Hydrological extremes (floods and droughts) have major impacts on society and ecosystems and are expected to increase in frequency and severity with climate change. Although both at the extreme ends of the hydrological spectrum, floods and droughts are governed by different processes, which means that they operate on different spatial and temporal scales and that different approaches and indices are needed to characterise them. However, there are also many similarities and links between the two extremes that are increasingly being studied.
This session on hydrological extremes aims to bring together the two communities in order to learn from the similarities and differences between flood and drought research. We aim to increase the understanding of the governing processes of both hydrological extremes, find robust ways of modelling and analysing floods and droughts, assess the influence of global change on hydroclimatic extremes, and study the socio-economic and environmental impacts of both extremes.
We welcome submissions that present innovative flood and/or drought research, including insightful case studies, large-sample studies, statistical hydrology, and analysis of flood or drought nonstationarity under the effects of climate change, land cover change, and other anthropogenic influences.
This session is jointly organised by the Panta Rhei Working Groups “Understanding Flood Changes”, “Changes in Flood Risk”, and “Drought in the Anthropocene” and will further stimulate scientific discussion on change detection, attribution, and the feedbacks between hydrological extremes and society. The session is linked to the European Low Flow and Drought Group of UNESCO´s IHP-VIII FRIEND-Water Program, which aims to promote international drought research. Submissions from early-career researchers are especially encouraged.
Floods, Droughts, or both
Large-sample hydrology or insightful case studies
Flood and drought nonstationarity
New approaches for analysis of extremes
Spatial and temporal variability
vPICO presentations: Tue, 27 Apr
The predicted increase in drought occurrence and intensity will pose serious threats to global future water and food security. This was hinted by several historically unprecedented droughts over the last two decades, taking place in Europe, Australia, Amazonia or the USA. It has been hypothesised that the strength of these events responded to self-reinforcement processes related to land–atmospheric feedbacks: as rainfall deficits dry out soil and vegetation, the evaporation of land water is reduced, then the local air becomes too dry to yield rainfall, which further enhances drought conditions. Despite the 'local' nature of these feedbacks, their consequences can be remote, as downwind regions may rely on evaporated water transported by winds from drought-affected locations. Following this rationale, droughts may not only self-reinforce locally, due to land atmospheric feedbacks, but self-propagate in the downwind direction, always conditioned on atmospheric circulation. This propagation is not only meteorological but relies on soil moisture drought, and may lead to a downwind cascading of impacts on water resources. However, a global capacity to observe these processes is lacking, and thus our knowledge of how droughts start and evolve, and how this may change as climate changes, remains limited. Furthermore, climate and forecast models are still immature when it comes to representing the influences of land on rainfall.
Here, the largest global drought events are studied to unravel the role of land–atmosphere feedbacks during the spatiotemporal propagation of these events. We based our study on satellite and reanalysis records of soil moisture, evaporation, air humidity, winds and precipitation, in combination with a Lagrangian framework that can map water vapor trajectories and explore multi-dimensional feedbacks. We estimate the reduction in precipitation in the direction of drought propagation that is caused by the upwind soil moisture drought, and isolate this effect from the influence of potential evaporation and circulation changes. By doing so, the downwind lack of precipitation caused by upwind soil drought via water vapor deficits, and hence the impact of drought self-propagation, is determined. We show that droughts occurring in dryland regions are particularly prone to self-propagate, as evaporation there tends to respond strongly to enhanced soil stress and precipitation is frequently convective. This kind of knowledge may be used to improve climate and forecast models and can be exploited to develop geo-engineering mitigation strategies to help prevent drought events from aggravating during their early stages.
How to cite: Miralles, D. G., Schumacher, D. L., Keune, J., and Dirmeyer, P. A.: Drought spatiotemporal propagation via land feedbacks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1505, https://doi.org/10.5194/egusphere-egu21-1505, 2021.
Recent droughts have shown that national water systems are facing increasing challenges over the last few years. As such, the Netherlands has seen increasing needs to adapt their water management in order to improve their preparedness for current and future drought events. Ideally, the necessary information needed for operational water management decisions should be readily available ahead in time and/or computed in a flexible and efficient way to ensure the various management actions. In this study we show that in addition to the physically based hydrological models, the upcoming and promising trend of incorporating machine learning (ML) in hydrology can increase the information produced to support national and operational water management.
To investigate the potential of ML for this case, we assessed 5 different ML methods to predict the following hydrological variables relevant for water management at a national scale: timeseries of discharge, groundwater levels, surface water levels and surface water temperatures. We developed a unified workflow for all the methods and variables of interest. As inputs, we only used a limited set of hydro-meteorological variables and general water management policies that are readily available on a daily basis and that can be used when the ML methods are used in seasonal forecasting mode.
We show that all methods have a good performance, with a normalized RMSE ranging between 0.0 and 0.4, and Random Forest outperforming other methods. This performance remains stable for low flows, where we observe that complex ML methods outperform simpler algorithms. The addition of water management in the ML routine increases overall performance, although limited. Finally, we observe that locations further upstream show a better performance due to the limited water management influence and close proximity to input observations.
Our study shows that ML has potential in predicting different hydrological variables at various locations at a national scale with only a simple input data set of 5 meteorological and hydrological variables. We additionally were able to capture and incorporate water management information in our analysis, creating a base for future experiments where a combination of seasonal forecasting and scenario analysis might reveal ML-informed mitigation strategies. As such, our approach may improve the preparedness of the national water system of the Netherlands for future drought events.
How to cite: Hauswirth, S. M., Bierkens, M., Beijk, V., and Wanders, N.: The potential of data driven approaches for quantifying hydrological extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2624, https://doi.org/10.5194/egusphere-egu21-2624, 2021.
In the context of climate change, it is important to understand whether drought conditions over the growing season of agricultural crops have changed over the past decades. Common drought metrics used for such assessments compare hydrometeorological anomalies using a static time window. However, the growing season varies among crops as well as in space; driven by climatic differences, and time; driven by e.g. changes in climate or crop-genotypes. Focusing on Southwestern Germany, we aim to investigate how the ranking of drought years varies between crops as well as among static and spatiotemporally varying growing season scenarios. First, we derived annual information on the timing of different phenological phases of two crops, winter wheat and maize, resp. early and late covering, from observations available from the German Weather Services. We then interpolated the timing of these phenological phases to 1 km resolution grids covering all agricultural areas in the study region, using static and spatiotemporally varying interpolation scenarios. Following, we extracted climatological timeseries for all agricultural grid cells and used those to simulate the climatic water balance as well as soil moisture for each grid cell with the hydrological model TRAIN. Finally, we derived for each year different drought metrics, i.e. anomalies in precipitation, temperature, climatic water balance and minimum soil moisture, and correlated those with crop yield anomalies. Results revealed distinct differences in the start and end of the growing season among considered crops. Further, the timing of different phenological phases varied by over a month in both space and time. During the most prominent drought years (2003, 2015, 2018), the growing season of both crops was particularly dry, independent on whether a fixed or variable growing season was considered. On the other hand, there were also some crop specific drought years, e.g., 1991 for maize or 2008 for winter wheat. The difference in hydrometeorological anomalies derived for static and variable growing seasons mainly relates to differences in temperature, but also affected the ranking of some drought years according to other hydrometeorological variables. More apparent were differences between drought metrics, e.g. between the climatic water balance and minimum soil moisture. From these metrics, especially minimum soil moisture correlated well with maize yields, whereas correlations with winter wheat were generally weak for all metrics. To conclude, crop specific agricultural drought assessments could benefit from a crop-relevant growing season specific definition of drought.
How to cite: Tijdeman, E. and Menzel, L.: Crop specific assessment of droughts in the growing season considering the spatiotemporal variability in phenology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14740, https://doi.org/10.5194/egusphere-egu21-14740, 2021.
In 2018, large areas of central and northern Europe were affected by an extreme drought. The water deficit propagated through the hydrologic cycle causing precipitation, soil moisture and, towards the end of 2018, streamflow and groundwater deficits. In Germany many socio-economic sectors were severely affected by the drought, e.g. the forestry sector has still not recovered. Main drivers for drought propagation are precipitation deficits. However, the natural variability of dry and wet precipitation patterns over time and space make characterization of droughts and predictions of impacts still challenging.
This study investigates German meteorological drought characteristics within general wet and dry spells since 1901 using station based daily precipitation data. Daily, monthly and seasonal aggregated indices such as the Standardized Precipitation Index (SPI) were used to characterize duration, severity and spatial extent of the 2018 drought. These characteristics were then compared with events of extreme droughts since 1901. Even though the meteorological drought of 2018 was extreme considering only precipitation data, we found comparable extremes in the past, for instance 1949 or 1964. However, based on what we observe in the SPI-12, clusters of extreme dry years in the 20th century were often followed by clusters of above average wet years, probably leading to a reduction of impacts in the following years. Since 2003, however, dry patterns predominate. Even though annual precipitation amounts are predicted to increase slightly in the study region this analysis shows the importance of analyzing sub annual as well as multi-year characteristics of precipitation patterns.
Including both wet and dry conditions when characterizing the severity of current drought events may improve our understanding of extreme meteorological drought events causing severe and long lasting impacts.
How to cite: Erfurt, M., Glaser, R., and Stahl, K.: Characterizing Germany’s 2018 drought in the context of wet and dry spells since 1901, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1037, https://doi.org/10.5194/egusphere-egu21-1037, 2021.
The 2013-2015 drought in the southeast Brazil was considered the worst since 1930, affecting more than 21 million people in the Sao Paulo metropolitan region. Previous studies have focused on the meteorological mechanisms and their impact based on low-resolution remote sensing datasets. Here, we simulated this entire drought event at 1 km2 resolution using the Joint UK Land Environment Simulator (JULES). The simulated domain covers large portions of the state of Sao Paulo and Minas Gerais with total area of about 200 thousand km2 (458 km by 463 km). We first investigate the impact of using both global and local datasets (soil and vegetation cover maps) on model performance by comparing the simulated evapotranspiration against the Global Land Evaporation Amsterdam Model. We found that using additional local land cover information together with vegetation-specific leaf area index from remote sensing has significantly improved the model performance while the local soil information has limited influence. Preliminary results suggest a lag of about one month for the drought to propagate from rainfall decrease in December/2013 to soil moisture depletion in January/2014. In addition, we combined the predicted results from JULES with a cluster analysis within the region to further categorized the domain into five groups clusters based on climatic and soil properties. This was done to better understand and explain the key controlling factors associated with the drought over these groups. Overall, we found that clusters with larger soil water storage capacity and slower drainage present more resilience to the drought. This study presents a detailed analysis on the impact of the extreme drought based on a high-resolution land surface model for a large domain in southeastern Brazil, and reveal the specific characteristics of drought propagation processes throughout the 2013-2015 period, adding a more hydrologically-oriented view on the impacts of the 2013-2015 drought to the meteorological findings discussed previously.
How to cite: Rosolem, R., Zhang, J., Pontes, L., da Rocha, H., and Domingues, L.: Investigating the hydrometeorological impacts of the 2013-2015 extreme drought in southeast Brazil by combining cluster analysis with land surface modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10879, https://doi.org/10.5194/egusphere-egu21-10879, 2021.
Drought is one of the most complex phenomena which may have a strong impact on agriculture, society, water resources, and ecosystems. In Romania, drought has a very strong impact on agriculture and affects 7.1 million ha, which represent 48% of the total agricultural land. The south, southeast, and eastern parts of Romania, including the Dobrogea region, are the most affected areas. During extremely dry years the average yields of various crops represent only 35% ÷ 60% of the potential yields. By employing three different drought indices (e.g. the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI), we show that there is a significant trend towards aridity especially from the 1980’s up to present in the south-eastern part of Romania. The Standardized Precipitation-Evapotranspiration Index (SPEI) at Sulina station (situated in the Doborgea region) for 12 months (SPEI12) indicates that over the last 30 years, this region was continuously affected by prolong droughts and there is a statistically significant shift towards dryer periods over the last 30 years compared to the period 1877 – 1990, thus indicating a critical situation for this region. Over the last 30 years, the long-term drought variability (SPI12, SPEI12, and PDSI) has increased both in duration and intensity up to maximum rates. The driest summers on record, over the region, are 2001, 2003 and 2007. These extremely dry summers are unprecedented throughout the observational record (~145 years). The history of drought in Dobrogea includes also many dry years, of which are to be mentioned: 1894, 1888, 1904, 1918, 1934, 1945.
How to cite: Nagavciuc, V., Ionita, M., and Roibu, C.-C.: Extreme aridity in the south-eastern part of Romania, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1181, https://doi.org/10.5194/egusphere-egu21-1181, 2021.
There are many ways to identify and monitor drought conditions. Scarcely are tools that calculate drought characteristics, The "SDF Calculator" works to bring monitoring tools to the public so they can assess drought conditions, this tool is used to assess and identify drought and its intensity.
Drought severity refers to the absolute sum of consecutive SDI values below a given threshold level while drought duration is the number of consecutive months that SDI is below that threshold, and drought frequency is a number of months with drought condition (means SPI < -0.5 or any given threshold that is desire, the threshold of drought index is a value that an index faces to drought condition. In every index, this value can be changed. For example, in many indices, the threshold of drought starts from zero or less zero. In other words, when the value of an index is calculating then all the values located in the drought classes, refer to the severity of the drought.
Droughts and exceptionally wet periods are regional phenomena, which are considered as major environmental extremes, especially in semiarid regions of the world. The development of severity-duration-frequency (SDF) relationships of droughts and wet periods is important in hydrological and climatic plannings in any country.
In this study, we aimed to offer a novel software model to be used for a quantitative description of droughts and wet periods to provide an overview of drought intensity and analyzing their severity, frequency, and duration. In addition, we have been able to develop a state-of-the-art bespoke software application, so the users are able to analyze drought based on the regional thresholds. While most of the analysis applications have used programming languages such as R or Python, due to the lack of software libraries in the .NET development environment, we have managed to offer our development environment based on .NET Core and C# programming language. The software application accepts inputs from various file formats or APIs, processes the data, and demonstrates the outcome in different graphs and maps depending on the geographical location of study areas. The outputs are not only can be exported as different formats to be used in big data applications but also might be exposed as web APIs to be used in live applications.
Keywords: Drought characteristics, SDF Calculator, API, Standardized Drought Indices (SDI)
How to cite: Mottaghi Zadeh, M. and Habibi, M.: Practical tool for drought characteristics calculation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14328, https://doi.org/10.5194/egusphere-egu21-14328, 2021.
How to cite: Cavus, Y., Eris, E., Aksoy, H., Burgan, H. I., Aksu, H., and Boyacioglu, H.: Spatio-temporal analysis of precipitation-based drought indices in Kucuk Menderes River Basin, Turkey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1531, https://doi.org/10.5194/egusphere-egu21-1531, 2021.
Drought is not only a multiscale (e.g., temporal, spatial) but also a multidimensional (e.g., onset, offset, duration, frequency, magnitude, intensity) phenomenon, and ecosystem production and respiration may respond to each drought dimension differently. Although multiple reports exist in literature on the drought impact on ecosystem productivity, it remains unclear how each component of drought impacts ecosystem gross primary production (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) and how the different drought dimensions interacted with each other on their impacts. In this study, we conducted a comprehensive drought impact assessment on forest GPP, NEE, and RECO including all the drought dimensions using FLUXNET observations and multiple time-scales of Standardized Precipitation-Evapotranspiration Index (SPEI). Our results indicated that while most earlier drought studies focused on simultaneous and post-drought conditions, the cumulative drought impacts and drought timing are more significantly impacting forest carbon uptake than simultaneous drought severity. Temporal standardization based meteorological drought indices could be used to accurately reflect plant water stress if antecedent and cumulative drought conditions are considered.
How to cite: Jiao, W. and Wang, L.: Quantifying the responses of ecosystem production and respiration to drought time scale, intensity, timing and lagged response: A FLUXNET synthesis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1841, https://doi.org/10.5194/egusphere-egu21-1841, 2021.
The 2018-2019 drought in northwestern Europe caused severe damage to a wide range of sectors, and has made clear that even in temperate-climate countries adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited quality of available data. We set up a time series modelling-based method for data preparation to map the spatiotemporal development of the 2018-2019 groundwater drought in the southeastern Netherlands, based on a large amount of monitoring data. The data preparation method was evaluated for its usefulness and reliability for groundwater drought studies and prediction. The analysis showed that the 2018-2019 meteorological drought caused extreme groundwater drought throughout the southeastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the geological-topographic setting. The time series modelling-based data preparation method was found a useful tool for the given situation to enable a detailed, consistent record of groundwater drought development. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series may bias drought estimations especially at a local scale, and underestimate spatial variability. Finally, time series modelling was also found a promising tool for regional-scale drought nowcasting and prediction. Further development of time-series based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future.
How to cite: Brakkee, E., van Huijgevoort, M., and Bartholomeus, R.: Mapping the spatiotemporal development of groundwater drought from data: the 2018-2019 drought in the Netherlands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-473, https://doi.org/10.5194/egusphere-egu21-473, 2021.
In the environment of the changing climate in Central Europe, the seasonality and magnitude of low flow events and hydrological droughts are projected to change in the near future. Ongoing increases in the air temperature, rates of evaporation and decreasing snow cover will significantly affect the summer deficit volumes even in the rivers of humid montane and highland areas in mid-latitudes. However, what if the significant changes have already been happening during the last decades? Therefore, this research is focused on analysis of the variability and seasonality of low flow events and hydrological drought events in fifteen near-natural catchments along the Czech–German and Czech–Polish national borders. To quantify the low flow regime changes of the study regions in the last 52 years (1968–2019), we applied tools from the R package lfstat. The 30-year moving averages of seasonality ratio (SR) and the seasonality index (SI) were derived to address the degree of change in each catchment. Moreover, the 7-day and 30-day mean summer minimum discharges were computed, as well as the streamflow deficit volumes for every episode of hydrological drought. The results showed a continual increase in the proportion of summer low flow and drought events during the study period along with a significant shift in the average date of low flow occurrence towards the beginning of the year. The most marked shifts in low flow seasonality were found mainly in catchments with the average altitude 800–1000 m a. s. l. Conversely, the low flow regime in catchments above 1000 m a. s. l. and also in the catchments below 800 m a. s. l. remained nearly stable throughout the 1968–2019 period. Moreover, the analysis of 7- and 30-day mean summer minimum discharges indicated a much-diversified pattern in the behavior of long-term trends than it was expected.
How to cite: Vlach, V., Ledvinka, O., and Matouskova, M.: Changing low flow seasonality in Central European headwaters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-71, https://doi.org/10.5194/egusphere-egu21-71, 2020.
In recent decades, an increasing frequency and severity of meteorological and hydrological droughts has been observed in most parts of Europe, including Poland. This is due to (among other factors) increasing atmospheric water demand, longer rainless periods, especially during the growing season, and decreasing winter snow retention. In consequence, a widespread soil moisture drying cascades to evaporative stress limiting the ecosystems productivity. Thus, a quantification of such events might give a better understanding of underlying inter-connected mechanisms. A range of different single or multiple indices are already in use to quantify the drought duration, severity and intensity. Moreover, recently introduced dedicated software tools help to conduct the spatial-temporal analysis of drought propagation through the hydrological system. In this study, I try to answer the question when, where and how the most severe droughts have been occurring during the last four decades, and in particular in the 21st century. Resulting from the weather extremes (precipitation and air temperature anomalies), the cascading impacts are analyzed as they subsequently occur through a subsurface soil system, and then translate into the evaporative stress and vegetation health conditions. The underlying assumption is that relevant drought indices might be derived from the reanalysis products including variables such as precipitation, air temperature, evapotranspiration and corresponding soil moisture estimates. For a relatively large territory (in this case over 300 thousand sq. kilometers) such data provide consistent set of variables allowing the multi-year analysis. Here, I used recently developed ERA5-land data, validated against basic variables acquired from the E-OBS data. First, drought events were identified using standardized indices at the 1-3-6 month time scales. Then, following a threshold approach, Contiguous Drought Area analysis was conducted in each time step for the growing season. Subsequently, the imprints of soil moisture depletion were detected in vegetation health quantified independently by remote sensing indices at relevant resolution. This study provides an evidence of moderate, severe and extreme drought occurrence. Recent biggest drought events occurred in 2003, 2005, 2006, 2015, 2018 and 2019 as a consequence of high monthly precipitation deficits reaching 100% of the long-term norm, and the air temperature 1-5 degree C higher as referred to average monthly thermal conditions.
How to cite: Somorowska, U.: Severe drought events inducing soil moisture depletion and evaporative stress across Poland during 1981-2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6509, https://doi.org/10.5194/egusphere-egu21-6509, 2021.
Several approaches to identify hydrological drought exist, which result in differences in drought frequency, timing, duration, and deficit volume (drought characteristics) using the same hydrometeorological data as input. This has created confusion within the hydro-meteorological community, as well as in operational water management services on the difference in drought characteristics obtained with the different approaches. The aim of this study, therefore, is to provide a comprehensive overview of the differences of hydrological drought, i.e. streamflow drought, using different identification approaches for the pan-European river network (>10,000 river grid cells). Time series of daily streamflow data were obtained from the LISFLOOD hydrological model forced with gridded meteorological observations from 1990 to 2018. Streamflow droughts were detected using the daily and monthly Variable Threshold methods (VTD and VTM), daily and monthly Fixed Threshold methods (FTD and FTM), and the Standardized Streamflow Index with 1-month accumulation period (SSI-1). For the threshold methods the Q80 (flow that is equaled or exceeded 80 percent of the time) is applied, whereas for the SSI a threshold of about -1 is used. We applied a centered 30-day moving average (30DMA) smoothing technique to the daily flow data to reduce the number of minor droughts. This is the first study that compares all these drought identification approaches in such a systematic way at this large scale. Our results (pan-European maps, tables) clearly show that characteristics of streamflow droughts derived with different approaches deviate, partly associated with different climate regions across Europe. The daily threshold methods (VTD and FTD) identify twice as much drought events than the monthly threshold methods (VTM and FTM) due to the daily resolution and minor droughts, even with smoothing. Average duration of FT droughts is longer than VT droughts. In addition, FT droughts have higher drought deficit volumes than VT droughts (~ 30-60%, dependent on climate region), whereas using monthly data (VTM and FTM) result in higher deficits (~10-60%) than daily data (VTD and FTD). In northern and central European regions (Köppen- Geiger Dfb, Dfc and ET climates), the variable threshold methods (VTD and VTM) generally detect drought earlier (March-July) than the fixed thresholds (FTD and FTM) (July-October). In the western European regions and the Mediterranean differences in timing among identification approaches are not so clear. The characteristics of SSI-1 drought, in general, are close to what is being identified with the VTM approach. Differences in drought characteristics highlight the importance of whether end-users should take seasonality into account or not (VT and SSI-1 versus FT) and consider temporal variability (daily versus monthly). Certainly, there is no unique hydrological drought definition that fits all purposes; hence we suggest that users should clearly agree among themselves upon a sharp definition on which type of streamflow drought is required to be identified for a specific application.
How to cite: Van Lanen, H. A. J. and Sutanto, S. J.: Implication of multi drought definitions to identify streamflow drought across Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4063, https://doi.org/10.5194/egusphere-egu21-4063, 2021.
Drought is considered one of the costliest climate extremes that have wide impacts on humans and ecosystems. Understanding different drought stages, for example, onset, propagation, and its recovery, especially for tropical (the vulnerable region in Earth’s climate system) catchments are crucial for ecosystem sustainability and food security. Utilizing gauge-based quality-controlled daily streamflow records from 98 catchments of rain-fed Peninsular River Basins (PRB) in India, here we investigate different phases of hydrological droughts in a multi-stage framework. While several studies so far have investigated the propagation of hydrological droughts at a monthly resolution, a credible understanding of drought dynamics requires analyzing low-flow series at a higher spatial and temporal resolution, ensuring the issuance of timely alerts related to regional water scarcity. Owing to high seasonality in the daily streamflow records, a variable threshold approach is adopted to delineate streamflow-based drought events. To assess the temporal evolution of droughts, the events are categorized into various inter-related phases, i.e., growth, persistence, and recovery stage over the study period 1965 – 2018. For most of the gauges, the mean timing of drought onset mostly lies between August and September revealing failure of monsoon as the primary causal factor for drought development in peninsular catchments. Furthering this, we identify four distinct hydrological drought regimes, which includes, Regime 1: persistent droughts with longer duration and moderate deficit volume with average termination during mid-monsoon (in September). These gauges are mostly situated in Central India and typically show a longer recovery time coincided with shorter return times (i.e., the time between two consecutive drought events), making it one of the most vulnerable regions in PRB; Regime 2: droughts with a shorter duration, least deficit volume with average termination in October, the post-monsoon period. These gauges are located in the western part of the country; Regime 3: droughts with the highest variability in drought deficit volume with the largest subsurface contribution from groundwater recharge. These sites are primarily located in eastern India and do not show any specific trend in the termination period; Regime 4: droughts with least regularity in drought termination with the average termination month clustered around November. These gauges are mostly concentrated in the southwestern part of the country. Our findings add value to the systematic understanding of hydrological drought propagations in rain-fed catchments, which serves as a basis for exploring future changes in droughts under concurrent shifts in rainfall and temperature extremes in a warming climate.
How to cite: Ganguli, P., Singh, B., and Raut, A.: Spatiotemporal Clustering of Hydrological Droughts in Peninsular India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-87, https://doi.org/10.5194/egusphere-egu21-87, 2020.
In Mediterranean climates, prolonged droughts lead to a significant shift in the precipitation -runoff relationship, usually in the direction of proportionally less precipitation allocated to runoff compared to wet periods. This shift may impact discharge predictions, as many hydrological and land surface models assume that hydrological processes are stationary even under a significant change of the climate (i.e., multi-year droughts) and are generally calibrated with more weight on discharge peaks than low flows.
Here, we investigate whether multi-year droughts result in a change in the precipitation-runoff relationship over continental European climates (which has never been fully explored before). 30-year records of annual rainfall and runoff from a dataset (>200) of small- and medium-scale (150 to 10000 km2) European catchments were used to test the existence of statistical shifts in the precipitation–runoff relationship. This was achieved by fitting a multivariate regression across annual cumulative full-natural flow, basin-wide annual precipitation, and a categorical variable denoting multi-year drought and non-drought years.
Results demonstrate that multi-year droughts cause a shift in the precipitation–runoff relationship regardless of predominant climate , with the magnitude of this shift ranging between 20 and 80%. We explore mechanisms of these shifts and potential explanatory factors, including catchment properties and characteristics.
Understanding changes in the precipitation-runoff relationship is paramount to make models and water resource management more robust to droughts, especially in a warming and more variable climate.
How to cite: Massari, C., Avanzi, F., Bruno, G., Gabellani, S., and Camici, S.: Observed exacerbation of the European water-budget deficit during multi-year droughts , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10966, https://doi.org/10.5194/egusphere-egu21-10966, 2021.
Agricultural droughts are becoming more frequent and severe, triggering a range of pervasive effects on society, environment, and economy. In drought-prone areas, multiple interventions aimed at efficient water use and protecting water resources have been used as preventive drought management measures. However, many of these solutions are colloquial or implemented inconsistently, and the actual contribution to drought preparation and response is limited or unclear. This study evaluates the applicability and effectiveness of preventive drought management measures (Hydrological-based measures). To achieve this goal, we divided the work into two stages. First, a quantitative analysis consisted of a review, classification, and mathematical representation of potential preventive drought management measures. Second, a modelling-based analysis compared droughts characteristics before and after implementing three selected measures from the first stage (rainwater harvesting reservoirs, afforestation, and intercropping). The study was developed in the Torola basin, a drought-prone area located in Honduras northeast. We applied the threshold level method to detect and analyse drought characteristics and the Soil Water Assessment Tool (SWAT) for hydrological modelling and representing the selected measures. We defined three scenarios for evaluating the effects of each measure. Results showed that selected measures increase infiltration and soil moisture content alleviating the severity and duration of drought events locally, but enhance the drought situation in surrounding areas.
Keywords: Agricultural droughts, preventive drought management measures, SWAT model.
How to cite: paez, A. and Corzo, G.: Evaluation of agricultural drought changes due to implementation of preventive drought management measures. Study case Torola basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9275, https://doi.org/10.5194/egusphere-egu21-9275, 2021.
Central Europe has experienced a severe drought almost every April for the last 14 years consecutively, driven by record high temperatures, low flows, high evapotranspiration, and high soil moisture deficit. The dynamic of this recent and recurrent mid-spring dryness is not yet understood. Here we show that the period 2007 – 2020 was characterized by a reduction of ~50% of the usual April rainfall amount over large areas in central Europe. The precipitation deficit and the record high temperatures were triggered by a multiyear recurrent high-pressure system centered over the North Sea and northern Germany and a decline in the temperature gradient between the Arctic region and the mid-latitudes, which diverted the Atlantic storm tracks northward. From a long-term perspective, the precipitation, temperature, and soil moisture anomalies observed over the last 14 years have reached the highest amplitudes over the observational record. This study provides an in-depth analysis of the hydroclimate extremes in central Europe over the last 140 years and their atmospheric drivers, enabling us to increase our dynamical understating of long-term dry periods, which is vital to enhance forecasting and mitigation of such events.
How to cite: Ionita-Scholz, M., Nagavciuc, V., Kumar, R., and Rakovec, O.: On the curious case of the recent decade, mid-spring precipitation deficit in central Europe , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9556, https://doi.org/10.5194/egusphere-egu21-9556, 2021.
Climate change is expected to change the pattern of rainfall resulting in changed flood magnitude. However, in large part due to interannual variability, identifying a climate change signal in flood magnitude remains difficult. As an alternative to investigating trends in flood magnitude, it has been suggested that trends in flood timing, that is, the day of annual streamflow maxima, may be a detectable trend due climate change.
Here, using high-quality data from around the world, trends in flood and center timing are investigated. We begin by standardizing the data on a local definition of water year. We find an interesting property, that after standardization, the flood and centre timing of streamflow can be approximated by a normal distribution. Moreover, we find that without the standardization on local water year the calculated trend can reverse. We proceed by analyzing trends in centre and flood timing globally using linear regression.
Results are commensurable with large-scale climatic change. But, unlike changes in extreme rainfall, trends are not spatially consistent. Flood timing is shifting to earlier in the year in the tropics, and later in the year in the extra-tropics, consistent with changes in mean rainfall and flood magnitude. There is evidence of a reversal of trends post-drought, suggesting that the mechanisms controlling flooding at a catchment scale are changing as a result of climate change. It is concluded that trends in flood timing are related to flood generating mechanisms, and largely modulated by changing antecedent moisture conditions.
How to cite: Wasko, C., Nathan, R., and Peel, M.: Flood and center timing in a changing world, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10, https://doi.org/10.5194/egusphere-egu21-10, 2020.
Standard flood frequency analyses hinge on unrealistic asymptotic assumptions, use a small portion of the data available (annual maxima or a few values above a high threshold only), and are ill-suited for short time series. Lately, the Metastatistical Extreme Value Distribution (MEVD) has gained momentum in the study of extremes, as it relaxes the assumptions on which traditional methods are based and makes a more effective use of the information at hand. Moreover, it is more flexible in the choice of the distribution of the ordinary events (i.e., events belonging to the bulk of the distribution, in contrast to annual maxima), hence giving room for selecting the statistical method that better describes the data.
In this work, we leverage the flexibility of the MEVD and develop an approach to a priori select the distribution of ordinary peaks according to the ratio between their empirical 99th and 90th percentiles, and apply it to daily mean streamflow time series from 183 gauges in Germany. Based on the value of this ratio, we choose either the Generalized Gamma or the Log-Normal distributions to describe ordinary peaks that show lighter or heavier tails respectively. This distinction allows us to improve the estimation of the magnitude of floods with high return periods in 117 basins of a 64 % on average and to reduce under-/over-estimation issues, when compared to a MEVD application in which the ordinary distribution is chosen regardless the tail features of the underlying data.
How to cite: Mushtaq, S., Miniussi, A., Merz, R., and Basso, S.: How to a priori select ordinary distributions in flood frequency analysis when applying the Metastatistical Extreme Value approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3003, https://doi.org/10.5194/egusphere-egu21-3003, 2021.
For sustainable water resources planning and management, it is necessary to redefine the concept of return period, risk, and reliability of hydrologic extreme under non-stationary condition. Thus, the present study aims to examine the return period, risk introducing physical based covariates in the location parameter of the generalised extreme value (GEV) distribution. The study is performed over the Godavari River basin, India. The expected waiting time (EWT) approach is used to make comparison of return period, risk between stationary and non-stationary approaches. From the analysis, it is found that 50% of the gauging stations are impacted by large scale modes/oscillations and regional hydrological variability, primarily by Indian Summer Monsoon Index (ISMI) and precipitation. The EWT interpretation estimates that the non-stationary return period, risk, and reliability are significantly different from stationary condition. Hence, it is concluded that return period analysis and risk assessment using non-stationary approach can be beneficial to water managers and policy makers in order to devise sustainable and resilient water resources infrastructure under climate change scenario.
Keywords: Extreme value analysis; Return period; Risk; Non-stationarity; Uncertainty
How to cite: Das, J., Umamahesh, N., and Jha, S.: Analysing the Return Period and Risk under the Influence of Physical Covariates on Hydrological Extreme, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-404, https://doi.org/10.5194/egusphere-egu21-404, 2021.
Assessment of flood and drought risks, and changes to these risks under climate change, is a critical issue worldwide. Statistical methods are commonly used in data-rich regions to estimate the magnitudes of river floods of specified return period at ungauged sites. However, data availability can be a major constraint on reliable estimation of flood and drought magnitudes, particularly in the Global South. Statistical flood and drought magnitude estimation methods rely on the availability of sufficiently long data records from sites that are representative of the hydrological region of interest. In the Philippines, although over 1000 locations have been identified where flow records have been collected at some time, very few records exist of over 20 years duration and only a limited number of sites are currently being gauged. We collated data from three archival sources: (1) Division of Irrigation, Surface Water Supply (SWS) (1908-22; 257 sites in total); (2) Japan International Cooperation Agency (JICA) (1955-91; 90 sites); and, (3) Bureau of Research and Standards (BRS) (1957-2018; 181 sites). From these data sets, 176 contained sufficiently long and high quality records to be analysed. Series of annual maximum floods were fit using L-moments with Weibull, Log-Pearson Type III and Generalised Logistic Distributions, the best-fit of these being used to estimate 2-, 10- and 100-year flood events, Q2, Q10 and Q100. Predictive equations were developed using catchment area, several measures of annual and extreme precipitation, catchment geometry and land-use. Analysis took place nationally, and also for groups of hydrologically similar regions, based on similar flood growth curve shapes, across the Philippines. Overall, the best fit equations use a combination of two predictor variables, catchment area and the median annual maximum daily rainfall. The national equations have R2 of 0.55-0.65, being higher for shorter return periods, and regional groupings R2 are 0.60-0.77 for Q10. These coefficients of determination, R2, are lower than in some comprehensive studies worldwide reflecting in part the short individual flow records. Standard errors of residuals for the equations are between 0.19 and 0.51 (log10 units), which lead to significant uncertainty in flood estimation for water resource and flood risk management purposes. Improving the predictions requires further analysis of hydrograph shape across the different climate types, defined by seasonal rainfall distributions, in the Philippines and between catchments of different size. The results here represent the most comprehensive study to date of flood magnitudes in the Philippines and are being incorporated into guidance for river managers alongside new assessments of river channel change across the country. The analysis illustrates the potential, and the limitations, for combining information from multiple data sources and short individual records to generate reliable estimates of flow extremes.
How to cite: Hoey, T., Tolentino, P., Guardian, E., Williams, R., Boothroyd, R., David, C. P., and Paringit, E.: Flood estimation for ungauged catchments in the Philippines using multiple archival data records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4905, https://doi.org/10.5194/egusphere-egu21-4905, 2021.
Estimation of extreme design floods with a short series of a few decades remains challenging because the statistical extrapolation of observed floods to extreme floods induces great uncertainties. Several alternative methods take advantage of the use of additional information: regional methods (e.g. the index flood method), Monte Carlo rainfall-runoff simulation methods, or specific statistical methods adapted to historical series. Here we present a flood frequency analysis on the upper Rhine River, using long historical series in Basel (1808-2017) and Maxau (1815-2018). We used a Bayesian framework to fit the parameters of the GEV distribution. Each value of the annual maximum discharge has uncertainties, which vary from ± 5-7% for the last decades to ± 22-42% for the oldest period depending on the station. At the local scale, without prior assumption on the three parameters of a GEV distribution, we found that the credibility intervals of the Basel and Maxau flood distributions are consistent. However, beyond a 1000-year return period, flood quantiles are incoherent with Q(Maxau) < Q(Basel) although Maxau (50 000 km2) is located downstream of Basel (36 000 km2). The floods at Basel are almost Gumbel distributed (shape parameter k = 0.066), whereas the floods at Maxau are Weibull distributed (shape parameter k = 0.219) with an asymptotic maximum value. Assuming that the shape parameter k has a certain regional consistency, we have performed a second iteration, with a prior interval [-0.1; +0.4] on k. The width of this interval corresponds to the union of the posterior distribution of k parameter of each local distribution: [-0.1; +0.2] at Basel and [0.0; +0.4] at Maxau. The second version of each distribution is almost the same up to a return period of 100 years, but there is no more crossing for extreme values. Using the predictive distribution with a regional prior on the shape parameter of the GEV distribution, the result is hydrologically consistent from upstream to downstream.
How to cite: Lang, M., Renard, B., and Le Coz, J.: Hydrological consistency between the upstream and downstream estimates of Q1000 flood on the upper Rhine River, using historical series in Basel (1808-2017) and Maxau (1815-2018), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8697, https://doi.org/10.5194/egusphere-egu21-8697, 2021.
Flood frequency analysis (FFA) has long been the standard procedure for obtaining design floods for all kinds of purposes. Ideally, the data at the basis of the statistical operations have a high temporal resolution, in order to facilitate a full account of the observed flood peaks and hence a precise model fitting and flood quantile estimation.
Unfortunately, high-resolution flows are rarely disposable. Often, average daily flows pose the only available/sufficiently long base for flood frequency analysis. This averaging naturally causes a significant smoothing of the flood wave, such that the “instantaneous” peak can no longer be observed. As a possible consequence, design floods derived from these data may be severely underrated.
How strongly the original peaks are flattened and how this influences the design flood estimation depends on a variety of factors and varies from gauge to gauge. In this study we are looking at a range of errors arising from the use of daily instead of instantaneous flow data. These include differences in the observed individual flood peaks and mean annual maximum floods, as well as the estimated distribution parameters and flood quantiles. The aim is to identify catchment specific factors that influence the magnitude of these errors, and ultimately to provide a means for error assessment on the mere basis of local hydrological conditions, specifically where no high-resolution data is available.
The analyses are carried out on an all-German dataset of discharge gauges, for which high-resolution data is available for at least 30 years. The classical FFA approach of fitting distributions to annual maximum series is utilized for error assessment. For identification of influencing factors, both the discharge series themselves and a catalogue of climatic and physiographic catchment descriptors are screened.
How to cite: Fangmann, A. and Haberlandt, U.: Flood frequency from maximum daily vs. instantaneous peak flows, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4911, https://doi.org/10.5194/egusphere-egu21-4911, 2021.
Flood peak quantiles for return periods up to 10 000 years are required for dam design and safety assessment, though flood series usually have a record length of around 20-40 years that leads to a high uncertainty. The utility of historical data of flooding is generally recognised for estimating the magnitude of extreme events with return periods in excess of 100 years. Therefore, historical information can be incorporated in flood frequency analyses to reduce uncertainties in high return period flood quantile estimates that are used in hydrological dam safety analyses.
This study assesses a set of existing techniques to incorporate historical information of flooding in extreme frequency analyses, focusing on their reliability and uncertainty reduction for high return periods that are used for dam safety analysis. Monte Carlo simulations are used to assess both the reliability and uncertainty in high return period quantile estimates. Varying lengths in the historical (Nh = 100 and 200 years) and systematic (Ns = 20, 40 and 60 years) periods are considered. In addition, a varying number of known flood magnitudes that exceed a given perception threshold in the historical period are also considered (k = 1-2). The values of Nh, Ns and k used in the study are the most usual in practice.
The reliability and uncertainty reduction in flood quantile estimates for each technique depend on the statistical properties of flood series. Therefore, a set of feasible combinations of L-coefficient of variation (L-CV) and skewness (L-CS) values should be considered. The analysis aims to understand how each technique behaves in terms of flood quantile reliability and uncertainty reduction depending on the L-moment statistics of flood series. In this study, L-CV and L-CS regional values in the 29 homogeneous regions identified in Spain for developing the national map of flood quantiles by the Centre for Hydrographic Studies of CEDEX are considered.
The results show that the maximum likelihood estimator (MLE) and weighted moments (WM) techniques show the best results in the regions with small L-CS values. However, the biased partial probability weighted moments (BPPWM) technique shows the best results in the regions with high L-CS values. While the expected moments algorithm (EMA) tends to underestimate flood quantiles for high return periods, the unbiased partial probability weighted moments (UPPWM) technique tends to overestimate them. In addition, including historical information of flooding in flood frequency analyses improves flood quantile estimates in most cases regardless the technique that is used. Uncertainty reduction in high return period flood quantile estimates are higher for short systematic time series, regions with high L-CS values and long historical periods.
Acknowledgments: This research has been supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.
How to cite: Soriano Martín, E., Jiménez, A., and Mediero, L.: Assessment of techniques to include historical information in flood frequency distributions for hydrological dam safety assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9424, https://doi.org/10.5194/egusphere-egu21-9424, 2021.
Climate change has led to increased droughts and floods over mainland Australia, resulting in water scarcity, excessive surplus and socioeconomic losses. Therefore, it is of great significance to comprehensively evaluate droughts and floods from the meteorological and hydrological perspective. Firstly, we determine the Standard Precipitation and Evapotranspiration Index (SPEI) by correlation analysis to represent the meteorological conditions. To characterize the hydrological conditions, we calculate the hydrological drought indices including Standard Runoff Index (SRI), Soil Moisture Deficit Index (SMDI), and Total Storage Deficit Index (TSDI), using the runoff and soil moisture data from the Global Land Data Assimilation System (GLDAS) and the Terrestrial Water Storage Change (TWSC) data from Gravity Recovery And Climate Experiment (GRACE) respectively. Results show that the most severe hydrological drought over mainland Australia during the study period occurred from May 2006 to Jan. 2009 with the drought severity of -58.28 (cm months) and the most severe flood from Jun. 2010 to Jan. 2013 is with the severity of 151.36 (cm months). The comprehensive analysis of both meteorological and hydrological drought indices shows that both meteorological and hydrological drought indices can effectively detect the droughts and floods over mainland Australia. Moreover, the meteorological drought and flood are of higher frequency, while hydrological drought and flood have a relatively longer duration. Based on the cross-correlation analysis, we find that the SPEI can firstly reflect the droughts or floods over mainland Australia, and then the SRI, SMDI and TSDI reflect with the time lags of one, three and six months respectively. Furthermore, we calculate the frequency of drought and flood at the basin scale and find that SPEI and SMDI are equally sensitive to drought and flood, while TSDI is more sensitive to flood than drought. This study reveals the relationship between meteorological and hydrological conditions in mainland Australia in the last two decades and highlights its intensifying extreme climate conditions under the circumstances of the increasing temperature and complex changing precipitation.
How to cite: Wang, W., Shen, Y., Wang, F., and Li, W.: Assessment of Meteorological and Hydrological Droughts and Floods over mainland Australia based on Drought Indices, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8147, https://doi.org/10.5194/egusphere-egu21-8147, 2021.
The drought and floods are a natural phenomenon of ecosystems. Many studies found that the frequency and intensity of individual events of floods and drought are increasing in recent decades due to climate change. However, it is still unclear whether the frequency of combined flood-drought events is increasing in the same year or not under the climate change scenario. To identify drought and flood characteristics, we used the Standardized Weighted Average of Precipitation (SWAP), and copula bivariate distribution concept to estimate the joint probabilities of combined flood-drought events of the same year. We utilized gridded rainfall data from the India Meteorological Department at 0.25 degree for the present study. We estimated drought, flood and combined flood-drought events for the base period (1901-1930) and the current period (1991-2018). The analysis demonstrates that about 51.97% of the total grid points show an increasing monthly SWAP values trend in the summer monsoon season. However, in winter, only 15.55% of the total grid points show an increase in the trend of monthly SWAP values. The univariate flood and drought analysis revealed that 83.98%, 83.98% and 81.90% of total grids show a significant percentage change of drought at 5, 10 and 25-year return periods, respectively when the current period is compared with the base period. Still, only, 27.88%, 16.32% and 13.82% of the total grids show a significant change in the flood 5-year, 10-year and 25-year return periods, respectively. We also found that combined flood-drought events' frequency increases in 39.21%, 36.49% and 20.71% of total grid points corresponding to 5, 10 and 25-year return period values, respectively. This study concluded that less intensity drought, flood, and combined flood-drought events are increasing in more grid points. The study outcomes will help the decision-makers to make efficient decision to overcome the impacts of the hydroclimatic extremes.
How to cite: Chetan Kumar, S., Gupta, V., and Jain, M. K.: Impact of Climate Change on Combined Flood and Drought events in India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9718, https://doi.org/10.5194/egusphere-egu21-9718, 2021.
Hydrological extremes such as floods and droughts are the most common and threatening natural disasters worldwide. Particularly, tropical Andean headwaters systems are prone to hazards due to their complex climate conditions. However, little is known about the underlying mechanisms triggering such extremes events. In this study, the Generalized Additive Models for Location, Scale and Shape (GAMLSS) were used for investigating the relations between the Annual- Peak-Flows (APF) and Annual-Low-Flows (ALF), respecting to climate and land use/land cover (LULC) changes. Thirty years of daily streamflow data-sets taken from two Andean catchments of southern Ecuador are used for the experimental research. Global climate indices (CI), describing the large-scale climate variability were used as hypothetical drivers explaining the extreme’s variations on streamflow measures. Additionally, the Antecedent-Cumulative-Precipitation (AP) and the Standardized-Precipitation-Index (SPI), and LULC percentages were also included as possible direct drivers – synthetizing local climate conditions and localized hydrological changes. The results indicate that AP and SPI clearly explain the extreme streamflow variability. Nonetheless, global variables play a significant role underneath the local climate. For instance, ENSO and CAR exert influence over the APF, while ENSO, TSA, PDO and AMO control ALF. Furthermore, it was found that LULC changes strongly influence both extremes; although this is particularly important for relative more disturbed catchments. These results provide valuable insights for future forecasting of floods and droughts based on precipitation and climate indices, and for the development of mitigation strategies for mountain catchments.
How to cite: Avilés, A., Contreras, J., Mendoza, D., and Pacheco, J.: The influence of global climate and local hydrological features over streamflow extremes. Case of study in a tropical Andean basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-55, https://doi.org/10.5194/egusphere-egu21-55, 2020.
Hydrological extreme events such as droughts and floods have a wide range of impacts on society and sectors such as agriculture and energy production. The impact of these extremes are projected to increase with future climate change and there is an urgent need to develop adaptation measures to reduce and manage the impacts. Long-term analysis of hydrological extremes, using a combination of models and climate data, helps better plan and manage water resources under global change. In this study, we modelled and analyzed hydrological extremes of the Volta river basin at very high-resolution (>10000 river reaches) using the Variable Infiltration Capacity (VIC) hydrological model, the vector-based river network routing model (RAPID), and high-resolution meteorological forcing datasets. The output from the VIC model is evaluated at multiple time scales (daily to annual) and for extreme events (droughts and floods) using observed streamflow data during the period 1979-2013. The model performed very well in areas less affected by dams, with performance increasing from daily to annual time scale. The modelled streamflow data is used to assess changes and variability in droughts (duration days and severity) and floods (annual daily maximum). The results show a decreasing and increasing trend in moderate and severe droughts in northern-eastern and southern parts of the basin, respectively. An increasing trend in floods is observed in the upper part of the basin (Black and White Volta) and the main river of the Lower Volta and we found a strong correlation with changes in precipitation and soil moisture.
How to cite: Gebrechorkos, S. H., Pan, M., Lin, P., Pritchard, D., Forsythe, N., Fowler, H., and Sheffield, J.: Modelling hydrological droughts and floods in the Volta Basin, West Africa , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7992, https://doi.org/10.5194/egusphere-egu21-7992, 2021.
Remote sensing and crowdsourcing data are new sensing methods that have the potential to improve significantly inundation modelling. That is especially true in data-scarce situations, for example when resources for acquiring sufficient traditional data are limited or when field conditions are not favourable. Crowdsourced water depths and velocities have been demonstrated to be useful for improving inundation models, ranging from the calibration of 1D models to data assimilation in 2D models. In this study, we aim to further evaluate how much the amount and type of crowdsourced data influence model calibration and validation, in comparison with data from traditional measurements. Further, we aim to assess the effects of combining both sources. For that, we developed a 2D inundation model of the Sontea-Fortuna area, a part of the Danube Delta in Romania. This is a wetland area, where data was collected during two 4-day field campaigns, using boat navigation together with the involved citizens. Citizens obtained thousands of images and videos that were converted into water depth and velocity data, while technicians collected ADCP data. We calibrated and validated the model using different combinations of data (e.g. all water depth data, half water depth and half water velocity). Results indicated that velocity data by themselves did not yield good calibration results, being better used in conjunction with water depths or by combining them into discharge. We also observed that calibration by crowdsourced water depths is comparable to the use of water depths from traditional measurements.
How to cite: Assumpção, T. H., Popescu, I., Jonoski, A., and Solomatine, D.: Calibrating and validating an inundation model with and without crowdsourced water depths and velocities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13252, https://doi.org/10.5194/egusphere-egu21-13252, 2021.
Estimating design floods at location with no measurements or short records is a major challenge for operational hydrology. The aims of this study are to (i) develop a regional flood frequency model that consists of a regression model for the index flood and the parameters in the growth curve; (ii) assess and attribute the uncertainty to the components of the regional flood frequency model, (iii) develop flexible approaches for combining a regional flood frequency model with local data and provide recommendations for how to combine local and regional data. Annual maximum flood data from 529 gauging stations were used for the model development. We re-parametrized the Generalized Extreme Value (GEV) distribution into an index flood component and growth curve component, and we used the median flood as the index flood. The model was estimated using a MCMC algorithm within a Bayesian framework. The Bayesian approach was also used to combine local and regional data. Two approaches were used (i) combining local and regional data to estimate the index flood (ii) combining local and regional data to estimate both the index flood and the growth curve. Simulation experiments were carried out to assess the performance of these approaches. We see that in particular for data records shorter than 10 years, we can benefit from combining the local and the regional model by both approaches. We also constructed a prior for use in local analysis that complied with the distribution of the regional model for three key quantiles. For the index flood, the regression model was successfully estimated and evaluated using a three-step cross validation approach. The most important variables for predicting the index flood were mean annual runoff, river length and lake percentage. The attribution of uncertainty showed that most of the uncertainty was found in the index flood component.
How to cite: Engeland, K., Reitan, T., Stenius, S. M., and Glad, P.: Design flood estimation at locations with no data or short records in a Bayesian framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13317, https://doi.org/10.5194/egusphere-egu21-13317, 2021.
Successive occurrence of floods across north-west England over the course of the past few years has resulted in the need for the local authorities and decision makers to (re-) assess several flood management schemes. However, ongoing decision-making on how flood control measures are constructed, is frequently still made on the basis of the assumption that the flood characteristics of catchments have remained constant over time (i.e., stationarity). To verify the validity of this assumptions, non-parametric tests alongside change-permitting flood frequency frameworks based on Generalized Logistic distribution model (as the recommended model in the UK catchments) have been applied to a dataset of extreme peak river flow measurements across the region (39 catchments with up to 75 years of records). Allowing the location parameter of the model to change linearly with time, cumulative annual rainfall and cumulative annual temperature as covariates, one stationary as well as six non-stationary models have been introduced. The regional non-stationary frequency results indicate a notable improvement over the stationary predictions, estimating design flood quantiles (i.e., 100-year events) up to 75% larger than classic stationary estimates. Moreover, the vast majority of rivers demonstrate statistically significant changes (mainly driven by cumulative annual rainfall), specifically in the late 1990s. This indicates that non-stationary models should be taken into consideration, along with the traditional stationary ones to help understanding the changes in the peak river flow regimes across the north-west England.
How to cite: Hesarkazzazi, S., Arabzadeh, R., Hajibabaei, M., Rauch, W., R. Kjeldsen, T., Prosdocimi, I., Castellarin, A., and Sitzenfrei, R.: Comparison of stationary and non-stationary frequency models for assessing design discharges in variable climate: north-west England case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10303, https://doi.org/10.5194/egusphere-egu21-10303, 2021.
Climate changes affect streamflow variability on the long-term and short-term temporal scale. Consequently, analysis of changes in hydrological regime, but also in intensity and frequency of short-time flood events, enables better understanding of hydraulic and geomorphological processes in rivers. Changes in streamflow variability and flood wave characteristics may lead to intensifying riverbed erosion and lowering infrastructure safety, such as bridges over rivers. The aim of the study is to analyse hydrological regime for historical data from the selected gauging stations on the two large lowland rivers in Croatia: the Sava River and the Drava River. Analysis of the magnitude, frequency, variability, and timing of streamflow is conducted. Additionally, deterministic and probabilistic approach to determination of metrics that describe hydrograph shape is performed for the different threshold levels. Results obtained from this study will help in exploring riverbed erosion processes which may entail the increased scouring around bridge piers and consecutively impair the infrastructure reliability in the changing climate.
This work has been supported in part by Croatian Science Foundation under the project R3PEAT (UIP-2019-04-4046) and DOK-2020-01.
How to cite: Kovačević, M., Potočki, K., and Gilja, G.: The analysis of streamflow variability and flood wave characteristics on the two lowland rivers in Croatia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2563, https://doi.org/10.5194/egusphere-egu21-2563, 2021.
Due to climate variability and reservoir regulation worldwide, it is fundamentally challenging to implement holistic assessments of detection, attribution and frequency analysis on non-stationary flood peaks. In this study, we proposed an integrated approach that combines the prewhitening Mann-Kendall test technique, Partial Mutual Information-Partial Weights (PMI-PW) method and Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS) method to achieve reliable non-stationary flood frequency analysis. Firstly, the prewhitening Mann-Kendall test was employed to detect the trend change of flood peaks. Secondly, the PMI-PW was employed to attribute the contribution of climate change and reservoir regulation to non-stationarity of flood peaks. Lastly, the GAMLSS method was employed to quantify the change in flood risks under the non-stationary condition. The applicability of the proposed approach was investigated by long-term (1931-2017) flood series collected from 32 big river catchments globally. The results suggested that global flood trends varied from increasing +19.3%/decade to decreasing −31.6%/decade. Taking the stationary flood frequency analysis as the benchmark, the comparative results revealed that the flood risk in 5 rivers under the non-stationary condition in response to warming climate significantly increased over the historical period whereas the flood risk in 7 rivers in response to increasing reservoir storage largely reduced. Despite the spatiotemporal heterogeneity of observations, the changes in flood peaks evaluated here were explicitly associated with the changing climate and reservoir storage, supporting the demand for considering the non-stationarity of flood peaks in the best interest of social sustainability.
Keywords: Flood peaks; Large catchments; Non-stationarity; Frequency analysis
*This work was supported by the Research Council of Norway (FRINATEK Project 274310).
How to cite: Zhou, Y., Xu, C.-Y., Ngongondo, C., and Li, L.: Detection, attribution and frequency analysis of non-stationary flood peaks in 32 big rivers worldwide , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10635, https://doi.org/10.5194/egusphere-egu21-10635, 2021.
Progressing towards a sustainable society implies the availability of reliable boundary conditions for various hydrodynamic flood models, including an extensive consideration of uncertainties. With an ever growing availability of data and models, the uncertainty sources are constantly increasing. Hence, an elaborate uncertainty analysis strategy has become a must. One way to deal with part of this uncertainty is by applying an ensemble approach, using different hydrological models in combination with various climate scenarios. However, impact modellers may find the growing number and the increasing length of input series for hydraulic models more challenging, since computing time, reliability of the analysis and project deadlines can cause a conflicting situation. In this context, there is a need for approaches that offer a compromise between computing the vast amount of long input series and adequately addressing the uncertainties within a reasonable time span. We present an approach which reduces the computation time, but simultaneously recognises the importance of robust results and the consideration of the different sources of uncertainty. By a stratification of the probability domain for extreme events (discharges, water levels,…) a set of hydrodynamic boundary conditions is generated. Each of these synthetic events gets a probability of occurrence, which changes according to either the considered confidence level or the considered ensemble member. In addition to the stratification approach, a tool for selecting synthetic events for design is developed. This tool allows end-users to create a subset of synthetic events which can be used as design events for a specific area and are representative for the full set of events. The approach is demonstrated for the River Dender catchment in Flanders using 40 years of hydro-meteorological data, an ensemble of 3 hydrological models and a detailed hydraulic model.
How to cite: Leyssen, G., van Uytven, E., Blanckaert, J., Adams, R., Franken, T., Nossent, J., and Pereira, F.: How to embrace big data and uncertainties within reasonable time constraints? A detailed flood study in Flanders, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16238, https://doi.org/10.5194/egusphere-egu21-16238, 2021.
The African continent is severely impacted by floods, with an increasing vulnerability to these events in the most recent decades. Our improved preparation against and response to this hazard would benefit from an enhanced understanding of the physical processes at play. A database recently compiled on Africa allows to conduct studies at the continental scale: the African Database of Hydrometric Indices (ADHI: https://doi.org/10.5194/essd-2020-281). Daily river discharge data have been extracted for 399 African rivers to analyze the seasonality of observed annual maximum discharge. In addition, extreme precipitation from CHIRPS and ERA5, and soil moisture from ERA5-Land between 1981 and 2018 have been also considered as potential flood drivers. The database includes a total of 11,302 flood events, covering most African regions. The analysis is based on directional statistics to compare the annual maximum river discharge with annual maximum rainfall and soil moisture. Results show that the annual peak flow in most areas is more strongly associated with the annual peak of soil moisture than of extreme precipitation. In addition, the interannual variability of flood magnitudes is better explained by the variability of annual maximum soil moisture or the precipitation summed over 5 days prior to an event, than by changes in the annual maximum daily precipitation. These results have important implications for the design of efficient flood forecasting systems or the investigation of the long-term evolution of these hydrological hazards.
How to cite: Tramblay, Y., Villarini, G., El Khalki, E. M., Gründemann, G., and Hughes, D.: On the influence of extreme rainfall and antecedent soil moisture on flood hazards in Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2908, https://doi.org/10.5194/egusphere-egu21-2908, 2021.
Information on flood seasonality is required in many practical applications of hydrology and water resources management. However, an understanding of flood seasonality and how flood frequencies may have changed over time has not been established for the Congo Basin. The main objective of this study is therefore to identify flood seasonality and change in frequency the Congo Basin (CB). The analysis based on six major drainage areas of the CB, where we used a Peaks Over Threshold (POT) flood time-series with three peaks per year. The relative frequency method is applied to identify flood seasons, and then a cluster analysis is performed to classify flood into type based on monthly frequency. The directional statistics method is used to determine the mean day of flood and the flood variability measure. To identify flood frequency changes, the analysis of variance was applied to test the difference between two flood frequency time series blocks before and after the change point year. Results show that four gauging stations exhibit a unimodal flood seasonality distribution while two gauging stations have bimodal flood seasonality distribution, and two significant flood rich months are observed in all studied gauging stations. The cluster analysis results in four spatially flood types with distinct seasonality characteristics. Mean flood dates show that the time interval between adjacent flood events in the south and south-east is shorter compared to time interval between flood events in the north and north-west. It is observed that, in almost all gauging stations, there is strong flood seasonality, and the geographical location of a watershed is indicative of its flood pattern. Most of significant decreasing frequencies are found in the southern part of the Congo Basin. There are no significant changes in flood frequency identified in the northern and eastern part of the Basin. However, flood frequencies have been increasing in the centre and western part of the Basin. This study suggest that, exploring flood generating factors and the drivers of change can provide insights for understanding the influence of these factors on floods as climate models projected changes in extreme precipitation and aridity in the future.
How to cite: Gode, B. B., Jeff, N., Raphael, T., Lauwrence, H., Mark A, T., Vincent, L. M., and Paul, P. B.: Flood Seasonality in the Congo River Basin , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-64, https://doi.org/10.5194/egusphere-egu21-64, 2020.
Hourly precipitation extremes can intensify with higher temperatures at higher rates than theoretically expected from thermodynamic increases explained by the Clausius-Clapeyron (CC) relationship (~6.5%/K), but local scaling with surface air temperature is highly variable. Here, we use daily dewpoint temperature, a direct proxy of absolute humidity, as the scaling variable instead of surface air temperature. Using a global dataset of over 7000 hourly precipitation gauges, we estimate the at-gauge local scaling across six macro-regions; this ranges from CC to 2xCC for more than 60% of gauges. We find positive scaling in subtropical and tropical regions in contrast to previous work. Moreover, regional scaling rates show surprisingly universal behaviour at around CC, with higher scaling rates in Europe. Our results show a much greater consistency of scaling across the globe than previous work, usually at or above the CC rate, suggesting the relevance of dewpoint temperature scaling to understand future changes.
How to cite: Ali, H., Fowler, H., and Lenderink, G.: Consistent large-scale response of hourly extreme precipitation to temperature variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2156, https://doi.org/10.5194/egusphere-egu21-2156, 2021.
California’s large network of dams is under increasing scrutiny as hydrologic extremes are becoming more frequent and dams are aging. Typically, dam spillways are sized for the most severe flood that is likely in a given watershed, called the Probable Maximum Flood (PMF). PMF is obtained from the Probable Maximum Precipitation (PMP), which is the greatest 72-hour depth of precipitation that is “meteorologically probable”. Historically, PMP has been estimated by scaling depth-area-duration relationships obtained from severe historical storms. The scaling factor was estimated as the ratio of moisture available during that storm to the climatological maximum for the region. This PMP estimation approach, after which the spillways of most existing dams in California have been sized, has long been criticized as being somewhat arbitrary, although in practice it has led to relatively conservative spillway designs. Advances in both atmospheric models now facilitate a more rational basis for specifying PMP. Over the last decade, model-based PMP estimation frameworks have been developed whereby a severe historical storm is reconstructed and “maximized” using a regional atmospheric model. The most common approach to date, called relative humidity maximization (RHM) consists of setting relative humidity to 100% at the model boundaries, which has the effect of generating more precipitation (“maximum”) than occurred in the actual storm. This addresses major limitations of earlier PMP techniques through (1) more realistic representation of storm physics, (2) applicability of the method to future climate, and (3) suitability for forcing hydrologic models for improved PMF estimation.
The work I present here addresses concerns regarding the sources of uncertainty in the RHM approach, such as choice of storm to reconstruct and maximize, and choice of model physics parametrizations that directly affect model-based PMP estimates. To do so I produce an ensemble of PMP estimates (rather than a single value) that samples the above-mentioned sources of uncertainty. I focus on three California study basins, all of which have large reservoirs and different topographic and hydroclimatic conditions: the Feather, Russian and Santa Ana River basins. Using the WRF model forced with ERA5 reanalysis, I first create an ensemble of 40 reconstructions based on 10 combinations of physics parametrizations for 4 severe historical storms (Dec. 1964, Feb. 1986, Jan. 1997, and Feb. 2019). Next, I modify the 40 reconstructions by maximizing the model boundary moisture fluxes. This results in an ensemble of 4 storm events, 10 physics combinations, and 2 PMP methods, yielding 80 PMP estimates from which to better assess uncertainty in PMP. Differences among the PMP estimates I obtain based on different storm events, model physics and PMP methods confirm the value of such an ensemble in providing a measure of uncertainty in PMP estimates . Focusing on large dams in California, this work is intended to improve confidence in and utility of PMP estimates, which form the cornerstone of dam safety, and ultimately enable safer and more effective reservoir management as the climate continues to change.
How to cite: Tarouilly, E. and Lettenmaier, D.: Uncertainty in Probable Maximum Precipitation for Dam Safety , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2861, https://doi.org/10.5194/egusphere-egu21-2861, 2021.
How should design flood magnitudes be estimated under climate change? Apart from assuming stationarity, the two main approaches are hydrologic simulation and informed-parameter, which is generally based on either trend or climate covariates. Here, we compare these approaches across a large set of hydro-climatologically diverse basins located throughout the contiguous United States, splitting the historic record into a calibration and validation time period. We evaluate performance when the approaches are forced with observed climate as well as simulated climate from general circulation models. We also investigate how the strengths of the climate informed and hydrologic simulation approaches can be combined to improve projections; here, we use the outputs of hydrologic simulation as covariates in the climate informed approach. The results provide a quantitative perspective on key long-term flood projection issues and provide a route forward to improving projections given the identified strengths and weaknesses of each approach.
How to cite: Schlef, K., François, B., and Brown, C.: Comparing and Advancing Approaches to Long-Term Flood Projection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3351, https://doi.org/10.5194/egusphere-egu21-3351, 2021.
Within risk modelling, event ‘footprints’ are used to demonstrate how an extreme event impacts different locations at a similar time. Currently, estimates of future impacts from extreme events are derived by applying climate change allowances to at-site flood frequency estimates based on observations from the current period. These modified flow frequency estimates are then used to calculate flood risk and associated losses using a variety of means.
The present work brings together these two strands to develop spatially resolved projections of changes in river flow and, together with new analyses of the spatial coherence, to generate a wider collection of plausible events to improve risk modelling of the rarest events. This wide collection of extreme flood events provides the foundational input for an event-based assessment of risk.
The research extends proven methods to generate extreme, widespread flood events directly based on outputs from a 1km grid-based hydrological model driven by UKCP18 datasets. These modelled events provide coherent and highly credible descriptions of changes in flow based on spatially coherent climate change information. In addition to the small number of widespread extreme events generated directly from the gridded hydrological model, copula-based methods have been extended and applied on a regional and even national scale at a 1km resolution over the GB river network. These extensions to the Heffernan-Tawn model and Empirical Copula models are being used to generate a collection of plausible extreme based on the climate of 1980-2010 and on climate projections for 2050-2080. The collection of events is then used to compare the characteristics and variability of widespread events across different climate ensemble members and compare between present and future estimates.
How to cite: Griffin, A., Stewart, L., Kay, A., Bell, V., Sayers, P., and Carr, S.: Simulating spatially coherent widespread flood events for risk modelling in the UK, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9671, https://doi.org/10.5194/egusphere-egu21-9671, 2021.
Flash Floods are damaging natural hazards which often occur in the European Alps. Precipitation patterns and intensity may change in a future climate affecting their occurrence and magnitude. For impact studies, flash floods can be difficult to simulate due the complex orography and limited extent & duration of the heavy rainfall events which trigger them. The new generation convection-permitting regional climate models improve the intensity and frequency of heavy precipitation (Ban et al., 2021).
Therefore, this study combines such simulations with high-resolution distributed hydrological modelling to assess changes in flash flood frequency and occurrence over the Alpine terrain. We use the state-of-the-art Unified Model (Berthou et al., 2018) to drive a high-resolution distributed hydrological wflow_sbm model (e.g. Imhoff et al., 2020) covering most of the Alpine mountain range on an hourly resolution. Simulations of the future climate RCP 8.5 for the end-of-century (2096-2105) and current climate (1998-2007) are compared.
First, the wflow_sbm model was validated by comparing ERA5 driven simulation with streamflow observations (across Rhone, Rhine, Po, Adige and Danube). Second, the wflow_sbm simulation driven by UM simulation of the current climate was compared to a dataset of historical flood occurrences (Paprotny et al., 2018, Earth Syst. Sci. Data) to validate if the model can accurately simulate the location of the flash floods and to determine a suitable threshold for flash flooding. Finally, the future run was used to asses changes in flash flood frequency and occurrence. Results show an increase in flash flood frequency for the Upper Rhine and Adige catchments. For the Rhone the increase was less pronounced. The locations where the flash floods occur did not change much.
This research is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to support climate adaptation and mitigation decisions for the coming decades by developing a regional climate prediction and projection system based on high-resolution climate models for Europe.
N. Ban, E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, …, M.J. Zander (2021): “The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation”, manuscript accepted for publication in Climate Dynamics.
S. Berthou, E.J. Kendon, S. C. Chan, N. Ban, D. Leutwyler, C. Schär, and G. Fosser, 2018, “Pan-european climate at convection-permitting scale: a model intercomparison study.” Climate Dynamics, pages 1–25, DOI: 10.1007/s00382-018-4114-6
Imhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2020. “Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river.” Water Resources Research, 56. Doi: 10.1029/2019WR026807
Paprotny, D., Morales Napoles, O., & Jonkman, S. N., 2018. "HANZE: a pan-European database of exposure to natural hazards and damaging historical floods since 1870". Earth System Science Data, 10, 565–581, https://doi.org/10.5194/essd-10-565-2018
How to cite: Zander, M., Viguurs, P., Sperna Weiland, F., and Weerts, A.: Using convection-permitting climate models and a high-resolution distributed hydrological model to assess future changes in Alpine flash floods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11484, https://doi.org/10.5194/egusphere-egu21-11484, 2021.
The rapid increase in heavy precipitation flooding events highlights the need for efficient flood forecasting techniques to facilitate flood hydrological research and effective flood management by civic bodies. The current study aims to develop a near-real-time flood forecasting framework by integrating a 3-way coupled hydrodynamic flood model framework with numerical weather modelling based rainfall forecasts. The proposed framework has been demonstrated over Mumbai city in India, which is subjected to flooding every year during the monsoon months. A fine-resolution atmospheric simulation with the Weather Research and Forecasting (WRF) model has been performed for rainfall forecasts, which serve as an input to the flood model. To access the impact of urbanization on rainfall extremes, three scenarios are considered in the WRF simulations, i.e., WRF model: (1) without Urban canopy model (WRF-NoUCM), (2) coupled with a single-layer Urban canopy model (WRF-SUCM), and (3) coupled with a multi-layer Urban canopy model (WRF-MUCM). Further, a three-way coupled flood model has been developed where the MIKE 11 model (streamflow) with the drainage network (stormwater drains) and the MIKE 21 model (overland flow) have been considered for flood inundation and subsequently hazard mapping. In addition, the tidal elevation is provided along the coastline in the model setup. The flood maps developed by three WRF forecasted rainfall scenarios have been compared with that of the maps developed with observed rainfall. The extent to which the scenarios have been able to imitate the pattern and extent of flooding generated by observed rainfall has been investigated to decide the best scenario to be adapted in the comprehensive flood forecasting network. This state-of-art flood forecasting approach may be implemented in other flood-prone coastal regions as a major non-structural flood management strategy to reduce flood risk and vulnerabilities for the people dwelling in those regions.
How to cite: Ghosh, M., Paul, S., Karmakar, S., and Ghosh, S.: Near-real-time flood forecasting for an urban coastal catchment: An approach in combination of numerical weather and 3-way coupled hydrodynamic flood modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12834, https://doi.org/10.5194/egusphere-egu21-12834, 2021.
This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.
How to cite: Kam, J., Kim, S., and Roundy, J.: NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1957, https://doi.org/10.5194/egusphere-egu21-1957, 2021.
Spatially extensive multi-year hydrological droughts threaten water resources availability and incur significant environmental and socio-economic consequences. Given the impacts of climate change, the UK is expected to remain vulnerable to future multi-year droughts. Existing approaches to quantify hydrological impacts of climate change are often scenario-driven and may miss out plausible outcomes with significant impacts. Event-based storyline approaches aim to quantify “storylines” of how a singular event with significant impacts could hypothetically have unfolded in alternative ways from plausible changes to its causal factors under present and future climate. This study uses the 2010-2012 UK drought, the most recent period of severe hydrological drought, as a basis, to create counterfactual storylines based on changes to 1) precondition severity, 2) temporal drought sequence and 3) climate change. Model simulations are performed using the GR4J hydrological model and drought characteristics for each counterfactual storyline is calculated using the Standardized Streamflow Index at multiple accumulation periods.
The storylines show that maximum intensity, mean deficit and duration of the 2010-2012 drought were highly conditioned by its meteorological preconditions. Recovery time from progressively drier preconditions reflect both spatial variation in drought characteristics and the influence of physical catchment characteristics, particularly hydrogeology, in the propagation of multi-year droughts. Plausible storylines of an additional dry year with dry winter conditions repeated before the observed drought or replacing the observed dramatic drought termination confirm the vulnerability of UK catchments to a “three dry winter” scenario. Application of the UKCP18 projections at four global warming levels explore the impacts of the drought in a warmer world. Drought conditions of the storylines could have matched and exceeded that experienced in past severe droughts, especially for southern catchments. The construction of storylines based on observed events can complement existing methods to stress test UK catchments against plausible unrealized droughts.
How to cite: Chan, W., Shepherd, T., Smith, K., Darch, G., and Arnell, N.: Storylines of UK drought based on the 2010-2012 event, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1544, https://doi.org/10.5194/egusphere-egu21-1544, 2021.
In the present study, an evaluation of the past, present, and future variability of droughts in the Bundelkhand region of Central India are analyzed. Bundelkhand is a severe drought-prone region with intense water stress, where in the last five years four were drought. Therefore, understanding the drivers of drought over the region and its future projection is quite crucial for regional water management. The assessment has been made by analyzing the observational dataset from 1951-2018 to understand the regional drought dynamics. The future projection is made using a multi-model ensemble from a regional climate model over the CORDEX South-Asia domain under the highest emission scenario. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) indices are used to understand present drought and its future projection. In addition to this, drought driving parameters like precipitation, temperature, sea-surface temperature wind circulation has been assessed to understand the regional drought dynamics. The composite analysis of drought indicates that the moisture-laden low-level jet from the Arabian Sea branch generally weakened compared to Bay of Bengal branch for monsoon season. Teleconnections of drought over Bundelkhand region shows that nearly half of the droughts are linked to El-Nino events that have become stronger in recent past. The model result reveals that regional climate variability is reasonably captured over the region. In addition, we found increasing drought frequency since the beginning of the 21st century. The detailed results from the analysis will be shown briefly in the general assembly.
Acknowledgement: This work is jointly supported by the Department of Science and Technology (DST), Govt. of India, grant number DST/INT/RUS/RSF/P-33/G and the Russian Science Foundation (Project No.: 19-47-02015). The first author is also thankful to the Department of Science and Technology (DST), Govt. of India for providing DST INSPIRE fellowship (Grant No. IF160281).
How to cite: Saharwardi, M. S., Dubey, A. K., Kumar, P., and Sein, D. V.: Drought dynamics and variability over Bundelkhand region of central India: Past, Present and Future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15248, https://doi.org/10.5194/egusphere-egu21-15248, 2021.
In this contribution future changes of surface water availability over the Austrian domain is investigated. We use an ensemble of downscaled and bias-corrected regional climate model simulations of the EURO-CORDEX initiative under moderate mitigation (RCP4.5) and Paris agreement (RCP2.6) emission scenarios. The climatic water balance and its components (rainfall, snow melt, glacier melt and potential evapotranspiration) are used as indicators for surface water availability and we focus on different altitudinal classes (lowland, mountainous and high alpine) to depict a variety of processes in complex terrain. Apart from analysing the mean changes of these quantities we also pursue a hazard risk approach by estimating changes in return periods of drought events of a given magnitude as observed in the reference period. The results show in general wetter conditions over the course of the 21st century over Austria. Considering seasonal differences, winter and spring will be getting wetter due to an increase in precipitation along with a higher rainfall/snowfall fraction as a consequence of rising temperatures. In summer only little changes in the ensemble median of the climatic water balance are visible, hence uncertainties are large due to a considerable ensemble spread. However, by analysing changes in return periods of drought events, a robust signal of increasing risk of moderate and extreme drought events during summer is apparent. It emerges from an increase in interannual variability of the climatic water balance, which likely stems from intensified land-atmosphere coupling under climate change sustaining and intensifying spring preconditions towards even wetter or dryer summers.
How to cite: Haslinger, K., Laaha, G., Schöner, W., Konrad, A., Olefs, M., Koch, R., and Abermann, J.: Elevation-dependent drying signals under future climate change – a case study for Austria, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6533, https://doi.org/10.5194/egusphere-egu21-6533, 2021.
Droughts are anticipated to intensify or become more frequent in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought definition, makes it difficult to compare drought projections and hampers overviewing future changes in drought. This issue is widely known and underscored in recent reports of the Intergovernmental Panel on Climate Change, but the relative importance of the issue and its spatial distribution have never been quantitatively evaluated compared to other sources of uncertainty.
Here, using a multi-scenario and multi-model dataset with combinations of three climate change scenarios, four global climate models and seven global water models, we evaluated changes in the frequency of three categories of drought (meteorological, agricultural, and hydrological droughts) by a consistent standardized approach with four different temporal scales of accumulation periods to show how differences among the drought definitions could result in critical uncertainties. For simplicity, this study focuses on one drought index per drought category. Firstly we investigated the disagreement in the sign of changes between definitions, and then we decomposed the overall uncertainty to estimate the relative importance of each source of uncertainty. By a multifactorial ANOVA, uncertainty was decomposed into four main factors, namely drought definitions, climate change scenarios, global climate models and global water impact models, and their interactions.
Our results highlight specific regions where the sign of change disagrees between drought definitions. Importantly, changes in drought frequency in such regions tended to be statistically insignificant with low ensemble member agreement. Drought definition attributed to18% of the main factor uncertainty at the global scale, and the definition was the dominant uncertainty source over 11% of the global land area. The contribution of difference in the drought category showed a higher contribution to overall uncertainty than the difference in scales. The contribution of scenario uncertainty was the least among the main factors in general, though it is a dominant factor in the far-future in a couple of hotspot regions such as the Mediterranean region. Overall, model uncertainties were the primary source of uncertainty, and the definition issue was less important over large areas. However, definition uncertainty was the primal uncertainty source with significant changes in particular regions, such as parts of high-latitude areas in the northern hemisphere. One needs to pay attention to these regions in overviewing future drought change. Nonetheless, what this study quantified is the relative importance of uncertainty stemming from drought definition that should be avoidable or reducible if one treats drought specifically. Our results indicate that we can reduce uncertainty in drought projections to some extent and get a clearer picture by clarifying hydrological processes or sectors of interest.
How to cite: Satoh, Y., Shiogama, H., Hanasaki, N., Pokhrel, Y., Boulange, J., Burek, P., Gosling, S., Grillakis, M., Koutroulis, A., Schmied, H., Thiery, W., and Yokohata, T.: Decomposing the uncertainties in global drought projection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6780, https://doi.org/10.5194/egusphere-egu21-6780, 2021.
Understanding how climate change affects droughts guides adaptation planning in agriculture, water security, and ecosystem management. Earlier climate projections have highlighted high uncertainty in future drought projections, hindering effective planning. We use the latest CMIP6 projections and find more robust projections of meteorological drought compared to mean precipitation. We find coherent projected changes in seasonal drought duration and frequency (robust over >45% of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of the land area). Future drought changes are larger and more consistent in CMIP6 compared to CMIP5. We find regionalised increases and decreases in drought duration and frequency that are driven by changes in both precipitation mean and variability. Conversely, drought intensity increases over most regions but is not simulated well historically by the climate models. These more robust projections of meteorological drought in CMIP6 provide clearer direction for water resource planning and the identification of agricultural and natural ecosystems at risk.
How to cite: Ukkola, A., De Kauwe, M., Roderick, M., Abramowitz, G., and Pitman, A.: Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1889, https://doi.org/10.5194/egusphere-egu21-1889, 2021.
Extreme droughts can cause enormous ecological and economic damage, and are expected to become more severe in some regions due to climate change. For water managers, it is crucial to understand extreme droughts and how they are projected to change compared to previous droughts, in order to plan for resilience to these events.
Changes in water resources do not only result from changes in precipitation and periods of below normal precipitation (meteorological droughts), they are also shaped by changes in atmospheric moisture demand, characterized here by potential evaporation. Therefore we use two standardized indicators, the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evaporation Index (SPEI) to isolate the impact of projected changes in precipitation and potential evaporation. We consider the contribution of precipitation deficits and potential evaporation changes to projected changes in future drought duration, severity and frequency. We explore droughts and their development across different time scales, as their diversity – from flash droughts to creeping multi-year droughts – adds to the challenge.
We make use of the recently released 12-member 12-km horizontal resolution perturbed parameter ensemble of spatially coherent regional UKCP18 climate projections (with and without bias adjustment). This ensemble of projections was produced by the UK Met Office by dynamically downscaling a perturbed parameter ensemble of HadGEM3-GC3.05 simulations with a regional variant. The skill of the UKCP18 regional ensemble members for drought simulation is evaluated by comparison with observed drought metrics for the baseline period.
Projected changes in UK climate according to the UKCP18 projections include wetter winters, drier summers and generally stronger temperature increases in summer than in winter. We assess how these changes contribute to changes in drought characteristics using SPI and SPEI for each member of the ensemble.
While this work focusses on meteorological droughts, it will be followed by a future analysis of their propagation to hydrological droughts. This project aims to support adaptation to droughts in the region of East Anglia and is conducted in collaboration with the water company Anglian Water.
How to cite: Reyniers, N., Addor, N., Darch, G., He, Y., Zha, Q., and Osborn, T.: Understanding changes in meteorological drought in regional UK Climate Projections (UKCP18), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8713, https://doi.org/10.5194/egusphere-egu21-8713, 2021.
The summer of 2018 in North-Western Europe was exceptionally warm and dry, which negatively impacted many sectors. The drought of 2018 was followed by the dry summer of 2019 and the dry spring of 2020. Such multi-year droughts bring unique challenges to the agricultural sector, water authorities and society, and require different adaptation strategies compared to ‘normal’ single-year droughts. The succession of these dry years raises a question: is it pure coincidence that North-Western Europe experienced such a multi-year drought, or are there physical processes that cause multi-year droughts? Furthermore, in the present era it is obvious to ask whether anthropogenic climate change will amplify multi-year droughts in the region.
We aim to find drivers of multi-year droughts by using ERA5 reanalysis data and state-of-the-art Large Ensemble simulations from seven climate models. We select multi-year droughts in these datasets based on the Standardised Precipitation and Evapotranspiration Index and compare drought characteristics in the 1991-2020 reference period with multi-year droughts towards the end of the century. The models show a strong increase in multi-year drought risk from present-day to the end of the century. The frequency of multi-year droughts near doubles and the median duration of selected drought events increases from 16 months to 50 months. Model differences are substantial, mostly due to differences in temperature trends, but all models agree on the increase in multi-year drought risk. Internal variability is large, indicating a large ensemble approach is indeed required to study this problem.
Next we discuss geophysical drivers of multi-year droughts. Slow-varying ocean processes (through sea surface temperatures) and land processes (through soil moisture) are investigated as potential sources of meteorological conditions that lead to multi-year droughts. We consider the full Earth system, including ocean-land-atmosphere feedbacks, as potential forcing for these events. Summarizing, we will show that anthropogenic warming has potentially large impacts on the frequency, duration and therewith societal risk of multi-year droughts, warranting detailed studies of this topic.
How to cite: Batelaan, T. J., van der Wiel, K., and Wanders, N.: The Influence of Anthropogenic Climate Change on (drivers of) Multi-Year Droughts in North-Western Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3261, https://doi.org/10.5194/egusphere-egu21-3261, 2021.
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