Displays

Climate change will likely exacerbate droughts, increase regional water demands and affect agricultural yields. In addition, projected population growth combined with lack of  ‘good’ governance is likely to enhance the negative impacts of droughts and crop failure in the future as agriculture increasingly expands onto marginal lands. There is a global concern about these trends, because crop failure, droughts, increasing pressure on suitable agricultural land and rangeland for livestock, and changes and quality of governance can also increase the risk of conflict and (organized) violence.

In this presentation we explore the strength and impact of the climate-conflict trap., We use historical drought simulations and future drought projections to study the link between conflict and drought. Conflict data are taken from the Uppsala Conflict Data Program and combined with hydrological simulations from the global hydrological model PCR-GLOBWB.

The results show that drought occurrence is expected to increase under all climate scenarios, with stronger impacts for the higher emission scenarios.  On the other hand, at the global scale conflicts are likely to reduce as increased economic wealth compensates for the increased climate vulnerability.

This work helps us to better understand the interplay between the natural hydrological system and society. To better understand unsustainable and potentially devastating pathways for the coming decades, we have the greater aim to start unravelling the complex dynamics between changes in drought, society and risk of conflicts.

How to cite: Wanders, N., von Uexkull, N., Buhaug, H., and di Baldassarre, G.: Unravelling the complex interplay between drought and conflict, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10793, https://doi.org/10.5194/egusphere-egu2020-10793, 2020.

Australia’s climate is widely recognised as oscillating between drought and flood, with these cycles potentially intensifying under climate change. To reduce the impacts of both, being better prepared for, and more resilient to climate extremes is required . To develop management strategies that address these issues, improved prediction and an understanding of both drought onset and termination is required. Here, a whole-catchment assessment of drought from onset through to propagation and then termination for a 585 km² agricultural catchment in eastern Australia was conducted. Meteorological and hydrological measurements of drought were combined with vegetation and soil moisture data to assess how the catchment responded to drought and then recovered during drought termination. Thirteen meteorological drought periods persisting more than six months were identified during this period. During these, vegetation health, soil moisture and streamflow declined, however, all indicators recovered quickly when rainfall surplus returned. Drought onset was tightly coupled to the combined state of large-scale ocean-atmosphere climate drivers and termination was caused by synoptic-scale events. The combination of climatic factors, topography, soils and vegetation are believed to be what makes the study catchments more resilient to drought than others in eastern Australia. The study diversifies traditional approaches to assessing hydrological extremes at the catchment-scale by examining the drought to flood cycle using a range of globally available measures. This is a key step towards improved drought prediction and management.

How to cite: Gibson, A., Verdon-Kidd, D., Hancock, G., and Willgoose, G.: From Drought to Flood: The Life Cycle of Drought, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4278, https://doi.org/10.5194/egusphere-egu2020-4278, 2020.

California's climate is projected to have more droughts and heatwaves in the future. A combination of heat and drought stress may significantly affect vegetation health of the Mediterranean ecosystems than drought stress alone. Based on multi-source remote sensing and surface data, we investigated the impacts of drought and climate change on the Mediterranean-climate vegetation of California at different scales, i.e. the entire state, southern California, and Los Angeles urban area. For entire California, we find that a hydroclimatic dipole regulated by El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) intensifies the aridity in southern California compared to the north. At a regional scale of southern California, we utilized a bootstrapping regression model to analyze the geographical influences on the relationships between vegetation and drought. Results suggest a warmer climate can significantly increase vegetation sensitivity to drought. In addition, soil texture and elevation seem to also play an important role in adjusting the wildland vegetation susceptibility to drought. In the Los Angeles urban area, we find socioeconomic conditions is the decisive influence in intensifying or mitigating the vegetation response to water-scarce seasons and years. The projected hotter climate in the 21^st century may reshape the future landscapes of the coupled human-natural system in California by exacerbating drought severity and duration, differentiating mortality, and increasing wildfires.

How to cite: Dong, C., MacDonald, G., Okin, G., and Gillespie, T.: Responses of Mediterranean-climate vegetation to drought and climate change across scales in California, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7138, https://doi.org/10.5194/egusphere-egu2020-7138, 2020.

The global importance of groundwater as a resilient water supply has increased in recent years as groundwater is the major water supply for over 2 billion people worldwide. Global population growth and expansion of irrigated agriculture have caused groundwater depletion particularly in semi-arid and arid regions, and efforts are underway to achieve groundwater sustainability in these areas. As groundwater flow is slow, and most aquifers have very long residence times, time horizons of 50-100 years are often suggested for setting up groundwater sustainability goals. However, aquifer response time to various stressors is site specific and depends on aquifer properties, climatic conditions, and frequency and intensity of droughts. Here, we utilize daily groundwater observations from unconfined aquifers across the conterminous United States to quantify groundwater recovery time to meteorological droughts during 1981-2017 period. We consider two metrics to quantify groundwater recovery time: 1) the “time-lag” between the end of the precipitation drought and the termination of groundwater storage loss, and 2) the “time of rise”, the time that it takes until the aquifer storage reaches the pre-drought conditions. Our results indicate that the average time lag of aquifer response time to drought is 15 months, and the time lag can increase up to 15 years for some aquifers. Analysis across 634 wells reveal that depth to water table is the primary factor that determines whether aquifer physical properties or precipitation characteristics control this time lag to droughts. In regions with shallow water tables, aquifer physical properties determine lag time while in aquifers with deep groundwater tables precipitation properties are more important. The average recovery time of a shallow water table aquifer is about 3 years, and the recovery time is longer during severe droughts. It is expected that with projected increases in intensity and frequency of droughts in the future, the buffering capacity of aquifers will decrease, increasing the need for developing groundwater sustainability plans that consider conjunctive water use.  

How to cite: Ajami, H. and Schreiner-McGraw, A.: Characterizing Groundwater Response Time to Droughts Across the United States , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6245, https://doi.org/10.5194/egusphere-egu2020-6245, 2020.

Droughts in India affect food production, gross domestic product (GDP), livelihood, and socio-economic condition of a large population associated with agriculture. Recent drought (2015-2018) caused groundwater depletion and affected about one-fourth of the Indian population. However, it remains unclear if the drought of 2015-2018 was among the most severe droughts that occurred in India. Here we use a long-term (1870-2018) data to identify the top five ("deadly") meteorological/hydrological droughts based on overall severity score in the last century and half period. Out of a total of 18 meteorological droughts, the deadly droughts occurred in 1899, 1876, 2000, 1918, and 1965. Similarly, the deadly hydrological droughts occurred in 1899, 2000, 1876, 1965, and 1918 during 1870-2018. All the five deadly droughts were associated with the positive phase of El Nino Southern Oscillations (ENSO). Results show that the relationship between ENSO and monsoon (June to September) precipitation in India has weakened while the role of Indian and Atlantic Oceans has strengthened during the recent decades. Notwithstanding the longest (41 months) duration, the 2015-2018 drought did not feature among the deadly droughts. The 2015-2018 drought affected surface (reservoir storage) and groundwater availability in both southern and northern parts of India and was linked to El-Nino and Indian Ocean Dipole. Droughts and rapidly declining groundwater together can pose serious challenge to ensure fresh water security in India.

How to cite: Mishra, V.: Long-term (1870-2018) drought reconstruction in India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12383, https://doi.org/10.5194/egusphere-egu2020-12383, 2020.

The 21st-century droughts in Europe are regarded as exceptionally severe
and negatively affecting a wide range of socio-economic sectors due to
increases in temperature together with a lack of precipitation during
the spring/summer months [1]. In this study, we synthesize a space-time
evolution of soil moisture droughts in the period of 1766-2019
to better understand the evolution of large-domain multi-year droughts
reflecting the long-term historical changes in hydroclimate variability across Europe.

Following steps are taken to quantify the prolonged (multi-year) soil
moisture droughts: (1) simulate soil moisture (SM) with
the mesoscale Hydrologic Model (mHM, [2]) forced using several bias-corrected
meteorological merged products [3-5]; (2) estimate
quantile-based soil moisture index (SMI) based on a 254-year long
monthly dataset, which is estimated with a kernel density approach [6];
(3) perform a spatio-temporal clustering algorithm to track droughts
through space and time along their evolution, for a given threshold of
SMI<0.2 [6]; (4) estimate drought statistics such as areal extent,
duration, intensity for all identified soil moisture drought events. 

The results from the period 1766-2019 show that total drought intensity
over Europe has an increasing trend, while the average
drought area remains unchanged.  In terms of total drought magnitude,
the ongoing recent 2018-2019 drought is ranked as the most extreme,
followed by 1920-1922, 1947-1948, 1857-1860, and 1988-1991
events. All these exceptional summer droughts were initiated in spring
primarily as a result of compounding effects of low precipitation and
high temperatures leading to extreme soil water
deficits. The 2018-2019 event exhibits average drought area covering
50% of the study domain, which is same as in 1947-1948. Our analysis
suggests that the 2018-2019 event is a new European drought benchmark,
replacing the well-documented 2003 drought event.

References:

[1] https://doi.org/10.1038/s41598-018-27464-4
[2] https://www.ufz.de/mhm
[3] https://doi.org/10.1007/s00382-007-0257-6
[4] https://doi.org/10.1002/joc.3711
[5] https://doi.org/10.1029/2009JD011799
[6] https://doi.org/10.1175/JHM-D-12-075.1

How to cite: Rakovec, O., Hari, V., Markonis, Y., Samaniego, L., Hanel, M., Thober, S., Maca, P., and Kumar, R.: The 2018-2019 European drought sets a new benchmark over 250 years, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6881, https://doi.org/10.5194/egusphere-egu2020-6881, 2020.

Droughts are multidimensional hazards that can lead to substantial negative environmental, societal and economic impacts. To understand drought processes, multiple perspectives need to be considered. Numerous studies have investigated drought propagation from meteorological droughts via soil moisture to hydrological droughts. Regional variation in drought regimes and anthropogenic influences make it difficult to find a direct connection between the multiple aspects of drought. Additionally, the lack of a comprehensive long-term multi data compilation limits our understanding of the severity and frequency of current drought events and therefore drought risk management strategies.

This study developed a multidisciplinary long-term dataset of drought indices and impact records in southwestern Germany for the time period between 1800 and 2018. It is based on meteorological data, streamflow records and tree-ring data as well as reported information on drought impacts. Drought events were classified into moderate, severe and extreme events based on each datatype separately, leading to a regional drought catalogue. Within this catalogue, 22 extreme drought events were identified as common events among different archives and data types. Ranking the ten most severe droughts per indicator uncovers extreme events in the 19th century. However the development of drought frequency and severity over the last two centuries highlights a unique intensification of drought events in the 21st century.

The multidisciplinary approach provides new insights into similarities but also unique aspects of different drought indicators. The catalogue identifies and includes numerous drought events of the past, which can be used for further risk related analysis as well as for planning and management of future events.

How to cite: Erfurt, M., Skiadaresis, G., Tijdeman, E., Blauhut, V., Bauhus, J., Glaser, R., Schwarz, J., Tegel, W., and Stahl, K.: Exploring the added value of a long-term multidisciplinary dataset in drought research - a drought catalogue for southwestern Germany dating back to 1801, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-181, https://doi.org/10.5194/egusphere-egu2020-181, 2020.

Regional flood and drought events often have more severe impacts than localized events in terms of damages and costs, the number of affected people, and habitat changes. Understanding which regions may be jointly affected by such extreme events can help us to derive reliable regional risk estimates, plan and manage resource flows, and develop suitable adaptation measures. However, the spatial dimension of droughts and floods is often neglected when deriving hazard estimates and we know little about the processes governing their spatial dependencies. Therefore, we investigate how and why spatial dependencies in droughts and floods vary seasonally and regionally over the United States. We aim to gain new insight into processes governing spatial dependencies of droughts and floods by contrasting their regional and seasonal patterns.

To map regions with a similar seasonal flood and drought behavior, respectively, we introduce a measure of connectedness, which quantifies the number of catchments with which a specific catchment co-experiences flood or drought events. We then summarize the spatial dependencies by identifying regions with a similar flood behavior and regions with a similar drought behavior. To do so, we use a hierarchical clustering procedure on the F-madogram, which is a measure of spatial dependence for extremes. We look at regional and seasonal differences in spatial dependence both for floods and droughts and subsequently compare the two phenomena.

We find that spatial dependence is over all seasons stronger for droughts than for floods. Both types of extremes, however, show regional and seasonal differences in spatial connectedness. Droughts show the strongest spatial dependence in fall. In contrast, the Rocky Mountains show the highest spatial dependence of droughts in winter because of snow accumulation. Very low spatial dependence is found in spring. The seasonal, spatial dependence patterns of floods are opposed to the one of droughts. Spatial flood dependence is highest in spring, especially in mountainous areas, high in winter at the Pacific coast and the Appalachian Mountains, and high in summer in the Rocky Mountains. In contrast, spatial connectedness is very weak in fall.
We conclude that spatial dependence patterns are stronger for droughts than floods because of the slower processes and longer durations associated with the phenomenon.   Furthermore, we conclude that both meteorological and land surface processes such as snowmelt and the availability of soil moisture shape the spatial dependence patterns of each extreme.

How to cite: Brunner, M. I., Gilleland, E., Swain, D., Wood, A., and Clark, M.: Spatial dependence of floods and droughts: learning from differences in regional and seasonal patterns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1850, https://doi.org/10.5194/egusphere-egu2020-1850, 2020.

The frequency of flood events has been increasing across large areas of the central United States since the second half of the 20^th century; these increasing trends have been largely related to changes in precipitation. The aim of this presentation is to provide insights into the possible reasons responsible for these changes, providing basic information that may enhance our capability of predicting and projecting these changes.

This study highlights the role of weather types in explaining the observed changes in precipitation and, consequently, in the frequency of flood events. More specifically, we identify five weather types from daily 500-hPa geopotential height using the k-means cluster analysis. Consistent with their distinct large-scale atmospheric patterns, these weather types exert different effects on precipitation in the central United States. Because of the strong moisture transport, strengthened low-level jet stream and wavy upper-level polar jet stream located in the western United States, among the five weather types weather-type 1 exerts the strongest impacts on precipitation, accounting for up to 40% of the total precipitation over the study region. Moreover, we detect a significant upward trend in the number and persistency of these two weather types for 1948–2019, suggesting a rising risk of heavy and long-lasting precipitation across the central United States.

How to cite: Villarini, G. and Zhang, W.: Increasing Frequency of Flood Events across the Central United States: A Weather-Type Perspective, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3773, https://doi.org/10.5194/egusphere-egu2020-3773, 2020.

Floods can be caused by a large variety of different processes, such as short, but intense rainfall bursts, long rainfall events, which are wetting up substantial parts of the catchment, or rain on snow cover or frozen soils. Although there is a plethora on studies analysing or modelling rainfall-runoff processes, it is still not well understood, what rainfall and runoff generation conditions are needed to generate flood runoff and how these characteristics vary between catchments. In this databased approach we decipher the ingredients of flood events occurred in 161 catchments across Germany. For each catchment rainfall-runoff events are separated from observed time series for the period 1950-2013, resulting in about 170,000 single events. A peak-over-threshold approach is used to select flood events out of these runoff events. For each event, spatially and temporally distributed rainfall and runoff generation characteristics, such as snow cover and soil moisture, as well as their interaction are derived. Then we decipher those event characteristics controlling flood event occurrence by using machine learning techniques.

On average, the most important event characteristic controlling flood occurrence in Germany is, as expected, event rainfall volume, followed by the overlap of rainfall and soil moisture and the extent of wet areas in the catchment (area with high soil moisture content). Rainfall intensity is another important characteristic. However, a large variability in its importance is noticeable between dryer catchments where short rainfall floods occur regularly and wetter catchments, where rainfall intensity might be less important for flood generation. To analyse the regional variability of flood ingredients, we cluster the catchments according to similarity in their flood controlling event characteristics and test how good the flood occurrence can be predicted from regionalised event characteristics. Finally, we analyse the regional variability of the flood ingredients in the light of climate and landscape catchment characteristics.

How to cite: Merz, R., Tarasova, L., and Basso, S.: Ingredients of German flood events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11579, https://doi.org/10.5194/egusphere-egu2020-11579, 2020.

Flood is one of the most devastating natural disasters of severe societal, economic, and environmental consequences. Understanding the characteristics of floods, especially at fine spatial and short temporal scales, can be critical for improving forecast and risk management efforts. Due to the limited availability, in-situ observations have been inadequate for meeting the challenges at global extent. Existing global flood modeling efforts also lack the sufficient spatial/temporal resolutions for capturing rapid/local flood events, e.g., those developed in less than a day. Here we implement a carefully-designed modeling framework to reconstruct global river discharge at very high resolution (5-km and 3-hourly for runoff calculation and ~2.94 million river reaches derived from 90-m DEM for river routing) for 40 years (1979-2018). The Variable Infiltration Capacity (VIC) model with calibrated parameters, is coupled with the Routing Application for Parallel computation of Discharge (RAPID), serving as the core of the modeling framework. The state-of-the-art merged precipitation product, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and flowlines vectorized from the MERIT Hydro are used. Pixel-level model calibration and distributional bias correction are performed against global runoff characteristics derived from observations and machine learning. Skill assessments are carried out both globally at daily sale and over contiguous U.S. (CONUS) at 3-hourly scale, using both general discharge performance metrics (Kling-Gupta Efficiency and it three components) and sub-daily flood-specific metrics (probability of detection, false alarm rate, flood volume error, peak magnitude error, timing error, etc.). The work here aims to provide some first-time understanding of local scale rapid flooding over the global domain. We also expect to learn more about the modeling tools developed for analyzing/monitoring fine scale flooding globally – their efficacy and lack thereof, why, and where to improve.

How to cite: Yang, Y., Pan, M., Lin, P., Beck, H., Yamazaki, D., Lu, H., Yang, K., Hong, Y., and Wood, E.: Global long-term sub-daily reanalysis of fluvial floods through high-resolution modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10129, https://doi.org/10.5194/egusphere-egu2020-10129, 2020.

We use three hydrological models and the stochastic storm transposition (SST) framework to investigate the validity of implicit assumptions in the empirical methodology of regionalization of flood frequencies (RFF) for prediction in ungauged basins. In particular, we investigate the long-standing hypothesis that for a set of catchments physical homogeneity of meteorological and infiltration processes implies statistical homogeneity of flood peak distributions. Our modeling (theoretical) results do not support this hypothesis. We also show that power-law regressions (i.e. log-log linearity) do not seem to be an appropriate model to connect distributions across scales (either quantiles or distribution parameters). Finally, even though our results support the most fundamental hypothesis in RFF that the underlying distribution of peak flows is invariant under translation in the river network, our results do not support the simple-scaling or multi-scaling frameworks. First, we show that some moments of the distribution cannot be inferred from area alone, violating the definition put forward by Gupta et al. (1994). Second, the resulting scale invariant distributions that we identified are different from LP-III and GEV and cannot be rejected by data as valid distributions. Our framework provides a new avenue to test methods for flood data analysis and it opens the door towards a unified physics-informed framework for prediction of flood frequencies in ungauged basins embedded in gauged regions.

How to cite: Mantilla, R., Perez, G., Velasquez, N., Wright, D., and Yu, G.: Insights from Physics-based Hydrologic Models and Stochastic Storm Transposition into the Underlying Assumptions of Flood Quantile Regionalization Techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12755, https://doi.org/10.5194/egusphere-egu2020-12755, 2020.

Nuisance flooding, which is repetitive flooding caused by both tidal and rainfall-driven events, is increasing in frequency and severity for many coastal communities. As climate change causes sea level rise and more frequent and intense storm events, these nuisance flooding events are producing significant disruptions and impacts to coastal communities. The objective of this study is to improve modeling and decision support activities around nuisance flooding and, in particular, its impact on transportation infrastructure. Our study region and partner in the research is the City of Norfolk, Virginia, USA. Norfolk is home to the largest Navy base in the world, the second busiest port on the United States East Coast, and is the second most populous city in Virginia. It is also one of 100 Rockefeller Resilient Cities in the world, committed to taking progressive aims at combating nuisance flooding. Using real-time observational networks, crowdsourced data, physics-based and machine learning modeling approaches, model predictive control, and economic and social science methods, we are exploring ways to better understand and mitigate the impacts of street-scale flooding. Our research is showing how real-time control of stormwater infrastructure systems can help to improve the resilience of these systems during nuisance flooding events by strategically holding back rainfall runoff and preventing tidally driven stormwater backups. We are also showing physics-based and machine-learning methods can be combined for real-time decision support and how reputation system approaches can be used to measure trust in crowdsourced rainfall datasets. This presentation will provide an overview of these and related activities, each aimed at the common goal of leveraging real-time data from a variety of sources, innovative modeling techniques, and community-driven decision making to improve community resilience to nuisance flooding.

How to cite: Goodall, J. L., Behl, M., Bowes, B., Campbell, B., Chen, A., Chen, T. D., Sadler, J., Spencer, K., Gorman, M., Praharaj, S., Shen, Y., Zahura, F. T., and Zeng, L.: Nuisance Flooding in Coastal Communities: Real-time Modeling and Decision Support to Improve Transportation Infrastructure Resilience, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11464, https://doi.org/10.5194/egusphere-egu2020-11464, 2020.

Several studies showed an increasingly negative impact of droughts and floods due to the combined effects of socio-economic and climatic factors. To better understand changes in hydrological risk over time, it is fundamental to unravel the complex interactions between increasing urbanization, population growth, water management strategies and increasing frequency and intensity of hydrological hazards. To this end, various socio-hydrological models have been developed over the past decade to explain the dynamics of risk generated by either human-flood or human-drought interactions. This study proposes a new analytical framework to represent, for the first time, the deeply intertwined interactions between humans and both hydrological extremes, i.e. floods and droughts. A new system dynamic model is developed and then applied to explore the phenomena generated by human-water interactions in relation to different water management strategies. The results show the ability of the proposed model to capture multiple socio-hydrological phenomena that have been empirically observed (levee effect, supply-demand cycle, reservoir, rebound, and sequence effects). In particular, our model is capable to capture dynamics that did not emerge in previous socio-hydrological models where human, drought and flood interplay is considered independently. Given its explanatory value, the model can contribute to a better interpretation of changes in drought and flood risk associated with anthropogenic influences.

How to cite: Mazzoleni, M., Odongo, V., Mondino, E., and Di Baldassarre, G.: Modeling the interplay between droughts, floods and human activities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1012, https://doi.org/10.5194/egusphere-egu2020-1012, 2020.

Drought quantification is carried out by indicators like (1) Standardized Precipitation Index (SPI) and (2) Standardized Precipitation Evapotranspiration Index (SPEI), which encompass the historical characteristics of meteorological variants (precipitation and temperature). The behavioral pattern of these variables has changed significantly due to the recent changes in the climate, posing a question of the mutual agreement among SPI and SPEI in defining drought over regions with different climatic characteristics. India is chosen as the study area owing to the long term data availability, diverse climatic zones (from tropical monsoon belts to dry arid regions), and increasing drought likelihood in the mainland area. Daily, gridded precipitation (0.25º × 0.25º) and temperature (1º × 1º) data, from January 1951 to December 2013, was utilized for the calculation of SPI and SPEI. While the average annual precipitation in India is consistently declining, the yearly average temperature exhibits three distinct trends (decreasing from 1951 to 1975, neutral from 1975 to 1990, increasing abruptly from 1990 onwards). Such variations in the trend behavior are replicated in the increasing divergence of the two indices, represented as percentage area under drought. A robust regional divergence between the indices is detected from east to west, highlighting the arid and semi-arid regions as hotspots of significant deviations.  SPEI showcases an overall increasing drought hazard in India since the 1970s in terms of frequency, magnitude, and duration, when compared to SPI.

How to cite: Singh, G. R., Dhanya, C. T., and Chakravorty, A.: Investigating the divergence between Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in defining drought over different climate zones of India., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-454, https://doi.org/10.5194/egusphere-egu2020-454, 2020.

Drought is considered as one of the most complicated natural disasters, whose adverse effects span over different domains such as agriculture, ecosystem, and economy. One of the widely used meteorological drought index is Standardized Precipitation Index (SPI), which is based on the stationary assumption (i.e., the statistical parameters does not change over time). Nevertheless, numerous studies have reported that the precipitation series has undergone remarkable changes, which emphasizes the need for developing a drought index incorporating the dynamic behavior of the precipitation. Hence, in this study, a non-stationary SPI (NSPI) is developed to capture the temporal dynamics of the precipitation and to identify the meteorological drought-prone areas over India. Before modelling, the non-stationarity in the distribution parameters of precipitation series are detected. If non-stationary is observed in any of the parameters, then that particular parameter is modelled as non-stationary, otherwise it is modelled as stationary. The proposed index provides a probability-based description of drought status and its uncertainty bounds, which are computed using Bayesian Inference. Results reveals that the traditional SPI is biased by the lowest magnitude of precipitation leading to overestimation of drought where frequent severe dry events are clustered, which is overcome by NSPI. Additionally, NSPI captured the historical drought, capturing the temporal dynamics of precipitation series in India and is more reliable than SPI. The proposed NSPI is found to be a potential index for drought monitoring in a nonstationary climate.

How to cite: Vinnarasi, R. and Dhanya, C. T.: Time-Varying Meteorological Drought Index for a Changing Climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-621, https://doi.org/10.5194/egusphere-egu2020-621, 2020.

A robust characterization and risk assessment of meteorological droughts is the need of the hour considering its pervasiveness and consequences; however, their precise physical quantification is a difficult geophysical endeavor. This becomes a serious issue for India, having 18% of the world’s population and 4% of global freshwater, out of which 83% is used in agriculture. In this study, a detailed spatiotemporal assessment of the meteorological droughts characterized by standardized precipitation index (SPI) at annual scale is carried out over the Narmada Basin, India using the monthly rainfall data from 24 stations for 63 years (1951- 2013). The entire duration was divided into two epochs of 31 years (i.e. 1951-1981 and 1982-2012) for a comparative assessment of drought characteristics. The non- parametric Mann- Kendall (MK) test is applied to investigate the trend of droughts. Further, to predict the environmental Flow (EF) conditions from rainfall data only, the linkage of SPI with the average annual flow (%AAF) is examined over four sub-catchments (Mohegaon, Hridaynagar, Manot, and Sher) of the basin. The results reveal that the Narmada basin is prone to droughts with a frequency of once in 3 to 5 years. The frequency and severity of droughts have significantly increased in 1982-2012 as compared to 1951-1981. The severity of recent droughts shows a more widespread aerial extent in the region. The MK test results indicate an increasing trend in the droughts over most of the stations. An exquisite agreement between SPI and %AAF (used to describe the EF condition) is observed with R² ranging from 0.757 to 0.988, which shows that coupling SPI with %AAF can be effective for ungauged catchments. This study suggests that appropriate measures must be taken for better management of the water resources in the basin, and also for mitigation droughts, considering the increased risk of the severe drought events in recent decades.

How to cite: Swain, S., Mishra, S. K., and Pandey, A.: Assessment of droughts and their linkage to environmental flow conditions over a large Indian river basin , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1048, https://doi.org/10.5194/egusphere-egu2020-1048, 2020.

The characterization of droughts is very dependent on the time scale that is involved. To obtain an overall drought assessment, the cumulative effects of water deficits over different times need to be examined together. For instance, the joint deficit index (JDI) is based on multivariate probabilities of precipitation over various time scales from 1- to 12-months, and was constructed from empirical copulas. We examine the Gaussian copula model for the JDI, and we model the covariance across the temporal scales with a two-parameter function that is commonly used in the specific context of spatial statistics or geostatistics. The validity of the covariance models is demonstrated with long-term precipitation series.

Next, we assess the impact of climate change on future droughts, based on the JDI. We select an ensemble of CORDEX regional climate model simulations, under the emission pathways RCP4.5 and RCP8.5. The CORDEX resolution used is 0.11 degree (EUR-11). In particular, distributional changes in the JDI are analysed for the Brussels-Capital Region. This area contains climatological and synoptic stations that are operated by the Royal Meteorological Institute of Belgium, with long-term series.

How to cite: Van de Vyver, H., Van den Bergh, J., and Van Schaeybroeck, B.: Drought assessment in a changing climate with the joint deficit index, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1679, https://doi.org/10.5194/egusphere-egu2020-1679, 2020.

Climate change has been proved to exacerbate drought events and further cause huge economic and ecological losses worldwide. Therefore, it is of great significance to study the long-term evolution characteristics of drought events and quantify the impact of drought events on typical ecological indexes. Based on the measured historical precipitation data, the standardized precipitation index of different time scales was extracted to measure water deficit. The leaf area index with wide range and high precision was generated based on the Modis remote sensing image and denoising processing to represent vegetation growth. Trend analysis and change point analysis were carried out to study the spatiotemporal evolution characteristics of the concerned drought indexes. Then, with hypothesis test, appropriate copula multivariate analysis method was innovatively introduced to construct joint distribution of the standardized precipitation index and leaf area index. The contribution of drought on vegetation growth was expected to be quantified by deriving the conditional copula and preset marginal distributions. The upper Yangtze River where biomass is extremely sensitive to climate change was taken as a study area. The results show that drought events in this region have significant spatial heterogeneity. The leaf area index is highly influenced by the meteorological drought index. From no drought to severe drought, the vegetation index is distributed more and more toward the low value. Copula is very potential to find the inner relationship of the standardized precipitation index and leaf area index. The study is useful to deepen the understanding of the internal mechanism of drought events and discuss reasonable disaster prevention and mitigation countermeasures.

How to cite: Zhu, S.: Spatiotemporal evolution of drought events and its contribution on vegetation growth in the river source region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1871, https://doi.org/10.5194/egusphere-egu2020-1871, 2020.

Droughts were climate anomalies that occurred naturally, affected a large area and persist for a long time. However, climate change and human interventions have altered the characteristics of droughts, resulted in a new type of drought that has a rapid onset and severe impacts without sufficient early warning. This is termed as “flash drought” that occurred frequently worldwide in recent years. There has been progresses regarding flash drought definition, impact identification and analysis of spatiotemporal variations, but whether human interventions play an important role in altering the long-term changes of flash droughts or increasing the risk of the occurrence of a specific flash drought event remains unknown. Here, we propose a new method for explicitly characterizing flash drought events based on soil moisture deficit, and attribute historical trends and project future changes of flash drought risk by conducting climate-hydrology multimodel ensemble simulation over China, where natural and anthropogenic climate change scenarios provided by 11 CMIP5 models are used to drive 3 land surface hydrological models (CLM4.5, VIC, Noah-MP) for superensemble simulations. We find a significant increase in flash drought risk over China during the middle and end of this century. The increasing flash drought risk is mainly caused by greenhouse gas-induced anthropogenic climate change, where both long-term warming and increasing rainfall variability lead to a drier but more variable soil condition over the flash drought hotspots. With an urgent need to adapting to the increasing flash drought risk, the latest CMIP6 soil moisture data are being used to diagnose a severe flash drought event occurred over the lower reaches of the Yangtze River in Eastern China in the summer of 2019, and the contribution of human-induced climate change on the 2019 flash drought event is being assessed.

How to cite: Yuan, X., Wang, L., Ji, P., Zhang, M., Chen, S., and Wang, Y.: Human-induced intensification of flash drought risk in the Anthropocene, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1911, https://doi.org/10.5194/egusphere-egu2020-1911, 2020.

Drought is one of the most widespread and threatening natural disasters in the world, which has terrible impacts on agricultural irrigation and production, ecological environment, and socioeconomic development. As a critical ecologically fragile area located in southwest China, the Yarlung Zangbo River (YZR) basin is sensitive and vulnerable to climate change and human activities. Hence, this study focused on the YZR basin and attempted to investigate the spatiotemporal variations of drought and associated multi-scale response to climate change based on the scPDSI (self-calibrating Palmer drought severity index) and CRU (climate research unit) data. Results showed that: (1) The YZR basin has experienced an overall wetting process from 1956 to 2015, while a distinct transition period in the mid 1990s (from wet to dry) was detected by multiple statistical methods. (2) Considering the spatial variation of the scPDSI, areas showing the significantly wetting process with increasing scPDSI values were mostly located in the arid upstream and midstream regions, which accounted for over 48% area of the YZR basin, while areas exhibiting the drying tendency with decreasing scPDSI values were mainly concentrated in the humid southern part of the YZR basin, dominating the transition period from wet to dry, to which more attention should be paid. (3) By using the EEMD (ensemble empirical mode decomposition) method, the scPDSI over the YZR basin showed quasi-3-year and quasi-9-year cycles at the inter-annual scale, while quasi-15-year and quasi-56-year cycles were detected at the inter-decadal scale. The reconstructed inter-annual scale showed a better capability to represent the abrupt change characteristic of drought, which was also more influential to the original time series with a variance contribution of 55.3%, while the inter-decadal scale could be used to portray the long-term drought variation process with a relative lower variance contribution of 29.1%. (4) The multi-scale response of drought to climate change indicated that changes of precipitation and diurnal temperature range (DTR) were the major driving factors in the drought variation at different time scales. Compared with potential evapotranspiration, DTR was a much more important climate factor associated with drought variations by altering the energy balance, which is more obvious over the YZR basin distributed with extensive snow cover and glaciers. These findings could provide important implications for ecological environment protection and sustainable socioeconomic development in the YZR basin and other high mountain regions.

How to cite: Liu, L., Li, H., Niu, Q., Lun, Y., and Xu, Z.: Spatiotemporal Variation of Drought and Associated Multi-Scale Response to Climate Change in Southeast Qinghai–Tibet Plateau, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2518, https://doi.org/10.5194/egusphere-egu2020-2518, 2020.

Understanding the hook structure between storm runoff extremes and temperature is key to quantifying the complex response of flooding regime to anthropogenic climate warming, but its underlying mechanisms, shifting trajectories and environmental consequences are highly uncertain. Our in-situ observations suggest a spatially homogeneous negative sensitivity of relative humidity to rising temperatures, with a colder peak point temperature (Tpp) than that of precipitation and storm runoff extremes, implying that atmospheric moisture constraint plays an important but inferior role than thermodynamic drivers in extreme-temperature scaling. To probe into the complex interplay of hook structures and weather-related hazard evolution, we focus on flooding menace over China’s main catchments and project streamflow scenarios with model cascade chains combining 31 CMIP5 models, bias correction and four hydrological models. The ensemble projections confirm a severe enhancement of extremes, with involvement of the hook structure’s continuous shift towards a warmer temperature accompanying by an upward movement under future warming.

How to cite: Yin, J., Guo, S., and Gu, L.: Shifting pattern of hook structures and impacts on storm runoff extremes under anthropogenic climate warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2976, https://doi.org/10.5194/egusphere-egu2020-2976, 2020.

Assessing the risk of future flood events and the implications for flood risk in cities is an economically and socially costly problem. In this research, we assess the utility of documentary evidence of past flood events for contemporary flood risk assessments to reduce the uncertainty in flood frequency estimation due to the interpolation from short annual maximum series (AMS) records.

The historical city of Bath, United Kingdom, developed in close relation to the River Avon, and evidence of flooding in the city of Bath can be traced back to Roman occupation. For this research a particularly rich record of historical evidence was chosen occurring from the 19^th century onwards with flood marks on buildings through-out the city as well as documentary evidence in contemporary newspapers and technical reports. The earliest flood mark found in the city of Bath dates to 1823 with 15 more extreme floods after that marked as well. The extensive flooding in 1947 initiated work on what eventually became the present-day Bath flood protection scheme (BFS) which was implemented after the 1960 catalyst flood event.

Using an existing one-dimensional hydraulic model representing the current hydraulic system of the River Avon in Bath, a historical survey of how the river and its management has changed over time was conducted. The model was developed using historical evidence (e.g. maps, flood marks, photographs, newspaper articles etc), surveyed river cross sections, recorded and design hydrographs from National datasets.

The 1960 flood is reconstructed numerically using all available data, from flood marks to old surveyed river cross sections.  The resulting hydraulic model is used to investigate the effect of the Bath Flood Defence Scheme. Sensitivity studies with different values for the roughness coefficient are also presented in order to assess the uncertainty on water levels during extreme events. Finally, the numerically reconstructed historical peak flood discharge is compared with the results obtained using a simple Manning equation approach to assess the two methods. This paper demonstrates how hydraulic modelling can be applied to historical data and offers considerable potential to further investigations in the improvement of design flood flows.

How to cite: Stamataki, I. and Kjeldsen, T.: Reconstructing a hydraulic model for historic flood levels in the city of Bath, United Kingdom, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3751, https://doi.org/10.5194/egusphere-egu2020-3751, 2020.

How drought changes in the context of global warming is a concerning issue that influences the strategies of drought mitigation and drought management. Based on the simulations of the version 2 of Global Land Data Assimilation System (GLDAS-2.0) during 1948-2016, we revisited the drought trend over China and analyzed the individual contributions of precipitation and potential evapotranspiration (PET) on varied drought patterns. Four composite drought indices including the Aggregate Drought Index (ADI), Joint Drought Deficit Index (JDI), self-calibrating Palmer Drought Severity Index (scPDSI) and Standardized Palmer Drought Index (SPDI) were employed for trend detection. Results showed that all four composite drought indices suggested a significant drying belt spreads from northeastern China to southwestern China, and a significant wetting trend in the “Three river sources” areas. Controversial patterns were mainly located in the northwestern China, Xinjiang districts, and the middle and lower reaches of the Yangtze River, where the SPDI and JDI respectively, overestimated and underestimated the moisture conditions at varying degrees. According to the change point tests, it is found that the drying pattern in the northeastern China occurred since 1970s, where precipitation deficits and expanded PET jointly aggravated the drying process, while for the “Three river sources” areas, the increased precipitation since 2000s is the main driver for the wetting pattern.

How to cite: Ren, L.: Revisiting drought trend over China during 1948-2016: a multivariate perspective, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4555, https://doi.org/10.5194/egusphere-egu2020-4555, 2020.

Standardized drought indices such as SPI are frequently used around the world to assess drought severity across a continent or a larger region covering different meteorological regimes. But how standard are the standardized indices? In this paper we quantify the uncertainty of SPI and SPEI based on an Austrian data set to shed light on what are the main sources of uncertainty in the study area. Here we analyze the uncertainty contributions by a linear mixed model that employs a restrictive maximum likelihood estimator in order to produce unbiased variance and covariance components. Five factors that either defy the control of the analyst (record length, observation period), or need to be subjectively decided during the steps of the calculation (choice of the distribution, parameter estimation method, and GOF-test of the fitted distribution) are considered. The results show that, overall, the choice of the distribution and the observational window are the most important sources of uncertainty. We quantify the relative uncertainty contributions in greater detail in order to give guidance how to make estimates most accurate for a given data set. We finally analyze the total uncertainty of SPI and SPEI to shed light on our main question whether the indices are skillful enough to provide a quantification of atmospheric drought that is standardized enough to allow the intended comparisons across various data situations and meteorological regimes.

How to cite: Laimighofer, J. and Laaha, G.: How standard are standardized drought indices? Uncertainty contributions for the SPI & SPEI case, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4716, https://doi.org/10.5194/egusphere-egu2020-4716, 2020.

Traditionally, availability of consistent, high quality, high-resolution data for Sub-Saharan Africa (SSA) has been limited, with political barriers, poverty and slow technological advancement all contributing to this issue. Over the past 30 years, a rapid increase in the advancement of satellite technology has led to the new era of ‘big data’, which includes a number of high-resolution, global remote sensing datasets. With an overwhelming amount of data now being downloaded and processed, we need to be sure that the best products are being used, in the most appropriate way, to determine the onset and evolution of extreme hydrological events and to influence policy implementation. This study uses scaling analysis of a number of hydrological and agricultural variables to investigate how spatial resolution influences monitoring of drought events. By studying the 2016/17 drought in Kenya, and assessing the drought footprint at various resolutions, it is evident that the data and its scale largely influences the apparent drought signal. Across all the variables, coarser data showed a significantly reduced drought extent than finer data, with a number of regions appearing to not fall below the drought threshold, when in reality, that area was experiencing drought. The implications of these scale issues could be significant, as drought policies in Kenya are implemented on a county level basis. By understanding the importance of effective scaling between the decision-making scale (policy), the data used for drought assessment (products) and the impacts of drought on the ground (processes), updated drought management and mitigation techniques can be used, with potential to reduce vulnerability to future drought events.

How to cite: Baulch, J., Sheffield, J., and Dash, J.: Processes, Products & Policy: Investigating how drought events are perceived differently across spatial scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5846, https://doi.org/10.5194/egusphere-egu2020-5846, 2020.

Understanding historical patterns of changes in drought is essential for drought adaptation and mitigation.
While the negative impacts of drought over east Africa have attracted increasing attention,
a comprehensive and long-term spatiotemporal assessment of drought is still lacking. Here, we provided
a comprehensive spatiotemporal drought pattern analysis during the period of 1964–2015 over
the GHA. The Standardised Precipitation-Evapotranspiration Index (SPEI) at various timescales (1 month
(SPEI-01), 3 month (SPEI-03), 6 month (SPEI-06), and 12 month (SPEI-12)) was used to investigate drought
patterns on a monthly, seasonal, and interannual basis. The results showed that despite regional differences,
an overall increasing tendency of the drought was observed across the GHA over the past 52 yr, with trends of
change of -0.0017 yr^-1, -0.0036 yr^-1, -0.0031 yr^-1, and -0.0023 yr^-1 for SPEI-01, SPEI-03, SPEI-06, and
SPEI-12, respectively. Droughts were more frequent, persistent, and intense in Sudan and Tanzania, while
more severe droughts were found in Somalia, Ethiopia, and Kenya. Droughts occurred frequently before
the 1990 s, and then became intermittent with large-scale impacts occurred during 1973–1974, 1984–
1985, and 2010–2011. A turning point was also detected in 1989, with the SPEI showing a statistically significant
downward trend during 1964–1989 and a non-statistically significant downward trend from 1990
to 2015. Seasonally, droughts exhibited an increasing trend in winter, spring, and summer, but a decreasing
trend in autumn. The research findings have significant implications for drought adaptation and mitigation
strategies through identifying the hotspot regions over east Africa at various timescales. Area-specific
efforts are required to alleviate environmental and societal vulnerabilities to drought events.

How to cite: Haile, G. G., Tang, Q., Leng, G., Jia, G., Wang, J., Cai, D., Sun, S., Baniya, B., and Zhang, Q.: Spatial and temporal variation of drought patterns over East Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6575, https://doi.org/10.5194/egusphere-egu2020-6575, 2020.

Soil drought has an important influence on plant development. The SoilClim model was used to investigate episodes of soil drought with 10-year return periods at the 0–100-cm profile during the 1961–2017 period for four selected regions of the Czech Republic (North-western Bohemia, Southern Bohemia, North-eastern Moravia, and Southern Moravia). It emerged that the frequency of soil drought significantly increases in the summer half-year (SHY) and exhibits insignificant trends in the winter half-year (WHY). The dynamic climatology of soil drought is based herein upon synoptic situations as classified by the Czech Hydrometeorological Institute, in terms of which changes in the occurrence and precipitation intensity of drought episodes in the four individual regions were studied. Drought episodes are generally related to decreases in the frequency of precipitation-rich situations and in their precipitation intensity. This is particularly true of situations C (central cyclone over central Europe), B (trough over central Europe) and Bp (travelling trough). Situations B and Bp, together with south-west cyclonic situations SWc_1-3, appeared as the most relevant to regional differences in drought episodes during SHY in the four regions studied, while western cyclonic situations (Wc and Wcs) emerged as particularly important in WHY. Regional differences are clearly marked between the Bohemian and Moravian regions, especially in SHY. Discussion of the results obtained concentrates on the uncertainty of soil drought data, differences between SHY and WHY, the effects of synoptic situations, and the broader context of soil droughts.

How to cite: Řehoř, J., Brázdil, R., Trnka, M., Řezníčková, L., Balek, J., and Možný, M.: Regional effects of synoptic situations on soil drought in the Czech Republic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6721, https://doi.org/10.5194/egusphere-egu2020-6721, 2020.

An extreme meteorological and hydrological drought occurred in Northern Europe in 2018, with widespread impacts including vast amounts of forests destroyed by wildfires, major crop losses, hydropower shortage, freshwater ecosystem stress, and water usage restrictions. Drought impacts are commonly felt on the ground and many are related to freshwater rather than solely to the atmosphere. A better understanding of the hydrological aspect of drought propagation is therefore vital in order to mitigate drought impacts. This study aims at assessing the drought propagation in 2018 in the (continental) Nordic countries at a monthly resolution, with a special emphasis on the streamflow and groundwater aspect. We used the E-OBS gridded observational datasets for temperature and precipitation, as well as high quality near-natural streamflow and groundwater data from the Nordic countries provided by national agencies. The extremeness for each variable was assessed by ranking each month of 2018 relative to that month in a 60-year record of data (30-year for groundwater due to data limitations). Whereas record-breaking high temperatures and precipitation deficits emerged over the Nordic region in May (Bakke et al., in prep.), streamflow stations did not experience extreme conditions before June in Norway, Sweden and Finland. This delay reflects the effect of various catchment properties and in particular the contribution of catchment water storages (mainly snowmelt) that dampens and delays streamflow response to meteorological conditions. The extent of record low streamflow maximized in July. In mid-August, high precipitation replenished the rivers in western and northern parts of the Nordic region. In the southeastern region, however, extremely low streamflow persisted throughout 2018 despite the return to more normal temperature and precipitation conditions after July. Catchments in western Denmark did not experience extremely low streamflow conditions during the summer of 2018, likely due to large groundwater reservoirs feeding the rivers. The response in groundwater levels was also delayed, with unusually low levels emerging in June and expanding in July. However, there was no clear spatial pattern of extremely low groundwater levels, even wells located very close together showed different results, reflecting the various hydrogeological properties and depths of the wells. Nevertheless, extremeness in groundwater are seen in about half of the wells throughout 2018. The response delay (estimated by the precipitation moving average window best correlated with the groundwater time series), depth and soil type help explain part of the variability in the results amongst the wells. In addition to assessing the uniqueness of the 2018 northern European drought, this study emphasises the added complexity of drought propagation, and the need of incorporating more variables than weather alone to understand hydrological drought development.

Reference: Bakke, S.J., Ionita, M., Tallaksen, L.M. (in prep.). The 2018 Northern Europe Hydrological Drought and its Drivers in a Historical Perspective.

How to cite: Bakke, S. J., Ionita, M., and Tallaksen, L. M.: Drought Propagation as Illustrated by the 2018 Nordic Drought Event, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7056, https://doi.org/10.5194/egusphere-egu2020-7056, 2020.

African countries are highly vulnerable to floods, with the fatalities and economic impacts associated with this hazard that have increased over the last decades. Despite the importance of the topic, little is known about the changes in the flood hazard over the second half of the 20^th and the first two decades of the 21^st century. Here we quantify the temporal changes in flooding using a newly assembled database of daily river discharge observations. The dataset contains over 700 stations having at least 20 years of daily data between 1950 and 2017. The database includes rivers from most sub-regions of Africa and sample a wide range of catchment sizes. Flood analyses are based on both annual maxima and peak-over-threshold to examine the changes in the frequency and magnitude of these events. Seasonal patterns of flood occurrence are also investigated through a regionalization based on directional statistics and monthly flood occurrence. Results indicate that, at the continental scale, there are more rivers with statistically significant downward trends. Yet, the spatial patterns exhibit regional variations, with several rivers showing increasing trends in central and South Africa. These findings are robust when considering longer times series or different sampling strategies for extremes.  

How to cite: Tramblay, Y. and Villarini, G.: Flood trends in Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7104, https://doi.org/10.5194/egusphere-egu2020-7104, 2020.

The North Atlantic Oscillation (NAO) is one of the primary atmospheric circulations which influence weather patterns in Great Britain. Its two phases (either positive or negative depending on differences in sea level pressure) result in characteristic precipitation patterns, the effects of which cascade down to signatures in streamflow. However, in relation to streamflow response to the NAO, these studies have been spatio-temporally limited as they have been undertaken using a small number of measurement sites with relatively short records.

The release of new historic datasets from the UK Centre for Ecology and Hydrology (CEH) provides a new opportunity to undertake a broad spatio-temporal analysis of the relationship between NAO and streamflow. This research used reconstructed daily flows for 291 catchments and the associated Standardised Streamflow Index (SSI) to explore the relationship between the North Atlantic Oscillation Index (NAOI) for the period January 1900-November 2015. Spearman correlations were calculated at monthly intervals between the NAOI and SSI (with a 1-month accumulation period), and the historic flows dataset was used to explore the variability in flows across the catchments under NAO+ and NAO- phases.

This analysis revealed distinct wet and dry spatio-temporal signatures in streamflow. The winter months are characterised by a north-west and south-east divide in this relationship; catchments in the northern and western regions show strong positive correlations between the NAOI and SSI and NAO+ is associated with higher than normal flows in many north-western catchments, and vice versa under NAO-. While catchments in the south-eastern and central regions are negatively correlated and therefore show and opposite wet-dry response. However, during the summer months, while there are some wet-dry signatures under NAO positive/negative phases - the reverse to that seen in winter, almost all catchments show weak NAOI-SSI negative correlation values. 

Finally, we compare the wet-dry responses to the NAO observed in streamflow to NAO-precipitation patterns, measured via correlations between the NAOI and Standardised Precipitation Index with a 1-month accumulation period over the same study period. The two sets of correlations (NAO-SPI and NAO-SSI) were analysed for spatio-temporal similarity through a Geographically Weighted Regression (GWR) analysis and a space-time clustering analysis. This revealed that in winter, as described above, the correlations with SPI and SSI generally behave similarly during the winter months – i.e. the wet-dry signatures in rainfall cascade down and are identifiable in streamflow patterns. In the summer months the NAOI-SPI correlations for the majority of catchments are negative. In the NAOI-SSI correlations, the summer values, while still negative, are notably weaker. The catchments with the weakest NAOI-SSI correlations are those generally in the central/southern region. These catchments have very slow response times due to their characteristics which may moderate the NAO wet/dry rainfall deviation.

How to cite: West, H., Quinn, N., and Horswell, M.: Mapping Wet-Dry Signatures of the North Atlantic Oscillation (NAO) in British Catchments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8530, https://doi.org/10.5194/egusphere-egu2020-8530, 2020.

The understanding of the propagation of meteorological droughts to hydrological droughts is an important phenomenon to take pre-emptive action to mitigate the effects of droughts. In this study, we have correlated the Standardized Streamflow Index (SSI) with the Standardized Precipitation Index (SPI-n) for 224 stations across India to analyze drought propagation time, which is specific to each catchment. Results indicate higher propagation times for basins lying in arid and semi-arid climate regions whereas lower propagation time is found for basins lying in wetter climate regions. The run theory is applied to SSI to identify all streamflow drought events for each station and the optimal Hydrological Drought Instantaneous Development (IDS) and Instantaneous Recovery Speeds (IRS) are calculated along with parameters; duration and severity. Drought propagation speeds can be used to simulate drought duration and severity for a catchment. Simulated drought duration for various catchments shows good agreement (R² > 0.7) with observed drought duration, therefore calculated optimal IDS and IRS can be used in forecasting drought conditions for each catchment. The effect of catchment characteristics on drought parameters was evaluated statistically by using heatmaps and bivariate correlation. This study provides comprehensive catchment-specific drought analysis for all major basins of India, which can be used by water managers to promptly and effectively avert drought and related disasters.

How to cite: Bhardwaj, K. and Mishra, V.: Role of catchment and climate characteristics in Hydrological Drought parameters and propagation in major river basins of India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10208, https://doi.org/10.5194/egusphere-egu2020-10208, 2020.

The intensity and frequency of extreme precipitation events have increased globally and are likely to rise further under the warming climate. The Clausius-Clapeyron (CC) relationship (scaling) provides a physical basis to understand the relationship of precipitation extremes with temperature. Recent studies have used global sub-daily precipitation data from satellite, reanalysis and climate model outputs (due to the limited availability of long term observed sub-daily data at global scales) and have reported a higher sensitivity of sub-daily precipitation extremes to surface air temperature than for daily extremes. Moreover, at higher temperatures, moisture availability becomes the dominant driver of extreme precipitation, therefore, dewpoint temperature can be a better scaling variable to overcome humidity limitations as compared to air temperature. Here, we used hourly precipitation data from the Global Sub-daily Rainfall (GSDR) dataset and daily dewpoint temperature data (DPT) from the Met Office Hadley Centre observations dataset (HadISD) at 6695 locations across the United States of America, Australia, Europe, Japan, India and Malaysia. We found that more than 60% of locations (scaling estimated for individual location) show scaling greater than 7%/K (CC rate). Moreover, more than 55% of locations across Europe, Japan, Australia and Malaysia show scaling greater than 1.5CC. Furthermore, when locations across selected regions are pooled within similar climatic zones (based on Koppen Geiger classification), scaling curves show around 7%/K scaling. The scaling curves for locations at greater altitude (>400m MSL) are flat compared to locations at relatively lower altitude. The difference in scaling rates at-station and for pooled regions highlight the importance of understanding the thermodynamic and dynamic processes governing precipitation extremes at different spatial scales and indicate that local processes are driving the super-CC sensitivities in most regions.

How to cite: Ali, H., Fowler, H., Lenderink, G., and Lewis, E.: Global scaling of observed sub-daily precipitation extremes to dewpoint temperature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10643, https://doi.org/10.5194/egusphere-egu2020-10643, 2020.

A multi-drought study that covers several draught types is required to better understand future drought. It is anticipated that drought will be exacerbated under climate change due to altered precipitation patterns and/or increased evapotranspiration. However, IPCC AR5 and SREX report stated with barely medium confidence that drought is expected to intensify over several regions in the world by the end of the 21st century, while elsewhere there is overall low confidence.

One of the reasons for these confidence levels stems from a definitional issue. As drought is a complex phenomenon and involves several processes, there are multiple hydrological variables and relevant-indicators used to quantify drought. Nonetheless, very few studies have comprehensively discussed future drought considering several drought types within a single study, hence leaving a gap on the holistic picture of future drought. Besides, most studies referred to in AR5 and SREX are based on coarse general circulation model (GCM) or regional climate model projections which have inherent model biases. Also, scenario uncertainties need to be examined more on drought projections, using the latest greenhouse gas emission scenarios.

This study presents a comprehensive multi-drought-type assessment on a global-scale until 2099. Using a set of multiple state-of-art global hydrological model (GHM) simulations forced by four bias-corrected GCM projections, meteorological (precipitation), agricultural (soil moisture) and hydrological (runoff) droughts are investigated by using the Standardized method at monthly-scale and another hydrological drought (discharge) by using a variable threshold method. The multi-model data set, which was developed in the Inter-Sectoral Impact Model Inter-comparison Project phase2b under a consistent simulation protocol, provides finer and detailed hydrological simulations at 0.5°x0.5° resolution. To explore potential pathways of drought changes, this study examined the Representative Concentration Pathways (RCP) 2.6, 6.0 and 8.5 scenarios. For each case, four drought features; drought intensity, spatial extent, the number of events, dry spell length, were studied, compared to those of the period before the 1960s.

The results highlight the hotspots of future droughts and show the development of each drought type for each RCP scenarios. As well as consistencies, differences among drought types were found in change trends and drought features. For instance, meteorological drought will decrease in some parts of middle-latitude in the northern hemisphere but the other two drought types will increase due to an increase in evapotranspiration over the regions. Or, dry spell length tends to be longer in runoff > soil moisture > precipitation drought in this order. These differences indicate that it is crucial to clearly define drought in discussing the phenomenon and it is critical to properly select drought types and index for one’s interest. Also, differences among RCP scenarios pose a question for mitigation discussions from the viewpoint of drought. Two types of uncertainties in this projection concerning model (GHMs and GCMs) uncertainty and parameter uncertainty in the drought analysis methods are also presented along with the drought projections.

How to cite: Satoh, Y., Yokohata, T., Pokhrel, Y., Hanasaki, N., Boulange, J., Burek, P., Veldkamp, T., Takata, K., and Shiogama, H.: Multi-type global drought projection using multi-model hydrological simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10748, https://doi.org/10.5194/egusphere-egu2020-10748, 2020.

Recent studies project a significant increase in drought frequency over most continents over the 21^st century. However, few studies have specifically looked at extreme droughts, defined here as having a return period larger than 20 years. In this work, two large climate model ensembles, the 50-member Canadian Earth System Model (CanESM2) and the 40-member Community Earth System Model (CESM1), both under the RCP8.5 scenario are used to project the evolution of the extreme drought frequency in the near (2036-2065) and far future (2070-2099) relative to the 1980-2009 historical period. The use of a large ensemble allows for a robust estimation of the frequency of very large droughts. Frequency changes for the 2, 20 and 100-year droughts were computed.

Extreme meteorological droughts were globally assessed using the short-term (1-month) and long-term (24-month) Standardized Precipitation Index (SPI). Extreme hydrological extreme droughts were assessed by the 1-month Streamflow Drought Index (SDI), using a lumped hydrological model on 5797 North American catchments to transform climate model outputs into catchment streamflows.

Results show that both climate models project increases of extreme meteorological drought frequency over many of the world’s regions, with a typical two or three-fold increase. The spatial distribution of regions with increasing meteorological drought frequency mostly matches those projected changes in future mean annual precipitation. Changes in future extreme hydrological droughts are dramatically more severe than for meteorological droughts, with up to a 27-times increase in frequency for the 100-year hydrological droughts, outlining the large impact of temperature change. The frequency change is the largest for the 100-year compared to the 2 and 20-year hydrological droughts.

How to cite: Zhao, C., Brissette, F., Chen, J., and Martel, J.-L.: Evolution of future extreme drought frequency in two climate model large ensembles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11449, https://doi.org/10.5194/egusphere-egu2020-11449, 2020.

Whilst the hydroclimatic drivers of drought are well-established, the forces which lead to the termination of drought conditions are less well understood.  An enhanced knowledge of the associations between these phenomena and drought termination is an important prerequisite for more robust operational forecasting of the end of droughts, a phase which assumes critical importance during protracted droughts which can span multiple years and substantially deplete water resources.

The influence of high integrated vapour transport (IVT) on drought termination has been established in the western USA, and atmospheric rivers have been linked to the ending of droughts in the mid-latitudes including North America and Asia.  Whilst high IVT has been demonstrated to be influential in major flood events in western Europe, a potential link with drought termination has not previously been identified.

This study systematically identifies drought termination events in river flow reconstructions for 302 catchments in the UK spanning the 1900-2010 period, and assesses the correspondence with high IVT values extracted from the ERA-20C reanalysis dataset spanning the same period.  Event coincidence analysis is used to quantify this association, with the Precursor Coincidence Rate (PCR) assessing the likelihood of high IVT preceding drought termination, and the Trigger Coincidence Rate (TCR) considering how often high IVT leads to drought termination.

PCRs were moderate to high across most of the UK, indicating that in most catchments a majority of drought terminations are triggered by high IVT.  TCRs were highest in the west of the UK, suggesting that in these regions a majority of high IVT episodes during droughts lead to its termination.  The combination of prevailing direction of landfalling high IVT with upland, wet and responsive catchments in the west (and vice versa) was supported by regression analysis.  In addition to determining occurrence, high IVT was also found to be influential on the characteristics of drought termination.

Metrics of PCR and TCR have the potential to inform management decisions in drought-impacted catchments, quantifying the likelihood of termination in instances of forecast high IVT events.  The importance of establishing associations between high IVT and drought termination is underlined by the higher confidence in IVT forecasts than direct rainfall forecasts over certain medium-range lead times.

How to cite: Parry, S., Lavers, D., Wilby, R., Wood, P., and Prudhomme, C.: Drought terminations in the UK driven by high integrated vapour transport, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12607, https://doi.org/10.5194/egusphere-egu2020-12607, 2020.

Knowledge of available water resources is essential in water management and water policy. In accordance with the EU Water Framework Directive, Hungary reviews and updates its assessment of water balance through the River Basin Management Plan (RBMP) every 7 years. In many cases, the available water resources in the RBMP for small streams are based on expert judgment, since it is not always possible to measure the actual water level or discharge. Consideration of climate change and its effects is also a big question: what are the effects of these changes on small streams, and is there a trend in the runoff?

For water managers, water scarcity and abundance are major concerns. To address this issue, our study focused on high-flow and low-flow signatures. This paper presents flow trends during the last 36 years in Western Hungary. During the period 1980-2016, daily discharge measurements were collected at 74 small streams. Twelve flow signatures were selected for trend analyses. Trends were determined for three time periods: the full measured time period at each station, and two eighteen-year periods between 1980-1998 and 1999-2016. At each location, trends were determined with 10% significance using the Mann-Kendall test.

The results show that in the low-flow signatures, no significant changes in flow trends occur at the individual watershed and regional scales during the two eighteen-year time periods, as well as during the full time period. In contrast, high-flow signatures have significantly changed for all three time periods, at both the individual and regional scales.

This work was undertaken as part of a project funded by the EFOP-3.6.1-16-2016-00017.

How to cite: Mátyás, K., Bene, K., and Koch, R.: Flow trends in Western Hungary, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13339, https://doi.org/10.5194/egusphere-egu2020-13339, 2020.

Groundwater systems are susceptible to droughts. However, the relationship between driving meteorological droughts and resulting groundwater droughts is particularly complex due to spatially and temporally varying climate drivers and spatially heterogeneous catchment and aquifer characteristics, as well as the potential effects of longer-term groundwater overexploitation and of groundwater abstraction and management interventions during episodes of meteorological drought. Consequently, many previous studies of the propagation of meteorological drought to groundwater systems have typically been geographically limited in scope, focussing on characterising aquifer units within a catchment or basin, and/or temporally limited to specific episodes of drought. Based on a new European-wide dataset consisting of groundwater level data from over 6,000 sites, here we describe the results of a multidecadal analysis of the expression of major episodes of meteorological drought at the continental scale in groundwater systems, independent of local hydrogeological setting.

In this study, raw groundwater level time series are modelled using an impulse response function of precipitation to obtain monthly groundwater levels that are then standardised. Sites with long-term trends in groundwater level are identified and usually inferred to be associated with overexploitation or other anthropogenic influences. Cluster analysis of the modelled standardised hydrographs is used to identify spatially coherent ‘type’ groundwater hydrographs. These type hydrographs can be characterised by differences in the autocorrelation of the underlying groundwater hydrographs, but may also reflective continental-scale variations in the driving meteorology. Finally, episodes of groundwater drought are extracted from the type groundwater hydrographs and compared with the driving meteorological droughts. The data provides evidence for the coherent response of groundwater systems to droughts across large areas of Europe depending on driving meteorology and the ‘memory’ of the groundwater system, and drought events such as in 2011-12, 2015 and 2017-18 showed spatial coherence across different European regions.             

How to cite: Brauns, B., Bloomfield, J. P., Cuba, D., Hannah, D. M., Marchant, B. P., and Van Loon, A. F.: Characterising the response of groundwater systems to major, continental-scale droughts: a multidecadal European case-study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16435, https://doi.org/10.5194/egusphere-egu2020-16435, 2020.

The climatological changes that have been forming for years have led to several different effects within the Wupper catchment area: the increasing of strong to extremely pronounced convective events lead on one hand (exacerbated by the strong topography within the “Bergische Land” region) to major damage events (e.g. in 2018 with several million of known damage costs), but also mean lower inflows to the drinking and usage water reservoirs due to the high level of interception and evaporation potential of the natural catchment areas (high proportion of forests, drinking water protection zones). The prolonged dry periods exacerbate the problem, because of the reduced groundwater recharge. While the annual precipitation is in the normal range, the changes in the distribution (in intensity, duration and seasons) cause big changes. In addition to the water volume capacity, this also affects the water quality of the streams and reservoirs. Resulting low levels in the reservoirs (often in combination with high temperature) e.g. to blue-green algae growth and require further efforts to achieve the needed water quality.

Purely stationary approaches are not sufficient for describing the processes properly and to transfer the results in a way that decision makers can understand the characteristics. Only a relative change of single precipitation periods in of percentage, cannot give any reference to the resulting effects and impacts. In addition, the different kind of data sets for hydrological and limnological impact modeling makes it difficult to compare the results. Historical point measurements (such as from climate stations or levels), areal (grid-based) historical recording e.g. precipitation by rain radar or soil parameters by satellite, weather forecasts in the range of hours to months and climate forecasts (e.g. decadal) or scenarios are each self-sufficient data sets, but must be linked in order to be able to derive appropriate measures.

Therefor methods to correlate past critical situations with indices / predictors, which are statistically sufficiently robust are suitable. This will enable us to make statements for the development in future periods and to represent changes in an impact-related manner. The presentation shows examples of how such approaches can be implemented for the phenomena described above (heavy rain / drought).

How to cite: Scheibel, M.: Approaches to describe different hydrological extremes related to their impact and derived measures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17728, https://doi.org/10.5194/egusphere-egu2020-17728, 2020.

Copula-based bivariate drought frequency analysis has been widely employed to evaluate drought risk in the context of point frequency analysis. However, the relatively significant uncertainties in the parameters are problematic when available data are limited. This study developed a bivariate regional frequency analysis model using Copula function within the Bayesian modeling framework. An experimental study is first performed to assure ourselves whether the proposed model can accurately reproduce drought characteristics. The proposed model is capable of effectively representing the recent drought events and can provide drought risk information along with its uncertainty. The results confirm that the proposed model is not only effectively representing correlation with regional dependencies of drought, but also providing the uncertainty of parameters.

KEYWORDS: Copula, Bayesian, Bivariate drought regional frequency analysis, Uncertainty

Acknowledgement

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI 2018-07010.

How to cite: Kim, H. J., Kim, J.-G., Cho, H. M., and Kwon, H.-H.: Bayesian Parameter Estimation to Bivariate Drought Regional Frequency Analysis Model: Application to Han-River Watershed, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20713, https://doi.org/10.5194/egusphere-egu2020-20713, 2020.

 Increase in frequency of the floods is one of the noticeable climate change impacts. The efficient and optimized flood analysis system needs to be used for the reliable flood forecasting. The credibility and the reliability of the flood forecasting system is depending upon the framework used for its parameter optimization. Comprehensive framework has been presented to optimize the input parameters of the computationally extensive distributed hydrological model. A large river basin has the high spatio-temporal heterogeneity of aquifer and surface properties.  Estimating the parameters in fully distributed hydrological model is a challenging task. The parameter optimization becomes computationally more demanding when the model input parameters (30 to 100 even greater) have multi-dimensional parameter space, many output parameters which make the optimization problem multi-objective and large number of model simulations requirement for the optimization. Aforementioned challenges are met by introducing the methodology to optimize the input parameters of fully distributed hydrological model, following steps are included (1) screening of the parameters through Morris sensitivity analysis method in different flow periods, so that optimization would be performed for sensitive parameters, different scalar output functions are used in this regard (2) to emulate the hydrologic response of the dynamic model, surrogate models or meta-models are used (3) sampling of parameters values using the optimized ranges obtained from the meta-models; the results are evident that the parameter optimization using the proposed framework is efficient can be effectively performed.  The effectiveness and efficiency of the proposed framework has been demonstrated through the accurate calibration of the model with fewer model runs. This study also demonstrates the importance and use of scalar functions in calculating sensitivity indices, when the model output is temporally variable. In addition, the parameter optimization using the proposed framework is efficient and present study can be used as reference for optimization of distributed hydrological model. 

Keywords: Calibration, parameter ranking, Sensitivity analysis, Hydrological modeling, optimization

How to cite: Nusrat, A., Farooq Gabriel, H., Haider, S., and Shahid, M.: A meta-model assisted framework of optimization of the Hydrological model parameters for accurate calibration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21083, https://doi.org/10.5194/egusphere-egu2020-21083, 2020.