HS2.4.4

Drought-affected regions often contain high densities of small reservoirs, usually informally built, as drought-coping mechanism. These structures influence socio-hydrological dynamics and have the potential to alter hydrological processes relevant to drought emergence and development. This study aimed to analyze the influence of a high concentration of small reservoirs on the intensification and evolution of drought events. We present an innovative method, which we call “Drought Cycle Analysis”, that tracks the concomitance of precipitation and water storage deficit and associates this with four drought stages: Wet Period, Meteorological drought, Hydro-meteorological drought and Hydrological drought period. The methodology was tested for the Riacho do Sangue River watershed located in the semi-arid region of northeast Brazil. We used a combination of satellite imagery (Landsat 5, 7 and 8) and an empirical equation to estimate the volume stored in the dense network of small reservoirs. Using the Drought Cycle Analysis, we show that the unmonitored small reservoirs induced and modified drought events, extending the duration of hydrological drought on average by 30%. Furthermore, this extension can double for specific drought events. The Drought Cycle Analysis method proved useful for monitoring and comparing the evolution of different drought events, in addition to being applicable as an auxiliary tool in the improvement of water resources management of large reservoirs. This study demonstrates the importance of considering small reservoirs in water resource management strategy development for drought-prone regions.

How to cite: Ribeiro Neto, G., Melsen, L., S. P. R. Martins, E., W. Walker, D., and van Oel, P.: Drought Cycle Analysis to evaluate the influence of a dense network of small reservoirs on drought evolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8930, https://doi.org/10.5194/egusphere-egu22-8930, 2022.

Prediction of hydrological drought requires good process understanding of streamflow generation. It is known that during progressing drought the water availability in rivers is a composition of different delayed contributions from various stores in the catchments. However, the composition of delayed contributions often differs a lot across landscapes and hydrogeological settings. Solutes or isotope data sets are often limited to separate different contributions only during the campaign period in a specific catchment. Hydrological models incorporate delayed contribution typically with different soil and groundwater storages to simulate total streamflow. Here we analyzed the output from different storages of the water balance model LARSIM which is a well-established operational river forecasting model in Southern Germany. We hypothesized that the different storages' contributions can also be estimated by an advanced hydrograph separation method splitting total streamflow in four delayed contributions (i.e., storm flow, fast and slow interflow and baseflow). We used the Delayed Flow Index (DFI) for hydrograph separation as, compared to a BFI index, it estimates multiple storage-outflow components. Though not entirely similar in their results, the combined analysis of model simulations and DFI hydrograph has the potential to inform water management and forecasters about relevant time scales of different streamflow contributions and drought severity. The information of streamflow storage states and contributions may help hydrological drought prediction of time periods outside the model calibration period and also for periods when faster delayed contributions consecutively cease to sustain streamflow.

How to cite: Stoelzle, M. and Stahl, K.: Deciphering streamflow composition during drought – model simulations as benchmark for advanced hydrograph separation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1935, https://doi.org/10.5194/egusphere-egu22-1935, 2022.

Many streams in the Swiss Plateau experienced in 2003, 2011, 2015 and 2018 extremely low streamflow conditions. In 2018, on several stream and groundwater gauging stations the lowest values since the start of observations were registered. Climate and hydrological impact models project that by the end of the century in the future summer half such dry periods with similar duration and intensity might occur more frequently. According to the models, extreme dry periods will last even longer as the drought in summer 2018.

To be prepared on that, the public authorities want to know and to get an overview on how streams react in case of such scenarios predicting dryer conditions. A requirement for that is to understand the relevant factors controlling the available storages for low-flow generation and the drainage behavior of small to mesoscale catchments during low flow under recent climate conditions.

Analysis of flow duration curves of over 200 gauged catchments in Swiss Plateau with information of mean annual precipitation and evapotranspiration, geological maps and some topographic properties allowed to identify factors reducing and increasing the low flow q95 percentile and controlling low flow behavior. With analysis of the resulting spatial pattern of specific discharges (l s^-1 km^-2) of more than 200 additional singular discharge measurements on selected sites during low flow periods, the effects of the relevant factors could be isolated more precisely. The relevant factors essentially consist of:

• Lithological construction of bedrock
• Infiltration and exfiltration of stream water in and from sediments of river bed
• Extend and permeability of quaternary deposits like morains, rubbly deposits, talus or sagging.
• Water withdrawal for water supply, irrigation and power plants.

The spatial low flow pattern all over the Swiss Plateau will be presented with maps and the effects of the mentioned factors will be shown on expressive examples.

A new automatic method is developped to calculate so-called master recession curves from the 200 available long-term discharge data series. With the intention to transfer the recession behavior from gauged to ungauged catchments, further analysis are planned in gauged catchments to identify the correlation between the recession curves and the effect of the above mentioned factors.

How to cite: Margreth, M., Zappa, M., Wanner, C., Hug-Peter, D., Lustenberger, F., and Schlunegger, F.: A new approach to transfer the discharge recession behavior from gauged to ungauged catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2902, https://doi.org/10.5194/egusphere-egu22-2902, 2022.

Extensive knowledge of hydrological processes occurring during droughts is required for a sustainable water resources management, especially in a changing climate. Large-sample analyses are particularly informative in this sense, because they allow us to extend the understanding beyond specific catchments. Data from experimental catchments and observatories showed that water stored within the catchment can sustain evapotranspiration and discharge during dry periods and previous multi-catchments studies on droughts highlighted the storage control on hydrological drought characteristics, through the quantification of hydrological signatures and catchment properties. However, few studies have explicitly quantified across different climates and catchment types the contribution of subsurface storage changes (ΔS) in the annual water balance, in drought propagation (from the meteorological to the hydrological one), and in drought recovery. Here, we assembled a dataset blending ground-based precipitation and discharge data, and remote-sensed actual evapotranspiration data to study drought propagation and recovery in a water-balance and data-based perspective for 102 catchments across various climatic and morphological properties in Italy. This region experienced severe drought years over the study period (hydrological years 2010 - 2019), as detected by the Standardised Precipitation Index for an accumulation period of 12 months. This large-sample analysis revealed that (i) subsurface storage is a non-negligible term in the annual water balance, as ΔS mean annual value represents on average the 11% of precipitation across the catchments, (ii) its depletion sustains discharge during drought years (median annual ΔS anomaly equal to -97 mm for catchments attenuating the hydrological drought with respect to the meteorological one), and (iii) it recovers from precipitation deficits over shorter time scales than evapotranspiration, but similar as those of discharge. These findings emphasize the need of explicitly considering subsurface storage in drought analyses to properly inform policy makers and water managers, as it is a key driver in drought propagation and recovery across climates and catchment properties.

How to cite: Bruno, G., Avanzi, F., Gabellani, S., Ferraris, L., Cremonese, E., Galvagno, M., and Massari, C.: Subsurface storage drives drought propagation and recovery across climates and catchment properties, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4848, https://doi.org/10.5194/egusphere-egu22-4848, 2022.

This study investigated the calibration performance of hydrological models applying a series of split-sample to crash-test potential combinations of calibration-validation periods under drought type (dry/wet) using lumped models: BILAN and GR2M. A sub-period focused on the drought was systematically selected for model calibration based on a particular climate characteristic (precipitation, temperature, runoff) and a 7-year moving window. This approach gives perception into calibrated parameters transferability overtime under similar or different climate conditions (drought). 

Both lumped models yielded similar results over a set of 6 catchments in a main West African river basin located in Senegal: the Gambia river basin. The Kling-Glupta Efficiency (KGE) was the objective function to assess models’ efficiency. A dependency was found between the model performance and the extent of input data. 

Results have shown that the calibration performance decreases within an extending simulation period width. A focus on the impact of drought type on calibration performance revealed models simulating better dry than wet years. The analysis on how model performance would be affected when calibrated in a climate condition different to the validation (e.g. calibrated in dry(wet) and validated into wet (dry) revealed that calibration over a wetter or dryer condition than the validation and vice-versa may lead to an over(under)estimation of the simulated runoff. 

The results also indicate a general performance loss due to the transfer of calibrated parameters to independent validation periods of −5 to −25%, on average. The shift of model parameters in time (validation) may generate a significant level of errors. The outcome of this study may lead to a master of the uncertainty associated with one hydrological model and a better assessment of runoff in a real-world application.

Keywords: Gambia river basin; calibration; crash test; rainfall-runoff model; BILAN; GR2M; lumped hydrological models;  

How to cite: Ba, D., Máca, P., Langhammer, J., and Bodian, A.: Modelling of changes in hydrological balance in Gambia river basin using two lumped models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6262, https://doi.org/10.5194/egusphere-egu22-6262, 2022.

A particular location in a region may suffer from drought conditions while another may experience normal or even wet conditions. This study, therefore, performs spatial analysis using station-based monthly precipitation data for 19 meteorological stations over the Seyhan River basin in Turkey. Six major droughts each 2-year long at minimum were identified from the basin-average annual precipitation deficit. The Standardized Precipitation Index (SPI) was calculated for the meteorological stations at different time scales to characterize the severity and spatial extent of the major droughts. For the most severe month of each major drought, severities were interpolated over the river basin to form drought severity maps by using the Inverse Distance Weighted (IDW) technique. The study illustrates that the river basin experienced drought at least once every decade, which can be as severe as to impact the region and the whole country. Drought severity does not vary greatly over the river basin and it decreases with increasing accumulation time scales. The distribution of the most severe droughts changes depending on the characteristics of the major drought. We observed that the major drought in 1989-1990 was the most severe event in the time period. This is a significant statement for water resources planning with reference to the Seyhan River basin. Focusing only on the major droughts observed in the past 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: Cavus, Y., Stahl, K., and Aksoy, H.: Spatial analysis of major droughts in Seyhan River Basin, Turkey, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-330, https://doi.org/10.5194/egusphere-egu22-330, 2022.

Drought causes hydroclimatic stress on terrestrial ecosystems and we see that these effects can have a long duration even when the drought is alleviated. The impacts of a drought are commonly dependent on the severity and duration of the hydrological drought event. At the same time, we see that the recovery from a severe drought is also impacted by catchment characteristics and regional climatology. In this study, we focus on Chile which has frequently experienced multi-drought periods with severe impacts.

In this study, we quantify the recovery of discharge, vegetation productivity (kNDVI), and soil moisture after hydrological droughts to quantify the drought termination (DT) and drought termination duration (DTD). We used the CAMELS-CL data set from 1988-2020 to study drought recovery in natural catchments. Using a composite analysis we obtain the average response of discharge, vegetation, and soil moisture after severe drought events for catchments throughout Chile. We estimate the impact of different explanatory variables and catchment properties from the CAMELS-CL data on DT and DTD using lasso regression for discharge, vegetation productivity, and soil moisture without selecting strongly correlated variables.

Our study demonstrates that the drought recovery of discharge can be explained by local characteristics while these relationships are less pronounced for vegetation and soil moisture droughts. Longer recovery times were found in environments with less precipitation and higher temperatures, with mainly shrub land cover. Shorter recovery times, at higher latitudes with increasing precipitation and lower temperatures under higher vegetation cover. The explanatory variables for discharge DT and DTD are associated with precipitation, potential evapotranspiration, and baseflow, or by a combination of them with catchment characteristics related to storage and release (e.g. land use). To that end, this work can help to identify drought vulnerability in regions where observations are lacking and help to predict drought recovery periods.

How to cite: Vega-Briones, J., Galleguillos, M., de Jong, S., and Wanders, N.: Drought recovery of southern Andes natural catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-368, https://doi.org/10.5194/egusphere-egu22-368, 2022.

Past drought years, characterized by large scale precipitation deficits and high summer temperatures, such as 2018 in Europe, have resulted in extreme low flow situations with negative ecological, economic and social consequences. In large river basins that originate in high mountain ranges, such as the Rhine river originating in the Swiss Alps, glaciers and snowpack alleviate drought situations by continuously providing meltwater to downstream reaches during summer. However, this meltwater contribution is under threat due to ongoing climate warming and retreating glaciers. Here, we designed a stresstest model experiment to answer the question ‘what if a historical drought year reoccurs in future conditions with retreated glaciers?’ A model framework was used combining the HBV model for the glacierized headwater catchments of the Rhine and the LARSIM model for the rest of the basin. Three historical drought and low flow years, 1976, 2003 and 2018, were selected and their meteorological conditions were transferred and used as stresstest model input to three future conditions in time, namely now (2018), near future (2031) and far future (2070). These three model states were obtained by transient simulations up to the respective moment in time using meteorological observations or an ensemble of bias-corrected climate model output from the RCP 8.5 scenario, using coupled glacio-hydrological model runs. The results show an aggravation of downstream low flows, especially when drought years happen under conditions in the far future. From the three years, 2003 has the strongest effect in the future, because the ice melt contribution was highest in that year in the past. During August, flows reduce up to 80% upstream for highly glacierized catchments, compared to the streamflow of the original drought years. At downstream gauges, where flows were already critically low in the past, streamflow reduces by 5%-20%. This model experiment shows a glimpse in future low flow events and emphasizes the importance of upstream cryospheric changes for downstream streamflow dynamics during drought. 

How to cite: Van Tiel, M., Weiler, M., Freudiger, D., Moretti, G., Kohn, I., Gerlinger, K., and Stahl, K.: Stress-testing the buffering role of glaciers in the Rhine basin: How much worse could summer low flows get under future glacier retreat?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7345, https://doi.org/10.5194/egusphere-egu22-7345, 2022.

Globally around half of the population resides in the tropics. Understanding the regional trends and physical controls of streamflow drought in the present day helps us to recognize the future changes in the hydrological cycle impacting the freshwater supplies. Few studies have examined the climatic and catchment controls on the propagation of streamflow droughts in tropical catchments. This study unveils the regional pattern of drought onset and deficit volume and identifies their evolution by analyzing the dominant climatic and physiographic controls. Two crucial streamflow drought signatures, the time of onset and deficit volume, are extracted from daily observed streamflow records of 82 rain-dominated catchments from peninsular India (8-24º N, 72-87º E). A daily variable threshold approach with an 80% exceedance probability of the flow record is used to identify drought events. Despite a decreasing trend in deficit volume, we show a delayed shift in drought onset of about a week for more than 50% of catchments. However, droughts with mean onset time clustered around the summer (Mar-May) season show a sharp rise in deficit volume trends. We show that while dynamic (precipitation and soil moisture) factors influence the onset of droughts, both static (catchment, topographic, and soil properties) and dynamic properties play a significant role in deciding the deficit volume. Based on a statistical approach (Taylor skill score and non-parametric dependence metrics), we identify static features, surface (30 cm) and sub-surface (up to 1m) soil organic stock and cation exchange capacity (CEC) to be dominant soil controls, also topographic ruggedness index, slope, topographic wetness index, and curvature are found to be the main controlling catchment attributes. The derived insights add new avenues in understanding the causal chain of physical processes linking climatic and physiographic controls on streamflow drought mechanisms, elucidating tropical climate response to water availability in a changing climate.       

How to cite: Raut, A., Ganguli, P., Reddy, N. N., Wöhling, T., Kumar, R., and Das, B. S.: Regional Trends and Physical Controls of Streamflow Drought Characteristics in Tropical Catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1239, https://doi.org/10.5194/egusphere-egu22-1239, 2022.

Our goal here is to answer the question, “what drives the demand side of drought?” We achieve this by decomposing atmospheric evaporative demand (Eo) anomalies during periods of drought into contributions from all of its drivers, using the US Midwest as a study region. In drought, anomalies in Eo are driven by anomalies in moisture availability, but Eo reacts quickly and so is a robust drought indicator. Thus, asking to what extent each meteorological driver determines evaporative demand in drought conditions is of value both academically and operationally.

We define drought as a sustained imbalance between the supply of moisture from the atmosphere to the surface (Precipitation) and the demand in the atmosphere for moisture from the surface, in favor of the demand. The demand arm is atmospheric evaporative demand (Eo; (sometimes referred to as “potential evaporation”); evapotranspiration (ET), the actual return flux to the atmosphere, is determined as the extent to which this demand can be met by the moisture available at the surface.

In this context, Eo can be thought of as the “thirst of the atmosphere.” It is a function of meteorological and radiative drivers at the surface: specifically temperature, solar radiation, wind speed, and humidity (and to a lesser degree, surface pressure). For Eo we use daily reference ET (ETo) from the Penman-Monteith equation, which provides a fully physical estimate that incorporates the effects of both advective and radiative forcing. We drive ETo by inputs from the North American Land Data Assimilation System phase-2 (NLDAS-2), which are distributed across CONUS at a spatial resolution of 0.125 degrees, and available from 1979 to the present.

Drought periods are determined using various spatially distributed drought-monitoring tools: specifically, the US Drought Monitor (USDM); the Evaporative Demand Drought Index (EDDI); the Standardized Precipitation Index (SPI); and soil moisture percentiles from the NLDAS-driven Noah land surface model.

We conduct a first-order analysis of the anomalies in Eo that exist during drought conditions. This technique assumes that the contributions from anomalies in all drivers sum to the anomaly in Eo; each driver’s contribution is the product of the sensitivity of Eo to, and the anomaly in, the driver. As our expression for Eo (i.e., Penman-Monteith ETo) is differentiable, the sensitivity to each driver can be derived explicitly by partial differentiation. Drivers’ anomalies are observed by querying the reanalysis during drought periods and deriving deviations from the drivers’ long-term means for the same periods across the entire reanalysis period.

Here we present the (i) general methodology for both the development of Eo and its decomposition and (ii) the results of the decomposition of drought-period Eo anomalies into the relative contributions from each driver across the Midwest Drought Early Warning System (DEWS) region.

How to cite: Hobbins, M., Jackson, D., Hughes, M., and Woloszyn, M.: A rigorous attribution of the demand side of drought: a case study in the Midwest US., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13137, https://doi.org/10.5194/egusphere-egu22-13137, 2022.

From 2018 to 2020, large parts of Central Europe experienced an unprecedented multi-year summer drought with severe impacts on society and ecosystems. While strong reductions of water quantity were reported, our study is one of the first to analyze its impacts on water quality by looking at nitrate export dynamics in a nested mesoscale catchment in Germany with heterogeneous landscape settings. We used concentration-discharge (CQ) relationships to analyze catchment functioning in terms of nitrate retention and release, and the mechanistic process based mHM-SAS model (Nguyen et al., 2021) to simulate the underlying belowground nitrogen (N) fluxes and associated hydrologic transit times. For the three drought years, we found an amplification of the seasonality in nitrate export with lower concentrations in summer and higher concentration in winter, compared to normal conditions. Compared to the long-term average behavior, the catchment exhibited a disproportionally high annual load export relative to the discharge available for its transport. We argue that this loss in nitrate retention capacity was driven by a complex mixture of changes in N cycling and heterogeneous transit times among different contributing areas. During dry periods, long transit times and sufficient subsurface denitrification likely caused the low in-stream nitrate concentrations in the upper, more mountainous catchment, while reduced soil denitrification and plant uptake resulted in an accumulation of N in the soils. During the following wet periods, this accumulated N was rapidly exported to the stream (median transit times < 2 month) causing a steep increase in nitrate concentrations and load export. In the downstream (lower) sub-catchment, long median transit times (> 20 years) prevent such an immediate export of the accumulated soil-N to the stream. Our modeling analysis, however, suggests that the build-up of soil N-stores and the lack of fast, shallow flow path may exacerbate N legacies in the downstream part of the catchment, which might become visible as higher N export to the stream decades later. Hence, the more immediate concentration response to drought observed at the catchment outlet was dominated by the flushier upstream catchment. Overall, this increased temporal variability of nitrate export and intensified within-catchment differences caused by a multi-year drought call for a higher spatiotemporal resolution of monitoring and more site-specific management plans for site-specific problems.

Nguyen, T. V., Kumar, R., Musolff, A., Lutz, S. R., Sarrazin, F., Attinger, S., & Fleckenstein, J. (2021, July 13). Disparate Seasonal Nitrate Export from Nested Heterogeneous Subcatchments Revealed with StorAge Selection Functions [preprint]. https://doi.org/10.1002/essoar.10507516.1

How to cite: Winter, C., V. Nguyen, T., Musolff, A., Lutz, S. R., Rode, M., Kumar, R., and Fleckenstein, J. H.: A multi-year drought can alter the nitrate retention capacity of a catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3618, https://doi.org/10.5194/egusphere-egu22-3618, 2022.

Droughts pose enormous challenges to food security and freshwater availability across the globe. Reservoirs serve as a lifeline for drought mitigation, and as a prime source of irrigation water during extreme dry conditions. Despite the critical role reservoirs play during droughts, reservoir-based hydrological droughts have only been explored in a limited manner. Specifically, droughts based on reservoir storage levels and evaporative losses have not been accounted for at a global scale. Here, we use NASA’s new MODIS global water reservoir product, GRACE TWS, and GLDAS meteorological data to evaluate global reservoir-based hydrological droughts for 164 reservoirs located in different climatic zones and geographic settings. We introduce an Integrated Reservoir Drought Index (IRDI) that was developed using the concept of the copula, which incorporates the effects of reservoir storage and evaporation rates to effectively monitor reservoir-based hydrological droughts. From the observed climate data (2002-2020), we find that the frequency of reservoir based droughts is increasing in the western part of North America (NA), the eastern part of South America (SA), south-central Asia, and the majority of Africa, New Zealand, and Europe. Based on this information, we have reconstructed the drought characteristics (mean intensity, max intensity, max duration) using the IRDI for all of the 164 reservoirs during 2002-2020. We find that reservoirs in western Australia, southern Europe, southeast Asia, and the northern part of North America have gone through more prolonged droughts (of about 80-90 months, with a maximum intensity of about -2.6 to -3.2). We find contrasting trends of TWS anomalies and IRDI in south Asia, south Africa, eastern North America, and other places—which highlights the need for reservoir-based drought information (as TWS tends to be governed by groundwater changes, and does not necessarily give insights on reservoir droughts). Despite having high precipitation anomalies (wet conditions), reservoirs were mostly found to be under drought conditions, which points to the significant influence of human activities on reservoir-based droughts. Major factors contributing to this are inefficient water management policies and practices—especially during drought conditions—in different countries. Overall, our study highlights how human activities alter reservoir-based hydrological droughts, which has significant implications on sustainable and resilient water resources planning and management across the globe.

How to cite: Shah, D., Zhao, G., Li, Y., and Gao, H.: Do Human Activities Influence Reservoir based Hydrological Droughts?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-436, https://doi.org/10.5194/egusphere-egu22-436, 2022.

Drought-flood transitions greatly challenge water management and the development of adaption measures to extreme events. These transitions can occur rapidly but may also take many months or years and are often studied using climate instead of streamflow data – neglecting the role of surface processes. The time between one extreme and the other may depend on climate and catchment characteristics including topography, soil types and water use. However, it is yet unclear how drought-flood transition times vary regionally in dependence of climate characteristics, flow processes, and water storage.
In this study, we analyse how drought-flood transition times vary across different hydro-climatic zones. We show how transition times, i.e. the number of days between drought-termination and the following flood event, vary in space. We identify indicators and common patterns of rapid transitions by correlating drought and flood characteristics to transition times. To do so, we use large-sample datasets such as CAMELS and LamaH, which provide diverse catchment characteristics in addition to streamflow data. This information helps to identify catchments with a high likelihood of abrupt drought-flood transitions. Such identification is highly relevant because flood preparedness is often low during drought events, which potentially increases the severity of flood impacts.

How to cite: Götte, J. and Brunner, M. I.: Drought-flood transitions across different hydro-climates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4397, https://doi.org/10.5194/egusphere-egu22-4397, 2022.

To reduce the future negative impacts of hydrological extremes, it is crucial to understand the spatiotemporal variability of flood and drought hazard and social, economic, and environmental vulnerabilities. Such understanding is particularly important in developing countries which are strongly vulnerable to hydrological extremes because of greater economic dependence on climate-sensitive primary activities and infrastructure. Brazil is increasingly affected by hydrological extreme events and related losses. The sum of damages and losses caused by recurrent floods and droughts has a substantial impact, especially in small and medium-sized municipalities. Developing disaster resilience and adaptation strategies requires an understanding of the risks related to floods and droughts and how they are spatially distributed and related across the country. Therefore, we present a risk analysis of how floods and drought are distributed and spatially connected across Brazil. To assess flood hazard, we rely on frequency analysis and spatial dependence measures. Moreover, the vulnerabilities towards drought and flood hazard were derived using data from the Brazilian atlas of natural disaster: damages and losses($), people affected, deaths, and homelessness. Based on the results, we divide the country into regions suitable to risk pooling, i.e. groups of municipalities that can cooperatively share costs, liabilities and risks.These regions can implement adaptation measures at the regional level, i.e. flood-drought risk insurance framework, using conjugate return periods, extended losses, multi-risk coverage, and composite willingness to pay and adapt. Our risk analysis will support the development of adaptation plans to hydrological extremes at the catchment and regional scale.

How to cite: Gesualdo, G., Benso, M., Brunner, M., and Mendiondo, E.: Spatiotemporal distribution of hydrological extremes in Brazil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-410, https://doi.org/10.5194/egusphere-egu22-410, 2022.

Land use and land cover changes are majorly associated with the anthropogenic causes as well as human growth and urban expansion. The rapid increase in urban development is interlinked to fragmentation of landcover changes leading to a rise in flood events. The growth rate of urbanization has been quite significant in Ireland over the past 30 years. Ireland’s annual growth rate of urbanization was found to be 3.1% between 1990 to 2012. Several studies have further documented that Ireland experienced an overall higher degree of land conversion relative to other European countries, concluding that the urban land expansion in Ireland has been among the highest in Europe. The majority of the physically-based hydrological models that are used to simulate the discharge dynamics from river basin outlets require land cover information. One of the major limitations of those models is that they, in general, assume that the land cover information remains the same over time. However, changes in land cover are evolving over time, which needs to be considered while simulating runoff at a river basin. In situations where the hydrological model is used to simulate runoff for a short period of time, ranging from a few months to a few years, a static land cover information might be sufficient, however, when runoff simulations are required for longer periods of time (more than a decade), it is important to consider the changes in land cover over such a period. This study investigates how changes in land cover impact hydrological runoff simulations using a rainfall-runoff model called soil water assessment tool (SWAT). The study area considered is the Dodder River basin located in southern Dublin, Ireland. Runoff at the basin outlet was simulated using SWAT for 1993–2019 using five landcover maps obtained for 1990, 2000, 2006, 2012 and 2018. The hypothesis specifically points to the consideration of dynamic and time-varying landcover data during the development of hydrological modelling for runoff simulation. Furthermore, two composite quantile functions were generated by using a kappa distribution for monthly mean runoff and GEV distribution for monthly maximum runoff, based on model simulations obtained using different landcover data corresponding to different time-period.

How to cite: Sarkar Basu, A. and Pilla, F.: Estimating the variation in runoff due to landcover changes using the SWAT model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9047, https://doi.org/10.5194/egusphere-egu22-9047, 2022.

Investments in flood protection are often made as a reaction to recent events. Flood risk communication and awareness creation might help to raise the motivation for preventive action in flood risk management, rather than implementing reactive measures after an event has occurred. In order to develop a way to convey scientific insight and data on flood risk, together with stakeholders, we are developing an interactive online tool to generate storymaps of local and national extreme flood events. To learn about the needs of every stakeholder, how flood risk information could be communicated and how practitioners can finally apply a resulting tool, we carried out an iterative co-development process. Stakeholders from local emergency intervention forces, insurance companies, cantonal and federal environment offices were interrogated in semi-structured interviews and workshops in an iterative process. The result is an online tool, which allows a user to construct storymaps of extreme flood events by varying several boundary conditions (e.g. duration of the precipitation event, initial conditions, etc.). These storymaps depict how a flood produced by physically plausible storylines of extreme precipitation events evolves and recedes.

With the development of storymaps, we connect physically plausible storylines with dynamical mapping. Both methods address the episodic memory and allow the user to connect new information to personal experiences or the collective memory, and hence create an emotional element. Storylines furthermore allow focusing on single possible realisations of an extreme event rather than an ensemble mean.

The storylines are constructed with the help of a comprehensive model chain: extreme precipitation events (return period >= 100 years) extracted from 8490 years of two merged hindcast archives of ECMWF – ENSext (1998-2017) and SEAS5 (1981-2017) – are used as scenarios to run a hydrological model for the main rivers and lakes in Switzerland. Subsequently, the results of the hydrologic simulations are fed into a hydrodynamic model coupled with an impact module to assess the flood impacts on buildings, roads, and critical facilities such as schools and retirement homes, including the number of affected people. The generated storymaps can be directly used in emergency intervention planning and training, because they provide dynamic information on possible flood impacts in contrast to static hazard maps, which are used for this purpose today. Furthermore, the tool might help to raise flood risk awareness among professionals as well as the interested public by visualizing and localizing physically consistent flood hazard information in an intuitive way.

How to cite: Munz, L., Martius, O., Kauzlaric, M., Mosimann, M., Fehlmann, A., and Zischg, A.: Participatory Development of Storymaps to Illustrate the Spatiotemporal Dynamics and Impacts of Extreme Flood Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9712, https://doi.org/10.5194/egusphere-egu22-9712, 2022.

How to cite: Sharples, W., Bende-Michl, U., Vogel, E., Holgate, C., Bahramian, K., Khan, Z., and Carrara, E.: Charting Australia’s changing hydrological extremes from the past to the present through to the future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6536, https://doi.org/10.5194/egusphere-egu22-6536, 2022.

Hydrologic extremes (floods and intense precipitations) are among Earth’s most common natural hazards and cause considerable loss of life and economic damage. Describing their space-time variability in relation to climate is hence important for scientific and operational purposes. This presentation describes the use of an innovative probabilistic framework to jointly analyze global datasets of floods and extreme precipitations. This framework is based on the idea that the temporal variability of the data is induced by hidden climate indices that are unknown and therefore have to be estimated directly from the data. This is to be contrasted with the usual approach using predefined standard climate indices such as ENSO or NAO for this purpose. In statistical terms, a two-level hierarchical model is used. The first level jointly describes floods and intense precipitations, with hidden climate indices treated as latent variables. The second level describes the temporal variability of the hidden climate indices (including trend and persistence components), and the spatial variability of their effects.

This model is applied to station-based datasets describing seasonal maxima of streamflow and precipitation at the global scale, corresponding to more than 3,000 stations over a 100-year period (1916-2015). Several hidden climate indices governing the joint temporal variability of streamflow and precipitation data are identified. They affect floods and intense precipitations over large (continental) spatial scales and in a highly structured way. Overall these hidden climate indices do not present noticeable trend or persistence components, suggesting that they represent mostly interannual modes of variability. By contrast, when the same model is applied to precipitation data only, the estimated hidden climate indices are affected by stronger and mostly upward trends: this confirms that increasing intense precipitations do not identically translate into increasing floods, as highlighted by the latest IPCC report. Finally, we demonstrate that hidden climate indices can be predicted to some degree from atmospheric variables such as pressure, wind, temperature etc. This allows reconstructing the probability of occurrence of hydrologic extremes in the distant past using long reanalyses such as 20CR.

How to cite: Renard, B., Westra, S., Kavetski, D., Thyer, M., Leonard, M., McInerney, D., and Vidal, J.-P.: A Global-Scale Analysis of Hydrologic Extremes using Hidden Climate Indices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7122, https://doi.org/10.5194/egusphere-egu22-7122, 2022.

Design flood obtained under stationary conditions is obsolete in capturing the modulations induced by anthropogenic climate change. This leads researchers to employ the non-stationary approach to analyze the flood frequency. In that regard, hydroclimatic connections between extreme events and large scale climate drivers can be incorporated in flood frequency analysis by conditioning the distribution parameters with the climate covariates. However, more than one mechanism can drive floods, thus consideration of flood events as one category may neglect potential links or give rise to spurious connections. This necessitates the clustering of flood events based on its flood generating mechanisms to incorporate the climate drivers better. Therefore, this study focuses on grouping flood events based on the shape of hydrographs by using the time series clustering technique, based on the concept that the statistical shape of the flood hydrograph represents the hydrologic processes over the region. Germany, divided into three different streamflow regimes & driven by multiple flood mechanisms, is considered as the study area. Climate network is employed to identify the climate drivers of each cluster. The non-stationary flood frequency analysis is then carried out on the individual clusters using the identified climate covariates. Return flood values obtained from this study’s results are compared against the traditional stationary and climate conditioned non-stationary values. Thus, this study better represents the local/climate drivers' influence on the flood frequency at the changing climate conditions.

How to cite: Ganapathy, A. and Agarwal, A.: Event clustering approach for flood frequency analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-93, https://doi.org/10.5194/egusphere-egu22-93, 2022.

Changes in flooding have substantial economic consequences. Increases in flooding increase economic losses while decreases in flooding increase water scarcity. Greater extreme rainfalls due to climate change are expected to cause greater flood magnitudes. This is particularly true for urban and developed catchments where there is a lack of impervious surfaces and surface water storage. However, in rural and undeveloped catchments historical trends are mixed, with many catchments experiencing decreases in flood magnitude.

Here we argue the observed increase in flood variability is due to (1) changes in the extreme rainfall patterns and antecedent soil moisture conditions that drive flood response, and (2) the influence of event rarity on the interaction of these flood drivers. To investigate this hypothesis, we use 2776 stations from the Global Runoff Data Centre paired with rainfall and soil moisture from the Global Land Data Assimilation System. Flood events are chosen to isolate the flood driving rainfall volume, rainfall peak, and antecedent soil moisture, and trends are analysed in each of these variables alongside the subsequent flood peak. The analysis is limited to stations with 30 years or more of active record with the majority of stations in North America, Europe, Brazil, Oceania, and southern Africa.

We find that, while peak rainfall magnitudes are increasing globally, storm volumes are not increasing as greatly, resulting in a decrease in storm durations. Antecedent soil moisture on the other hand is consistently drier across the world. The result is a mixed flood response that depends on the local climate and the event rarity. In temperate and cold regions of the world floods are generally increasing in magnitude – these increases are less for more frequent events (those expected to occur on average once per year) and greater for more rare events (those expected to occur once every 10 years). The increases in flood magnitude are consistently less than the increases in the peak rainfall and rainfall volume because of drying soils.

In tropical and arid regions, there is a decline in flood magnitude for frequent flood events, with increases in flood magnitude only for the rarest events. This is because, in these regions, the antecedent moisture decreases outweigh the increase in rainfall. These results point to a worst of both world’s scenario where small floods, responsible for filling our water supplies, are decreasing, while the large flood events which pose a risk to life and infrastructure, are increasing.

How to cite: Wasko, C., Nathan, R., Stein, L., and O'Shea, D.: Historical increases in flood variability due to changing storm volumes and soil moisture, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-128, https://doi.org/10.5194/egusphere-egu22-128, 2022.

The genesis of riverine floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels threatening cities and communities along the river banks. In this study, we develop an analytical framework that captures and shares the story behind major historic and projected streamflow peaks in the large and complex basin of the Rhine River. Our analysis is based on hydrological simulations with the mesoscale Hydrolgical Model (mHM) forced with an ensemble of climate projections. The spatio-temporal analysis of the flood formation includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, flood extent and the excess runoff from major sub-basins up to ten days before a streamflow peak. An interactive web-based viewer provides easy access to result figures of major historic and projected streamflow peaks at four locations along the Rhine River. Our results indicate that each streamflow peak is driven by a specific sequence of precipitation and snowmelt from different areas in the Rhine River basin. Furthermore, we map how rising temperatures increase liquid precipitation in the Alps, in turn, increasing streamflow peaks along the Rhine River. The highest streamflow peak simulated at Cologne using climate projections exceeds the historic record by almost 50 % and was driven by excessive rainfall over several days over large parts of the Rhine River basin. Such an event taking place today would have catastrophic consequences. Further research is required to assess the impacts of changes in the persistence of circulation patterns on flood extent and hazard.

How to cite: Rottler, E., Bronstert, A., Bürger, G., and Rakovec, O.: Rhine flood stories: Spatio-temporal analysis of historic and projected flood formation in the Rhine River basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3469, https://doi.org/10.5194/egusphere-egu22-3469, 2022.

The intensity and occurrence of groundwater flooding have been found to increase considerably in the past two decades. In general, groundwater flooding occurs over a larger area and for a longer duration, when compared to other types of flooding. The associated damage and disruption caused by groundwater flooding led researchers to focus on the development of groundwater flood forecasting models that can be used for flood risk assessment and development of flood mitigation planning and strategies. This study develops a nonlinear time series model to predict total flooded volume (TFV) in a lowland karst area of Ireland. A Nonlinear Autoregressive model with Exogenous variables (NARX) was developed in the karst region, where rainfall and tidal amplitude had been considered to influence the TFV. The developed NARX model was found to predict TFV with considerable accuracy up to 30 days ahead, with a Kling-Gupta Efficiency (KGE) value of 0.9 or above. The efficiency deteriorates beyond 30 days ahead prediction and becomes 0.81 KGE when the prediction window is 90 days. Comparison of the developed NARX model with a linear time series model in TFV forecast indicates the importance of considering the nonlinear terms while developing the forecasted model. The developed NARX model has the potential to create an early warning system for flooding. The model has further been used to predict freshwater discharge from the inter-tidal spring into the Atlantic Ocean in southern Ireland.

How to cite: Basu, B. and Gill, L.: Nonlinear time series based forecast modelling of groundwater flooding at karst region in Ireland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7539, https://doi.org/10.5194/egusphere-egu22-7539, 2022.