During the period 2018-2020, Europe experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences. Yet, the extremity of these multi-year dry conditions is not recognized. Here, we provide a comprehensive spatio-temporal assessment of the drought hazard over Europe by benchmarking past exceptional events during the period from 1766-2020. We identified the 2018-20 drought event as a new benchmark having an unprecedented intensity that persisted for more than two years, exhibiting a mean areal coverage of 35.6% and an average duration of 12.2 months. What makes this event truly exceptional compared with past events is its near-surface air temperature anomaly reaching +2.8 K, which constitutes further evidence that the ongoing global warming is exacerbating present drought events. Our analysis shows that exceptional agricultural droughts enhanced by record-breaking near-surface air temperature anomalies have a significant impact (decline) on major crop yields (wheat, grain maize, and barley) across the European countries.  Furthermore, future events based on climate model simulations (CMIP5) suggest that Europe should be prepared for events of comparable intensity as the 2018-2020 event but with durations longer than any of those experienced in the last 250 years. Soil moisture drought projections synthesized in this study, even under a moderate emission scenario, indicate that decision-makers in Europe should be prepared for drought events of comparable intensity in future. Thus, the 2018--20 drought event could be considered as a wake-up call on agricultural policies. In this study, we compared and contrasted this event with earlier events of similar magnitudes and showed the role of increasing temperature rises. 

DOI of dataset: https://zenodo.org/record/5801249

Reference: 

Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S., Hanel, M., Kumar, R. (2022). The 2018-20 multi-year drought sets a new benchmark in Europe. Under Review, resubmitted version

How to cite: Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S., Hanel, M., and Kumar, R.: The 2018-2020 multi-year drought sets a new benchmark in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3626, https://doi.org/10.5194/egusphere-egu22-3626, 2022.

In recent years, drought impacts have been perceived as more severe and frequent than those of past events throughout Europe. Due to the heterogeneity of Europe’s hydro- climatological situation as well as the multiple Nations on the continent, drought events and their impacts vary with respect to location, sector, extent, duration and scale. In order to understand recent effects of drought and their possible drivers, national representatives distributed a uniform questionnaire to water management related stakeholders at different scales of 28 contributing countries. The survey focused on collecting information on stakeholders’ perceptions of drought, impacts on water resources and beyond, water availability and current drought management strategies at national and regional scales. The survey results were compared with the actual drought hazard information registered by the European Drought Observatory (EDO) for 2018 and 2019. The final results of the study highlight the diversity among national drought event perception and the value of implemented drought management strategies. Only few countries practise drought management, an absence of drought management is mostly attributed to lacking of resources, but also lacking political will for implementation and lacking political advice. Supported by the national representatives’ perspectives, the study concludes with an urgent need to further reduce drought impacts by constructing and implementing a European macro-level drought governance approach, such as a directive, which would strengthen national drought management and lessen harm to human and natural potentials.

How to cite: Blauhut, V., Stölzle, M., Ahopelto, L., Brunner, M., Teutschbein, C., and Wendt, D. and the Drought Risk Europe - a Panta Rhei working group: Lessons from the 2018-2019 European droughts: A collective need for unifying drought risk management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6146, https://doi.org/10.5194/egusphere-egu22-6146, 2022.

Drought represent one of the extreme aspects of the water cycle with impact on water resources, agriculture and socio-economic activities. Currently, droughts are used as an indicator of climate variability and change and are mainly driven by changes in the hydrological cycle and the large-scale atmospheric circulation. In order to have a long-term perspective on the drought variability and change, beyond the instrumental record, one needs to use different proxies and/or historical evidences. In this study we will present documentray evidences regarding the occurence of extreme droughts in Transilvania region (Romania) during the Little Ice Age (AD 1500-1800) and their socio-economic impacts. Between AD 1500-1800 we identify 126 drought events, frm which 33 are considered extreme droughts (e.g. with the character of a calamity). Of the 33 extreme droughts, 3 occurred between AD 1500-1600, 14 between AD 1600-1700 and 16 between AD1700-1800. These events have been driven by anomalous large-scale atmospheric and oceanic patterns in combination with strong variation in the solar and volcanic variability. In conclusion, our results will contribute to the knowledge of extreme events of Central Eastern Europe during the Little Ice Age and may be used as an indicator to predict their influences in the context of the climate changes.

How to cite: Badaluta, G., Badaluta, C.-A., Ionita, M., and Mindrescu, M.: Extreme droughts in Transilvania during Little Ice Age derived from documentary evidences, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10903, https://doi.org/10.5194/egusphere-egu22-10903, 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.

Flood and drought events can lead to severe socio-economic impact and damages. Thus, there is a need for early warnings of such extreme events, especially for decision-makers in sectors like hydropower production, navigation and transportation, agriculture, and hazard management. To improve the predictability of sub-seasonal streamflow, we propose the approach of a hybrid forecasting system, where a conceptual hydrological model PREVAH is combined with a machine learning (ML) model. The PREVAH model provides catchment level hydrological forecasts and the role of the ML model is to emulate a runoff routing scheme. Such a hybrid setup allows the forecasting system to benefit from the statistical power of ML while maintaining the understanding of physical processes from the hydrological model.

The objective of this study is to investigate the predictability of a hybrid forecasting system to provide monthly streamflow predictions for three recent extreme events. These include the drought event in summer 2018, the drought event in spring 2020, and the flood event in summer 2021 in selected large Swiss rivers. We also investigate different predictability drivers by considering additional input features to the ML model, such as initial streamflow, European weather regime indices, and a hydropower proxy.

We demonstrate that the proposed hybrid forecasting system has the potential to provide skillful monthly forecasts of the interested events. Informed ML models with additional input features achieve better performance than results obtained using hydrological model outputs only. This study sheds light on using hybrid forecasting for sub-seasonal hydrological predictions to provide useful information for medium-term planning at a monthly time horizon and reduce the impact of flood and drought events.

How to cite: Chang, A. Y.-Y., Jola, S., Bogner, K., Domeisen, D. I. V., and Zappa, M.: Hybrid Forecasting of Recent Flood and Drought Events in Switzerland , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1893, https://doi.org/10.5194/egusphere-egu22-1893, 2022.

Snow plays a significant role in the hydrology of numerous regions across the globe. A major portion of precipitation above 45O N latitude falls as snow. The accumulated snow melts slowly and contributes to infiltration and runoff processes. Therefore, it is important to study the quantity and fate of water from snowmelt. Reduced snow storage would lead to snow droughts, which can have an enormous impact on the water resources of snow-dominated catchments, such as those in the western United States. For that reason, it is essential to efficiently identify the time and severity of snow droughts. This study proposes SnoDRI, a new index that could identify and measure snow drought events. SnoDRI is a machine learning-based index that is estimated from several snow-related variables utilizing novel machine learning algorithms. The model uses a semi-supervised learning algorithm of an autoencoder and feature extraction to assess the importance of each variable. We use ERA5-Land reanalysis data from 1981 to 2021 for the Western United States to study the efficacy of the new snow drought index. The results are validated by verifying the coincidence of actual snow drought events and the interpretation of our new index. We will discuss how well the new drought index can perform and help in better identification of snow droughts.

How to cite: Rasiya Koya, S., Kar, K. K., Srivastava, S., and Roy, T.: SnoDRI: A Machine Learning Based Index to Measure Snow Droughts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10665, https://doi.org/10.5194/egusphere-egu22-10665, 2022.

In this presentation, I highlight key lessons learned following a career within drought research. From its start with focus on low flows in the 1990'ties to the definition of hydrological drought and its spatial and temporal patterns in more recent work - it has been an interesting and learning journey. A period that coincides with an increasing awareness of drought as a natural hazard at the local, regional and continental scale. It is also a period when the influence of climate change on the hydrological cycle, water resources and extremes, became prominent. Accordingly, time series could no longer be assumed stationary, which had been the basis for our hydrological training at the time, and the research focus shifted from more traditional hydrological analysis to the detection, attribution and projection of the climate change signal on hydrology. When analyzing drought and its wide range of impacts, it is important to distinguish between the different types of droughts, and when analyzing changes and trends, it is important to distinguish between natural variability and changes due to climate change or human interventions. How drought is defined and perceived in different sectors and regions across the world influences the choice of methodology and how the results are interpreted and communicated. The importance of drought terminology and awareness of the diversity in drought behavior - within and among regions - are aspects that have been essential throughout my research and that I will reflect upon in my talk.

How to cite: Tallaksen, L. M.: Hydrological Drought – 12 lessons learned in 12 minutes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4951, https://doi.org/10.5194/egusphere-egu22-4951, 2022.

Droughts are the major natural disasters affecting water availability leading to social, economic, and environmental challenges. Due to climate variability, investigation of climate change impact on droughts is of vital importance for sustainable societal and ecosystem functioning.  Western Ghats, a humid tropical region of India is selected as a case study to investigate the change in drought characteristics subjected to future climate change. In the last few decades, due to monsoon failure, the intensity of droughts has been identified to increase in this region making this study important. For future climatic variables, the ensemble of five Global Climate Models (GCMs) in the Coupled Model Intercomparison Project (CMIP5) are considered for the Representative Concentration Pathways (RCPs) 4.5 and 8.5. Standardized Precipitation-Evapotranspiration Index (SPEI) for the 12-month accumulated period is considered to assess the change in the drought characteristics during near (2011–2040), mid (2041–2070), and far future (2071–2100). The results indicate an increase in drought events in the future with maximum change during the far future for both RCP 4.5 and 8.5 scenarios. The major areas affected are in the southern part of Kerala and Karnataka. The change in total severity and duration of these drought events are high during the near future, moderate during the mid-future, and very high during the far future in both scenarios with RCP 8.5 being more severe. The findings show the variability in drought characteristics both spatially and temporally across the study area. The results will be helpful in identifying the hotspots prone to drought risk. This will serve as important guidance in improving the identification of causes, minimizing impacts, and enhancing the resilience to droughts in the study area. Further, the important implication of the study will be for water resource planning and management to strategize policies that emphasize on providing water in water-scarce regions during extreme drought situations.

How to cite: Sadhwani, K., Eldho, T. I., and Karmakar, S.: Projected Climate Change Impact Assessment on Drought Characteristics Over a Humid Tropical Region in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9070, https://doi.org/10.5194/egusphere-egu22-9070, 2022.

Atmospheric variability in the North Atlantic region is known to modulate hydrometeorological variables across Europe. In this context, oscillatory systems, such as the NAO, may be used to indicate future water resource behaviours, such as hydrological droughts. Existing hydroclimate studies have identified a sensitivity of certain water resources to multiannual periodicities in systems such as the NAO and have highlighted that these long-term behaviours may be valuable to existing drought forecasting systems; for instance, by indicating multi-year periods of increased drought risk. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here, we explore the role of NAO periodicities in defining water resource and drought behaviours over the past 90 years using a large dataset of 136 groundwater level records and 767 streamflow gauges in the UK. We identify significant relationships between the NAO and a calculated index of wide-spread water resource drought and find several abrupt shifts in drought frequency driven by non-stationarities in multiannual NAO behaviour. This includes a 7.5-year periodicity that has predominated water resource behaviour (and extremes) since the 1970s but has been weakening over recent years, suggesting a new shift in drought frequency may soon impact water resources. Furthermore, we show that the degree to which these periodicities have influenced recorded water resource anomalies is comparable to the projected effects of a worst-case climate change scenario. We discuss the potential origins for these modes of non-stationarity and their implications for existing water resource forecasting and projection systems, as well as the utility of these periodic behaviours as an indicator of future water resource drought in Europe.

How to cite: Rust, W., Bloomfield, J., Cuthbert, M., Corstanje, R., and Holman, I.: Multiannual Atmospheric Controls on Drought Stationarity: What the NAO can tell us about past behaviours and future climate change projections?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-641, https://doi.org/10.5194/egusphere-egu22-641, 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.

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.

This study intended to understand the effect of climate change on spatiotemporal characteristics of multivariate drought risk over the Vidarbha region of India. The Standardized Precipitation Evapotranspiration Index (SPEI) is employed to characterize droughts in the region. Gridded daily precipitation and temperature data produced by the Indian Meteorological Department (IMD) and Coupled Model Inter-comparison Project Phase 6 (CMIP6) were utilized for estimating the SPEI. The drought events were identified and subsequently characterized by duration, severity, and peak. Different goodness of fit tests was applied to select the best fitting marginal distributions of the individual drought characteristics. Several symmetric and asymmetric Archimedean trivariate copulas and seven bivariate copula families were evaluated for joint distribution modeling. Maximum pseudo-likelihood and genetic algorithms have been applied to estimate the copula parameters accurately. The asymmetric Frank copula was selected to construct the trivariate distribution of the drought characteristics. Frank, Student’s t and Clayton copulas were chosen to build the bivariate distribution of duration-severity, duration-peak, and severity-peak, respectively. The joint distributions were applied for computing the joint return periods of drought events. The drought risk over the region was illustrated using zoning maps for historical along with near and far future periods. The inferences derived from the study will help policymakers to prepare better mitigation strategies under the changing environment.

How to cite: Datta, R. and Reddy, M. J.: Multivariate Analysis and Assessment of Regional Drought Risks under Climate Change using Copulas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11772, https://doi.org/10.5194/egusphere-egu22-11772, 2022.

Water resource managers all over the world require timely and robust climate projections to support planning on a range of timescales. In the UK, water companies are obliged to produce long-term water resource management plans to ensure security of water supply, and these are required to take account of climate change. One of the challenges is having ready access to the latest climate projections, at an appropriate scale to support regional- and national-scale planning. In particular, a major gap has been the availability of a national, spatially-coherent dataset of river flows and groundwater projections.

This presentation introduces the ‘enhanced Future Flows and Groundwater’ (eFLaG) project, that has delivered nationally consistent hydrological (river flow and groundwater) projections for the UK, based on the latest UK Climate Projections (UKCP18). The hydrological projections are derived from an ensemble of river flow models (Grid-to-Grid, PDM, GR4J and GR6J) and groundwater models (AquiMod and ZOODRM) to provide an indication of hydrological model uncertainty. A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge were produced using bias-corrected data from the UKCP18 Regional (12km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies sampling across the diverse hydrological and geological conditions of the UK. An evaluation, using multiple metrics, will be presented for the national scale.

The eFLaG project also undertook a national-scale analysis of hydrological droughts. We present results showing the future evolution of hydrological drought severity compared to a current baseline – generally showing significant increases in drought severity in future. We also show the evolution of low flows through the 21^st century, demonstrating the benefit of having long, transient ensemble runs of river flows and groundwater levels. While there are wide uncertainties, reflecting the diversity of RCM ensemble members and hydrological models alike, generally these results point towards decreasing low flows and minimum groundwater levels through the coming century. Finally, we also undertook an analysis of spatial coherence of drought, showing how inter-regional coherence of drought changes under anthropogenic warming – results which could have implications for water transfers that are integral to the latest round of Water Resources Management Plans (WRMPs).

eFLaG is designed to provide a demonstration climate service to enhance the resilience of the water sector to drought events. In this regard, we will also describe three contrasting case studies (Thames, Wales and Scotland) where the team collaborated with the water industry to demonstrate the utility of eFLaG for water resources management applications. These demonstrators illustrate the potential benefit of coherent, multimodel, transient projections, while also the challenges in integrating them with current statutory WRMPs produced by water companies.

While eFLaG was developed with drought applications as the primary focus, the evaluation metrics show that river flows and groundwater levels are generally well simulated across the regime. The eFLaG dataset can potentially be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose.

How to cite: Hannaford, J. and the the eFLaG Team: enhanced future FLows and Groundwater (eFLaG):  future hydrological projections to enhance drought resilience in the UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12040, https://doi.org/10.5194/egusphere-egu22-12040, 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.

Climate change impacts in the Swiss Alps have been seen through changes in the hydrologic regime. These include glacier mass loss, shifts in peak snowmelt and changes in seasonal precipitation type and timing (e.g., more rain-on-snow events in winter). In addition, it is expected that global warming will lead to more intense extreme rainfall events in the Alps in the future. Combined, all of these changes pose a challenge to planning, management and optimization for hydropower producers operating in the Alpine region. In particular, because the impacts are not expected to be felt homogeneously across the region, it is difficult to ascertain how the risks will differ between individual reservoirs without more localized assessment. This study focuses on the use of statistical tools that are specifically adapted for application to small datasets (such as the extended generalized pareto distribution), in order to calculate return periods of extreme precipitation and high reservoir inflow events for individual Alpine reservoirs. In addition, we show how the return periods of persistent low inflow periods (droughts), which have the potential for significant negative impacts on hydropower production, are determined using extreme value theory. The result is effectively a risk profile comprised of return periods for both extreme high precipitation/inflow and impactful low precipitation/inflow events that can be used by researchers and practitioners alike to further understanding of local climate change risks to individual reservoirs.

How to cite: Milojevic, T., Blanchet, J., and Lehning, M.: Risk Profiles of Impactful and Extreme Hydrological Events for Alpine Reservoirs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12338, https://doi.org/10.5194/egusphere-egu22-12338, 2022.

The compound occurrence of two extremes of the hydrological spectrum (droughts and floods) either in space and/or time, could aggravate the associated socio-economic impacts with respect to those caused by the individual extreme event. Both extreme events share the potentially linked driving mechanisms and interconnected characteristics, therefore the better understanding of the dependence structures of the contributing variables is essential to avoid the underestimation of the possible risks of compound hazards. To this end, the present study focusses on spatio-temporally compound extreme events under a changing climate and identify/locate the most vulnerable hotspots in the Upper Jhelum Basin (South Asia), paving the way for adaptation and mitigation measures. Climate models are the main tools to assess climate projections and, particularly, to provide relevant information for sectoral applications. They often present systematic biases, thus some sort of bias adjustment is performed in impact assessments. The framework of the present study is two-fold: (i) evaluation of bias correction (BC) of climate model historical simulations and (ii) projection of extreme compound events in the near future (2040-2059) and far future (2080-2099) for three different Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5). Droughts and floods are characterized by using a multivariate drought index (namely the Standardized Precipitation Evapotranspiration Index, SPEI), which is derived from daily precipitation (P) and maximum (Tmax) and minimum temperatures (Tmin). In the first step, the intercomparison of different state-of-the-art BC methods (uni- and multi-variate) and BC approaches (direct and component-wise) for climate model simulations stemming from different experiments (CMIP6, CORDEX -WAS-44- and CORDEX CORE -WAS-22) is performed following a multivariate framework. The added value/performance of BC and climate model simulations is examined in terms of inter-variable physical coherence of involved key essential variables (P, Tmax and Tmin) and characteristics of extreme events (duration, severity, intensity, and frequency of floods and droughts) during the historical period. In the second step, projected changes in the extreme events characteristics and their compounding in space and time are analyzed for the near and far future under all available scenarios. Climate projections of this kind of extreme events, spanning different scenarios and other sources of uncertainty is essential to better implement adaptation and mitigation solutions that can help reduce the negative impacts of climate change.

How to cite: Ansari, R., Casanueva, A., and Grossi, G.: Climate projections of spatio-temporally compound extreme events (floods and droughts) using a multivariate drought index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10376, https://doi.org/10.5194/egusphere-egu22-10376, 2022.

The quantitative description of uncertainty in future projections of hydrometeorological variables provides valuable information for a better interpretation of climate change impact for informed policy decisions and actions to mitigate the associated risk. Several methods have been developed and used for decomposing the uncertainty in projections. The relative importance of the uncertainty associated with the choice of uncertainty decomposition methods compared to the other sources of uncertainty has however never been quantitatively investigated. We scrutinize where and to what extent the rate of fractional uncertainties could vary across the globe depending on the choice of uncertainty decomposition methods. We characterize drought by the standardized precipitation evapotranspiration index (SPEI) using a large ensemble of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) general circulation models (GCMs). Flood and extreme precipitation are quantified by fitting a generalized extreme value distribution (GEV) to the annual maxima time series of the ISIMIP2b global hydrological model and CMIP5/CMIP6 simulations. The uncertainty in future projections of extreme precipitation, flood, and drought is then split in the variance contributions using the traditional ANOVA, quasi-ergodic ANOVA (QE-ANOVA), HS09, and variance decomposition-same sample size (VD-SSS) methods. Finally, the uncertainty arising from the choice of uncertainty analysis methods is quantified and compared across different types of hydrological extremes and the IPCC reference regions.

How to cite: Tabari, H. and Willems, P.: Uncertainty of uncertainty decomposition approaches for projections of hydrological extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2389, https://doi.org/10.5194/egusphere-egu22-2389, 2022.

In the UK, land use and land management change are being considered as part of a strategy to tackle flooding; the term natural flood management (NFM) is used in the UK to refer to this approach. Although evidence is limited, it is thought that practices considered as NFM, such as woodland planting or cover cropping, increase soil water storage and infiltration through improved soil structure, potentially reducing flooding within catchments where applied. Moreover, we know that trees and woodland have higher interception than shorter vegetation, increasing soil moisture deficit. We simulate what effect these land use and management changes have in the River Coln catchment (Upper Thames, UK), which is characterised by permeable geology and arable farming. The catchment has experienced both surface water and groundwater-driven flooding in recent history and is also important in maintaining summer low flows in the River Thames.  

The land surface water balance was modelled with the detailed, field-scale hydrological model SWAP. Recharge from the SWAP model was passed to a semi-distributed groundwater model, which produces groundwater baseflow, and direct runoff from SWAP was passed to a linear reservoir model to produce surface runoff. Soil information (such as soil hydraulic parameters) was based on the NATMAP database; vegetation parameters (e.g. typical crop rotations or forest types) were derived from stakeholder workshops, surveys and interviews, and expert elicitation. Soil and farm management decisions were implemented by perturbing the SWAP soil and vegetation parameters. The parameters of the surface water routing and groundwater components were calibrated within a Monte Carlo framework.

Overall, we found that land use and land management measures have very limited potential for reducing flooding in permeable catchments, where the primary driver of high flows is high winter groundwater levels. We found that only large-scale coniferous planting had the potential to reduce winter peak flows and flooding in the Coln (e.g. reduction of 21−27% with two-thirds of catchment as coniferous woodland), but that this would decrease summer mean flows (12% reduction in July mean flow, 8% in August). The levels of coniferous woodland required to achieve these reductions in winter high flows are unrealistic, given the large area of productive arable land that would need to be converted to woodland and the limited ecological benefits of coniferous woodland.

How to cite: Collins, S., Verhoef, A., Mansour, M., and Macdonald, D.: Modelling the impact of land use change on floods and drought in a groundwater-dominated catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2349, https://doi.org/10.5194/egusphere-egu22-2349, 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.

Before-after monitoring of small-scale restoration activities in blanket peatlands, e.g., revegetation and gully blocking, suggests they can also deliver significant Natural Flood Management (NFM) benefits (reduce and delay floodpeaks). However, we still lack a clear understanding of the underlying processes driving NFM effects; and doubts remain about whether interventions will retain their impact when implemented at scales large enough to reduce flooding in downstream communities. We examine the impact of the two interventions at a range of scales from the 1 hectare micro-catchment scale at which a Before-After-Control-Intervention (BACI) study has been undertaken, to the 40 km² scale (at which flooding begins to affect residential properties). We calibrate the Generalised Multistep Dynamic (GMD) TOPMODEL rainfall-runoff model to different BACI experimental catchments each representing an intervention scenario. Through numerical experimentation with the calibrated parameters, we estimate the impact-magnitude of different process drivers. Our findings confirm the NFM benefits of these restoration-focused interventions at the micro-catchment scale. In both interventions and in our largest storms, floodpeak attenuation is primarily due to roughness reducing the floodwave speed and thus thickening the overland flow (kinematic storage). More conventional, static storage (i.e. interception + ponding + evapotranspiration), becomes important only in smaller storms. Finally, we use the parameter-sets identified by calibrating to the BACI catchments to extend our findings to the 40 km² Glossop catchment. Glossop has experienced several damaging floods in the last 50 years and has received appreciable recent restoration activity. Here we use GMD-TOPMODEL in a second set of modelling experiments to estimate downstream impact of existing interventions and to examine the impact of alternative scenarios of spatially distributed intervention configurations.

How to cite: Goudarzi, S., Milledge, D., Holden, J., Allott, T., Edokpa, D., Evans, M., Kay, M., Shuttleworth, E., and Spencer, T.: Investigating process drivers of Natural Flood Management and its flood risk reduction potential across scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9475, https://doi.org/10.5194/egusphere-egu22-9475, 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.

Large ensemble simulations can be exploited to generate larger data samples than the observed record and consequently better assess the likelihood of rare events. Such simulations have the potential to inform storylines of ‘unseen’ flood episodes, i.e., that are more extreme than those seen in historical records. This method has, for example, been used to improve design levels of storm-surges in the river Rhine and to anticipate rainfall extremes over the UK. However, adequate evaluation of simulated ‘unseen’ events is a complex task.
Here, we showcase simulated Amazonian mega-floods from the combination of global climate model EC-Earth and global hydrological model PCR-GLOBWB. We introduce a three-step procedure to assess the realism of these mega-floods based on the model properties (step 1), as well as the statistical features (step 2) and physical credibility of the simulation (step 3). For the Amazon example, we find that the unseen floods were the result of an unrealistic bias correction of precipitation. 
We reflect on the different types of models that can be used to generate large sample sizes, and discuss the difference between storylines from large ensembles as compared to targeted model experiments to identify mega-floods. We conclude that understanding the driving mechanisms of unseen events may guide future research by uncovering key model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers. 

How to cite: Kelder, T., Wanders, N., van der Wiel, K., Marjoribanks, T., Slater, L., Wilby, R., and Prudhomme, C.: Amazon mega-flood naratives from large ensemble simulations – are they unseen or unrealistic?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-485, https://doi.org/10.5194/egusphere-egu22-485, 2022.

Floods have large impacts on the populations and the economy of Africa, but little is known about the dominant flood generating mechanisms across this continent. This study is based on a large set of African basins, with the aim of identifying the main mechanisms causing floods. A total of 13815 flood events between 1981 and 2018 in 529 catchments are classified to identify the main flood drivers in different African regions. The classification is based on daily river discharge data together with precipitation and soil moisture from the ERA5-Land reanalysis, to identify flood events associated with short rains, long rains, or excess rain over saturated soils. Results indicated that processes related to soil saturation, either before floods or during long rainfall events, are strongly associated with the occurrence of floods in Africa. Excess rain in Western Africa, and long rain for catchments in Northern and Southern Africa, are the two dominant generating mechanisms, contributing to more than 75% of all flood events. Overall, no significant changes were detected in the relative importance of these drivers over the last decades. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to precipitation intensity, to analyze the evolution of flood hazards or implement flood forecasting systems.

How to cite: Tramblay, Y., Villarini, G., Saidi, M. E. M., Massari, C., and Stein, L.: Classification of flood-generating processes in Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1430, https://doi.org/10.5194/egusphere-egu22-1430, 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.

Flash Floods are damaging natural hazards which often occur in the European Alps. Precipitation patterns and intensity may change in a future climate affecting their occurrence and magnitude. For impact studies, flash floods can be difficult to simulate due the complex orography and limited extent & duration of the heavy rainfall events which trigger them.

The new generation convection-permitting regional climate models (CP-RCMs) improve the representation of the intensity and frequency of heavy precipitation. Therefore, this study combines such simulations with high-resolution distributed hydrological modelling to assess changes in flash flood frequency over the Alpine domain.

 We use output from a state-of-the-art CP-RCM to drive a high-resolution distributed hydrological wflow_sbm model covering most of the Alpine mountain range on an hourly resolution.  First, the hydrological model was validated by comparing ERA5 driven simulation with streamflow observations from 130 stations (across Rhone, Rhine, Po, Adige and Danube basins). Second, a hourly wflow_sbm simulation driven by a CP-RCM downscaled ERAInterim simulation was compared to databases of past flood events to evaluate if the model can accurately simulate flash floods and to determine a suitable threshold definition for flash flooding. Finally, simulations of the future climate RCP 8.5 for the end-of-century (2096-2105) and current climate (1998-2007) are compared for which the CP-RCM is driven by a Global Climate Model. The simulations are compared to assess if there are changes in flash flood frequency and magnitude using a threshold approach. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the future climate simulations indicate an increase in the flash flood severity in both summer and autumn leading to more severe flash flood impacts.

How to cite: Zander, M., Viguurs, P., Sperna Weiland, F., and Weerts, A.: Future changes in flash flood frequency and magnitude over the European Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1693, https://doi.org/10.5194/egusphere-egu22-1693, 2022.

Climate change is increasing frequency and magnitude of precipitation extremes and floods. Increasing design floods in the future could lead to underestimated design capacities of current spillways, increasing the probability of dam overtopping. Therefore, new methodologies are required for assessing hydrological dam safety considering climate change. Moreover, uncertainty should be accounted to properly assess future changes in overtopping probabilities. This study presents a methodology to assess hydrological dam safety considering the impact of climate change on inflow hydrographs and initial reservoir water levels in flood events, quantifying the uncertainty in estimations. The methodology is applied to the Eugui Dam in the River Arga catchment (Spain), upstream the city of Pamplona.

The impact of climate change on inflow hydrographs in the Eugui reservoir is quantified by using the delta changes in precipitation quantiles extracted from climate projections in the Iberian Peninsula (Garijo & Mediero, Water 2019). The RIBS distributed hydrological model was used in Lompi et al. (Water, 2021) to quantify the expected changes in flood quantiles at the Eugui Dam for three-time windows (2011-2040, 2041-2070, and 2071-2100), two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and seven return periods (2, 5, 10, 50, 100, 500 and 1000 years).

The impact of climate change on initial reservoir water levels in flood events is assessed integrating the HBV continuous hydrological model with a reservoir operation model. HBV simulates daily inflow discharges in the Eugui reservoir by using rainfall and temperature climate projections as input data. HBV is calibrated with 13 years of precipitation, temperature, and reservoir inflow discharge observations. The reservoir operation model is developed to obtain daily reservoir water levels. It uses HBV inflow discharges as input data and considers reservoir operation rules, such as water supplies and environmental releases. Rainfall and temperature climate projections for an ensemble of 12 climate models are used to assess the changes in the expected daily reservoir water levels at the Eugui dam for each time window and emission scenario, including the control period.

A set of 10 000 peak inflow discharges are randomly generated from several GEV distribution functions fitted to the flood quantile outputs of the RIBS model. Given hydrograph shapes and initial reservoir water levels are assigned to each peak flow. The Volumetric Evaluation Method is used to simulate flow routing processes in the reservoir. The frequency curves of maximum reservoir water levels and maximum outflow discharges are obtained for each scenario, assessing the expected changes in the probability of exceedance of dam overtopping. In addition, a stochastic procedure quantifies the uncertainty chain of the methodology.

The results show an increase of both the maximum water level frequency and the probability of dam overtopping, especially in the period 2071-2100 for the RCP 8.5. Moreover, the maximum outflow discharge frequency also increases in all the time windows for the RCP 8.5, exacerbating the hydraulic risk for the downstream population in the Pamplona city.

Acknowledgments: This research is supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.

How to cite: Lompi, M., Mediero, L., Soriano, E., and Caporali, E.: A stochastic methodology to assess the impact of climate change on the Eugui hydrological dam safety (Spain) with an ensemble of climate projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11498, https://doi.org/10.5194/egusphere-egu22-11498, 2022.

Short-duration (hourly) precipitation extremes have intensified in the past and they are projected to increase more under the warming climate. The Clausius-Clapeyron (CC) relationship can be used to understand the sensitivity (scaling) of precipitation extremes with warming. According to the CC relationship, hourly precipitation extremes intensify at around a 7% (CC rate) per degree rise in temperature. However, the observed scaling rates deviate from the CC rate which can be due to multiple thermodynamic and dynamic factors which have been discussed in the recent scaling studies. Moreover, the choice of data and scaling methods may also lead to uncertainty in scaling rates. In this study, by using observed hourly precipitation and daily dewpoint temperature over the USA, we show that robust quality controlled precipitation data show differences in scaling. We also obtained higher scaling rates for the higher measurement precision data (0.25mm and 2.5mm). We further show the uncertainty in scaling rates using different four scaling methods. Our results highlight the need of using extensive quality controlled and finer precision observations for estimating accurate scaling rates.

How to cite: Ali, H., Pritchard, D., Fowler, H., and Lewis, E.: Uncertainty in estimating the observed relationship between hourly precipitation extremes and dewpoint temperature, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11887, https://doi.org/10.5194/egusphere-egu22-11887, 2022.