Scale dependencies are a core element of hydrological sciences. The inclusion of deterministic aspects in flood statistics requires a cross-scale approach that takes into account the differences in flood generation and, in particular, the importance of local and regional processes as well as the interdependencies of flood waves at different spatial scales. The interaction of meteorological drivers with the spatially and temporally variable catchment conditions, e.g. antecedent soil moisture, snow cover or confluences, determines a variety of flood events that are scale-dependent. For example, heavy rainfall floods usually lead to extremely large flood peaks if the catchment is small and has a critical size relative to the spatial extent of the rain cell. The situation is different for synoptic rainfall, where large basins are affected and the total rainfall and the superposition of floods from tributaries can lead to extreme events. Thus, the consideration of flood generation becomes more complex when large river basins are considered instead of small catchments. In addition to the meteorological and catchment-specific factors, the interaction between the individual sub-catchments must also be taken into account. In particular, the superposition of flood waves from tributaries can critically alter the flood hydrograph. How such differences can be determined statistically-deterministically and how the individual factors can be taken into account in a statistical model for estimating design floods is presented here. Univariate mixture models for local floods as well as multivariate statistical models for regional floods help to better understand floods and their development. Their relevance on different spatial scales is discussed here. The result forms the basis for improved flood modelling that extends the classical peak-based flood frequency analysis by essential spatial aspects.

How to cite: Fischer, S. and Schumann, A.: Scale Dependence of Stochastic-Deterministic Flood Statistics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1349, https://doi.org/10.5194/egusphere-egu23-1349, 2023.

Different flood-generating mechanisms are responsible for high flows in different catchments. This mixture of generating mechanisms could violate the homogeneity assumption of the extreme value distribution used often in flood frequency analysis. Thus, this study aims to classify flood samples into homogenous process-based groups and estimate the flood quantiles for different return periods. Furthermore, this study also deals with the sample inadequacy in the flood classification by pooling ensemble reforecast datasets based on the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach. The Dresden gauge in the Elbe River is selected as the study site. Daily discharge data are extracted from the GRDC, and flood events are separated based on our proposed ‘Peak-identification flood separation algorithm’, which follows four steps: 1. Identification of peaks, i.e., points with a higher streamflow value than its prior and next values, 2. Pruning based on 90^th percentile threshold value, 3. Application of independence criterion, 4. Identification of flood starting and ending position. After flood separation, hydrograph features-based flood grouping and ensemble data pooling are performed. We observe the difference in the distribution characteristics of the observed in comparison to the pooled datasets. A relative difference of 0.25 (cumecs/cumecs) is noticed for the 100-year return level between observed and pooled data. As our key contribution, we address the sample mixing problem using the flood classification technique and establish the importance of data pooling.

How to cite: Ganapathy, A., Hannah, D. M., and Agarwal, A.: Flood frequency analysis integrated with unprecedented flood samples and mixed probability distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4046, https://doi.org/10.5194/egusphere-egu23-4046, 2023.

Droughts are a recurring long-term problem in West Africa, but in recent years the region has also experienced a significant increase in damaging floods. Increasing trends in extreme discharges have been shown, reflected by an increase in return level magnitude since 1980s in Sahelian rivers. Our study targets flood prediction within the regional catchment of the Niger River at Niamey, the capital of Niger. The river hydrograph has changed significantly since the drought between the 1970s and 1980s, evolving from a single peak to a two-hump hydrograph: a first flood, coming mainly from three direct Sahelian tributaries of the right bank of the Niger River, and a second one, coming from the more remote Guinean basin. Predicting floods in Niamey is not straightforward because of the complexity of the hydrological system, which combines non-stationarity and the difficulty of deconvoluting the two floods and separate their own trends.

The objective of the study is to quantitatively assess the hydrological hazard on the Niger River at Niamey based on a flow data set covering the period 1950-2020 by developing a statistical modeling approach that allows to integrate both the hydrological complexity and the non-stationarity of floods. An important question addressed by the proposed approach is to evaluate the contribution of considering hydrological complexity in non-stationary statistical modeling. This is achieved by defining several flood samples and proposing different non-stationary models adapted to their complexity.

How to cite: Sauzedde, E., Vischel, T., Panthou, G., Tarchiani, V., and Massazza, G.: Statistical modeling of the flood hazard in a complex and non-stationary hydrological system: case study on the Niger River in Niamey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5265, https://doi.org/10.5194/egusphere-egu23-5265, 2023.

The issue of dependence and causality is fundamental for the study of compound events. Dependence measures are largely exploited in literature to study the interconnections among hazards and drivers. Classical dependence measures are symmetric, dependence in one direction is considered equal as the dependence in the other direction. Nevertheless, there are many situations in which there can exists a directionality on dependence. Considering the extremes in river network case study, there exists a physical link between upstream and downstream river sites, and this must be reflected in their dependence relationships. Upstream influences downstream more that in the other direction. In this work, we want to explore the concept of asymmetric dependence considering the extremes and the possible existing link with causality effects. A conditional version of the Kendall’s tau has been proposed and investigated to give some information about the directionality of the dependence.

As case study we use the UK river network considering daily discharge data and a POT analysis approach. Exploring the issue of asymmetry in the statistical pairwise dependence, it could provide a new tool/perspective to address the joint statistical behaviour of dependent variables.

How to cite: Deidda, C., Engelke, S., and De Michele, C.: Exploring the asymmetric dependence in hydrologic extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14153, https://doi.org/10.5194/egusphere-egu23-14153, 2023.

Quito, the capital of Ecuador, is an Andean city experiencing two water challenges: urban flooding driven by extreme precipitation events and water scarcity in the dry season. Climate change is expected to increase the probability of the occurrence of flash floods, sewer overflows, and landslides because of more intense precipitation. It might moreover reduce the river discharge in the dry season due to an increase in temperature and evapotranspiration. The previous studies in the region have used a limited number of climate models and have not focused on short-duration events, presenting biased impacts of climate change. 
To address this knowledge gap, this research employs an ensemble of 19 state-of-the-art CMIP6 general circulation models (GCMs) to analyze climate change impacts on intensity- duration-frequency (IDF) curves, urban flooding, and river discharge in Quito under four plausible future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Daily precipitation and temperature simulations are spatially downscaled with the delta change and quantile perturbation approaches. Temporal downscaling is then applied to obtain sub-daily precipitation time series and statistics in the form of IDF curves. The uncertainty contribution of the extreme value analysis is considered by including five statistical distributions for IDF estimations. Furthermore, composite design storms are built based on historical hyetographs recorded in the city and then applied to a calibrated hydraulic model (SWMM) for a part of Quito´s combined sewer system. Climate change impacts on the urban area are expressed as changes in the IDF curves and the flood volume. 
To analyze climate change impacts on river discharge, three conceptual hydrological models (NAM, GR4J, and VHM) are calibrated in one of the water-supplying catchments of the city, the San Pedro River. Here, the impacts are expressed as changes in the peak, mean, and low river discharges. The uncertainty contribution of the different components (climate models, emissions scenarios, hydrological models, and extreme value distributions) is quantified by a variance decomposition method. 
The findings suggest an increase in the intensity of short-duration extreme precipitation events by 10-30% for the near future (2021-2050) and by 20-50% for the far future (2070- 2099). As a result of this intensification, the flood volume in the sewer network of Quito magnifies at critical points. Moreover, in the San Pedro River, the peak discharges are projected to increase by 5-20% and 10-50% in the near and far future, respectively. In contrast, the low discharges in the dry season are projected to decrease up to 13-30% as fewer wet days are expected. The uncertainty analysis reveals that climate models dominate the total projection uncertainty, although the contribution of hydrological models and extreme value distributions is not negligible. The results of this research contribute to the planning of climate change adaptation strategies and actions to reduce future risks.

How to cite: Nuñez Mejia, S., Mendoza Paz, S., Tabari, H., Singaña-Chasi, M., Paredes, D., and Willems, P.: Climate change impacts on IDF curves, urban flooding, and river discharge in Quito,Ecuador, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17185, https://doi.org/10.5194/egusphere-egu23-17185, 2023.

Extreme floods estimation in a changing climate is a challenge facing two major methodological difficulties: extrapolation to low-probabilities events, in a non-stationary climate. Despite its complexity, such an estimation is a key input for the safety assessment,and the design of high-risk infrastructures (dam, nuclear powerplant), built for decades and supposed to withstand the future climate.

When significant, the trend on observed floods is dependent on the climatology and scale of the considered catchment and cannot be directly transposed to the high quantiles required for safety assessment. In the literature, three modeling frameworks can be distinguished to compute these estimations in a non-stationary climate:

• Non-stationary Flood Frequency Analysis performed with time or a climate covariable.
• Dedicated extreme flood estimation method (like those being based on rainfall-runoff stochastic simulation), integrating the time or a climate covariable.
• Complete climate and hydrology modeling chains (combining GCM, RCM and hydrological models).

The approach proposed here falls into the second category, with the application of the SCHADEX method in an evolving climate. The SCHADEX method  it is based on a semi-continuous rainfall–runoff simulation process which allows the generation of an exhaustive set of crossings between precipitation and soil saturation hazards.

In this study a regional surface temperature, modeled by 10 different GCMs from the CMIP5 project for the RCP 4.5 and 8.5 scenarios, is used to drive a non-stationary, temperature-varying, distribution of extreme rainfall. The temperature-quantile models are calibrated season by season thanks to the observations of the 1950-2019 period where trends are statistically significant. Another method downscales the same GCM models thanks to the analog method to generate projected series of areal rainfall and basin average temperature. These series are used as future climatological input of the SCHADEX method. For several 35-years windows up to 2099, and for the RCP 4.5 and 8.5 scenarios, SCHADEX computes the estimation of extreme flood quantile based on both the projected extreme rainfall distribution and climatology. These estimations are compared to the 1985-2019 reference period to assess the evolution of estimated high quantiles.

The study is based on a dataset of seven catchments ranging from 1200 to 7000 km² located in various regions of the South-East half of France with contrasted climates. Only the significant rainfall trends are modeled, assuming a stationary extreme rainfall distribution otherwise.

The most significant changes in extreme rainfall are for basins under Mediterranean influence. Due to the non-linearity on the catchment’s response to heavy rain, the changes in extreme flood estimation are generally higher than the changes in extreme rainfall. In most catchments, drier future pre-flood conditions do not significantly dampen the increase of rainfall. For mountainous catchments, increased temperatures lead to higher rain-snow limit during intense events in autumn, and higher pre-flood snowmelt in spring, globally increasing the efficiency of heavy rainfall.

Some perspectives of this study are drawn, among them the need for another climate variable in the models, aside the surface temperature, which could account for the cyclogenesis evolutions. Some discrepancies between the two modeling chains (extreme rainfall distribution and climatology) are also to be tackled.

How to cite: Paquet, E.: Estimation of extreme flood quantiles with the SCHADEX method in projected climatic conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11938, https://doi.org/10.5194/egusphere-egu23-11938, 2023.

Studies on the effects of climate change on hydrological extremes frequently use hydrological models whose parameters are determined through calibration techniques utilizing observed meteorological data as input force. However, when climate models are applied, this procedure result in a biased evaluation of the probability distribution of high streamflow extremes. As an alternative, we present a methodology called "Hydrological Calibration of eXtremes" (HyCoX), which involves maximizing the likelihood that the predicted and observed high streamflow extremes belong to the same statistical population by means of hydrological model calibrations driven by climate model output.

The application of HYPERstreamHS, a distributed hydrological model, to the Adige River watershed (southeastern Alps, Italy), shows that this technique retains statistical coherence and produce accurate quantiles of the yearly maximum streamflow.

How to cite: Avesani, D., Fiori, A., Bellin, A., and Majone, B.: Hydrological model calibration in high streamflow extremes climate change studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1409, https://doi.org/10.5194/egusphere-egu23-1409, 2023.

An intensified hydrological cycle with global warming is expected to increase the intensity and frequency of extreme precipitation events. However, whether and to what extent the enhanced extreme precipitation translates into changes in river floods remains controversial. Here, we demonstrate that previously reported unapparent or even negative responses of river flood discharge (defined as annual maximum discharge) to extreme precipitation increases are largely caused by mixing the signal of floods with different generating mechanisms. Stratifying by flood types, we show a positive response of rainstorm-induced floods to extreme precipitation increases. However, this response is almost entirely offset by the concurrent decreases in snow-related floods, leading to an overall unapparent change in total global floods in both historical observations and future climate projections. Our findings highlight an increasing rainstorm-induced flood risk under warming and the importance of distinguishing flood-generating mechanisms in assessing flood changes and associated social-economic and environmental risks.

How to cite: Zhang, S.: Reconciling disagreement on global river flood changes in a warming climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15900, https://doi.org/10.5194/egusphere-egu23-15900, 2023.

Over the past decades, the increasing frequency and intensity of extreme events due to climate change and anthropogenic climate change have greatly increased the threat to the production and livelihood of people along the riverbank. Hence, it is crucial to analyze the extreme variations of streamflow and sediment load observed in large rivers to better predict future changes in the world's water resources and hydrological extremes. In this study, we show the spatiotemporal variations of streamflow extremes and sediment load extremes in the mainstream of the Yellow River based on the daily streamflow and sediment load data from 1956 to 2019 and multiple mathematical and statistical methods. Then, we identify the main factors related to human activities and climate change by establishing quantitative relationships to figure out how these factors altered extreme streamflow and sediment load. Our results reveal that extreme streamflow and sediment load have decreased significantly since the 1950s (p < 0.05) except for the Yellow River source. However, the extreme streamflow increased significantly (p < 0.05) during 2000-2019, likely due to increased precipitation, and the extreme sediment load at most stations tended to stabilize. The contribution of extreme streamflow to the annual streamflow declines remarkably in the middle-upper reaches and increases significantly in the Lower reaches. While the contribution of extreme sediment load to the annual sediment load decreased significantly in the middle-lower reaches. Besides, the occurrence dates of extreme streamflow and sediment load showed an overall trend to disperse from the flood season to the four seasons of a year. We also have evidence that the fundamental cause of extreme water-sediment yield is extreme precipitation. Yet the extremity and hazard of water-sediment extremes are strongly affected by anthropogenic activities. Among them, mainstream dams can artificially change the water-sediment extremes, relationship, and synchronization, while anthropogenic engineering and vegetation measures can reduce the maximum possible peak of water-sediment extremes. Changes in the water-sediment relationships across the basin also confirm that changes in sediment source availability or erosive power dominate sediment reduction in each sub-basin. This study provides a scientific basis for risk management and water resources development and utilization in complex watersheds.

How to cite: Yin, S., Gao, G., and Fu, B.: Significantly reduced extreme streamflow and sediment load in the Yellow River Basin: Impacts of climate change and human activities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-247, https://doi.org/10.5194/egusphere-egu23-247, 2023.

Human behaviors have changed as ice-jam flooding has become more prevalent, impacting both flood hazard and vulnerability as a function of flood risk. These dynamic adaptations can be developed by both governments (e.g., artificial breakup and dike installation) and individuals (e.g., flood-proofing and elevating houses). The interaction between these top-down and bottom-up measures provides a complex socio-hydrological system. However, the traditional assessment of ice-jam flood risk lacks an appropriate consideration of evolving human behaviors and their interactions with static assumptions on human adaptations. We build an agent-based model to assess the ice-jam flood risk with top-down and bottom-up adaptive strategies (artificial breakup and flood-proofing). The individuals’ behaviors are influenced by the possible reduction in flood risk at the individual level by artificial breakage over time. Also, the government’s behavior is influenced by the possible reduction in total flood risk by the dynamic adaptive behavior of individuals (flood-proofing). Thus, micro levels’ behavior can dynamically lead to macro phenomena, and macro phenomena define micro levels’ behavior over time. This model is applied to Fort McMurray along the Athabasca River, Canada, with a long history of ice-jam flooding. Also, we perform a variance-based global sensitivity analysis to investigate the individual effect of model factors and their joint effects on ice-jam flood risk. The results show that although the artificial breakage by the government leads to a regime shift and a considerable decrease in the ice-jam flood risk, it decreases the number of the newly adapted residents to flood-proofing and the role of residents in ice-jam flood risk. This study can provide a good understanding of the important role of dynamic adaptive behavior in ice-jam flood risk and pave the way for better Building flood resilience.

How to cite: Ghoreishi, M., Das, A., and Lindenschmidt, K.-E.: Advancing Ice-jam Flood Risk: Integrating Dynamic Adaptive Behavior into Agent-based Model of Fort McMurray, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3664, https://doi.org/10.5194/egusphere-egu23-3664, 2023.

Extreme precipitation is projected to increase in many parts of the world, leading to concern over flooding and associated hazards. Despite the intuitive causal link between extreme precipitation and extreme flooding, observations and models suggest that other factors, in particular, low antecedent moisture may obscure or offset this relationship. Low antecedent soil moisture can arise through evaporation—driven by atmospheric warming that is projected to continue—but moisture is also reduced through an increase in the length of the time between storms, i.e., ‘storm intermittency’, which has been less studied and which can be derived directly from precipitation data. In this presentation, we focus on the modulating role of storm intermittency on the relationship between extreme precipitation and both flood potential, i.e., both flood peaks and volumes. We create an observation-based historical baseline of storm intermittency, 1950-2015, from a set of case-study basins to understand the relationship across a range of hydrometeorological settings. Next, the storm intermittency from a set of 16 CMIP6 climate models is evaluated relative to the baseline, and the evaluation is used to project future intermittency and likely outcomes on flood potential for the period 2016-2100. This projected flood potential is compared with hydrologic simulations from downscaled land surface models forced by the same CMIP6 models and differences between the projected and modeled outcomes are assessed in the context of simulated soil moisture and other hydrologic factors. Overall, we seek to understand the utility of storm intermittency as a predictor of flood potential and to understand the impacts of projected intermittency on future flood hazards.

How to cite: Livneh, B., Bjarke, N., Modi, P., and Furman, A.: Understanding the role of precipitation intermittency on changes in extreme flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4272, https://doi.org/10.5194/egusphere-egu23-4272, 2023.

Soil moisture is increasingly recognized as shaping fluvial flood trends, but it only represents a fraction of subsurface water storage. In contrast, groundwater in the saturated zone often contributes a significant proportion of river flow, but its effects on large-scale flood trends are poorly understood. We analyzed streamflow and climate records of thousands of catchments to show that baseflow (i.e., groundwater-sustained river flows) affects the magnitude of annual flooding at time scales from days to decades. Annual floods almost always arise through the co-occurrence of high precipitation (rainfall + snowmelt) and elevated baseflow. Consequently, trends and variations of flood magnitudes are often more strongly coupled to antecedent baseflow conditions than antecedent soil moisture and extreme precipitation. This reveals the importance of groundwater in shaping river floods and can decouple flood trends from shifting precipitation extremes and soil moisture.

How to cite: Berghuijs, W. and Slater, L.: Connecting flood trends to groundwater, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1856, https://doi.org/10.5194/egusphere-egu23-1856, 2023.

Floods and drought affect millions of people each year, but what if a riverine flood rapidly follows or occurs during a hydrological drought?

The 2022 summer drought in Europe, for instance, was punctuated by flash floods, affecting societies, economies and the environment already impacted by the persistent drought. In the same summer, in Iran and Afghanistan, devastating riverine floods followed a severe drought, causing displacement and human losses. Although the abrupt transitions between opposite hydrological extremes can pose huge risks for societies, the processes behind and effects of drought-flood interactions remain largely unknown, as most studies address droughts and floods separately. This research provides the first global study of compound and consecutive drought-flood events, shedding light on the underlying hydrological interactions between opposite hydrological extremes.

By analysing timeseries of hydro-meteorological and other biophysical variables for 8255 catchments globally, we reconstruct the propagation of droughts and floods through the hydrological cycle, thereby identifying and characterizing flood events that follow or compound with drought conditions. We use variable and fixed threshold-level approaches to detect extreme dry and wet conditions, and seasonality statistics to analyse the timing of riverine floods. Our results show that close succession between drought and flood occurs mainly during the transition between seasons: from winter to spring in mid-latitude areas and from dry to wet at the equator and polar regions. Although these events are rare, they have increased over time, especially in countries such as France and Germany, southern Brazil, and India. Furthermore, drought conditions often shift the flood timing, resulting in later winter floods in Europe, in the north-eastern coast of the United States and western Canada, and earlier summer floods in Central America and Northern Brazil.

This study shows that although drought and flood events evolve from different hydrological processes and atmospheric dynamics, these hydrological extremes interact with the same hydrological system, resulting in system alterations that may modify flood dynamics.

How to cite: Matano, A., Berghuijs, W., de Ruiter, M., Ward, P., Mazzoleni, M., and Van Loon, A.: Drought influence on flood dynamics: a global overview, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-430, https://doi.org/10.5194/egusphere-egu23-430, 2023.

Climate change has a large influence on the occurrence of extreme hydrological events. In this study we take advantage of two recent developments that allow for more detailed and local estimates of future hydrological extremes. New large climate ensembles (LE) now provide more insight into the occurrence of hydrological extremes as they offer order of magnitude more realizations of the future weather and thus floods and droughts. At the same time recent developments in Machine Learning (ML) based forecasting have enabled scientists to provide this LE information to a local scale relevant to water managers.

In this study we combine LE, consisting of 2000 years of global data for scenarios representing present-day, 2 and 3 degrees warmer climate ⁽¹⁾, together with a local, observation-based ML model framework for simulating hydrological extremes for the Netherlands ^{(2, 3)}.

We developed a new post-processing approach that allows us to use LE simulation data for local applications based on historical information. We test the application of the post-processing step based on historical simulations, before implementing in the different scenario runs.

The discharge simulation results for the different scenarios show a clear seasonal cycle with increased low flow periods (both average duration and number of events) from summer till end of autumn (~45% August-October) and increased high flow periods for early spring (~43% February-April) looking at national scale, with the 3-degree warmer climate scenario showing the highest percentages for both (52.5% and 48.3% respectively). Regional differences can be seen in terms of shifts (low flows occurring earlier in the year) and range (higher/lower percentages). These trends can further be detangled into location specific results, due to the added value provided by the ML setup.

We show that by combining the wealth of information from LE and the speed and accuracy of ML models we can advance the state-of-the-art when it comes to modelling and projecting hydrological extremes. The local modelling framework allows to simulate discharge under different climate change scenarios for national, regional and local scale assessments. The historically and locally trained models provide essential information for water management to be used in  long-term planning.

¹⁾ Van der Wiel, K., Wanders, N., Selten, F. M., & Bierkens, M. F. P. (2019). Added value of large ensemble simulations for assessing extreme river discharge in a 2 °C warmer world. Geophysical Research Letters, 46, 2093– 2102.
²⁾ Hauswirth, S. M., Bierkens, M. F., Beijk, V., & Wanders, N. (2021). The potential of data driven approaches for quantifying hydrological extremes. Advances in Water Resources, 155, 104017.
³⁾ Hauswirth, S. M., Bierkens, M. F., Beijk, V., & Wanders, N. (2022). The suitability of a hybrid framework including data driven approaches for hydrological forecasting. Hydrology and Earth System Sciences Discussions, 1-20.

How to cite: Hauswirth, S. M., van der Wiel, K., Bierkens, M. F. P., Beijk, V., and Wanders, N.: Simulating Hydrological Extremes for different Warming Levels – combining Large Scale Climate Ensembles with local observation based Machine Learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7677, https://doi.org/10.5194/egusphere-egu23-7677, 2023.

Extreme hydrological events have had several economic, ecologic, and social impacts on many regions around the world. Although the impacts of floods and droughts are equally important and severe, they are typically treated as separate phenomena due to their hydrological differences. However, in order to address and mitigate their impacts, it is important to analyse and model the interactions between the spatial and temporal characteristics of the events, not only separately but also in a joint framework. Moreover, understanding and simulating the effects of land-use land-cover changes on extreme events dynamics is crucial to improve the development of land use policies and risk management plans. The Central America Dry Corridor (CADC) is one of the regions in the world with the highest vulnerability to floods and droughts due to its marked precipitation seasonality and climate variability. Land use change is also an important variable in this mainly rural area, in which forest cover has declined rapidly during the last decades, modifying basin runoff and affecting extreme events generation. Therefore, this study proposes a methodology to analyse and represent in a joint framework the spatio-temporal characteristics of CADC’s floods and droughts, and identify their relationship with land-use land-cover change patterns. To achieve this, a hybrid modelling framework that integrates Machine Learning (ML) techniques with a spatially distributed hydrological model is presented. It is expected that the integration of ML techniques increases hydrological model capabilities to accurately simulate the effects of land-use land-cover change on floods and droughts propagation. It is also expected that the hybrid model can be used as a tool to assess the effectiveness of different risk management measures and land use policies in floods and droughts mitigation.

How to cite: Torres, E., Corzo, G., and Solomatine, D.: Spatio-temporal analysis of extreme hydrological events in a joint framework and its relationship with land-use land-cover change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13167, https://doi.org/10.5194/egusphere-egu23-13167, 2023.

Single extreme hydrometeorological events have been highly studied around the world, however, concerns related to the spatiotemporal variations have extended the studies to look for more insight into space and time dimensions. In this context, of increasing importance are the relations of extreme events properties over multiple spatial and temporal scales. Nevertheless, the study of these relations has not been widely developed. The interaction between events, like floods and droughts with different spatiotemporal characteristics, so far, has still yet to be further studied. Some studies show that there are complex relations linking between both extremes, since occurrences of both are observed in single catchment areas around the world. Furthermore, when analyzing time and space scales for concurrent or successive events, the complexity increase. Recent advances in the spatiotemporal analysis of droughts and floods include tracking approaches, data-driven probabilistic models and machine learning applications. Likewise, new studies have highlighted the usefulness of data mining techniques in extracting knowledge, identifying patterns and detecting anomalies from climate databases.

Therefore, the main objective of this research is to characterize and identify spatial and temporal patterns related to extreme hydrometeorological events generation and propagation using data mining techniques. The selected case study is the Magdalena River basin in Colombia. This basin produces most of Colombia’s Gross Domestic Production (GDP), which is highly dependent on the water resource. Because of this, extreme hydrological events such as floods or droughts have a large impact all over the basin.

ERA5-Land information (precipitation, temperature, surface pressure and wind U and V components) from 1980-2020 with a resolution of 0.1°x0.1° at multiple time scales (hourly and monthly) was collected for this study. This data was used to identify and characterize extreme hydrometeorological events for multiple time steps and indices thresholds. Temporal, spatial, climatic and geometrical properties of each extreme event region were calculated and stored in a hydrometeorological database. Unsupervised machine learning clustering algorithms (k-means, hierarchical clustering, DBSCAN and spectral) were applied on the database to cluster elements with similar property values. At last, a data mining association rules method (APRIORI) was applied to identify clear patterns between cluster elements of extreme hydrometeorological events. As a main result of this study, is expected an improved understanding of the extreme hydrometeorological events patterns and their associated hydro-climatic processes in the region. This knowledge can help to obtain more accurate and less uncertain estimations of extreme hydrological events, as these are major challenges of many water resources problems, such as monitoring and forecasting.

How to cite: Duarte, S., Corzo, G., and Solomatine, D.: Spatiotemporal Identification and Characterization of Extreme Hydrometeorological Events Patterns in the Magdalena River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14230, https://doi.org/10.5194/egusphere-egu23-14230, 2023.

Recently, due to the climate change, the frequency of extreme hydrological disasters such as drought and flood is increasing worldwide. Especially, sudden change in precipitation cause drought and flood often occur alternately in a short period of time, which is defined as a drought-flood abrupt alternation event. In this study, daily Standardized Precipitation Index (SPI) for the entire basin of South Korea is used to analysis the characteristics of the short-term abrupt alternation events of drought and flood, focusing on the short-term perspective rather than the monthly SPI. When the SPI value is less than –1.5 for 10 consecutive days, a drought event begins, whereas when the SPI value is more than 0.5 for 7 consecutive days, a drought event ends. On the contrary, when the SPI value is more than 1.5 for 10 consecutive days, a flood event begins, whereas when the SPI value is less than –0.5 for 7 consecutive days, a flood event ends. When the time interval between the end of drought event and the start of flood event is less than five days, a drought-flood abrupt alternation event is identified. The severity of drought-flood abrupt alternation event is defined similarly to the severity of drought using the SPI. We classified the severity into two types: SW(severity of whole period) and ST(severity of transition period). We used the additional statistical risk grade analysis. Nackdong River basin (southeastern region of Korea) has most severe grade of the SW rather than the other basins and the ST is lower than other basins. On the contrary, Yeongsan River basin (southwestern region of Korea) has most severe grade of ST rather than the other basins and the SW is lower than other basins. In conclusion, using daily SPI can determine the risk-prone areas through evaluating the frequency and severity of drought-flood abrupt alternation events. Due to climate change, increasing variability of precipitation, and frequent flood abrupt alternation events in the future, our results will cornerstone to predict the vulnerable and risk-prone region or preventing disasters.

Acknowledgement: This research was supported by a grant(2022-MOIS63-001) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Son, H.-J., Byun, S. H., Yoon, H.-C., Lee, J.-H., and Kim, T.-W.: Investigating Characteristics of Drought-Flood Abrupt Alternation Events in South Korea for Comprehensive Disaster Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3018, https://doi.org/10.5194/egusphere-egu23-3018, 2023.

With its arid and semi-arid climate, the African Sahel is a region highly drought-prone and vulnerable given its reliance on meagre natural resources for food security. However, the region recently also experienced flood occurrences. Against the background of food security, effects of floods and drought upon crop yield requires thorough investigation. To investigate the risk that flood and droughts pose to food crop we considered: 1) the case of the Sudanese Sahel in the eastern part of the region given its representation of one-third of the total area of the region, 2) traditional rainfed farming systems as the most fragile system in terms of marginality and regular experiences of challenges, including weather shocks and 3) sorghum as a main staple crop in the area. To identify risk and quantify its magnitude during the years when yield losses were encountered, we used gridded climate datasets, dynamic (yearly) land-use datasets and an unusually long national sorghum statistics for the last half a century. We expressed risk in % as hazard x vulnerability x 100%. Using a drought index for the growing season based on the ratio of rainfall to potential evapotranspiration, hazard is expressed here as a function of: i) severity of drought, ii) dry spell and iii) time frequency of drought. For the 51-year period from 1970 – 2020, 26 risk years were identified representing both hydrological extremes – floods and droughts. A risk year is defined here as a year with yield loss below the detrended yield data. In the decade 2011-2020, seven years were identified risk years. Four of those (2011, 2012, 2013 and 2015) were drought years and three (2017, 2019 and 2020) were exceptionally wet years. Sorghum yield varied significantly as a function of risk. Variations in the risk index explain 97.5% of the variation in sorghum yield, with a 1% increase in the risk leading to a decline of yield by 14.5 kg/ha. Nevertheless, the traditional farming sector achieved several high yield levels during the 26 risk years, namely 2020, 2019, 2017 and 2015 were years with the 3^rd, 4^th, 5^th and 7^th highest yield levels. Our findings show that: a) the traditional farming system experienced a high degree of vulnerability to hydrological extremes during the decade 2011-2020 and b) drought remains the most relevant hydrological risk whereas floods cause a small risk and may even favor yield.

How to cite: Elagib, N. A., Ali, M. M. A., and Schneider, K.: Do droughts and floods pose similar risks to Sahel staple food crop?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1867, https://doi.org/10.5194/egusphere-egu23-1867, 2023.

Hydropower regulations may significantly increase the variability of flow at especially short time scales when compared with the natural hydrological regime to which river ecosystems have evolved over long time periods. This can be detrimental for river habitats and for many organisms. Attenuation of the variability in rivers and lakes improves ecological status at some distance downstream of the introduced variability. Being able to accurately estimate this distance is critical for the evaluation of ecological status. The attenuation of introduced flow variability has only been studied previously for specific rivers and lakes, and the dominant mechanisms have not been analyzed in detail. In this work, the attenuation rate and its important drivers is studied for lakes and regulated rivers in all of Sweden by comparing the results of hydrological and hydrodynamic models with observations. We performed Fourier transformation of flow time series obtained with a) the Hydrological Predictions for the Environment (HYPE) model, b) an extracted model representing only river processes, c) the diffusion wave equation, and d) from observed flow at several hundred stations. The reduction of the amplitudes along rivers and in lakes was then analysed. This damping in rivers and lakes was further compared.

In many regulated rivers in Sweden, flow variability of periodicity 7 days is dominant among periods varying from a couple of days up to one month. The analysis further shows that variability with periodicity days to months typically attenuate with an exponential rate that is largest for short periods. Attenuation of these periods in rivers is mainly driven by processes within rivers, as opposed to catchment features such as the distribution of rain or soil properties. Further, rivers in regulated systems often resemble cascades with long stretches of rivers with low gradients in elevation between the dams. The associated attenuation in these “lake-alike” rivers can be well described by hydrological simulations with HYPE using a simple linear attenuation box. In contrast, the sometimes-used diffusion wave equation is often unable to replicate the observed attenuation here. Lakes have larger attenuation potential than rivers, especially at low flows.

Our work supports the assessment of ecological status and management decisions by improving the estimates of distances required for attenuation, and provides important insights on attenuation processes.

Elenius, M.T. and G. Lindström (2022) Introduced flow variability and its propagation downstream of hydropower stations in Sweden. Hydrology Research 53(11), 1321-1339. doi: 10.2166/nh.2022.138

How to cite: Elenius, M. and Lindström, G.: The role of rivers and lakes in damping flow variability introduced by hydropower, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16124, https://doi.org/10.5194/egusphere-egu23-16124, 2023.

Climate Change is likely to have a significant effect on river flows and associated floods and droughts in the coming decades. As such, it is key to understand the nature and severity of these changes and the potential costs and benefits of potential impact mitigation strategies at both a catchment and national scale. We have developed and demonstrated a methodology for national scale modelling of river flows under twelve climate forcing scenarios and have evaluated the impacts of potential adaptation strategies with regards to river flow.

This work forms part of the OpenCLIM project, which aims to create a scalable framework that enables users to integrate models and datasets from across a range of sectors (such as urban development, hydrology, and heat risk). This integrated, cross-sectoral approach is necessary to capture the complexities of climate response and will enable users to explore the potential impacts of climate change and adaptation strategies.

A spatially distributed, physically based hydrological model (SHETRAN-UK) has been setup for 701 catchments across Great Britain and Northern Ireland. Both uncalibrated and auto-calibrated simulations were run for historical periods and future climate scenarios (using UKCP18 regional climate projections). Strong model performance across the country allowed for analysis of the effect of climate change and storylined urban development on future river flows and the impact of potential adaptation strategies, specifically relating to floods and droughts.

Results from an urban development model were used to represent potential change in urban areas while natural flood management strategies were implemented by increasing woodland cover and storage in the model. Flows from the models were then fed into models for estimating economic flood damages.

This talk will discuss the methods and findings from the project, comparing them to other studies and will discuss the relevance of continued investigations into modelling climate/adaptation impacts as well as the lessons learnt regarding autocalibration and the high-performance computing approach (DAFNI & JASMINE).

How to cite: Smith, B., Lewis, E., and Birkinshaw, S.: National Hydrological Modelling of Climate Adaptation Impacts for the UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15606, https://doi.org/10.5194/egusphere-egu23-15606, 2023.

Assessing precipitation non-stationarity beyond the modern, instrumental record is valuable for disentangling the impacts of anthropogenic climate change from natural climate variability. This study evaluated changes in 3-month meteorological drought and pluvial extremes by merging tree-ring reconstructions, observations, and climate model simulations spanning 850 – 2100 CE across North America; to determine whether the Industrial era and projected future changes are outside the range of natural climate variability. To accomplish this, we utilized a non-stationary version of the commonly used Standardized Precipitation Index (SPI), modified to capture the slow progression of underlying statistical distributions through time. Within this non-stationary framework, multi-dimensional splines simultaneously model annually recurring seasonality and seasonally specific trends, constrained to mimic the WMO 30-year reference period, for each distribution parameter. The non-stationary SPI framework was further developed to merge tree-ring proxy data, 20th century observations, and CMIP6 climate model output into a common millennial-scale model by accounting for seasonal and data-specific biases.

Results show that many regions of North America have already experienced significant intensification of drought and pluvial extremes relative to the previous 1,000 years of presumed natural climate variability. These appear as widespread exacerbation of both extremes, especially summer drought and winter pluvials with consistent spatial signals: overall drying trends in the west and south, wetting trends in the northeast, and increased interannual variability across the east and north. Climate change projections indicate a continued intensification of these trends by 2100. These results underscore the need for reassessing severities of recent drought and pluvial events relative to a changing climatological baseline and a need for incorporating climate non-stationarity when assessing future drought and pluvial risk. 

How to cite: Stagge, J. and Sung, K.: Ongoing and projected future intensification of North American pluvial and drought extremes relative to the pre-Industrial millennium, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10691, https://doi.org/10.5194/egusphere-egu23-10691, 2023.

The Limpopo River Basin (LRB) is highly vulnerable to floods and droughts, and these recurrent extreme events seriously threaten the basin's water and food security. Implementing sustainable water management practices is essential to improving resilience to future flood and drought hazards. Identification of such sustainable practices can be done through evaluating alternative management scenarios. It is increasingly recognized that scenario analysis and management strategy identification requires collaboration between scientists and a broad range of stakeholders from local to (trans-) national scales. In this study, we demonstrate and evaluate a real-world application of a basin-scale hydrological model as a decision-support tool based on a multi-sector collaborative modelling approach to co-create management strategies and identify appropriate, inclusive water governance strategies to improve resilience to hydrological extremes in the LRB. To achieve the objectives, an integrated hydro(geo)logical model (WetSpass-MODFLOW) was set up using existing (i) hydro(geo)logical and climatic information and (ii) expert and local community knowledge collected through stakeholders' workshops. After successfully evaluating the model simulation capacity using the groundwater observation datasets, the model was used for evaluating the following management scenarios identified during the stakeholders workshops with inputs from local, national and transboundary governance actors: (1) increase groundwater abstraction; (2) deforestation; (3) afforestation; and (4) managed aquifer recharge (MAR), using (4a) injection well, (4b) rainwater harvesting (local ponds), and (4c) small water reservoirs (e.g. local ponds and sand dams). Though evaluating different identified management scenarios and stakeholder feedback, our results suggest that the most effective strategy is local rainwater harvesting and storage through small-scale (household to village) water reservoirs/ponds or well recharge. It reduces the risk and impact of floods as it can capture and store the excess water during flood into the groundwater aquifer and if upscaled over the entire LRB, can significantly increase the groundwater level across the basin. Additionally, this excess water can be an essential source of water during a drought. The results also show that the multi-sector collaborative modelling approach is effective to co-create management strategies and identify the appropriate and inclusive strategy to improve resilience to hydrological extremes even in data-limiting conditions, provided that the effective stakeholder’s involvement is ensured throughout the modelling study. Finally, the proposed modelling outcomes are helpful in making informed decisions regarding appropriate water management and transboundary cooperation in the LRB. 

How to cite: Mustafa, S. M. T., Franchi, F., Matano, A., Loon, A. V., Tirivarombo, S., Olabode, O. F., and Comte, J.-C.: Participatory groundwater modelling to build resilience to hydrological extremes in the Limpopo River Basin: Potential and challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16486, https://doi.org/10.5194/egusphere-egu23-16486, 2023.

Mediterranean mountainous basins provide critical water supply and ecosystem services, yet these environments are increasingly at risk due to anthropogenic stressors and competition for water across urban, agricultural and environmental demands. On the top of this, future climate projections suggest a drier and warmer Mediterranean with large increases in the frequency, duration, and severity of hydrological droughts with serious consequences for the management of water resources and natural ecosystems. So due to its vulnerability, it is crucial that land surface models (LSMs) correctly characterise these phenomena, as a first step to the provision of reliable projections of future water availability across the Mediterranean region.

As hydrological systems are intrinsically intertwined with climatological and ecological systems, the propagation of meteorological droughts (i.e., precipitation below than normal and higher temperatures) through them is modulated by a variety of mechanisms which are linked to carbon and water cycle interactions and specifically to how well LSMs represent evaporation fluxes and water storage.

The aim of this study is to analyse how Noah-MP LSM represents agricultural and hydrological droughts and in particular the propagation of the precipitation and evaporative demand anomalies to the soil moisture and streamflow anomalies in two typically and eco-hydrologically different Mediterranean catchments in the Upper Tiber River in Central Italy.

The analysis is carried out with the NASA Land Information System’s Noah-Multi Parameterization (Noah-MP) model configured with four soil layers with the layer thicknesses, varying from 0.1, 0.3, 0.6, and 1 m, from the surface and using a simple groundwater reservoir beneath the soil layer allowing for soil moisture–groundwater interaction and related runoff production. Noah-MP allows for the prognostic representation of vegetation growth in combination with a Ball–Berry photosynthesis-based stomatal resistance. The LAI is calculated from leaf carbon mass by multiplying by the specific leaf area. The model is validated with ground-based soil moisture and streamflow observations as well as with remote sensing-based products of evaporation, vegetation and soil moisture.

Results show a sub-optimal representation of runoff (magnitude) and LAI (with phase shift and magnitude) especially during some important drought events that have hit the region in 2012 and in 2022 (specifically over the more mountainous catchment) suggesting that improvements could be obtained from a better model parameterization of the vegetation and runoff schemes via calibration and assimilation techniques.

How to cite: Modanesi, S., Bechtold, M., De Lannoy, G. J. M., De Santis, D., and Massari, C.: Skills in the representation of the propagation of the meteorological droughts through the eco-hydrological system by a land surface model across two Mediterranean catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6346, https://doi.org/10.5194/egusphere-egu23-6346, 2023.

The vulnerability of large areas to drought events emphasizes the importance of a reliable probability analysis of drought events. A drought is notoriously considered as one of the most complex natural phenomenon, which, more than other natural hazards, remains difficult to quantitatively model due to the difficulty of sampling a sufficient number of events in the historical record. In fact, due to the persistence and often long interarrival times between droughts, occurring on time scales of years to decades or more, very long observational time series are necessary to study their statistical properties. It is indeed rare that such a large amount of observational information, at appropriate space-time resolutions and consistency, are available in practical applications.

One possible approach to overcome this problem relies on the use of proxy climatic data to extend the instrumental record. Additionally, since relatively few drought events occur even within records of several hundred years, techniques which optimally use available information, such as the metastatistical framework, may be highly beneficial in these analyses.

Motivated by the above considerations, this work exploits a publicly available tree-ring based Old World Drought Atlas (OWDA; Cook et., 2015), a reconstruction of the June–August self-calibrating Palmer Drought Severity Index (sc-PDSI), to model the stochastic nature of the drought characteristics. To gain a quantitative understanding of how well tree ring-based data capture drought occurrences, we compare the sc-PDSI computed with direct observations of precipitation and temperature, with those obtained from tree-ring proxies. Furthermore, we characterize drought events and their properties using the statistical “theory of runs”. We then explore the potential of the Metastatistical Extreme Value Distribution (MEVD) to estimate the probability of occurrence of drought events and compare its performance with that obtained by the use of traditional approaches. A cross-validation scheme, dividing the available data into independent calibration and test sub-sample, is used to quantify the estimation uncertainty associated with different sample sizes and estimation methods.

The analysis of extreme droughts in two case studies in Italy suggests that the MEVD-based formulations are more robust and flexible approaches with respect to traditional ones. The comparative analysis of the predictive estimation uncertainty is site-specific, but MEVD estimates outperform, in terms of bias and uncertainty, traditional GEV estimates.

The analyses also (1) confirm the usefulness of the paleoclimate reconstructions for improving the robustness of the statistical study of extreme droughts, and (2) highlight that the metastatistical formulations allow estimations of probability of intense droughts even when observational length is too short to apply traditional methods.

How to cite: Caruso, M. F., Peres, D. J., Cancelliere, A., and Marani, M.: Modeling extreme meteorological droughts from paleo-climatic reconstructions using a metastatistical framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12164, https://doi.org/10.5194/egusphere-egu23-12164, 2023.

Europe has experienced increasing frequency of climate extremes which caused negative impacts on the ecosystems and various socioeconomic sectors. In this research, we examined the drought conditions and impacts in Weisse Elster, a low-mountain watershed, in Central Germany. First, we studied the temperature and precipitation trends in the watershed. We found that seasonal and annual temperatures had an increasing trend. Precipitation had a decreasing trend during summer and an increasing trend in the winter and annual scales. By using drought indices, namely standardised precipitation-evapotranspiration index (SPEI) and standardised precipitation index (SPI), we found that drought conditions have been worsening. We used the Water Supply Stress Index (WaSSI), an integrated ecosystem services model developed by U.S. Forest Services, to simulate two key ecosystem services: surface water flow and carbon sequestration. The model showed satisfactory performance when evaluated against discharge, evapotranspiration and gross primary productivity (GPP) observations. To understand the drought vulnerability of different areas and ecosystems, we compared water yield (WY), net ecosystem productivity (NEP), and soil moisture (SM), averaged for the five most intense drought events, to the averages of the total study period (57 years). We found that droughts caused a significant reduction in WY (54%), NEP (18%), and SM (13%) in the region, with some areas being more affected than others. Urban landcover saw a 41% reduction in water flow, while agriculture and grasslands landcovers experienced significant reductions in generated water flow (63% and 60%, respectively). Deciduous forests had a 53% reduction in water flow and coniferous forests experienced a loss of around 37%. All landcover types saw a similar impact on carbon sequestration during droughts. Coniferous forests sequestered 21% less carbon while deciduous forests, grasslands, and agriculture landcover sequestered 18%, 17%, and 17% less carbon, respectively. We emphasise that there is an urgent need to improve climate resilience in the region and to reduce drought risks in different sectors to adapt to climate change.

How to cite: Al-Qubati, A. A. A., Zhang, L., and Pyarali, K.: Understanding drought conditions and impacts on key ecosystem services in a low-mountain watershed in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2443, https://doi.org/10.5194/egusphere-egu23-2443, 2023.

The realistic assessment of drought is subjected to substantial uncertainty in the presence of a multitude of drought indicators owing to their mutually exclusive methodologies, variant data sources employed, and changing variable behavior. Though temperature-driven divergence analyses among drought indicators are not unknown, in this study the authors attempt to unravel the quantum of disagreements the newly developed Standardized Net Precipitation Distribution Index (SNEPI) possesses with its contemporary counterparts. The inherent aim of the authors is to highlight that (1) climate change impacts propagating to drought dynamics are not solely driven by increasing global temperatures, but also by changing precipitation characteristics, (2) identify regions where the operational use of SNEPI is maximum, and (3) identify various physical processes that govern the evolution of index divergences through an attribution analysis. We found a persistent negative disagreement or an enhanced occurrence of dry extremes in the annual divergence of SPEI with SPI and SNEPI, which questions the reliability on the traditional drought indices. However, the seasonal dispersion of this annual divergence revealed a strong spatiotemporal signal of monsoon droughts by SNEPI, which the traditional SPEI neglected. Further, the attribution analysis revealed that the radiative fluxes governed the evolution of SPEI-SPI divergence. Consequently, the divergence between SPEI and SNEPI is driven by the characteristics of wet spells, with the relationship strengthening in the monsoon season and tropical climate zones. The authors suggest an expansion in the operational value of SNEPI in the tropical regions where these discrepancies/disagreements are profound.

How to cite: Singh, G. R., Chandrika Thulaseedharan, D., and Chakravorty, A.: Exploring the seasonal divergence among SPI, SPEI and SNEPI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-266, https://doi.org/10.5194/egusphere-egu23-266, 2023.

Accurate streamflow prediction is necessary both during wet and dry periods for efficient management of water resources. However, most hydrological models mainly focus on simulating high flows and perform poorly during low-flow or recession periods. Therefore, past studies have resorted to various calibration techniques to allow rainfall-runoff (R-R) models better capture recession flow dynamics. In the present study, we propose integrating two structurally different models and utilising their relative strengths to improve overall streamflow prediction. The proposed framework integrates a conceptual rainfall-runoff model (HBV) and a simple power-law regression (PLR) such that the former is utilised for high-flow prediction and the latter for low-flow prediction. We compared the performance of this integrated model framework (HBV-PLR) with the original HBV model using data from 108 basins in the United States. It was found that the 25^th, 50^th, and 75^th percentiles of mean absolute error (MAE) for HBV, respectively, improved from (0.47, 0.62, and 0.77) to (0.38, 0.50, and 0.67) using the HBV-PLR integrated framework. Similarly, the median Nash-Sutcliffe Efficiency (NSE) during the recession improved from 0.65 to 0.74. Here, we also argue that forcing HBV model to simulate low-flow dynamics by calibrating it using an objective function biased towards lower values may not lead to a prediction as accurate as HBV-PLR. Therefore, a model integration approach is a better option than using a single model to improve streamflow prediction during different flow regimes.

How to cite: Kadu, A. and Biswal, B.: Improving Streamflow Prediction Using a Model Integration Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6681, https://doi.org/10.5194/egusphere-egu23-6681, 2023.

Human influences can both intensify or mitigate hydrological droughts significantly altering their severity, duration, and frequency via non-linear and dynamic feedbacks. Despite their large influence, current understanding of when, where and to what degree, human-water interactions modify hydrological drought is lacking. One of the key reasons for this is the scarce availability of quantitative human water use data as they are typically considered commercially sensitive and hard to obtain. Consequently, we often rely on static, low-resolution indicators of human water use (such as global water use databases) or qualitative information on human water use, when in reality human-water interactions are highly place-specific and non-stationary over time due to changes in water management and policies.

In this study, we will disentangle human influences on hydrological droughts using observational hydro-meteorological and groundwater data and a unique dataset of spatially explicit, time-varying abstractions and discharges for a large sample of catchments across England. Building on recent work to quantify and detect human influences, we will use a suite of hydrological signatures to characterise deviations in droughts and low flows from a large sample of benchmark (i.e. near-natural) catchments. We will link these deviations to different characteristics of the abstractions data (e.g. seasonal catchment averages, abstraction purpose) and to key water management schemes (e.g. low flow alleviation schemes). In doing so, we will advance our current understanding of how humans influence hydrological droughts and how we can improve the collection of human-water use data for future environmental analyses.

How to cite: Coxon, G., Bloomfield, J. P., McMillan, H., Oldham, L., Pianosi, F., Salwey, S., Wendt, D., and Zheng, Y.: Disentangling human influences on hydrological drought, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3569, https://doi.org/10.5194/egusphere-egu23-3569, 2023.

The 2022 European drought affected the whole of France, leading to very severe summer low-flows. The outstanding characteristics of this event is questioned within a long-term historical context. It first makes use of 600+ daily streamflow series gauging near-natural catchments from all French regions. The historical background is provided by the 25-member ensemble hydrological reanalysis FYRE Hydro which covers the period 1871-2012 for the above stations. FYRE Hydro originates from a 25-member ensemble streamflow simulation using the GR6J lumped conceptual model. These simulations integrate three types of uncertainty: (1) the meteorological uncertainty through the use of the 25 members of the high-resolution FYRE Climate meteorological reanalysis (Devers et al., 2021) as forcings, (2) the uncertainty in streamflow measurement used to calibrate the hydrological models, and (3) the hydrological model error based on relative discrepancies between observed and simulated streamflow (Bourgin et al., 2014). An ensemble Kalman filter furthermore combined these ensemble simulations with available historical series together with their uncertainties to produce the FYRE hydro reanalysis. Streamflow observations from 2022 are compared to severe drought years in the FYRE Hydro reanalysis as identified by Caillouet et al. (2021). Results show that 150-year records were broken over a large number of stations for various low-flow indicators, confirming the exceptional nature of this hydrological drought event.

Bourgin, F., Ramos, M., Thirel, G., and Andréassian, V.: Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting, Journal of Hydrology, 519, 2775 – 2784, 85 https://doi.org/https://doi.org/10.1016/j.jhydrol.2014.07.054, 2014

Caillouet, L., Vidal, J.-P., Sauquet, E., Devers, A., Lauvernet, C., Graff, B., and Vannier, O.: Inter-comparison of extreme low-flow events in France since 1871, LHB: Hydroscience Journal, 107, 1-9 https://doi.org/10.1080/00186368.2021.1914463, 2021

Devers, A., Vidal, J.-P., Lauvernet, C., and Vannier, O.: FYRE Climate: a high-resolution reanalysis of daily precipitation and temperature in France from 1871 to 2012, Clim. Past, 17, 1857–1879, https://doi.org/10.5194/cp-17-1857-2021, 2021

How to cite: Vidal, J.-P., Devers, A., Lauvernet, C., Héraut, L., and Delaigue, O.: The outstanding 2022 hydrological drought in France within a 150-year historical context, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7409, https://doi.org/10.5194/egusphere-egu23-7409, 2023.

The 2022 hydrological drought in Europe was a significant event that resulted in widespread water shortages and economic disruption. The low water levels had significant implications for supply chains, transport capacity and water quality. In this study, we used a fully automated, neural-network based processing chain to semantically segment Sentinel-2 data. The processing chain was originally developed for flood detection. To map changes in surface-water extent during the drought, we compared reference water masks of the previous two years with the extent of summer 2022.
Our results show that the drought had a measurable impact on surface-water extents across Germany, with many rivers and lakes experiencing declines. A decline can be observed in all river basins. By providing detailed maps of these changes, our study offers valuable insights into the impact of droughts on surface water extent and can help inform future drought mitigation and management efforts in the region. The results presented in this contribution indicate, that the surface water extent in Germany 2022 declined by 3.1% compared to the previous two years. The most affected hydrological catchment area was the Weser river basin, which experienced a water extent loss of 7.4%.

How to cite: Groth, S., Fichtner, F., Wieland, M., Mandery, N., Shankar, S., Martinis, S., and Riedlinger, T.: Mapping changes in surface-water extent during the 2022 hydrological drought in Germany using Sentinel-2 data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9541, https://doi.org/10.5194/egusphere-egu23-9541, 2023.

The European continent has been struck by several extreme droughts over the last decades, associated with wide ranging societal, environmental and economic impacts. Changes in large-scale atmospheric circulation are vital for understanding the spatial patterns of changes in seasonal meteorological drought. The study presented herein is based on Bakke et al. (in review) and we begin by demonstrating the coherent pattern in the development of extreme meteorological drought and high-pressure systems during the extreme 2018 and 2022 drought events in Europe. Next, we investigate the relation between changes in large-scale atmospheric patterns and meteorological drought, as indicated by the geopotential height at 500mb (Z500) and the Standardised Precipitation-Evapotranspiration Index (SPEI), respectively. Calculations are done separately for four climate regions (North, West, Central-East and Mediterranean) over the growing season (March-September). Overall, the results show a low sensitivity to the Z500 data sets used (NCEP, ERA5, ERA20C and 20C), and the SPEI data sets (CRU and EOBS) at the regional level. We find coherent spatial patterns in 1979–2021 trends in seasonal and monthly Z500 and SPEI, with hot spots of significant changes towards higher pressure (increasing Z500) and drier conditions (decreasing SPEI) over West in spring and Central-East in summer. Strong correlations (at 1% significance level) between the variables are found for all regions throughout the growing season. A strong relation between high-pressure systems and meteorological drought is confirmed by a high degree of co-occurring regional anomalies since the beginning of the 20th century. The strongest links are detected in West, and the weakest links in North. Finally, we investigate projected Z500 according to a low-end (SSP126) and a high-end (SSP585) emission scenario. According to the projected changes, anomalously high-pressure systems will be the new normal regardless of scenario, and well exceeding the 2018 and 2022 levels in the case of the high-end emission scenario. The ability of the model ensemble to represent the spatial heterogeneity in historical Z500 variability and trends is limited. Thus, projected changes in large-scale circulation are highly uncertain. Consequently, due to the strong link between Z500 and SPEI, high uncertainties are associated with projected changes in drying trends and meteorological drought across Europe.

Bakke, S.J., Ionita, M. and Tallaksen, L.M.: Recent European drying and its link to prevailing large-scale atmospheric patterns. npj Climate and Atmospheric Science (in review). https://doi.org/10.21203/rs.3.rs-2397739/v1

How to cite: Bakke, S. J., Ionita, M., and Tallaksen, L. M.: Recent European drying and its link to prevailing large-scale atmospheric patterns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3255, https://doi.org/10.5194/egusphere-egu23-3255, 2023.

Projections indicate that agricultural and hydrological droughts' frequency, severity, and duration are expected to increase globally in the twenty-one century. A better understanding of droughts drivers is key to creating preparedness and resilience to projected events. Typically, droughts are caused by lower precipitation and/or higher evaporation than normal in a region. The region's characteristics and anthropogenic influences may enhance or alleviate the drought events. Evaluating the multiple factors influencing droughts is complex and requires innovative approaches. To address this complexity, this study applies a multivariate approach to evaluate the relationship between ten hydroclimatic characteristics and the severity of agricultural and hydrological droughts. A process-based model (Soil Water Assessment Tool) is used for hydrological modeling. The model outputs (soil moisture and streamflow) are used to calculate the indicators for the drought's analysis: Soil Moisture Deficit Index for agricultural droughts and the Standardized Streamflow Index for hydrological droughts. Then, the Multivariate decision tree approach is applied to evaluate the relevance and relationship between the hydroclimatic characteristics and the agricultural and hydrological drought severity at each subbasin. The approach is applied in the Cesar River basin (Colombia, South America), an area of ecological interest declared RAMSAR site.

Study outcomes indicate that evapotranspiration, precipitation, and percolation are the primary drivers of agricultural droughts. Other hydroclimatic parameters such as the curve number, water yield, solid yield, and slope play a relevant role in the subbasin's exposure to agricultural droughts. Subbasins with precipitation lower than 1318 mm, evapotranspiration higher than 1191 mm, percolation higher than 648 mm, and soil yield higher than 101 mm experienced more severe agricultural drought conditions during the period of analysis Regarding hydrological droughts; findings show that evapotranspiration and water yield are principal drivers. Results indicate that precipitation, percolation, and surface runoff also influence the severity of hydrological droughts. Most severe drought conditions during the evaluation period are observed in subbasins with evapotranspiration higher than 826 mm, water yield higher than 9 mm, and precipitation higher than 1398 mm. The outcomes of our analysis indicate that seven out of ten hydroclimatic characteristics evaluated influence the severity of agricultural and hydrological droughts. In addition, the results demonstrate that capturing the non-linear relationships between drivers of droughts and severity allows examining the hydroclimatic characteristics that influence droughts in a region.

How to cite: paez, A., Corzo, G., and Solomatine, D.: Multivariate regression tree approach to evaluate relationship between hydroclimatic characteristics and agricultural and hydrological droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-587, https://doi.org/10.5194/egusphere-egu23-587, 2023.

Low flows are generated by the interplay of climatic variability and basin water storage dynamics, which depend on basin attributes such as geology and soil properties. Even though low flows have been predicted worldwide, their controls and generation mechanisms remain elusive in many regions, such as South America. Here, we investigate the relative importance of climate and basin attributes in the spatial variability of minimum annual 7-day streamflow magnitudes (Q_min) of 1412 river basins in Brazil. We analyze time series of observed daily streamflow, precipitation and evaporation from 1980-2020; geology such as rock type and hydraulic conductivity; topography; soil properties such as sand content and class; and land cover. We estimate Q_min with a simple conceptual model that separates the roles of climate and basin attributes in regulating low flows. For each river basin, we identify the longest annual dry spells and estimate Q_min using an exponential decay model with three components: (i) initial flow, which indicates the basin’s water storage at the dry spell onset; (ii) dry spell length (T_dry), representing climate seasonality, computed from a 31-day precipitation minus evaporation series; and (iii) flow recession rate (Q_rec), indicating how quickly the basin releases the water stored. We estimate the initial flow component with the fraction (β) of mean annual precipitation minus evaporation (PE_m) that recharges the aquifer. This fraction is estimated from basin attributes using model-based recursive partitioning, a method similar to regression trees, in which we found soil properties as the predominant attributes. The flow recession component is estimated likewise, in which we found rock type and composition as the predominant attributes. We found that the model explains 56% of the variance in observed Q_min. The large-scale patterns show a close match. Results show that the relative importance of climate and basin attributes depends on the spatial scale of analysis. Climate and basin attributes are similarly important at the national scale, in which changing PE_m, T_dry, Q_rec, and β by one spatial standard deviation change estimated Q_min on average by 41%, 57%, 66%, and 31% respectively. On the other hand, basin attributes control low flow variability on subnational scales. Analyzing blocks sized 300 by 300 km, changing PE_m, T_dry, Q_rec, and β by one spatial standard deviation in each block change estimated Q_min on average by 19%, 11%, 36%, and 19%. Our interpretation is that the spatial variability of low flows is regulated mainly by the basin’s water storage capacity, here driven by rock type and composition, which even compensates for the highly seasonal climate from the South American monsoons. For example, most of the highest low flows (i.e., Q_min above 1 mm/d) are located in high-storage sandstone aquifers, a common aquifer type in Brazil. These highest low flows rarely occur in low-storage aquifers, which require a combination of high annual precipitation (i.e., above 3000 mm/yr) and the absence of a dry season such as in northwestern Amazonia. These findings can contribute to water security by estimating the impacts of climate change and variability on droughts.

How to cite: Chagas, V. B. P., Chaffe, P. L. B., and Blöschl, G.: Geological and climatic controls of low flows in Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6605, https://doi.org/10.5194/egusphere-egu23-6605, 2023.

Drought is the most damaging natural phenomena for large areas and populations. Places such as the Andean tropics have not been exempt from the presence of these events. Among these, hydrological droughts play a critical role because streamflow is closely related to the development of the population living in mountains. The study of these droughts has been neglected because, most of the time water availability has exceeded the water demand. However, this has caused a high vulnerability in the region due to the lack of knowledge and preparation for these events. In addition, the intensification of the water cycle due to climate change aggravates the situation. In this context, the objective of this study was to characterize streamflow droughts in an inter-Andean catchment. The study was performed in four near-natural headwater catchments distributed in a nested approach. Catchments are located in the southern Ecuadorian Andes, between 4550 and 2500 m a.s.l. where groundwater contribution is significantly reduced. Between 25 and 44 years of daily streamflow data was used. To identify streamflow droughts, the threshold method was used on a fixed and daily basis. In addition, different threshold levels obtained from the 70th, 80th, 90th, 95th and 98th percentiles of the duration curve were used. From the events identified, the characteristics of duration, magnitude and intensity were calculated. The five percentiles identified a minimum and maximum number of 40 and 670 events, respectively. On the other hand, the fixed threshold detected on average 27% more events compared to the daily threshold. The average duration, magnitude and intensity varied between: 3.2 and 12.85 days; 0.12 and 5.31 mm; and 0.02 and 0.24 mm day^-1, respectively. Despite the existence of events with more extreme characteristics, on average, the presence of events of short duration and magnitude prevail. These results are very different compared to those produced in lowlands, where the contribution of groundwater is important. Additionally, the lag between a meteorological and hydrological drought is very small, and therefore other ways to identify droughts should be studied. Results provide insight into the identification and characteristics of streamflow droughts in a poorly studied region such as the Andes. These can help to improve water resource management rules and evaluate water stress scenarios.

How to cite: Sucozhañay, A. and Célleri, R.: Establishing streamflow drought characteristics in an inter-Andean Mountain catchment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9704, https://doi.org/10.5194/egusphere-egu23-9704, 2023.

Climate change has led to new forms of extreme that until recently were not a topic of concern for the scientific community and general society. Terms such as flash droughts, megadrought, and anthropogenic droughts, among others, entered our vocabulary only recently, in the past decade or so. However, such categorizations are unclear and only come after the fact and with a considerable delay between impacts and definition, hindering preparedness potential. By the current projections and actions regarding climate change, one can only assume that extreme events of different magnitudes, multiple drivers and extensive impacts will keep surprising us. We propose in this work a novel frame to define extreme events. The aim is to allow their identification and increase preparedness against their impacts. The premise is to invert the pyramid of priorities and (1) in collaboration with stakeholders, policymakers and society, assess the potential impacts, defining different levels of “damage”. (2) From the identified levels of impact, use historical data, modelling, and projections to assess frequencies and drivers. Finally, (3) define the thresholds and patterns that lead to such impacts, supporting informed mitigation action and forecast. We present two examples of the application of this methodology regarding crop production in Germany. First, we identify what dry spell duration, thresholds, and seasonality are critical to crop yield. In the second, we identify the flashiness and intensity of flash droughts that lead to increasing crop losses, besides environmental drives to such events. This approach provides better results in identifying and defining such events. It also equips scientists with more didactic and direct measures of extreme events, facilitating communication and empowering action.

How to cite: Lima Alencar, P. H. and Paton, E. N.: A new framework for drought definition, identification, and preparedness, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13284, https://doi.org/10.5194/egusphere-egu23-13284, 2023.