Hydrologists are requested to quantify the response of catchments with respect to climatic variability or climatic changes: for this, they need to be able to assess the climate elasticity of streamflow. Here, we present a large sample study, based on 4122 catchments from four continents, investigating to which extent the climate elasticity of streamflow depends on aridity, i.e. the ratio of the long-term average values of potential evaporation to precipitation. After examining the example of the “Budyko-type” water balance formulas – which embed the dependency between elasticity and aridity – we use catchment data to verify empirically the existence of this link and we discuss the possibilities to impose the dependency to aridity in elasticity in order to obtain more physically-consistent elasticity coefficients.
How to cite: Andréassian, V., Mendoza Guimarães, G., Lerat, J., and de Lavenne, A.: Streamflow elasticity vs aridity – a large sample elasticity study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8866, https://doi.org/10.5194/egusphere-egu25-8866, 2025.
Whether flow is relatively young or old when it passes by the catchment outlet is a strong indicator of weathering processes, biogeochemical reactions, nutrient availability, pollution susceptibility and the hydrologic response of a catchment. It depends not only on individual catchment, climate, event and vegetation properties, it is also the result of a multitude of interactions between different processes and catchment states within the hydrologic system.
In order to begin to disentangle the cause-effect chains, we employed the physically-based, spatially explicit 3D model HydroGeoSphere in a virtual catchment running 270 scenarios with different combinations of catchment, climate and vegetation properties. For example, we looked at the influence of vegetation density and rooting depth while also considering different soil moisture conditions that modify the relationships between water ages and vegetation properties. In the same way, we varied the hydraulic conductivity of the soils and observed the water age relationships conditional on antecedent soil moisture. It became clear very quickly that simple, straightforward dependencies between individual catchment, vegetation, event and climate properties do hardly exist.
This is to show that, in order to make meaningful predictions about the age of hydrologic fluxes, it is inevitable to consider more than one variable when predicting biogeochemical responses at the catchment scale. Thus, it can be extremely helpful to look at the individual properties and the processes they control, their potential interactions and interdependencies, in a bottom-up approach within the framework of a hydrologic model.
How to cite: Heidbüchel, I., Yang, J., and Fleckenstein, J.: How climate, catchment and vegetation characteristics impact water flux partitioning and transit times at the catchment scale – a modeling study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4583, https://doi.org/10.5194/egusphere-egu25-4583, 2025.
Determining the respective attribution proportions of climate change and land use change to streamflow changes in river systems is of increasing interest to researchers and practitioners tasked with managing river basins. We propose an extension to established techniques of attributing the relative proportions of climate change (CC) and land use change (LUC) drivers to streamflow change by instead considering the proportions as distributed through a probability density function rather than as a point value. The novel method is demonstrated for parent catchments (catchments not nested within any other and not sharing any water flows with any other catchment) across Scotland. Results are determined by the flow, temperature and precipitation data, and by analysis of the change in these vectors. The ratio of the LUC and CC attribution proportions (LCAP) is then more appropriately expressed as a vector of values, each associated with its own probability value within a probability density function (pdf). Results demonstrate that the LCAP ratio pdf can vary considerably through time, can be expressed as a probabilistic estimate within confidence limits and that it is possible to track physical changes in the catchment in the evolution of the probability density function. Particular metrics for the LCAP ratio, such as the median value through time, can be derived from the pdf. The LCAP ratio resulting from analysis is also a function of the flow metric chosen – change in high flows (e.g. Q05) is generally more driven by CC whereas low flows (e.g. Q95) are more driven by LUC. It can also be concluded, with a high degree of confidence, that for Scottish catchments in general, and for much of the time, both CC and LUC are significant drivers of streamflow change, but it can also be shown that there is a relationship between the magnitude of the LCAP ratio and certain physical catchment descriptors such as area, median catchment height or shape compactness. Hence, these findings may have implications for future catchment flood management utilising nature-based solutions to reverse landscape degradation and mitigate effects of climate change, provided that the economic and social costs are outweighed by the benefits.
How to cite: Wray, N., Beevers, L., and Angeloudis, A.: A probabilistic approach to disentangling climate change & land use change effects on river flows, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4559, https://doi.org/10.5194/egusphere-egu25-4559, 2025.
The Sahel, the semi-arid fringe south of the Sahara, experienced severe meteorological droughts in the 70s-80s. During these droughts, several watersheds may have experienced a regime shift that led to an increase in the annual runoff coefficient (annual runoff divided by annual precipitation). This phenomenon, known as the first Sahelian hydrological paradox, has been attributed to soil crusting, a very typical feature of the sahelian region, which led to an increase in Hortonian runoff. The physical driver of this soil crusting is still debated in the literature. Standard explanations generally involve land use and cover changes (LUCCs). However, alternative explanations exist: soil crusting may have also been impacted by changes in precipitation regime (total precipitation, precipitation intensity, …). Here, we focus on the impact of precipitation regimes on annual runoff coefficient.
In this region, most hydrological processes occur at a sub-daily scale. However, existing observations of precipitation are only available at the daily scale. A classic way to resolve this issue is to downscale observations of precipitation at a sub-daily scale, and model processes at this scale. In practice, such downscaling always implies strong hypotheses concerning spatio-temporal dependencies of rainfall process at fine scale. Instead, we propose an alternative solution: to “upscale” sub-daily hydrological processes at a daily scale. Specifically, we use fine-scale rainfall series to force a simplified Green-Ampt (GA) infiltration model which predicts runoff at fine scale. Then we compute both annual statistics of rainfall regime from sub-daily rainfall series and annual runoff coefficients from the GA simulations ; and we infer a statistical link between annual rainfall statistics and annual runoff coefficients. This statistical emulator of the GA model predicts annual runoff coefficient based on several features: the saturated hydraulic conductivity of the soil (ksat), and several indicators of precipitation regime, such as the average and the maximum of daily precipitation.
In our results, this emulator is first trained and assessed using observations of precipitation from Sahelian stations. In this case, we show that this emulator can reproduce annual runoff coefficients produced by the GA model. We also note that ksat has more impact than annual indicators of precipitation, which may be due to the crystalline sedimentary context of our study. Then, we test this emulator on Sahelian watersheds where we have both rainfall series and observed runoff series (and thus runoff coefficients). Our preliminary results show that this emulator can reproduce observed trends in annual runoff coefficient.
How to cite: Dubas, O., Le Roux, E., Panthou, G., Vandervaere, J.-P., and Peugeot, C.: Can changes in precipitation regime explain the first Sahelian hydrological paradox ? An inquiry with a statistical emulator of sub-daily hydrological processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10775, https://doi.org/10.5194/egusphere-egu25-10775, 2025.
Several regions globally have recently experienced persistent shifts in the relationship between rainfall and runoff, triggered by multi-annual drought. These regions are climatically diverse; however, no assessments have yet been undertaken to draw parallels between the processes responsible. We present a comparative analysis of non-stationarity between south-west Australia and south-east Australia, two regions separated by over 2,000km and both experiencing non-stationary streamflow responses. We adopt existing methods of characterising hydrological non-stationarity and apply these to 254 catchments in Eastern and 54 in Western Australia. Of the catchments analysed, 51% of Eastern and 63% of Western catchments displayed a transition from the historical rainfall-runoff relationship to one of reduced flow generation, with the reduced flow state persisting in 31% and 63% of catchments, respectively. Interrogation of characteristics inherent in the transitioned catchments revealed positive correlation in Western Australia between catchment forest coverage and non-stationarity, while an inverse relationship is found in Eastern Australia. Similarly, catchment coverage by land cleared for agricultural purposes was positively correlated to non-stationarity in Eastern Australia and inversely so in Western Australia. We suggest a possible link to pre-existing trends in groundwater for cleared catchments, where those in Western Australia may have been experiencing rising groundwater levels due to clearing occurring recently relative to Eastern Australia. The hydrological non-stationarity in forested western catchments is consistent with previous studies showing the importance of groundwater connectivity in those catchments; in contrast, such links are impossible to explore in eastern forested catchments due to a spatial gap in groundwater monitoring. We recommend further comparative studies be conducted to create a more thorough understanding of this behaviour in order to better inform decisions regarding water planning.
How to cite: Campion, N., Fowler, K., Turner, M., and Hall, J.: Contrasts and contradictions comparing hydrological shifts across southern Australia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9203, https://doi.org/10.5194/egusphere-egu25-9203, 2025.
The increasing frequency of extreme climatic events challenges both science and water resources management. Flood risk assessment on the one hand, and drought risk assessment on the other hand are usually considered the tasks of different sub-disciplines. However, recent studies suggest that the two might be more closely related than widely assumed. There is some evidence that the information content of stream discharge in regard to groundwater systems is widely underrated and that groundwater dynamics is key for long-term flood risk assessment. Here we bring together two different lines of evidence in order to gain better understanding of how the interplay between groundwater and streams determines regional hydrological systems resilience to climate change.
On the one hand, findings from a joint analysis of 292 time series of stream discharge and groundwater head from a 36,000 km2 region covering a 43 years period are reported. Spatial variability, that is, different behaviour at different sites, could largely be traced back to spatially varying input reflecting regional climatological patterns, and to different degrees of damping and low-pass filtering of the hydrological input signal in the subsurface. Stream discharge and groundwater head dynamics differed in regard to the latter, but not without remarkable overlap. Both for stream discharge and groundwater head the degree of low-pass filtering was very closely related to long-term trends, similar as in other studies (Lischeid et al. 2021). Beyond that there was no clear distinction between surface and subsurface hydrological dynamics.
The second study aimed at determining the key drivers of extreme floods based on 73,350 synthetic hydrographs from a comprehensive modelling study, comprising 163 catchments with 450 model realizations each. On the one hand, tail heaviness of flood distribution was assessed by the shape factor of the extreme value distribution (Macdonald et al. 2024). On the other hand, a newly developed Cumulative Periodogram Convectivity (CPC) index was tested which is based on the degree of low-pass filtering of hydrological time series. Both indices were closely related to the extreme value behaviour of precipitation, to the upper subsurface storage and, to a lesser degree, to catchment area. However, these relationships were less close for the shape factor which suffered from the problem of fitting an extreme value distribution to bent flood frequency curves. In contrast, the CPC index was much more robust.
To conclude, low-pass filtering of hydrological signals in the subsurface proved to be the unifying element for stream discharge and groundwater head as well as for flood and drought hazard characteristics. Contrary to usual expectations, time series of deep groundwater head rather than of shallow groundwater or stream discharge turned out to be the most efficient early warning indicators for the effects of climate change in regard to extreme events. Thus common analysis of runoff and groundwater hydrographs is strongly recommended for science and water resources management.
References:
Lischeid et al. (2021), Journal of Hydrology 596, 126096, DOI: 10.1016/j.jhydrol.2021.126096
Macdonald et al. (2024), Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024
How to cite: Lischeid, G., Weyers, J., Macdonald, E., and Vorogushyn, S.: What determines hydrological systems’ resilience to climate change?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2701, https://doi.org/10.5194/egusphere-egu25-2701, 2025.
Potential Evapotranspiration (PET) is a crucial component in hydrological modelling. It represents the water demand of the region, and thus, it can influence drought assessments, partitioning of precipitation to evapotranspiration (Budyko framework), and climate change impact studies. Many studies have focused on the impact of PET on future runoff changes, with limited consideration of other hydrological components (actual evapotranspiration, runoff, soil moisture, and total water storage). However, few studies examine how different PET methods affect future changes in hydrological cycle components. Understanding these impacts and uncertainties is crucial for the intensification of hydrological cycle studies. This study aims to investigate the impact of potential evapotranspiration on the intensification of the hydrological cycle for the future across European catchments covering all European climates. A mesoscale Hydrological Model (mHM) is employed to assess hydrological cycle components for each catchment. Five ISIMIP climate models were used to simulate historical (1950-2014) and future (2015-2100) hydrological cycle components for three Shared Socio-economic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5. Twelve widely used PET methods were considered, ranging from the simplest (temperature-based) to the most complex approaches (radiation and combinational-type). In total, 557 catchments from energy-limited, mixed, and water-limited categories were analyzed. Our initial analysis reveals that annual-scale hydrological cycle components simulated by all climate models are broadly consistent with historical observation-based datasets of Thakur et al. (2024). At the monthly scale, temperature-based PET methods demonstrate greater variability than radiation and combination methods. In summer, complex PET methods overestimate mean monthly PET, while temperature-based methods align better with observations. Our findings improve the understanding of the potential evapotranspiration’s role in the future hydrological cycle intensification and its associated uncertainties across European catchments.
Reference: Thakur, V., Markonis, Y., Kumar, R., Thomson, J. R., Vargas Godoy, M. R., Hanel, M., and Rakovec, O.: Unveiling the Impact of Potential Evapotranspiration Method Selection on Trends in Hydrological Cycle Components Across Europe, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2024-341, in review, 2024.
How to cite: Thakur, V., Oldrich, R., Thomson, J. R., Kumar, R., Hanel, M., and Markonis, Y.: The Impact of Potential Evapotranspiration Methods on Future Hydrological Cycle Intensification Across European Catchments , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-392, https://doi.org/10.5194/egusphere-egu25-392, 2025.
Despite numerous past and ongoing efforts towards characterizing the propagation of rainfall estimation uncertainties in rainfall-runoff hydrologic models, modelers continue to struggle to identify the main features which impact the way rainfall errors are transmitted to simulated runoff. With this work, we introduce the concept of the rainfall elasticity function, i.e. the measure of how responsive the simulated event runoff is to a change in rainfall. We analytically derive the functions for two well-known runoff generation model types: the Probability Distributed Model (PDM), where the Pareto distribution is used to describe the distribution of soil-moisture storage capacity, and the Soil Conservation Service – Curve Number (SCS-CN) model. These functions are explored to examine the propagation of rainfall errors through the two models. It is shown that the two models are characterized by very different elasticity functions, which results in diverging propagation features of the rainfall errors. For the PDM case, increasing the precipitation depth, or decreasing the storage capacity, results in the elasticity growing from 1 to a peak whose value and location depend on the model parameters, and then asymptotically decreases again to 1. For the SCS-CN model, increasing the precipitation depth, or decreasing the maximum potential retention, makes the elasticity decrease from infinity to 1. The capability of the elasticity functions to describe the propagation of rainfall errors through the models is illustrated by using data from a number of flood events occurred in the last two decades in the Eastern Italian Alps. It is shown that the analytical functions closely resemble the results obtained by forcing the models with the actual distribution of rainfall errors, thus paving the way for the practical application of this approach, such as in hydrological model calibration and use of multi-model ensemble for flood forecasting.
This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).
How to cite: Borga, M. and Dallan, E.: Rainfall elasticity functions: a new metric to quantify divergent runoff sensitivity to rainfall errors in hydrological models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19061, https://doi.org/10.5194/egusphere-egu25-19061, 2025.
Understanding and robustly sampling climate variability is vital for producing reliable near- and long-term projections of water availability and drought. Climate variability on interannual to multi-decadal timescales can substantially influence precipitation, temperature, or humidity, shaping the intensity, frequency, and persistence of extreme hydrological events. Particularly for multi-year variability, such influences can lead to consecutive years of extreme hydrological stress, challenging the resilience of natural and human systems. Furthermore, sampling the worst-case, most extreme yet plausible conditions of extreme drought, potentially compounding with other system stressors, is crucial for producing comprehensive risk assessments. Regionally, climate variability can amplify or dampen the anthropogenic signal of global warming. Therefore disentangling its contribution from such anthropogenic changes is crucial to understand observed changes and how they may continue into the future, as well as to determine worst-case or unprecedented conditions plausible today.
Here, we showcase how climate variability sampling techniques such as Single Model Large Ensembles (SMILEs) and Ensemble Boosting can be used to assess how soon unprecedented extreme heat and drought stress could occur over Europe, whether it could happen successively year after year, and how intense worst-case heat and drought stress could become already today. SMILEs consists of several simulations from one climate model under the same forcing to capture the effect of freely evolving internal variability and generate a range of possible climate outcomes, from daily to centennial scales. Ensemble Boosting uses extreme conditions in a SMILE as a starting point, which are then re-run under a small butterfly-effect like perturbation to produce hundreds of physically consistent storylines that explore worst-case extremes, by amplifying the chaotic nature of climate variability around the original parent event itself. Together, these approaches are extremely powerful tools to produce risk storylines that remain physically consistent across time, space, and across variables, and that can be used to assess hydrological impacts to better prepare for the challenges posed by accelerating climate change and its influence on water resources.
How to cite: Suarez-Gutierrez, L., Fischer, E. M., Marotzke, J., Müller, W. A., and Vautard, R.: Exploring Climate Variability and Worst-Case Drought Storylines using Large Ensembles and Ensemble Boosting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16852, https://doi.org/10.5194/egusphere-egu25-16852, 2025.
Drought extremes in hydrological systems are closely tied to soil moisture dynamics. However, the influence of climate variability on soil moisture, particularly in the East Asia monsoon region, remains insufficiently explored. This study examines soil moisture in Taiwan, combined with satellite data, to investigate the impacts of summer monsoons. For instance, the positive phase of the Pacific-Japan Pattern has been identified as a key driver of interannual extremes in compound heat and drought events. These phenomena are exacerbated by soil moisture deficiencies, which intensify dry conditions, elevate air temperatures, and result in significant societal and agricultural damage. This research focuses on the linkage between drought characteristics and climate variability from a soil moisture perspective, in contrast to traditional indices in Taiwan that predominantly rely on rainfall data. Advanced analytical approaches, such as AutoEncoder (AE) and Principal Component Analysis (PCA), were utilized to enhance drought quantification by integrating satellite observations with high-resolution downscaled datasets. PCA results emphasize the critical role of interannual internal modes, like the Pacific-Japan Pattern, in driving drought conditions. However, discrepancies observed between AE and PCA outcomes highlight the need for further investigation into nonlinear relationships underlying drought dynamics.
How to cite: Lin, S.-Y., Tseng, W.-L., Lo, M.-H., and Wang, Y.-C.: Exploring Climate Variability and Soil Moisture Dynamics to Refine Drought Quantification in East Asia Monsoon Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10377, https://doi.org/10.5194/egusphere-egu25-10377, 2025.
Due to various characteristics of a catchment area, such as topography, size and shape, soil and climate, different flood types prevail. These types are categorized according to the flood-generating processes, which are rainfall over short to long durations and the influence of snow dynamics. Often, a flood typology is devised based on observational time series or single hydrological simulations. However, the influence of internal climate variability on flood types is not well understood and quantified yet.
Here, we apply a unique hydrological large ensemble over a central European domain featuring the catchments of the upper Danube and the southern parts of the Main and Elbe catchments. The driving climate stems from a 50-member high-resolution single model initial-condition large ensemble (SMILE), the CRCM5-LE at 12 km resolution. SMILEs are driven by the same external forcing and apply a single climate model – hence, the variability within the SMILE can be interpreted as a model representation of internal climate variability. After a bias adjustment, the CRCM5-LE is used to drive the physically-based hydrological model WaSiM at 3-hourly temporal resolution and 500 m spatial resolution yielding a 50-member hydrological SMILE for 98 river gauges in the study area.
For a 60-year historic period (1961 – 2020) we differentiate between five types of rain-induced and snowmelt-affected floods via a statistical flood typology analysing the hydrographs and the hydrometeorological drivers. The flood types feature short-rain floods, floods driven by medium- to long-duration rainfall, long-rain floods with frequent multiple peaks, rain-on-snow floods, and snowmelt-dominated floods.
We show that the frequency and intensity of the different flood types largely varies between the 50 simulations indicating a strong influence of internal climate variability on the flood typology. The highest degree of variability over all catchments is found for short-duration rainfall floods. The sensitivity of the flood typology to climate variability also varies greatly over the 98 catchments. We see a tendency for a higher sensitivity of smaller catchments to internal climate variability. Further, reservoirs and lakes are found to lower the effect of climate variability on the flood types due to their buffering behaviour.
We follow that using a single time series (may it be observational or simulated) might lead to a strong under- or overestimation of flood peaks per flood type, miss the catchment-specific flood typology, and induce an under- or overdetection of trends in the flood types. Hence, we suggest the application of SMILEs for the determination of flood peak return levels and the robust trend detection of certain flood types in order to incorporate the uncertainties of internal climate variability.
How to cite: Poschlod, B., Fischer, S., and Böhnisch, A.: How does internal climate variability propagate to the catchment-characteristic flood types? Insights from a hydrological large ensemble, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8290, https://doi.org/10.5194/egusphere-egu25-8290, 2025.
In the Netherlands, precipitation minus reference evaporation (Makkink) is a good indicator for groundwater recharge. In the following, this difference is referred to as potential recharge. The climate change since 1940 has led to systematic changes in precipitation and in evaporation. However, the 30-year running average of the potential recharge does not show a continuous trend over this period, but the 30-year running standard deviation steadily increases for aggregation intervals shorter than a year. This suggests that the variability of the groundwater heads may have increased also.
Analysis of a set of long timeseries of groundwater head in the Netherlands does not show such an increase in variability. In order to determine whether this is due to the properties of the groundwater system or due to other (anthropogenic) influences, simulations of the groundwater heads have been carried out using timeseries modelling with a timeseries of the potential recharge and transfer functions covering the range of responses determined in the Dutch national Groundwater head viewer. The simulations show that an increase of the variability of the groundwater heads due to the increased variability of the potential recharge is only to be expected for short response times. However, comparison of the aggregated trends of the groundwater heads with the trends of the potential recharge indicates that there is a strong anthropogenic impact besides the influence of the climate (change). Therefore, long term assessment of (geo)hydrological systems has to include land-use changes, groundwater abstraction and other anthropogenic influences.
How to cite: Zaadnoordijk, W.: How did the increasing variability of precipitation and evaporation over the past 50 years propagate to groundwater heads in the Netherlands?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7370, https://doi.org/10.5194/egusphere-egu25-7370, 2025.
Flood risk management institutions and practitioners need accurate and easy-to-use approaches that incorporate the changing climate conditions into flood predictions in ungauged basins. The present work aims at developing an operative procedure to include the expected variation in precipitation extremes in flood frequency analysis. We relate Flood Frequency Curves and Intensity-Duration-Frequency curves through quantile-quantile relationships. Assuming that the percentage variations of precipitation and flood quantiles are linked by the quantile-quantile relationship, we obtain modified Flood Frequency Curves accounting for the projected changes in precipitation extremes. The methodology is validated in a virtual world based on the Rational Formula approach where flood events are the result of the combination of two jointly distributed random variables: extreme precipitation and peak runoff coefficient. The proposed methodology is found to be reliable in basins where flood changes are dominated by precipitation changes rather than variations in the runoff generation process. To illustrate its practical usefulness, the procedure is applied to 227 catchments within the Po River basin in Italy using projected percentage changes of precipitation extremes from CMIP5 CORDEX simulations for the end of the century (2071-2100). With projected changes in 100-year precipitation ranging from 5 to 50\%, the corresponding variations in 100-year flood magnitudes are expected to span a broader range (10 to 90\%), reflecting substantial heterogeneity in catchment responses to rainfall changes.
How to cite: Cafiero, L., Bertola, M., Mazzoglio, P., Laio, F., Blöschl, G., and Viglione, A.: How changes in future precipitation impact flood frequencies: a quantile-quantile mapping approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3736, https://doi.org/10.5194/egusphere-egu25-3736, 2025.
The escalating frequency of extreme hydroclimatic events, driven by climate variability and change, rapidly alters hydrological patterns and thus renders the traditional assumption of stationarity in hydraulic design and water resource planning obsolete. This study addresses the challenges posed by high spatial and temporal variability of extreme events, particularly in tropical and semi-arid regions, where understanding the processes driving short- and long-term climate changes remains complex. A novel non-overlapping block-stratified random sampling (NBRS) framework is proposed, integrating multiple nonparametric statistical tests to assess non-stationarity (NS) in hydroclimatic extremes. A modified NBRS framework incorporates a nonparametric clustering approach to detect spatial clusters of NS, caused by shifts in mean, variance, and distribution, or combinations of these factors. The NBRS framework distinguishes between weak and strict forms of stationarity and is further enhanced by a modified variant that identifies the stochastic processes influencing NS. A comparative assessment of the NBRS framework and its modified version with conventional trend and change point methods demonstrates its ability to identify time-invariant characteristics, especially in heteroscedastic variables like extreme rainfall and streamflow. This framework is applied to 28 hydroclimatic indices derived from over four decades of data from west-flowing river basins of India, which are characterized by diverse physio-climatic conditions. The modified NBRS approach effectively identifies NS in extreme hydroclimatic indices, elucidating its root causes and significant implications for hydrologic design. The findings reveal that traditional trend and change point tests are less effective in capturing time-invariant characteristics, particularly in heteroscedastic variables such as extreme rainfall and streamflow. Also, the distributional shifts predominantly drive NS in rainfall and streamflow extremes, whereas temperature extremes are influenced by changes in both mean and distribution properties. Valuable insights into the evolving patterns of hydroclimatic extremes under a changing climate can be drawn from this study.
Keywords: Non-stationarity, Hydroclimatic extremes, Statistical analysis, Climate change, Spatial clustering, Extreme weather events.
How to cite: Singh, A., J. Sharma, P., and S. V. Teegavarapu, R.: A Novel Approach for Assessing Non-stationarity in Hydroclimatic Extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-929, https://doi.org/10.5194/egusphere-egu25-929, 2025.
The complexity of hydrological models can significantly influence the accuracy of streamflow predictions. While more complex models may seem advantageous due to their robustness, previous research found that simpler models can often yield comparable or even superior results for some applications, particularly if they are able to adequately represent key hydrological processes and features. Here, we investigated the impact of model complexity on the streamflow projection over the century in Brazil, a continental-scale country that presents hydrological and landscape heterogeneity. We employed projected climate change data from 10 models of the CLIMBra dataset over 735 Brazilian catchments, forced by the CMIP6-based SSP2-4.5 and SSP5-8.5 scenarios. We used five hydrological models representing a full range of model complexity: 1. Functional forms, 2. Grunsky method, 3. HYMOD model, 4. MISDC model, and 5. a regional model based on the Long Short-Term Memory (LSTM) algorithm. On a daily basis (models 3 to 5), when comparing the traditional models with the LSTM, we found that LSTM overperformed HYMOD and MISDC, with a median KGE of 0.72. The MISDC presented the worst performance in daily predictions. All the models were evaluated on a long-term basis, with KGE ranging from 0.62 (Grunsky method) to 0.85 (LSTM model). The conventional hydrological models, HYMOD and MISDC presented KGE of 0.78 and 0.80, showing great suitability for Brazilian catchments, but with the disadvantage of needing local parametrization. It is also worth noting that performance metrics were improved in all cases from a daily to a long-term basis, due to the longer timescale. Regarding the streamflow over the century, when comparing an ensemble mean of climate projections, different models estimated, on average, changes from -21% to +15% in long-term mean daily streamflow. Simpler models projected a slight increase in the mean streamflow, while more complex models projected greater changes. We also found some spatial patterns of variation according to the model complexity, with greater differences in the arid catchments (where the KGEs were lower). We further discuss model complexity and performance in view of climate models' inherent uncertainties. The comparative performance presented in our study showed that while complexity can enhance performance, some simpler models can show similar outputs and might be preferred for some applications.
How to cite: Almagro, A., Ballarin, A., and Oliveira, P. T.: The role of model complexity in streamflow projections on Brazilian catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14071, https://doi.org/10.5194/egusphere-egu25-14071, 2025.
For effective adaptation planning and water resources management, it is essential to assess the intrinsic uncertainties in future runoff changes over the next decades. Over Europe, runoff variations are mainly driven by precipitation, which, in turn, is influenced by large-scale atmospheric circulation over the North Atlantic. Previous studies have emphasized strong teleconnections between the European climate and the Atlantic Multidecadal Variability through air-sea interactions using reanalysis and observational datasets. However, these teleconnections have been suggested to be poorly represented in climate models. Here, we aim to quantify the influence of internal variability on future runoff changes in state-of-the-art climate models, to analyze the influence of these teleconnections on the internal variations of runoff at multi-decadal timescales, and to assess the realism of these internal variations.
We show that the intrinsic uncertainty accounts for at least 50% of the total uncertainty in near-term runoff changes over different European regions. At the source of the intrinsic uncertainty, we find a predominant role of atmospheric noise in the models, through the modulation of precipitation. The past multi-decadal variability of precipitation and large-scale atmospheric circulation is then evaluated using different observational datasets. These analyses suggest that the intrinsic uncertainty in future runoff projections is well represented in northern, western and central Europe but is underestimated over the Mediterranean region.
How to cite: Deman, J. and Boe, J.: Intrinsic uncertainties in future runoff changes over Europe in the next decades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11042, https://doi.org/10.5194/egusphere-egu25-11042, 2025.
Global warming and human activities are altering the global hydrologic cycle, raising concerns about water availability. The rainfall-runoff relationship (RRR), determining how much rainfall becomes runoff, remains poorly understood globally, particularly regarding the influence of socioeconomic development. Here, we analyze rainfall and runoff data from 1,492 global basins over the period 1990–2015, finding that 80.5% experienced significant changes in RRRs. Using a hydrologic model-based attribution framework, we attribute these changes to natural environmental factors in 67.7% of basins and to socioeconomic factors in 32.3% of basins. Notably, among basins where socioeconomic factors dominate RRR changes, 65.9% show reduced runoff coefficients, indicating that human activities are decreasing runoff generation. Our findings demonstrate that socioeconomic developments such as population growth and GDP increase—reduce runoff by enhancing water withdrawals and consumption, thereby exacerbating water scarcity. This study highlights the substantial human impact on hydrologic processes under climate warming.
How to cite: Zhang, X., Liu, P., Zhang, L., Yin, J., Liu, W., Cheng, Q., and Li, X.: Socioeconomic development dominates changes in runoff response over 1/3 of global river basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8478, https://doi.org/10.5194/egusphere-egu25-8478, 2025.
Posters on site: Wed, 30 Apr, 08:30–10:15 | Hall A
The magnitude, frequency and spatial extent of hydrological extremes are changing because of climate change. However, the sign and magnitude of projected future changes in high flows remain uncertain in many regions, including Switzerland, because of internal climate variability, which causes streamflow fluctuations on annual to decadal timescales. To disentangle the changes in high flows that can be attributed to climate change from those related to internal variability, one needs to quantify this uncertainty. For this task, one can use single-model initial-condition large ensembles (SMILEs), which are climate models composed of different members representing equally plausible realisations of the climate. In this study, we use the climate variables projected by a 50-member SMILE at the daily time step as inputs to a hydrological model to project future changes in high-flows for a large set of catchments in Switzerland. We calculate changes in high streamflow quantiles and changes in the seasonality of maximum annual streamflows to investigate the changes in high flows between current climate and future conditions until the end of the century. We then calculate the signal-to-noise ratio and the time-of-emergence to determine where and when these changes are significant. We show that under the RCP8.5 scenario (1) high flows are likely to decrease at high elevations and increase at low elevations; (2) annual streamflow maxima are projected to occur earlier at high elevations and later at low elevations; and (3) the high flow change signal emerges from internal climate variability before 2050 at high elevations and after 2050 at low elevations. Our findings will likely have implications on flood frequency estimation in the alpine region.
How to cite: Astagneau, P. C., Wood, R. R., and Brunner, M. I.: Earlier emergence of high-flow changes at higher elevations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3083, https://doi.org/10.5194/egusphere-egu25-3083, 2025.
Climate change significantly impacts the hydrological system, river flows, and available water resources. In this study, we have investigated the potential impact of climate change on the Lake Tana water resources by using data generated from two Global Circulation Models (GCMs) (i.e., MIROC5 and MPI) under two Representative Concentration Pathways (RCPs), i.e., RCP4.5 and RCP8.5. Climate data, mainly precipitation and temperature, was generated for the two future time horizons (the 2040s (2020-2049) and 2070s (2050-2079). The lake temperature is increasing for all RCP scenarios and time domains. The result confirmed that lake evaporation and rainfall increased for all future scenarios. The ungauged surface water inflow also increased in the 2040s time domain, while gauged watershed surface inflow increased for RCP4.5 (2070s) and RCP8.5 (2040s) and decreased for RCP4.5 (2040s) and RCP8.5 (2070s). Performance indices such as reliability, resilience, and vulnerability were used to assess the performance of the Lake Tana reservoir under climate change. The time-based and volumetric reliability have an average value of less than 80% in both the 2040s and 2070s under all scenarios. The resilience values are below 50%, which indicates that the reservoir will take a long time to recover from the shortage. The dimensionless vulnerability has also a value of less than 50%, indicating that the reservoir will be discharged by sufficient inflow to satisfy the demands. From the performance measures, the reservoir will not somehow have good performance due to the increase in upstream abstractions (small and large-scale irrigation). Facing future climate and according to hydrological changes, reservoir rule curves have been developed that can help for the sustainable use of the resources.
How to cite: wubneh, M. A., Stumpp, C., and Strohmeier, S.: Reservoir performance assessment and operations in Lake Tana, upper Blue Nile Basin, Ethiopia, in response to climate change and water management., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4439, https://doi.org/10.5194/egusphere-egu25-4439, 2025.
As the supply of freshwater for several coastal towns worldwide, as well as for agricultural and industrial uses, coastal aquifers are crucial to the sustainability of communities. However, because of rising sea levels and altered rainfall patterns, climate change has a significant effect on these aquifers. The issue is made worse by anthropogenic effect from excessive pumping. When combined, they can result in substantial seawater intrusion, which has an impact on coastal towns and users and presents difficulties for sustainable development.
A limited number of studies have been done on the hydrogeological and environmental conditions in Saudi Arabia's Eastern Province, especially on seawater intrusion and climate change impact. Mitigating the effects of climate change requires an understanding of the relationship between seawater intrusion and climate change. this study fills this knowledge gap by examining the long-term impacts of climate change on seawater intrusion in the area.
A density-dependent transport model was developed using SEAWAT to simulate seawater intrusion under three scenarios. These scenarios accounted for projected sea level rises of 0.58 m, 0.70 m, and 0.91 m, respectively, with recharge rates ranging from 4.5 to 5.89 mm/year. Simulation time extends until the year 2100. The results indicated a substantial inland shift of the freshwater-saltwater interface, with the horizontal extent of saltwater encroachment increasing over time.
The study shows that the major contributor of seawater intrusion effects results from sea level rise, and the effect of changing precipitation in minimal and could be considered negligible.
Based on the results of this study, it is recommended to follow adaptive water management strategy to deal with this problem. Some measures such as lowering groundwater extraction and combining it with injection wells can have a good impact on the seawater intrusion issue. Results show that these measures demonstrated effectiveness in mitigating the impact, reducing the affected saline area in the aquifer, and reversing the saltwater-freshwater interface.
How to cite: Baalousha, H. and Fahs, M.: Modelling Climate Change Impact on Seawater Intrusion using Density-Dependent Flow model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5400, https://doi.org/10.5194/egusphere-egu25-5400, 2025.
Sub-Saharan Africa (SSA) is increasingly experiencing unprecedented drought-to-flood events, posing critical challenges to water and food security. These rapid or seasonal transitions between extreme hydroclimatic conditions underline the urgency of advancing climate adaptation strategies and enhancing risk management frameworks in the region. However, the role of large-scale climate variability, such as the El Niño-Southern Oscillation (ENSO), Atlantic Multidecadal Variability (AMV), and Indian Ocean Dipole (IOD), in influencing decadal trends in these events across SSA remains inadequately understood.
This study aims to address this gap by evaluating how well eight single-model initial-condition large ensembles (SMILEs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulate the spatiotemporal patterns of drought-to-flood events in SSA. ERA5-Land data is used as the observational reference. We also investigate potential seasonal links between the probability of drought-to-flood events and large-scale modes of climate variability.
Drought-to-flood events are defined as the sequential occurrence of a flood following a drought. To capture these events, we employ a variable threshold approach for identifying droughts, while floods are characterized using absolute thresholds (50th to 90th percentiles). To assess potential differences between meteorological and hydrological definitions of drought and flood, we compare results derived from precipitation, soil moisture, and runoff datasets.
How to cite: Dieppois, B., Ekolu, J., Rubinato, M., Onyutha, C., Okia, C., Musinguzi, D., Bogere, R., Mombo, F., Binego, L., Fried, J., and Van De Wiel, M.: Large-scale climate drivers of drought-to-flood events in Sub-Saharan Africa: Insight from CMIP6 large-ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6234, https://doi.org/10.5194/egusphere-egu25-6234, 2025.
Alpine streams supply water to mountains and downstream regions, but their sensitivity to climatic variability is complex. Here we resolve seasonal and elevation-dependent patterns of streamflow sensitivity using long-term records from the European Alps. For each week of the year, we fit a multiple linear regression model that predicts streamflow as a function of temperature, precipitation, and storage (approximated by streamflow from the previous week). The resulting regression coefficients quantify the direction and magnitude of the influence of the three predictor variables and thus represent weekly sensitivities of streamflow in response to changes in temperature, precipitation, and storage. At high elevations with extensive glacier cover, weekly temperature and precipitation sensitivities peak in spring and summer when melt rates are high. At low elevations with no glacier cover, weekly temperature sensitivities are negative in summer, while precipitation sensitivities are highest under moist winter conditions. Storage sensitivities are particularly high at high elevations in winter, when streamflow is mostly sourced from subsurface storage. Our results indicate how the transition zone, which marks a change from negative to positive temperature sensitivities in spring and summer, could shift upwards with climate warming, showing that streamflow sensitivities are temperature-dependent and thus non-stationary. Weekly streamflow sensitivities enhance our understanding of the main drivers of the streamflow regime and can be used for the evaluation of hydrological simulation models.
How to cite: Gnann, S., Markus, W., and Stahl, K.: Streamflow sensitivity regimes of alpine catchments and their relationship with elevation, temperature, and glacier cover, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6409, https://doi.org/10.5194/egusphere-egu25-6409, 2025.
Droughts cause significant impact on the terrestrial vegetation ecosystem with water shortage propagating through ecohydrological processes. Understanding how drought affects the ecosystem under different hydrogeological conditions is crucial for ecosystem protection. However, it is not clear how ecosystems respond to meteorological drought (MD) under different hydrogeological conditions. This study used monthly standardized precipitation evapotranspiration index (SPEI), soil moisture index (SSMI), normalized difference vegetation index (SNDVI) and solar-induced chlorophyll fluorescence (SSIF) to investigate the characteristics and mechanisms of propagation from MD to agricultural drought (AD) and ecological drought (ED) during 2000~2014 in the Jinsha River Basin. Based on the maximum correlation coefficients (MCC), the differences in drought propagation time of MD to AD and ED were explored in positively and negatively correlated areas. Random Forest was used to identify the impacts of driving factors on drought propagation. Results show that (1) AD was mainly positively correlated with MD while the correlation coefficients between ED and MD ranged from negative to positive. (2) The propagation time from MD to AD was shorter in summer and autumn. The propagation time from MD to ecological drought indicated by NDVI (EDndvi) was shorter than that to ecological drought indicated by SIF (EDsif) in the positively correlated areas while the result in the negatively correlated areas was in contrast. (3) Random forest results indicated that temperature (T), solar radiation (S) and precipitation (P) were key factors influencing ED in positively correlated areas, T was an important factor in controlling the occurrence of ED in negatively correlated areas. (4) SIF was more sensitive to MD and had a shorter response time in positively correlated areas. It has great potential for monitoring the response of vegetation growth to drought. MD is not the main factor threatening vegetation growth in negatively correlated areas. The findings in this study have significant implications for accurately understanding the mechanisms of the response of vegetation growth to meteorological drought and offer scientific guidance for maintaining terrestrial ecosystem health.
How to cite: Li, C., Zhang, X., and Li, H.: Heterogeneous Influences of Driving Factors on the Propagation from Meteorological Drought to Agricultural and Ecological Droughts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6676, https://doi.org/10.5194/egusphere-egu25-6676, 2025.
In winter 2023/24 along the Elbe River catchment in Germany, river flows were considered high or severe (European Flood Awareness System), and flooding impacted approximately 1.6 million people across Europe (International Disaster Database). Winter river floods are driven by both immediate seasonal weather events and the antecedent conditions accumulated over the preceding summer and autumn. Understanding the relative contributions of antecedent conditions and weather, as well as their interplay, to a location’s flood risk is essential for effective flood risk management. Using a lumped hydrological model and historical records, we will show that the elevated river flow along the Elbe was primarily influenced by high antecedent conditions. Using the tools and capabilities of NVIDIA’s Earth-2 platform we are able to create a large range of AI-generated weather events and combine them with different antecedent conditions for the winter 2023/24 season. We then use our hydrological models to assess the wide range of plausible river flooding. This approach allows us to answer the question of how severe flooding across the Elbe would have been in winter 2023/24 if major storms had occurred, and the likelihood of this scenario happening in future years.
How to cite: Ashcroft, J., Poulston, A., Koch, M., and Ertl, G.: Winter flooding in the Elbe - antecedent conditions vs seasonal weather, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6712, https://doi.org/10.5194/egusphere-egu25-6712, 2025.
Water resources in mountainous regions are affected by climate change, necessitating accurate hydrological simulations to provide plausible future scenarios for effective management. This study evaluates the added value of a high-resolution regional climate model (RCM) for projecting water resources under future climate scenarios. The RegIPSL regional Earth system model, was employed to simulate the European South-West (SWE3) domain at convection-permitting scale with a horizontal resolution of 3 km. Precipitation and temperature outputs were compared to those simulated by the model with a 20 km horizontal resolution. The model simulations are available for different 10-year time periods: an evaluation period (2000-2009), a historical period (1996-2005), and a future period with the rcp8.5 scenario (2041-2050). Bias correction was applied to model outputs, using the CDF-t method with in-situ observations and satellite-based data. The corrected datasets were used to force the HYPE and CemaNeige-GR4J hydrological models, simulating river flows in 16 river basins located in the different mountainous regions of Morocco. Result indicated that the high-resolution model significantly enhanced the simulation of hydrological variables in the different basins. The range of different basins considered allowed to characterize the model's efficiency in different aridity and topographic contexts, using hydrological signatures for each basin to analyze past performance and explore future scenarios. This research underscores the added value of convection-permitting models in advancing hydrological impact studies for complex terrains.
How to cite: Lahnik, O., Tramblay, Y., Hanich, L., Bastin, S., Ben Ahmed, A., Lguensat, R., and Andersson, J.: Hydrological evaluation of the convection-permitting regional climate model RegIPSL in Morocco, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10507, https://doi.org/10.5194/egusphere-egu25-10507, 2025.
In recent decades, West Africa has been increasingly exposed to hydrological droughts and floods. However, the extent to which these changes are related to climate change and are likely to persist during the 21st century remains poorly understood. To address this gap, this study integrates plausible regional climate change storylines, derived from the 6th phase of the Coupled Model Intercomparison Projects (CMIP6), into physically based hydrological modelling experiments utilising the latest high-resolution setup of Open Source LISFLOOD (OS-LISFLOOD). Despite some limitations over the Sahelian region, OS-LISFLOOD shows good performances in representing the hydrological cycle and specific characteristics of hydrological droughts and floods. While CMIP6 models consistently project warming temperatures over West Africa, greater zonal contrasts and model discrepancies are found in projected rainfall changes. Overall, CMIP6 models tend to project more (less) rainfall, as well as more (less) intense rainfall, over the eastern (western) region of West Africa. However, wetter (drier) conditions are projected over larger regions in CMIP6 models simulating weaker (stronger) warming in the North Atlantic and Mediterranean air surface temperatures. Future changes in hydrological droughts and floods mirror changes in precipitation patterns. In the 21st century, we find robust significant increases (decreases) in the magnitude (duration) of floods across West Africa. Meanwhile, reduced (increased) frequency and magnitude of longer (shorter) duration hydrological droughts are found in the eastern (western and coastal) regions of West Africa. Our study stresses the importance of considering future changes in hydrological droughts and floods for effective water resource management and risk reduction across this highly vulnerable region.
How to cite: Ekolu, J., Dieppois, B., Diop, S. B., Bodian, A., Grimaldi, S., Salamon, P., Villarini, G., Eden, J., Monerie, P.-A., Van de Wiel, M., and Tramblay, Y.: How could climate change affect the magnitude, duration, and frequency of hydrological droughts and floods in West Africa during the 21st century? A storyline approach , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11021, https://doi.org/10.5194/egusphere-egu25-11021, 2025.
Monitoring the qualitative and quantitative state of groundwater is a fundamental tool for investigating and preventing the effects of climate change and anthropic activities on water resources. This study represents the first investigation into the dependency of shallow groundwater temperature (GWT) on climate variability in the Turin metropolitan area (Piedmont, NW Italy).
First, a study of GWT and air temperature (AT) data on a regional scale in the time period 2010-2019 was carried out in order to understand the relationship between the two temperatures. It was possible to observe that GWT shows a general increase throughout the Piedmont Po plain, with a mean rise of 0.85 °C/10 years while AT has a mean increase of 1.69 °C/10 years.
Given these results, a 3D groundwater flow and heat transport model for the Turin metropolitan area (approximately 130 km2) has been developed using the Smoker/Heatflow numerical code. For building the model 2 different measurement campaigns of GWT in the area has been carried out during 2022.
The objective of the modelling was to better understand flow and heat transport dynamics in the shallow aquifer; moreover, a further aim was to analyse how climate change and the urban heat island of the city influences GWT, also from a forecasting perspective.
Following calibration of the model with the available data, future predictions has been made using AT data from different IPCC scenarios for the city of Turin. It has been chosen to use RCP 4.5 and RCP 8.5. The results showed for the RCP 4.5 scenario, the maximum GWT reached is 17.9 °C with an average increase of 1°C from 2022 to 2099; for the RCP 8.5 scenario the maximum GWT reached is 19.2°C with an average increase of 1.5°C from 2022 to 2099.
The development and application of this model has made possible to simulate variations in GWT on a local and city-scale in order to better understand how urban GWT will respond to the different climate scenarios in the perspective of better future management of the resource.
How to cite: Egidio, E., Lasagna, M., De Luca, D. A., Zaniboni, L., and Molson, J.: Groundwater temperature variations in the Turin metropolitan area (Piedmont, NW Italy): what is the future?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16529, https://doi.org/10.5194/egusphere-egu25-16529, 2025.
In recent years, consecutive drought years have affected large areas of the world, such as Europe in 2018-2020 and Northern America in 2020-2023. Drought conditions on any given year pose considerable risk to agriculture, forestry, ecosystems or water and energy supply. Multi-year droughts bear the potential to aggravate such impacts due to water deficit build-up over a longer period without insufficient recovery during wet seasons. Adaptation strategies usually work for limited time and rely on recovery periods (e.g., storage lakes). Multi-year droughts thus strongly challenge current drought preparedness, adaptation and mitigation measures.
With changing climate, droughts are projected to increase worldwide in duration and frequency. Due to legacy effects of depleted soils and self-intensification processes, the risk for full years of water deficits rises further. For well-informed adjustment of adaptation to multi-year droughts, a comprehensive assessment of their risks under current and future climate conditions is required. Therefore, it is crucial to assess the skill of climate models in simulating multi-year droughts globally.
In this study, we identify models that perform best over different hotspot and regions where models share high, or more worryingly low skill in representing multi-year droughts. We also assess the sensitivity of multi-year drought definition to different drought metrics used, including the 6-month and 12-month standardized precipitation evapotranspiration index (SPEI). For the analysis, we focus on a minimum duration of 12 months with SPEI < -1.
To sufficiently sample climate variability and produce large enough samples of extreme, multi-year droughts, we use a range of CMIP6 single-model initial condition large ensembles (SMILEs). SMILEs provide multiple runs of shared forcing and model configurations, but different starting conditions and evolutions that sample the range of internal climate variability. Here, SMILEs are evaluated regarding their capability to depict multi-year droughts against reanalysis data for the recent past (1991-2020), and based on this evaluation we provide projections of the change in multi-year droughts based on best-performing models. This work presents first results on regional and global scales, acknowledging internal climate variability of the representation of multi-annual droughts.
How to cite: Böhnisch, A. and Suarez Gutierrez, L.: Evaluation of multi-year droughts in global SMILEs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17357, https://doi.org/10.5194/egusphere-egu25-17357, 2025.
Climate change is increasing the uncertainty of global hydrological cycles, leading to an increase in extreme weather events such as heatwaves, droughts, and floods. While global and continental-scale hydrological analyses based on CMIP6 (Coupled Model Intercomparison Project Phase 6) climate change scenarios have been actively conducted, detailed analyses for specific regions are still lacking. Furthermore, comparing model predictions with observation data for the initial 10-year period (2015-2024) of climate change models is important for validating short-term forecast accuracy and enhancing the reliability of long-term climate prediction. This study evaluates the performance of the CMIP6 prediction models for air temperature, evapotranspiration, precipitation, and soil moisture during the 2015-2024 period under the SSP 2-4.5 and SSP 5-8.5 scenarios. To validate the scenarios, a comparison with GLDAS (Global Land Data Assimilation System) and ERA5-Land reanalysis data is executed. Subsequently, SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) were calculated for the early (2015-2040), mid (2041-2070), and late (2071-2100) periods of climate change, to analyse the intensity and frequency of future droughts. The results show that air temperature and evapotranspiration exhibited a strong correlation, while precipitation and soil moisture showed relatively weak correlations. Based on this study, quantifying bias in climate models can contribute to improving the performance of regional climate predictions. Furthermore, it is expected to provide important information for future climate change forecasting.
Keywords: Climate Change, CMIP6, Drought Indices, Monsoon Regions
Acknowledgement
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).
How to cite: Jeong, J., Lee, S., Kim, D., and Choi, M.: Prediction and Evaluation of Hydrological Factors and Drought Indices under Climate Change using CMIP6 in Monsoon Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14262, https://doi.org/10.5194/egusphere-egu25-14262, 2025.
Water security is crucial for achieving most Sustainable Development Goals, especially health, food production, energy, and climate resilience. Given the many links between water and other SDGs, focusing efforts on achieving the water goal would inevitably facilitate the achievement of the rest. From this perspective, it is feasible for Southern Mediterranean countries, including Morocco, to partially achieve the SDGs by adopting integrated water management systems, of which hydrological modeling is an important part. However, most modeling tools and their structure often show inconsistencies in application from one basin to another, which can be explained by two factors: first, the insufficient and inaccurate input data, and second, the inherent artifacts in the model structure as well as its incompatibility with the characteristics of the basin. In this work, we propose a modeling scheme that seeks to solve the two problems related to data scarcity and model insufficiency in arid and semi-arid regions. We used a multi-source data approach combined with a multi-model approach to forecast water flow in a set of twenty sub-catchments of the Oum Er-Rbia River Basin in central Morocco, we mainly calibrated, validated and tested the model sets parameters as well as their performance behavior. This modeling exercise will lead to a comprehensive understanding of the model transferability, stability, and adaptability according to its application catchment. Our analysis of model’s performances and outputs reveals spatial and temporal variation in the prediction results of each model, where the set of models was divided according to accuracy, stability, and adaptability into high-performance models along the study field (MOPEX3/2, topmodel, hymod, GR4J, and HBV), medium-performance models (sacramento, newzeland1/2, xinanjiang, and mcrm), and failed models (MOPEX1, tank/2, and collie1). The proposed modeling sceme not only enhanced the predictive skills in the study area, but it’s also formed the basis for investigating the characteristics of the targeted catchment and thus facilitated the process of selecting the most appropriate model for each basin. Additionally, the remotely sensed data products helped to solve the problem of data scarcity in poorly or ungauged basins.
How to cite: El Garnaoui, M., Boudhar, A., Nifa, K., El Jabiri, Y., and Karaoui, I.: Contribution of EO Large-sample hydrology data and multi-model approach in enhancing model stability and accuracy in arid and semi-arid regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15231, https://doi.org/10.5194/egusphere-egu25-15231, 2025.
As observed in the last years, extreme events, floods and droughts, have been reported to be more likely due to climate change and environmental modifications, and Italy in particular, experienced more frequent and intense drought events, with an exceptionally severe drought in 2022.
To cope with this kind of phenomena and to update the existing numerical modelling for water resource management, in 2021 the Po River District Basin Authority (AdBPo) started the implementation of the GEOframe modelling system on the whole territory of the district, aiming to provide a better quantification and forecast of the spatial and temporal water availability in its territory.
The GEOframe modelling system (Abera et al. (2017)) is a completely open-source semi-distributed conceptual model, developed by a scientific international community led by the University of Trento, characterized by a high modularity and flexibility.
The model, after the meteorological data spatial interpolation, and the geomorphological analysis, enables the simulation of all the components of the hydrological balance (e.g.: evapotranspiration, snow accumulation, water storage and water discharge).
The reference time period is the 1991-2020 time range and all the simulations processes take place in the “Hydrological Reference Units” (HRU), namely the subbasins obtained from the geomorphological analysis.
In this case the average area of the subbasins is set at 10 km2 (generating nearly 3000 HRUs), considered as a good compromise between the simulation precision and the computational space and time needed.
Consequently, the model parameters calibration was carried out according to a “zonal calibration” strategy in which the parameters are calibrated in different hydrometers. This is the most computational time-consuming phase of the model implementation, for this reason a 3 hydrological years period was selected, on the basis of water discharge data availability in the different regions of the district.
The calibration was carried out with the KGE method and consists in the research of the values of the characteristic model parameters which fit the discharge evolution recorded in the hydrometers in the best possible way, comparing the simulated discharge trend with the measured one.
The calibration of the model, as its implementation, has started in the Valle d’Aosta region, the most upstream part of the district, and proceeded going downstream, through Piemonte, Emilia-Romagna and Lombardia, for a total of about 150 calibrated hydrometers.
Due to the huge surface and the high complexity of the study area and of the hydrometers distribution, different actions and strategies have been tested to improve the calibration results and efficiency.
With the completion of the calibration phase, it was then analysed the impact of water scarcity on agriculture and taking a particular attention to the snow precipitation contribution.
In conclusion, thanks to the modularity of the GEOframe model, it was possible to work collaboratively on the calibration phase, lowering the time needed and improving the calibration efficiency, exchanging the results obtained and the strategy to calculate them and to carry out an analysis on water availability in the Po River Basin District up to Pontelagoscuro (FE).
How to cite: Roati, G., Brian, M., Tornatore, F., Azimi, S., Andreis, D., Formetta, G., Salehi, H., Baig, S., Rigon, R., and Wani, J. M.: The GEOframe system deployment in the analysis of water availability and scarcity in the Po River Basin District and model calibration strategies , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19472, https://doi.org/10.5194/egusphere-egu25-19472, 2025.
