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.

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.