Sustainable water resources planning and management is critical in fulfilling the demands of present and future generation in limiting environment. River basins plays a crucial role in balanced and responsible management strategies, as they frequently serve vital role in freshwater supply, irrigation, hydropower generation, industrialization and to support a balance ecosystem. In the era of climate change and adverse environmental impacts, it is required to prioritize assessment for sustainable development and management of river basins to ensure a resilient water system. In this study, the Tawa Basin has been selected which is one of the important tributary of Narmada Basin and has a paramount importance in irrigation and hydropower generation. From a hydrological standpoint, basin response to climate change is critical for analysing hydrological extremes and developing long-term plans and strategies for water-related activities and policies. The basin experiences significant rainfall fluctuation throughout the year, particularly during the monsoon season (June to September) which adversely influence the runoff generation in the basin and consequently, the likelihood of catastrophic occurrences. However, the hydrological response of Tawa basin to climate change has been rarely investigated under socio-economic pathways. Present work sought to evaluate the hydrological response of Tawa basin under changing climate using Coupled Model Inter-comparison Project (CMIP6) scenarios. In this study, a comparative assessment has been done with the application of two conceptual lumped hydrological model to ensure the robustness of the Hydrological model. For this, the MIKE 11 NAM and GR4J model has been set up for period of 2009 to 2021. The model is calibrated at the downstream of the Tawa reservoir using the water balance at reservoir scale. For climate change assessment, the latest CMIP6 outputs has been incorporated for two shared socio-economic pathways; SSP2-45 and SSP5-85 for near (2040-2060) and far (2070-2100) future. In addition, evaluation was performed using the individual and ensemble output of climate models to ensure the uncertainty in hydrological responses. The findings of this study are critical for understanding how climate change will alter the hydrology of the Tawa River Basin. This research might lead to the adoption of a strategic strategy for sustainable water resources and improved societal resilience to climate change in the Tawa River Basin.

How to cite: Badika, P., Raghuvanshi, A. S., and Agarwal, A.: Climate Change Impact Assessment on Hydrological response of Tawa Basin for Sustainable Water Management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-618, https://doi.org/10.5194/egusphere-egu24-618, 2024.

The evolving landscape of extreme rainfall patterns, triggered by climate change and global warming, introduces nonstationary behavior, challenging conventional hydrologic design assumptions rooted in stationarity. This study addresses this paradigm shift by modeling distribution parameters with covariates, utilizing a 70-year high-resolution IMD gridded dataset to extract and model extreme annual rainfall across diverse Indian cities. Drawing on previous research and goodness-of-fit tests that favor the Generalized Extreme Value (GEV) distribution for modeling extremes, the study incorporates various indices, including Nino3.4, dipole mode index, global and local temperature and time, to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming trends. Performance assessment utilizes the Akaike information criterion and Likelihood ratio test, while quantile reliability is scrutinized through confidence intervals (CIs). The findings uncover widespread nonstationary trends in most grid points, resulting in broader CIs for estimated quantiles, return periods, and covariates in fitted models. Despite the broader confidence bands associated with nonstationary conditions, indicating higher uncertainty, the results affirm a nonstationary pattern in rainfall extremes. Consequently, the study underscores the imperative to develop nonstationary models that effectively capture these dynamic trends with reduced uncertainty.

How to cite: Ankush, A., Goel, N. K., and Rajendran, V.: Addressing Nonstationarity in Extreme Rainfall Patterns: A Case Study on Indian Cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-858, https://doi.org/10.5194/egusphere-egu24-858, 2024.

In the Slovakia for assessment of climate change is used as references period hydrological years 1981 - 2010. Although the first observations in groundwater began in 1956, the monitoring network was not dense enough and the objects were not evenly distributed in Slovakia. Even the expansion of monitoring network in the 1970s was not enough to obtain longer, 30-year time series. Based on the new reference period 1991 - 2020 recommended by the World Meteorological Organization WMO, this period was also evaluated from the point of view of groundwater. The aim of the contribution was to compare reference periods 1981 – 2010 and 1991 - 2020 and their changes in the groundwater in Slovakia. These two reference periods were compared with each other based on the ratio values of long-term time series of minimum and average values of the groundwater level and springs yield. The new reference period 1991 - 2020 and period 1981 - 2010 were also evaluated by trend analysis using the non-parametric Mann-Kendall test. The Mann-Kendall statistical test was use to assess whether a set of data values is increasing or decreasing over time and whether the trend in either direction is statistically significant. The Mann-Kendall test does not assess the magnitude of change. The advantage of this test is, that it is not affected by the current distribution of the data and at the same time is less sensitive to extreme values in the time series.

                The long-term average values of the groundwater level and the springs yield for the period 1991 - 2020 compared to 1981 - 2010 were lower in the outer West and Northwest, in the Northern parts of Slovakia, in the region of central Slovakia, and in the Southeast and outer East. Based on the comparison of the long-term minimum values of the compared periods, the values were steady in compared to the previous period at most of the evaluated objects.

                When evaluating the trends of long-term averages for the period 1991 - 2022, significant decreases in the average groundwater level and springs yield were detected in the outer West and Northwest, in the strip from Northern to central Slovakia, in the North of Eastern Slovakia, and in the Southeast and outer east of the territory. When evaluating the trends of long-term minima for the period of hydrological years 1991 - 2022, the situation was similar to the evaluation of long-term averages.

                By comparing the period of 30 annual series 1991 - 2020 to the period 1981 - 2010 based on the evaluated ratio values, we did not notice significant deviations.

Keywords: groundwater, spring yield, references periods

How to cite: Kurejova Stojkovova, M. and Slivova, V.: Comparison of the reference periods of the hydrological years 1981 - 2010 and 1991 - 2020 in groundwater in Slovakia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1424, https://doi.org/10.5194/egusphere-egu24-1424, 2024.

Australia suffers from frequent droughts but future drought changes have remained stubbornly uncertain over the continent, with CMIP projections indicating low model agreement across most regions. Here we constrain future changes in drought over Australia by combining a hierarchy of projections from coupled global and regional climate models and several offline hydrological models that are widely employed in Australia. We analyse changes across multiple types of droughts (meteorological, hydrological and agricultural) to understand robustness of trends across drought types. Using this multi-projections approach, we identify robust future increases in drought across key agricultural and densely populated regions that are consistent across different drought types. Our study demonstrates value in analysing multiple projections together to build confidence in future changes in other regions of the world where model uncertainty is high.

How to cite: Ukkola, A., Vogel, E., Thomas, S., Bende-Michl, U., Pitman, A., and Abramowitz, G.: Future drought changes in Australia from multiple projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1920, https://doi.org/10.5194/egusphere-egu24-1920, 2024.

The change of groundwater storage (GWS) on the Tibetan Plateau (TP) is vital for water resources management and regional sustainability, but its estimation has large uncertainty due to insufficient hydrological measurements and diverse future climate scenarios. Here, we employ high-resolution land surface modeling, advanced satellite observations, global climate model data, and deep learning to estimate GWS changes in the past and future. We find a 3.51±2.40 Gt yr^-1 increase in GWS from 2002–2018, especially in exorheic basins, attributed to glacier melting. The GWS will persistently increase in the future, but the growth rate is slowing down (0.14 Gt yr^-1 for 2079–2100). Increasing GWS is projected over most endorheic basins, which is associated with increasing precipitation and decreasing shortwave radiation. In contrast, decreasing GWS is projected over the headwaters of Amu Darya, Yangtze, and Yellow river basins. These insights have implications for sustainable water resource management in a changing climate.

How to cite: Wang, L. and Jia, B.: The slowdown of increasing groundwater storage in response to climate warming in the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2331, https://doi.org/10.5194/egusphere-egu24-2331, 2024.

Large-scale revegetation presents a new set of challenges by augmenting water consumption in arid regions, despite its positive impact on ecosystems. In water-stressed areas, where precipitation is the primary source of water, extensive afforestation may disrupt the balance between water supply and demand. Consequently, it is crucial to assess the maximum extent of vegetation coverage and productivity that can be sustained by rainwater resources. Our study characterizes the sustainability of revegetation by determining the upper limit of the leaf area index (LAI) supported by rainwater resources. The research focuses on the Loess Plateau (LP), a region known for both large-scale vegetation restoration and severe water shortages. The upper limit on LAI is computed based on evapotranspiration (ET) supported by rainwater resources, utilizing an optimized Shuttleworth-Wallace (S-W) model that incorporates dynamic vegetation and carbon dioxide components. Carbon sequestration capacity and efficiency are compared between the maximum and actual vegetation scenarios using an analytical water use efficiency (WUE) model. Both models exhibit good performance and align with empirical observations. Results indicate that under the maximum vegetation scenario, the LAI is 11.5% higher than the actual scenario when vegetation on the LP is restored to its maximum level. The average gross primary productivity under the maximum vegetation scenario surpasses the actual scenario by 25.0%, with a 17.9% increase in ecosystem WUE. It is important to note that the maximum scenario represents a theoretical upper limit based on ideal assumptions. The findings emphasize that enhancing rainwater utilization efficiency can unlock the potential for sustainable vegetation restoration, improving its efficiency. This study provides valuable guidance and theoretical support for planning vegetation restoration in water-scarce regions.

How to cite: Zhang, B., Tian, L., and Li, Y.: Estimating the maximum vegetation coverage and productivity capacity supported by rainwater resources on the Loess Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2402, https://doi.org/10.5194/egusphere-egu24-2402, 2024.

A favorable environment can induce vegetation overgrowth to exceed the ecosystem carrying capacity, exacerbating water resource depletion and increasing the risk of lagged effects on vegetation degradation. This phenomenon is defined as structural overshoot, which can lead to large-scale forest mortality and grassland deterioration. However, the current understanding of structural overshoot remains incomplete due to the complex time-varying interactions between vegetation and climate. Here, we used a dynamic learning algorithm to decompose the contributions of vegetation and climate to drought occurrence, trace the connection between antecedent and concurrent vegetation dynamics, thus effectively capturing structural overshoot. This study focused on the climate-sensitive hotspot in Northwest China drylands, where significant vegetation greening induced by a warming and wetting climate was detected during 1982–2015, leading to soil moisture deficit and aggravating vegetation degradation risks during droughts. We found that during this period, structural overshoot induced approximately 34.6% of the drought events, and lagged effects accounted for 16.7% of the vegetation degradation for these overshoot drought events. The occurrence of overshoot droughts exhibited an increasing trend over time, which was primarily driven by vegetation overgrowth followed by precipitation variation. Although the severity of overshoot and non-overshoot droughts were generally comparable in spatial distribution, the impact of overshoot droughts is still becoming increasingly obvious. Our results indicate that the expected intensified overshoot droughts cannot be ignored and emphasize the necessity of sustainable agroecosystem management strategies.

How to cite: Liu, L., Zhang, Y., Cheng, Y., An, Q., and Kang, S.: Intensified Structural Overshoot Aggravates Drought Impacts on Dryland Ecosystems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2953, https://doi.org/10.5194/egusphere-egu24-2953, 2024.

Hydrologic cycle has wide impacts on the ecosystem, atmospheric circulation, ocean salinity and circulation, and carbon and nitrogen cycles. Under anthropogenic global warming, previous studies showed that the intensification of the hydrologic cycle is a robust feature. Whether this trend persists in hothouse climates, however, is unknown. Here we show that mean precipitation first increases with surface temperature, but it decreases with surface warming when the surface is hotter than ~320-330 K. This non-monotonic phenomenon is robust to the warming trigger, convection scheme, ocean dynamics, atmospheric mass, planetary rotation, gravity, and stellar spectrum. The weakening is because of the existence of an upper limitation of outgoing longwave emission and the continuously increasing shortwave absorption by H₂O, and is consistent with the strong increase of atmospheric stratification and dramatic reduction of convective mass flux. Our results have wide implications for the climates and evolutions of Earth, Venus, and potentially habitable exoplanets.

How to cite: Liu, J., Yang, J., Ding, F., Chen, G., and Hu, Y.: Reversal of Precipitation Trend in Hothouse Climates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3504, https://doi.org/10.5194/egusphere-egu24-3504, 2024.

Changes in future climate are inevitable, strongly impacting water resources and their adaptation strategies. Northern Peru currently faces water scarcity issues, significantly limiting activities such as agriculture, a key economic and social pillar for the region. In addition, there are pronounced fluctuations in precipitation due to the effects of the El Niño phenomenon (ENSO), along with scarcity of information regarding various climatic variables in terms of quantity and quality, making it challenging to accurately account for the resources available for proper management.

Therefore, this research aims to, through cluster analysis, assess the importance of various variables such as elevation, precipitation, maximum and minimum temperature and stationarity index, for the period 1972-2015, in determining the hydrologically homogeneous regions currently present in the sub-basins of the northern region. Various multivariate cluster methods such as hierarchical clustering, partitioning around medoids (PAM), and K-means have been used for this purpose. To assess the validity of the analysis, Hopkins statistics and visual inspection methods were employed. Additionally, various validation tests, including internal and stability measures, were applied to evaluate the effectiveness of the cluster algorithm results. Alongside this analysis, a trend study of precipitation has been conducted, helping identify regions that may be experiencing changes in their rainfall patterns. This trend study was performed using the non-parametric Mann-Kendall test on the 49 stations located in the region. The same procedure was also applied to climate models with projections of precipitation and maximum and minimum temperatures up to the year of 2100 to understand the future behavior due to climate change.

Comparing hydrologically homogeneous regions and potential trend changes found between the current and future climate change situations would help identify potential areas where the analyzed climatic variables undergo significant changes. This would aid in identifying potential adaptation measures, since these variables are crucial for determining water availability.

How to cite: Chavez, A., Quevedo, V., Bravo, D., and Chapilliquen, D.: Evaluating the impact of climate change in Northern Peru by analyzing homogeneous regions based on different climate variables and precipitation trend changes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3690, https://doi.org/10.5194/egusphere-egu24-3690, 2024.

Reservoirs have made significant contributions to human access and management of surface water resources, as well as to the production of clean energy, thus playing a vital role in alleviating the water crisis and decarbonizing energy systems through hydropower generation. The rapid growth in reservoir construction has led to an increase in the surface water area, consequently escalating evaporative water losses. As a crucial component of water cycle, most estimation methods for evapotranspiration focus on the land surface, with a relatively rough estimate of the significant evaporation loss from open surface water. Meanwhile, limited by the availability of reservoir geographic information and monthly area series data, there is still a lack of comprehensive and accurate estimation of reservoir evaporation losses. As the country with the largest number of dams in the world, it is necessary to accurately estimate China's surface water evaporation losses associated with its prosperous and developing dam construction. Here, we used the China Reservoir Dataset and LandSAT based Global Surface Water Dataset to reconstruct the monthly area series of 4874 reservoirs in China from 1984 to 2020, and further considering the heat storage of water bodies, the monthly evaporation losses of these reservoirs from 1988 to 2018 were estimated. The results indicate that the average annual evaporation volume of these 4874 reservoirs is 18.55 × 10⁹ m³, equivalent to 31% of the total domestic water consumption of China (in 2021). During the research period, the evaporation rate shows a significant growth trend (p < 0.05, 0.15km³/year), attributed to the upward trend in evaporation rate (p < 0.05, 0.0046 mm/d/year) and total reservoir area (p < 0.05). The differences in economic development level and reservoir size result in significant spatial heterogeneity in the evaporation loss trend in different regions. The results can serve as a useful reference for water resources management and sustainable utilization.

How to cite: Zhu, Y. and Ye, A.: Estimation of Reservoir Evaporation Water Loss in Inland China over the Past 30 Years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3901, https://doi.org/10.5194/egusphere-egu24-3901, 2024.

As our climate system climbs through its current warming path, temperature and precipitation are greatly affected also in their extremes and there is a general concern about the effects on river floods. While a wide body of literature on the detection of flood changes is available, the identification of their underlying causes (i.e. flood change attribution) is still debated. In this work, we aim at better understanding how floods of different kind are related to climate extremes (of precipitation and temperature) and how these extremes are related to large scale predictors (e.g. climate oscillations, teleconnections). The study area is the Greater Alpine Region, which is an ideal laboratory for analysing complex effects of climate on floods because of the interplay of heavy precipitation and snow processes in controlling flood generation, and also because the European Alps divide the Mediterranean and Continental Europe with different responses to climate oscillations. Through a novel integrated modeling chain, we aim at identifying the climate extreme indices that better relate to river floods, the large-scale climate phenomena controlling their dynamics, their expected modifications due to climate change and the associated uncertainties. The research plan of a multidisciplinary team of climatologists and hydrologists will be presented together with preliminary results. We believe that this research will strengthen our knowledge on flood risk in the future and contribute to improve existing methods for disaster risk assessment and management.

How to cite: Viglione, A., Arnone, E., Corti, S., Ferguglia, O., Giuntoli, I.,  von Hardenberg, J., Lombardo, L., Mazzoglio, P., and Palazzi, E.: Mapping of climate to flood extremes in the European Alps: a multidisciplinary approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4014, https://doi.org/10.5194/egusphere-egu24-4014, 2024.

Long-term extending cultivation activities resulted in the world’s worst soil erosion on the Chinese Loess Plateau. By converting cropland into vegetated land, the Grain for Green Project (GfGP)—the world’s largest investment revegetation project—effectively alleviates the soil erosion on the  Loess Plateau. However, during the GfGP implementation, the positive effect of cropland to the revegetation and soil erosion control has been underestimated to date, hindering a comprehensive evaluation to the effect of cropland on ecological restoration. Here, we evaluated the effect of the GfGP on soil erosion control across the  Loess Plateau, analyzed the dominant driver of the  Loess Plateau vegetation greening, and further identified the contributions of croplands to this world’s largest revegetation project. We found that the vegetation of the  Loess Plateau was significantly improved and its leaf area increased by 1.23 × 10⁵ km² after the implementation of the GfGP, which contributed 42% to the decrease of the  Loess Plateau soil loss. Among them, our results show that cropland contributed 39.3% to the increased leaf areas of the  Loess Plateau, higher than grassland (36.3%) and forestland (14.3%). With the reduction of agricultural area, the contribution of cropland to the increased leaf areas in the  Loess Plateau was still the largest, which was mainly due to the increase in cropland utilization intensity. This study highlights the significance of the GfGP in soil erosion control and revises our understanding of the role of cropland in ecological restoration and society development.

How to cite: Lan, X. and Liu, Z.: Land-use Intensity Reversed the Role of Cropland in Ecological Restoration over the World's Most Severe Soil Erosion Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4397, https://doi.org/10.5194/egusphere-egu24-4397, 2024.

Extreme events such as floods and droughts are expected to become more frequent and intense due to the changing climate. However, it is still a challenge to monitor, understand, simulate, and anticipate the underlying hydrological processes. Large scale hydrological models and remote sensing observations (such as surface soil moisture from SMOS, SMAP and Sentinel, as well as total water storage changes (TWSC) from the GRACE and GRACE-FO gravity missions) provide a unique large-scale to global view on the changing hydrology. Sequential Calibration and Data Assimilation (CDA) provides opportunities to combine benefits from both, modelling and observing, and thus helps to improve our understanding of the impact of the climate change on water resources.

In this study, we present how the careful and consistent processing of GRACE/-FO data including a consistent estimation of the full error covariance matrix to represent the typical spatially correlated error structure supports the assimilation of satellite data into large-scale hydrological models. The first case study will tackle the question of selecting an appropriate multi-sensor data assimilation approach to combat the temporal and spatial resolution mismatch between data and model for high-dynamic frequencies. For this, daily GRACE data are assimilated for the Brahmaputra Basin that was subject to major floods, e.g., in 2004, 2007 and 2012. A reconstruction of these flood events allows a better understanding of the benefits and limitations of large-scale hydrological CDA in a changing climate. The second case study focuses on quantifying human-induced impacts on surface and groundwater storages under prolonged and intense droughts. Here, we assimilate two decades of monthly GRACE/-FO data for the Murray-Darling Basin, Australia, to better understand the impact of dry climatological conditions on our water resources.

How to cite: Schumacher, M., Retegui Schiettekatte, L., Yang, F., and Forootan, E.: The AAU Calibration and Data Assimilation (CDA) approach for improving large-scale hydrological models in a changing climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5467, https://doi.org/10.5194/egusphere-egu24-5467, 2024.

The estimation of actual evapotranspiration (ET) using both rescaled and non-rescaled complementary relationship (CR) models has become a hotspot in the research on terrestrial ET. This study explores the relationship between these two CR models and improves the method for calculating x_min in rescaled CR models. The rescaled and non-rescaled CR models can be functionally interconvertible, i.e., the non-rescaled CR model enables the rescaled simulation of ET and the rescaled CR model can also conduct a non-rescaled simulation. The parameter b or c in the non-rescaled CR models plays a role similar to x_min in the rescaled models. Based on the data from 15 catchments in the Loess Plateau of China, we validate this relationship between the two CR models. Meanwhile, we evaluate the formulation for x_min proposed by Crago et al. (2016) (the Crago’s x_min values) and the results show that the range of variation for the Crago’s x_min values is smaller than that for the x_min values obtained by inverse method from the models (the inversed x_min values) in the interannual process. The inversed x_min values of RCR-C2016 are mostly larger than that of RCR-S2017, while the Crago’s x_min values are in between these two values. The empirical function for x_min is developed using the aridity index (AI) and normalized difference vegetation index (NDVI) as independent variables in the interannual fluctuations. On the mean annual scale, the empirical function for x_min is expressed only using the AI. Cross-validation results show that the rescaled CR models combined with x_min determined by the empirical functions can more accurately estimate ET and simulate its interannual and spatial changes. (Supported by Project 41971049 of NSFC)

How to cite: Mu, Z., Liu, W., Ma, N., Cheng, C., and Zhou, H.: Relations of rescaled to non-rescaled complementary models and improvement of evapotranspiration estimates by incorporating both climatic and land surface conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6983, https://doi.org/10.5194/egusphere-egu24-6983, 2024.

The global streamflow plays a crucial role in the broader water cycle, intricately linked to human activities, ecology, and agriculture. The rise in atmospheric CO₂ has complex effects on global streamflow. In addition to feedbacks to climate change, CO₂ impacts on streamflow result from surface changes, including reduced streamflow induced by expanding vegetation and increased streamflow induced by reduced vegetation evapotranspiration due to stomatal closure. Global models, vital for policy planning, predict increased streamflow due to dominant positive impacts of elevated CO₂. More than 10 out of 14 global dynamic vegetation models concluded that increased CO₂ exacerbated runoff growth over the 1981-2020 period, especially in tropical and temperate regions. Yet, studying four decades of observed streamflow data, we find these models largely overestimate the increase in streamflow induced by elevated CO₂, particularly in tropical forest (tropical) and cold forest (cold), pointing to an unexpectedly drier world.

How to cite: Wei, H., Zhang, Y., Liu, C., and Huang, Q.: Global models overestimate streamflow induced by rising CO2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7046, https://doi.org/10.5194/egusphere-egu24-7046, 2024.

In order to compare the impacts of the choice of land surface model (LSM) parameterization schemes, meteorological forcing, and land surface parameters on land surface hydrological simulations, and explore to what extent the quality can be improved, a series of experiments with different LSMs, forcing datasets, and parameter datasets concerning soil texture and land cover were conducted. Six simulations are run for mainland China on 0.1^o×0.1^o grids from 1979 to 2008, and the simulated monthly soil moisture (SM), evapotranspiration (ET), and snow depth (SD) are then compared and assessed against observations. The results show that the meteorological forcing is the most important factor governing output. Beyond that, SM seems to be also very sensitive to soil texture information; SD is also very sensitive to snow parameterization scheme in the LSM. The Community Land Model version 4.5 (CLM4.5), driven by newly developed observation-based regional meteorological forcing and land surface parameters (referred to as CMFD_CLM4.5_NEW), significantly improved the simulations in most cases over mainland China and its eight basins. It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations, and it decreased the root-mean-square error (RMSE) from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations. This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

How to cite: Liu, J., Jia, B., and Xie, Z.: Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7416, https://doi.org/10.5194/egusphere-egu24-7416, 2024.

How to cite: Huang, P. and Ducharne, A.: The impact of increasing atmosphere CO2 concentration on hydrological trends in France over the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7788, https://doi.org/10.5194/egusphere-egu24-7788, 2024.

Rainfall interception loss (E_i) is one of the biggest unknowns in the global hydrological cycle. As a dynamic process, E_i depends on vegetation structure and canopy characteristics, but also on the precipitation and (micro)climatic conditions that determine the atmospheric demand for water. The spatial variability of these factors makes it difficult to reliably estimate E_i over large scales, and its sensitivity to non-stationary climate variability renders E_i trends highly uncertain. In this regard, process-based formulations accounting for key biophysical and climatic factors provide unique opportunities to examine the global patterns of E_i, as well as the driving mechanisms behind its variability.

Here we explore the estimates of E_i from the Global Land Evaporation Amsterdam Model (GLEAM v3; Martens et al., 2017), the Penman-Monteith-Leuning (PML v2; Zhang et al., 2019) model, and a recently proposed and validated global application constrained by a synthesis of global experimental data (Zhong et al., 2022). All three methods estimate long-term E_i based on Gash-type formulations (Valente et al., 1997; Van Dijk and Bruijnzeel, 2001). To reduce uncertainty, a multi-product approach is applied to examine the spatial-temporal trends in E_i. Moreover, we focus on a well-validated model (Zhong et al., 2022) to further isolate the relative contributions of precipitation, vegetation and evaporative demand to E_i variability. We find that E_i, described both in terms of the volume of evaporated water and as a percentage of precipitation, exhibits increasing trends globally. Contrasting regional changes are found, however, with a significant increase over Eurasia where the strongest vegetation greening occurs, and decreases over the Congo basin driven by a decline in precipitation. At decadal timescales, the increasing E_i is largely driven by global vegetation greening through an increase in the intercepting surface and storage capacity, while its inter-annual variations are mainly controlled by changes in precipitation. Moreover, the positive contribution of evaporative demand should not be overlooked, given the ubiquitous rise in global potential evaporation driven by atmospheric warming.

References

Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903– 1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.

Valente, F., David, J., and Gash, J.: Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models, J. Hydrol., 190, 141–162, https://doi.org/10.1016/S0022-1694(96)03066-1, 1997.

Van Dijk, A. and Bruijnzeel, L.: Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description, J. Hydrol., 247, 230–238, https://doi.org/10.1016/S0022-1694(01)00392-4, 2001.

Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.: Coupled estimation of 500m and 8day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.

Zhong, F., Jiang, S., van Dijk, A. I., Ren, L., Schellekens, J., and Miralles, D. G.: Revisiting large-scale interception patterns constrained by a synthesis of global experimental data, Hydrol. Earth Syst. Sci., 26(21), 5647-5667, https://doi.org/10.5194/hess-26-5647-2022, 2022.

How to cite: Zhong, F., Jiang, S., Koppa, A., Ren, L., Liu, Y., Wang, M., and Miralles, D. G.: Multi-decadal change in global rainfall interception and its drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7987, https://doi.org/10.5194/egusphere-egu24-7987, 2024.

The Base-Flow Indices (BFI), indicating surface and groundwater interaction, play a significant role in the hydrological cycle. The values vary widely across the globe, and are expected to change in the context of a changing climate. Predicting global “natural” BFI is challenging due to limited observations reflecting natural impacts. This study aims to predict annual BFI and their trends for a compiled dataset of annual streamflow and meteorological data covering the period 1982-2020 for more than 2250 small unregulated catchments worldwide. BFI were derived using three digital filtering methods, resulting in a trend of -0.0009 ± 0.01 per decade over the last four decades. To predict annual BFI and their trends in ungauged catchments, a Random Forest Regression approach was employed, incorporating static attributes and meteorological time series data as model inputs. Five-fold cross-validation demonstrated the effectiveness of the Random Forest Regression in predicting both BFI and their trends. The Quantile Regression Forest method was utilized to quantify the uncertainty, achieving a relatively low range for both BFI and their trends. Soil conditions and maximum temperature emerged as the most crucial variables for predicting BFI, while temperature-related variables also proved essential for predicting the BFI trends. The goal is to extend the understanding of "natural" BFI and their trends in ungauged catchments, as the Random Forest Regression model was trained under unregulated conditions. This study offers the possibility to predict "natural" BFI and their trendsacross the globe. This could support water authorities in managing water resources, particularly concerning base flow.

How to cite: Huang, Q., Seibert, J., and Zhang, Y.: Predicting annual base flow index and its trends using a large sample of dataset across the globe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8134, https://doi.org/10.5194/egusphere-egu24-8134, 2024.

The Sahara desert was potentially much wetter and vegetated in the past during the warm African Humid Period. Although debated, this climatic shift is a possible scenario in a future warmer climate. The most prominent reported evidence for past green periods in the Sahara is the presence of paleo-lakes. Even today, Saharan desert lakes get filled from time to time. However, very little is known about these events due to the lack of available in-situ observations. In addition, the hydrometeorological conditions associated with these events have never been investigated in a dedicated climatological approach. This study proposes to fill this knowledge gap by investigating the meteorology of lake-filling episodes (LFEs) of Sebkha el Melah – a commonly dry lake in the northwestern Sahara. Heavy precipitation events (HPEs) and LFEs are identified using a combination of precipitation observations and lake volume estimates derived from remote sensing satellite imagery. Weather reanalysis data is used together with three-dimensional trajectory calculations to investigate the moisture sources and characteristics of weather systems that lead to HPEs and to assess the conditions necessary for producing LFEs. Results show that hundreds of HPEs occurred between 2000 and 2021, but only 6 LFEs eventuate. The runoff coefficient, i.e. the ratio between the increase in lake water volume during LFEs and precipitation volume during the HPEs that triggered the lake-filling, ranges five orders of magnitude and is much smaller than the figures often cited in the literature regarding this arid area. We find that LFEs are generated most frequently in autumn by the most intense HPEs, for which the key ingredients are (i) the formation of surface extratropical cyclones to the west of the Atlantic Sahara coastline in interplay with upper-level troughs and lows, (ii) moisture convergence from the tropics and the extratropical North Atlantic, (iii) a premoistening of the region upstream of the catchment over the Sahara through a recycling-domino-process, (iv) coupled or sequential lifting processes (e.g., orographic lifting and large-scale forcing), and (v) the stationarity of synoptic systems. Based on the insights gained into Saharan LFEs in the present-day climate, future studies will be able to better assess the mechanisms involved in the greening of the Sahara in the past and also in a warmer future.

How to cite: Armon, M., Rieder, J. C., Dente, E., and Aemisegger, F.: The hydrometeorological ingredients needed to fill dry Saharan lakes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9824, https://doi.org/10.5194/egusphere-egu24-9824, 2024.

River flow is an essential component of the global water cycle and arguably the best monitored variable in land hydrology. Both anthropogenic climate change as well as direct human influences on the terrestrial environment influence river flow at local to planetary scales. Here, we survey recent scientific advances in quantifying past trends and projecting future changes in global river flow, with focus on trends in flow volumes and seasonality. Previously published evidence of past changes is complemented by an analysis of changes in river flow using in-situ observations and river discharge estimates from a global re-analysis product. Available literature on future projections is accompanied by an analysis of Global Climate Model projections routed through the global river network.

Systematic patterns of past changes in river flow emerge at the regional to global scales, despite significant uncertainties and small-scale spatial variability. These uncertainties are related e.g. to differences in study periods, considered indicators of change, availability of in-situ observations, and uncertainties of model-based reconstructions. Some of the most pronounced changes in past river flow include increasing river flow in the northern high latitudes and robust decreasing trends in significant parts of central and south America, the Mediterranean region, the southern tip of Africa as well as central and southern Australia. In other world regions, available in-situ observations are sparse or there is conflicting evidence, meaning that trends are still uncertain. We further show systematic shifts in the river flow seasonality with a tendency for earlier streamflow and a dampened seasonal cycle in across many parts of the world likely to due to higher temperatures and earlier snowmelt, with later streamflow in the Mediterranean linked to changes in rainfall.

Climate model driven assessments of future changes in river flow suggest that total global discharge to the oceans may increase with global warming, albeit with large regional differences in the direction of change and substantial model uncertainty. Across several studies based on different model ensembles, there is consensus for increasing river flow in the northern high latitudes and some evidence for increasing river flow in tropical Africa, the Indian subcontinent and eastern and tropical Africa. Finally, we highlight regions in which past changes in river flow are consistent with future projections, which include but are not limited to increasing river flow in northern North America and northern Europe, as well as drying tendencies in the Mediterranean, the southern tip of Africa and Southern Australia. The consistency between model projections and historical trends in these regions gives confidence to future water management decisions, while disparities between historical trends and projections highlights regions where better understanding of the processes governing past and future change in river flow will be required moving forward.

How to cite: Gudmundsson, L., Brunner, M., Döll, P., Fluet-Chouinard, E., Gosling, S. N., Hirabayashi, Y., Müller Schmied, H., Slater, L., Stein, L., Wasko, C., Yamazaki, D., and Zhou, X.: A survey of past and future changes in global river flow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10051, https://doi.org/10.5194/egusphere-egu24-10051, 2024.

Changes in vegetation are expected to influence terrestrial water and energy fluxes; however, the impacts of vegetation changes on water availability remain controversial. In this study, we applied the Community Land Model, version 4.5 (CLM4.5) coupled with the Variable Infiltration Capacity (VIC) hydrological parameterizations to the upper Yellow River Basin (UYRB), which is the most important water conservation area in the Yellow River Basin, to investigate the impacts of vegetation changes on water availability in the area. The results showed a pronounced greening trend in the UYRB from 1982 to 2018, resulting in increased evapotranspiration, decreased runoff, drier soil conditions, and decreased water yield. The water reduction effect of vegetation greening is more pronounced in water-limited areas than in energy-limited areas. This study highlights the diverse hydrological responses to vegetation changes under different climatic conditions and land cover types. It is crucial for ecological restoration policies in China to recognize these distinctions and their potential negative impacts on water availability, especially in water-limited regions.

How to cite: Wang, Y., Wang, G., Deng, X., and Ruan, Y.: Assessing impacts of vegetation changes on water availability in the upper Yellow River Basin, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10053, https://doi.org/10.5194/egusphere-egu24-10053, 2024.

Understanding long-term changes in evapotranspiration and their drivers is crucial due to direct impacts on water availability. Increasing evapotranspiration rates can exacerbate droughts and jeopardise water availability, especially in the summer months with higher water demands. Uncertainties of multi-decadal variations in evapotranspiration at local to regional scale and their drivers are, however, still large. In this data-based study, we derive changes in evapotranspiration from the catchment water balance for a large number of catchments in Central Europe over 1982–2016. We further analyse changes in potential drivers including vegetation and land cover based on a remote-sensing derived vegetation index and a land cover product, water availability based on changes in seasonal precipitation and available energy and atmospheric demand based on changes in reference evapotranspiration. We find wide-spread increases in catchment evapotranspiration until about the year 2000 and only small changes with a decreasing tendency after 2000. The observed variations in regional evapotranspiration are significantly correlated with variations in precipitation, reference evapotranspiration and vegetation activity. High evapotranspiration around 2000 can be related to high values of reference evapotranspiration, precipitation and vegetation activity. Lower evapotranspiration in the early 1980s despite relatively high precipitation is linked to lower values of reference evapotranspiration and vegetation activity, while the halt of further evapotranspiration increases after 2000 despite high values of reference evapotranspiration may be explained by low precipitation. The study contributes to expand our knowledge on the drivers of changes in the water balance in Central Europe over recent decades, which is of great importance for water management in a changing climate.

How to cite: Duethmann, D., Bruno, G., and Strohmenger, L.: Drivers of changes in catchment evapotranspiration in Central Europe over the past 40 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10062, https://doi.org/10.5194/egusphere-egu24-10062, 2024.

Heatwaves – short-term periods of anomalous warmth – can play an outsized role in shaping downstream water resources as they impact the timing and magnitude of snow and glacier melt.  Melt-driven runoff enhanced during spring heatwaves is particularly important as it can cause downstream flooding and damage.  It is well understood that heatwaves will become more frequent and more severe in the future due to climate change; however, it is not well understood how the hydrological response to heatwaves will change in the future.

Here, we quantify the streamflow response to heatwaves over the past century across Western Canada.  We investigate how such streamflow responses vary in space, time, by streamflow regime, and by hydroclimate conditions, and we present a simple mathematical framework to partition the responses into seasonal and heatwave-driven components.  We use freshet timing and winter snowfall as metrics that are expected to change under climate change, and we compare how streamflow responses to heatwaves differ between baseline historical years (later freshet and more snowfall) and future proxy years (earlier freshet and less snowfall). 

We find that in future proxy years, the streamflow response to spring heatwaves is diminished when seasonal streamflow is enhanced, indicating that peak streamflow during heatwaves does not necessarily increase under climate warming.  We also find that the proportion of spring streamflow generated by heatwaves is lessened relative to seasonal streamflow, and this proportion is diminished as the freshet progresses.

Our results contextualize how the streamflow responses to heatwaves have varied over the past century, to better understand how they may change in the future.  Importantly, our findings have implications for future heatwave-driven flooding in nival and glacial basins at both regional and global scales, and we present novel observational signals of change in heatwave-driven streamflow that can be further investigated by future modelling studies.

How to cite: Anderson, S. and Chartrand, S.: Spatiotemporal variability of heatwave-driven streamflow in nival and glacial basins: Future insights from a century of observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11575, https://doi.org/10.5194/egusphere-egu24-11575, 2024.

Tree planting to mitigate climate change has become a popular topic in recent years. While it has been widely reported that tree planting reduces annual water yield, it is not clear how tree planting affects a catchment’s storm runoff response for events of different magnitudes. China’s “Grain for Green Program” almost doubled the vegetation cover on the Loess Plateau two decades ago, and thus represents a large-scale experiment revealing the impact of tree planting on hydrological processes. Here we show how the storm runoff response to rainfall has changed as a result of tree planting using five sub-catchments in a 26,000 km² large basin that received different degrees of afforestation. Our dataset covers over 40 years of daily streamflows, allowing us to use new nonlinear Ensemble Rainfall-Runoff Analysis techniques to quantify the runoff response to rainfall events of different intensities. We find that after tree planting, the runoff response peak decreased up to 86%, proportional to the percentage increase in the Leaf Area Index (LAI). This attenuation of peak runoff is much larger than that of the decrease in average growing season runoff (59%). Surprisingly, the largest attenuation in peak runoff response occurs during high-intensity rainfall events rather than low-intensity rainfall events. This observation implies that the main mechanisms reducing runoff response cannot be increased canopy interception or soil moisture depletion, because these would be expected to have a larger effect on low-intensity events. Instead, we hypothesize that the main mechanisms are likely to be reductions in runoff-generating areas and increases in infiltration rates. Consistent with this hypothesis, low flows (i.e., Q95) do not decrease, but instead increase up to 25%, with the largest increases in low flows occurring in sub-catchments with the largest percentage increases in LAI. These findings highlight the positive effect of tree planting on reducing storm runoff peaks and increasing low flows, coincident with the reduction in annual water yield that has been widely reported in other studies. These substantial and persistent hydrological consequences of tree planting can inform future efforts at climate change mitigation through vegetation management.

How to cite: Liu, S., Seybold, H., van Meerveld, I., Wang, Y., and W. Kirchner, J.: Tree planting attenuates storm runoff response on the Chinese Loess Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13209, https://doi.org/10.5194/egusphere-egu24-13209, 2024.

Understanding the hydrological impacts of climate change is essential for robust and sustainable water management. This study assessed the hydrologic impacts of climate change in the Jinshajiang River basin, the source region of the Yangtze River, using the historical observations and the future hydrologic simulations under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), deriving from a hydrological model. For the historical period, there is an increasing trend in precipitation, evapotranspiration, snowmelt, and consequently an increasing in streamflow in the upstream, whereas a decreased streamflow in the downstream catchment. For future scenarios, a warmer and wetter climate was projected for the basin throughout the 21st century, and correspondingly an overall increase in mean and extreme streamflow, with a larger magnitude in the far future than in the near future, and under SSP5-8.5 than SSP2-4.5. The projected remarkable increase in precipitation cause the transition in changing trend of streamflow compared with the historical period. The projected continuing decline in snowfall and snow water equivalent result in a significant advance and decrease in snowmelt, followed by an earlier and more concentrated peak streamflow in July, especially for the upstream catchment. Ultimately, reservoirs in the basin are expected to gain more inflows, however, with larger variability and more floods and hydrological droughts, which impose potential challenges on reservoir operations. These outcomes indicate the importance of adaptive water resources management in the melting water contributed basin to sustain and enhance its services under global warming.

How to cite: Xu, H., Qin, P., Xia, Z., Liu, L., Wang, Q., and Xiao, C.: Hydrologic responses to climate change and implications for reservoirs in the source region of the Yangtze River, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13611, https://doi.org/10.5194/egusphere-egu24-13611, 2024.

Biotic factors have been identified as one of the most important controls on evapotranspiration (ET) variation in the scenario of future climate change. Land surface models have developed sophisticated canopy processes to emphasize the importance of vegetation. However, as the vegetation processes become more and more complicated, the relative importance of biotic impact in comparison with abiotic impact on ET has not been well quantified. Failing to understand the relative importance between abiotic and biotic impact may result model bias in water cycle prediction. We collected satellite-based

ET dataset (GLEAM, CRv1, P-LSH), climate data, biotic factor estimates, and apply the variance decomposition analysis to quantify the relative importance between biotic and abiotic impacts. Then, we compared with the model counterpart, i.e. the ensemble means of LS3MIP and CMIP6. Variance decomposition analysis on ET dataset show that about 70% of the ET inter-annual variation is contributed from abiotic factors, such as vapor pressure deficit (VPD), net radiation, and precipitation, whereas only 30% of ET variance is explained by biotic factors, such as stomata conductance and leaf area index (LAI). The abiotic contributions of the models show great uncertainties, which range from 36% to 60%. Overall, the abiotic factor contributions of most models are significantly higher than satellite-based ET dataset. ET variation of grassland is mostly explained by abiotic factors, which is consistent between models and ET dataset. VPD and precipitation explained most of the ET variation in ET dataset, especially in high latitude, whereas stomata conductance and LAI explained most of the ET variation in LS3MIP and CMIP6 models in boreal forest. The model overestimates of abiotic contribution indicate more complicated canopy processes require better constraints. Climate change leading to increase in VPD and more frequent extreme precipitation potentially play more important role in future ET changes. More efforts, such as model parameterization, calibration, new process development, still need to be made by modelers to improve model meteorological feedback.

How to cite: Cai, Y., Lu, X., Wei, Z., and Wei, N.: Assessment of Land Surface Model's Evapotranspiration Response, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13740, https://doi.org/10.5194/egusphere-egu24-13740, 2024.

The potential impacts of climate change in catchment hydrology are still unknown in most of the world and it is not different in Brazil. Conducting an integrated analysis of catchments based on similarity groups allows us to extract conclusions and observations about the overall controls of hydrological behavior, but also considering the specific and distinctive characteristics of each of these groups. This approach enables us to identify and comprehend the primary features influencing hydrological behavior within each distinct group, increasing hydrologic predictability and knowledge of catchments’ functioning, which is essential to better understand the impacts of climate change. In this study, we investigate the possible shifts in Brazilian catchment hydrology behavior in response to a changing climate. Employing a regional approach of the Long Short-Term Memory (LSTM) to understand and predict streamflow across 735 catchments of six hydrologically similar groups in Brazil, we simulated streamflow throughout the 21st century. This simulation utilized a multi-model ensemble comprising 19 bias-corrected Global Climate Models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), driven by intermediate and high-emission scenarios (SSP245 and SSP585). Our results show that the regional LSTM outperforms the conventional hydrological modeling (NSE≈0.60), underscoring the reliability of deep learning to estimate streamflow with simplified input. Interestingly, we found that substantial variations in projected temperatures across scenarios do not necessarily correspond to significant differences in projected streamflow. Moreover, changes in precipitation and temperature may not exert proportional impacts on streamflow. Further, we will investigate the dynamics of transitions between catchment groups. This innovative approach to assess the impacts of climate change enhances the reliability of projected streamflow trajectories, a critical consideration given the uncertainties associated with CMIP6 models. Furthermore, this study holds potential utility in developing strategies to mitigate the impacts of climate change on Brazilian water resources.

How to cite: Almagro, A., Zamboni, P., and Oliveira, P. T.: Assessment of climate change impacts on Brazilian catchments using a regional deep learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13880, https://doi.org/10.5194/egusphere-egu24-13880, 2024.

Background

Both blue water and green water contribute to agricultural water scarcity, which is subjected to impacts of escalating climate extremes, e.g., precipitation and temperature extremes. However, an explicit quantification of the possible effects of compound climate extremes on agricultural water scarcity index (AWSI) under historical and future climate is absent and current research often overlooks how different spatial scales influence agricultural water scarcity.

Methods

We applied an integrated AWSI, which incorporates blue water and green water, to estimate agricultural water scarcity in provincial and basin scales in China, and to determine the association of AWSI with compound climate extremes over the historical period 1971–2010 and for future period 2031–2070.

Conclusions
Our results indicate a marked escalation in AWSI during dry years and periods of elevated temperatures, and precipitation significantly impacts AWSI more than temperature variations. In secondary basins, AWSI was about 25.7% higher than the long-term average during dry years, increasing to nearly 49% in exceptionally dry conditions. Comparatively, in tertiary basins, the increases were 27.7% and 55%, respectively. In years characterized by high temperatures, AWSI rose by approximately 6.8% (7.3% for tertiary basins) from the average, surging to around 19.1% (15.5% for tertiary basins) during extremely hot periods. Future climate change would further intensify AWSI and amplify the effects of climate extremes, particularly in Inner Mongolia with changes of AWSI over 200%. Southwestern China could also experience expanding agricultural water scarcity under future climate scenarios. Improving irrigation efficiency has potential to alleviate water scarcity by up to 30%. Moreover, it illustrates that AWSI assessment at the tertiary basin level could better capture the influence of climate extremes on AWSI compared to assessments at the secondary basin level. As a whole, the investigation offers an in-depth evaluation of the influence of compound precipitation and temperature extremes and research scale on water scarcity.

How to cite: Liu, J. and Liu, W.: Impacts of climate extremes on agricultural water scarcity under historical and future periods and the spatial scale effect, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14467, https://doi.org/10.5194/egusphere-egu24-14467, 2024.

Forest fires are commonly expected to exacerbate local flood hazards. Yet, it's not well-established if such an effect is evident across broader geographic regions concurrently, particularly when considering the compounded influences of forest fires and climate variability on floods. Here, we show that recent 2019–2020 mega forest fires in southeast Australia, characterized by unprecedented burned areas, have significantly increased the flood peak discharges in the ensuing two years. The impact varied regionally, being more pronounced in areas with winter-dominated and uniform rainfall patterns, while it was insignificant in regions with summer-dominated rainfall. This regional divergence in fire impacts can be attributed to the differences in burned areas and dominant flood generating mechanisms. Given the increasing influence of climate change on fire activities, people living in these fire-prone regions might face escalating risks of cascading flood hazards following fires in the future.

How to cite: Xu, Z., Zhang, Y., and Blöschl, G.: Intensified floods after mega forest fires in southeast Australia , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14546, https://doi.org/10.5194/egusphere-egu24-14546, 2024.

Affected by global climate change, the variations of snow cover and snowmelt runoff in the high cold region has raised an increasing concern. Snowmelt water is an important component of spring runoff in the Lancang River basin, and it is of great significance for the scientific operation of cascade hydropower stations in the Lancang River basin to master the variation law of snow cover in the upper reaches of the Lancang River and accurately simulate the snowmelt runoff process. Based on the remotely sensed snow cover data from 2000 to 2019, the Mann-Kendall trend test method was used to analyze the spatio-temporal variation of snow cover in the upper reaches of Lancang River. An snowmelt runoff model was established, and the PSO algorithm was introduced to determine the model parameters to simulate the snowmelt runoff process. The results show that the snow cover in the upper reaches of Lancang River showed no significant increase in spring, autumn and winter, and no significant decrease in summer. The average annual snow cover in spring, summer, autumn and winter was 0.16, 0.06, 0.13 and 0.17, respectively. The snow cover in the southwest and north of the Lancang River source area increased in all seasons, while the snow cover in the southeast area decreased. Among them, the increase of snow cover in the northwest reaches the largest in winter, up to 3%/a. The SRM model has good applicability in the upper reaches of the Lancang River, and the certainty coefficients of calibration period and verification period are 0.87 and 0.78, respectively, and the results have a certain implication for the simulation of snowmelt runoff in the alpine region.

How to cite: Zhang, J.: Evolution trend of snow cover and simulation of snowmelt runoff in upper reaches of Lancang River, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14558, https://doi.org/10.5194/egusphere-egu24-14558, 2024.

Detailed changes in land surface water fluxes under vegetation greening is unclear especially in different patterns of climate change and land-use change. A typical vegetation greening region located in Loess Plateau was selected as a studying area. Because of high spatial heterogeneous site conditions, similar spectral reflectance and shapes of different vegetation types, there is a low accuracy of land cover mapping in mixed regions of multiple vegetation types, thereby leading to a pronounced underestimate of land use change, such as grain for green project. Besides spectrum, topography, and some usually used features, a novel land cover mapping framework is constructed with evapotranspiration which vary dramatically in vegetation types. Generally, the classification accuracy of all kinds of land cover is above 90%, and improved by 5.4-15.3%, 0-15.7%, 3.0-20.4%, of cropland, forest and grassland, respectively. Then water fluxes including precipitation, evapotranspiration, soil moisture, and runoff were analyzed in nine different vegetation types, considering the three types of vegetation found in cropland, forest and grassland along with respective stable, loss, and gain states. The result indicated that the cropland returning and afforestation has successfully facilitated a positive regional water cycle. This finding is useful for supporting ecological restoration and future water resources management, and enhancing the carrying capacity and resilience of the region.

How to cite: Bao, Z., Wang, J., and Ruan, Y.: Characterization of land surface water flux under vegetation greening introduced by changes in climate and land-use, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14579, https://doi.org/10.5194/egusphere-egu24-14579, 2024.

This study introduces a comprehensive suite of methodologies for estimating Intensity Duration Frequency (IDF) curves, critical for engineering planning in the face of expected variations in extreme precipitations induced by climate change. Indeed, in recent years the climate proofing design of hydraulic infrastructures (e.g. sewage systems) arose increasing interest but, at the moment, there is a lack of clear understanding of the differences between approaches and the relative weight of the individual phases of the process on the final estimates (approaches to fit the statistical parameters, differences between simulation chains, variations induced by socio-economic scenarios driving climate models). To investigate such issue, four consolidated approaches to assess the potential variations induced by CC in IDF curves are compared: Padulano et al., 2018 [doi.org/10.1002/hyp.13449, QDM-CMCC], Hassanzadeh et al., 2019 [doi.org/10.1016/j.advwatres.2019.07.001; QQD], Alzahrani et al., 2022 [doi.org/10.1007/s11269-022-03265-3; EQM], Hassanzadeh et al., 2019 [doi.org/10.1016/j.advwatres.2019.07.001; SIM]. More specifically: QDM-CMCC combines a simple delta change with quantile delta mapping; the Quantile-Quantile downscaling (QQD) spatiotemporally downscales extreme rainfall quantiles through a parametric relationship; Equidistant Quantile Mapping (EQM) spatiotemporally downscales extreme rainfall quantiles using a two-step parametric procedure; Scale-Invariance Method (SIM) derives the distributions of short-duration local extreme rainfalls based on those of longer duration using the scaling relationships between non-central moments over different rainfall durations.

Precipitation values are provided by 14 climate simulation chains made available in the framework of the EURO‐CORDEX initiative; 1981-2010 is adopted as the current period while, as the future time horizon, 2036-2065 is adopted under three different Representative Concentration Pathways, RCP2.6, RCP4.5 and RCP8.5. As pilot case, the reference IDF curve adopted to design hydraulic infrastructures in the Ischia Island (30 km from Naples, Southern Italy) is used.

The investigation is aimed at exploring not only the spread among the findings returned by exploiting the different approaches in a real-world scenario but also to improve the understanding about how the theorical differences in the approaches can lead to very different estimates. Results show that the three main sources of uncertainty (statistical parameter fitting, climate modelling and RCP scenarios) play a comparable role inducing an increasingly evident spread as the return times increase.

Finally, it is worth noting that two libraries in R and Python for the four approaches, available upon request, have been developed to permit assessments over test cases in different precipitation regimes and by exploiting different climate simulation chains to replicate the findings achieved in the present investigation.

How to cite: Napolitano, L., Rianna, G., Padulano, R., and Francalanci, V.: Development of an integrated suite for estimating Intensity Duration Frequency curves in a climate change perspective , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15132, https://doi.org/10.5194/egusphere-egu24-15132, 2024.

This research focuses on the estimation of extreme precipitation quantiles using climate change scenario data from the 6th Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). The study involved the analysis of precipitation data from 23 global climate models (GCMs), with a final selection of 10 models that best represented the characteristics of extreme precipitation in South Korea, based on statistical measures against observed rainfall data. Particularly, precipitation data from 71 observation points within the Chungju-Dam basin, a region of hydrological significance and susceptibility to extreme weather events, were collected for analyzing climate change impacts.

Furthermore, the study conducted the regional frequency analysis (index-flood method) to estimate rainfall quantiles, employing the Generalized Extreme Value (GEV) distribution and L-moment method for parameter estimation. The analysis resulted in the development of a multi-model ensemble (MME) incorporating the 10 selected GCMs and 4 Shared Socioeconomic Pathways (SSP) scenarios. This approach facilitated a comprehensive understanding of potential future climate changes, considering emission trajectories and socio-economic changes. Additionally, the study quantitatively evaluated the impact of climate change and associated uncertainties in the region, which is essential for devising adaptation and mitigation strategies in response to climate change conditions, particularly in areas susceptible to extreme weather events. This research provides valuable insights into the understanding of climate-induced extreme weather events and offers guidance for policymakers and environmental planners in preparing for the impacts of global climate change.

How to cite: Kim, S. and Heo, J.-H.: Assessment of extreme precipitation risks using multi-model climate projections: focusing on the Chungju-Dam basin in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16120, https://doi.org/10.5194/egusphere-egu24-16120, 2024.

Irrigation is one of the main human landscape management activity, and has seen a dramatic increase during the XXth century, with a direct local increasing effect on soil moisture (SM) and evapotranspiration (ET). To sustain the increase of ET, irrigation withdraws water from rivers and groundwater reservoirs. As a result, irrigation activities have a direct impact on water and energy balance, and drive the evolution of ET under the ongoing climate change in intensively irrigated regions. On the other hand, irrigation induces feedbacks from the atmosphere, that includes changes in precipitation patterns, air temperature cooling and changes in energy-related variables. Moreover, future climate change will complexify these interactions, and it is not clear what would be the future implications of irrigation activities in water resources management and key hydroclimate variables.

To assess the joint evolution of irrigation, water resources and hydroclimate variables, we use an irrigation scheme that was recently evaluated in ORCHIDEE, the land surface component of the IPSL climate model. This scheme calculates water demand based on a soil moisture deficit approach, and restrains water supply to water available in small and large rivers and in groundwater, considering the facility to access the water source and an environmental volume for ecosystems. To assess the effect of irrigation on water resources and climate, we use two transient coupled simulations at global scale, for the period 1950-2100, under SSP5-RCP8.5 scenario to have a strong climate change signal during the future period. One of the simulations runs with the irrigation scheme activated, while the second one runs without irrigation.

Preliminary results at global scale show that irrigation will increase under the chosen scenario, due to the prescribed increase of the irrigated surface from scenario SSP5-RCP8.5 and a warmer climate. This increase will counteract part of the increasing trend of groundwater storage and will complexify the evolution of river storage in irrigated areas. On the other hand, it will enhance the increase of ET at global scale. We will extend our analysis to water and energy-related variables, including key climate variables like precipitation and air temperature, at different seasons and regions. We will also focus our analysis in some intensively irrigated areas, to assess the causes of possible water supply shortages in irrigation activities. These results should help to understand future implications of irrigation in water resources management in irrigated areas, and also effects in non-irrigated zones via remote land-atmosphere feedbacks.

How to cite: Arboleda, P., Ducharne, A., Tiengou, P., and Cheruy, F.: Effect of irrigation on joint evolution of water resources and hydroclimate variables under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16155, https://doi.org/10.5194/egusphere-egu24-16155, 2024.

Anthropogenic climate change is profoundly altering the hydrological cycle in high-latitude regions. Iceland, with its abundant hydrological and glaciological data, provides a unique opportunity to study the effects of climate change on streamflow in snow- and glacier melt dominated catchments. The country's reliance on hydropower, as the top electricity producer per capita globally, highlights the critical need for understanding these changes.

In Iceland, the average temperature has risen significantly in recent decades, outpacing the global warming trend. Despite this warming, increased precipitation has led to more extensive snow cover and depth in some regions. Glaciers have experienced a loss in area and mass, soil temperatures have risen, and vegetation has increased. However, the impacts of these environmental shifts on streamflow remain largely unexplored.

Our study utilizes the newly released LamaH-Ice dataset, encompassing streamflow observations from mainly undisturbed watersheds, atmospheric forcings from climate reanalyses and catchment characteristics, to investigate Iceland's streamflow dynamics changes over recent decades. We analyze annual, seasonal, and monthly streamflow volumes, spring freshet timing, and extreme flow events, correlating these changes with environmental conditions and catchment attributes.

The results suggest that streamflow regime alterations are influenced by multiple factors, including geographic location, topography, and river type. The findings offer crucial insights into Iceland's hydrological changes amid rapid climatic shifts, with broader implications for reservoir operations and water resource management. This study not only enhances our understanding of Icelandic hydrology but also contributes to global knowledge on climate-induced hydrological changes.

How to cite: Helgason, H. B., Gunnarsson, A., Sveinsson, Ó. G. B., and Nijssen, B.: Understanding Changes in Iceland’s Streamflow Dynamics in Response to Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16654, https://doi.org/10.5194/egusphere-egu24-16654, 2024.

In order to alleviate the problem of water scarcity, adapt to and mitigate the negative impact of climate change on water resources, large-scale infrastructure projects have been built worldwide. In addition to bringing huge social benefits, reservoirs may also affect meteorological conditions near the surface and mesoscale or weather scale processes. A high-quality meteorological dataset is an important foundation for understanding climate change. Considering the complexity of the underlying surface in the Three Gorges Reservoir region, this study uses CLDAS multi-source fusion grid meteorological data to study the characteristics of changes in the Three Gorges Reservoir before and after water storage. Select four elements: temperature, precipitation, wind speed, and relative humidity, and analyze the climate effects before and after the Three Gorges Reservoir from different time scales such as year, season, and day. Based on the analysis of CLDAS multi-source fusion data, it is shown that for the average temperature, after the water storage, except for the areas along the southern side of the Yangtze River where the temperature is lower than before the water storage, most other areas are higher. On an annual scale, there is not much difference in average temperature before and after water storage. The average temperature effect in the Three Gorges region after water storage varies at different time periods. During the subsidence period and high water level period, it shows an overall warming effect, with an average temperature increase of 0.1 ℃ and 0.3 ℃, respectively. However, during the flood season and water storage period, it shows a cooling effect, with an average temperature decrease of 0.2 ℃ and 0.9 ℃, respectively. The cooling effect is more pronounced during the water storage period. After water storage, it shows an increase in temperature during the day and a decrease in temperature at night. For annual precipitation, except for some areas in the east, northwest, and central regions where precipitation has decreased, most of the remaining areas of the Three Gorges generally have more precipitation than before the water storage. At the annual scale and different time periods, the precipitation in the Three Gorges area is higher after the water storage than before, and on the annual scale, the precipitation after the water storage increases by 8.8% compared to before the water storage; During the subsidence period, flood season, storage period, and high water level period, the precipitation after storage increased by 10.2%, 1.3%, 21.7%, and 32.2% respectively compared to before storage. The precipitation during the high water level period after storage changed the most, while the precipitation during the flood season changed the least.

How to cite: Wang, Q., Chen, X., and Li, W.: Assessment of Climate Effects in the Three Gorges Reservoir Based on Multi-source Fusion Data CLDAS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17866, https://doi.org/10.5194/egusphere-egu24-17866, 2024.

The intensification of the water cycle over recent decades has produced changes in hydrologic extremes (floods and droughts) unevenly across the globe and the U.S. is not an exception. This has led to increased interest in coordination amongst federal agencies within the U.S. to improve readiness to respond to and mitigate the effects of these extreme events as well as for the U.S. to increase coordination with our international partners, as evidenced by a recent Quadrilateral Security Dialogue between the U.S., Japan, Australia, and India on extreme precipitation and its effects on water quality, inundation, and flooding. As the nation’s unbiased resource for land-surface information, the U.S. Geological Survey has responded by developing a set of interpretative studies and related datasets to understand changes in floods and droughts, the potential drivers of these changes, and strategies for updating frequency-based statistics for hydrological extremes. This presentation and discussion will highlight recent advancements in data and interpretation on hydrologic extremes as well as the detection of changes in, attribution of, and adjustment for observed changes.

How to cite: Archfield, S.:  U.S. Geological Survey datasets of hydrological extremes and their drivers to enhance security and improve understanding, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21007, https://doi.org/10.5194/egusphere-egu24-21007, 2024.

To estimate design-flood quantiles, such as the 100-year flood, the observed annual maximum flood (AMF) series is fitted to a probability distribution and then the design flood quantile is estimated from the fitted distribution. This is because, in most cases, historical records are not long enough to observe rare, design-flood events. Changes in the AMF series, which are usually detected using simple trend tests (e.g., Mann-Kendall test (MKT)), are hypothesized to result in changes in  design-flood estimates. This hypothesis is tested by using an alternate framework to detect significant changes in design-flood between two periods – rather than changes in the AMF series – and then evaluated using synthetically generated AMF series from the Log-Pearson Type-3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show that the MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design-flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design-flood quantiles were observed even without a significant trend in the AMF series and basins with statistically significant trends did not necessarily exhibit statistically significant changes in design-flood. If changes to design-flood quantiles are of interest, this framework can be useful rather than simple trend tests on the AMF series which may or may not indicate changes in the design-flood quantiles have occurred. We are now extending the application of the developed framework to mixed population scenarios where floods are generated from more than one causal mechanism under the hypothesis that two more causal mechanisms result in statistically different design-flood quantile estimates at the same river.

How to cite: Awasthi, C., Archfield, S. A., Reich, B. J., and Arumugam, S.: Can Trend Tests Detect Changes in Design-Flood Quantiles under Changing Climate?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21009, https://doi.org/10.5194/egusphere-egu24-21009, 2024.