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Impacts of climate change at 1.5, 2 and 3 °C mean global warming above preindustrial level are investigated and compared for runoff, discharge and snowpack in Europe. Ensembles of climate projections representing each of the warming levels were assembled to describe the hydro-meteorological climate at 1.5, 2 and 3 °C. These ensembles were then used to force an ensemble of five hydrological models and changes to hydrological indicators were calculated. It is seen that there are clear changes in local impacts on evapotranspiration, mean, low and high runoff and snow water equivalent between a 1.5, 2 and 3 °C degree warmer world. In a warmer world, the hydrological impacts of climate change are more intense and spatially more extensive. Robust increases in runoff affect the Scandinavian mountains at 1.5 °C, but at 3 °C extend over most of Norway, Sweden and northern Poland. At 3 °C, Norway is affected by robust changes in all indicators. Decreases in mean annual runoff are seen only in Portugal at 1.5 °C warming, but at 3 °C warming, decreases to runoff are seen around the entire Iberian coast, the Balkan Coast and parts of the French coast. In affected parts of Europe, there is a distinct increase in the changes to mean, low and high runoff at 2 °C compared to 1.5 °C, strengthening the case for mitigation to lower levels of global warming. Between 2 and 3 °C, the changes in low and high runoff levels continue to increase, but the changes to mean runoff are less clear. Changes to discharge in Europe’s larger rivers are less distinct due to the lack of homogenous and robust changes across larger river catchments, with the exception of Scandinavia where discharges increase with warming level.

Accurate and consistent annual runoff prediction in a region is a hot topic in management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, wavelet packet decomposition (WPD) is adopted to further decompose the IMF with the maximum SE into several appropriate components. Then long short-term memory (LSTM) model, a deep learning algorithm based recurrent approach, is employed to predict all components. Finally, forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to seven annual series from different areas in China, is evaluated based on four evaluation indexes (R, MAE, MAPE and RMSE). Results indicate that ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four evaluation indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, rendering it an efficient instrument for scientific management and planning of water resources.

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

Field‐based studies are limited in Himalaya‐Karakoram (HK); therefore, remote sensing and glaciohydrological modeling provide alternative solutions to investigate runoff evolution under changing climate conditions. Due to limited in situ runoff data in HK, glaciohydrological models are often calibrated using high‐resolution remote sensing data. This study introduces the calibration of the glaciohydrological model Spatial Processes in Hydrology (SPHY), at glacier catchment‐scale over 2000–2023 using satellite‐based Sentinel‐1 Synthetic Aperture Radar (SAR) wet snow maps, along with available geodetic mass balance estimates in the HK region. The selected calibrated model parameters are validated against in situ runoff data to test the robustness of satellite‐based calibration for Chhota Shigri Glacier (CSG), Dokriani Bamak Glacier (DBG), and Gangotri Glacier System (GGS) catchments in HK. The SPHY modeled and in situ catchment‐wide runoff estimates show good agreement. The Sentinel‐1 SAR‐derived wet snow percentage area shows strong spatial and temporal variability from 2015 to 2023. The mean annual runoff is 1.79 ± 0.15 m3s−1, 1.63 ± 0.09 m3s−1 and 39.40 ± 3.15 m3s−1 over 2000–2023 for CSG, DBG and GGS catchments, respectively. Maximum annual runoff occurred in 2021/2022, mainly due to heatwaves in early spring/summer 2022. Snowmelt runoff is highest in CSG (61%) and GGS (49%), while rainfall‐runoff dominates in DBG (42%). Satellite‐based glaciohydrological model calibration offers a unique opportunity to improve runoff estimates for glacierized catchments in data‐sparse regions. Applying present study to glacierized catchments lacking in situ runoff data will strengthen our past, present, and future glaciohydrological understanding of regions such as HK and Andes.

Estimating runoff components, including surface flow, baseflow and total runoff is essential for understanding precipitation partition and runoff generation and facilitating water resource management. However, a general framework to quantify and attribute runoff components is still lacking. Here, we propose a general formulation through observational data analysis and theoretical derivation based on the two-stage Ponce-Shetty model (named as the MPS model). The MPS model characterizes mean annual runoff components as a function of available water with one parameter. The model is applied over 662 catchments across China and the contiguous United States. Results demonstrate that the model well depicts the spatial variability of runoff components with R2 exceeding 0.81, 0.44 and 0.80 for fitting surface flow, baseflow and total runoff, respectively. The model effectively simulates multi-year runoff components with R2 exceeding 0.97, and the proportion of runoff components relative to precipitation with R2 exceeding 0.94. By using this conceptual model, we elucidate the responses of surface flow and baseflow to available water and environmental factors for the first time. The surface flow is jointly controlled by precipitation and environmental factors, while baseflow is mainly influenced by environmental factors in most catchments. The universal and concise MPS model offers a new perspective on the long-term catchment water balance, facilitating broader application in large-sample investigations without complex parameterizations and providing an efficient tool to explore future runoff variations and responses under changing climate.

The purpose of this paper is to simulate the future runoff change of the Yellow River Basin under the combined effect of land use and climate change based on Cellular automata (CA)-Markov and Soil & Water Assessment Tool (SWAT). The changes in the average runoff, high extreme runoff and intra-annual runoff distribution in the middle of the 21st century are analyzed. The following conclusions are obtained: (1) Compared with the base period (1970–1990), the average runoff of Tangnaihai, Toudaoguai, Sanmenxia and Lijin hydrological stations in the future period (2040–2060) all shows an increasing trend, and the probability of flood disaster also tends to increase; (2) Land use/cover change (LUCC) under the status quo continuation scenario will increase the possibility of future flood disasters; (3) The spring runoff proportion of the four hydrological stations in the future period shows a decreasing trend, which increases the risk of drought in spring. The winter runoff proportion tends to increase; (4) The monthly runoff proportion of the four hydrological stations in the future period tends to decrease in April, May, June, July and October. The monthly runoff proportion tends to increase in January, February, August, September and December.

Monthly conceptual hydrological models can provide simple yet effective descriptions of hydrological processes. Most hydrological models were designed to understand physical processes of catchments, focusing on individual sub‐processes (Newtonian paradigm). However, few were developed to represent the overall behavior of hydrological systems (Darwinian paradigm). This study adapted the objective functions from water resources systems to simulate catchment water partitioning. Building on this, a supply‐demand‐based hydrological model (SDM) was developed. The proposed SDM was validated using data from 640 CAMELS‐US and 171 CAMELS‐AUS catchments, and compared with five parsimonious models: the Two‐parameter Water Balance Model, the WatBal Model, the Dynamic Water Balance Model, the Génie Rural 5‐parameter Model, and the Time Variant Gain Model. Results indicate that: (a) The model shows satisfactory results, with median NSE values of 0.65 and 0.74 for CAMELS‐US and CAMELS‐AUS catchments, respectively, during the validation period. (b) Compared to the other five models, the SDM achieves better performance in terms of the structural risk minimization metric, with median values of 0.61 and 0.42 during the validation period for the two data sets, respectively. (c) The SDM shows greater improvement compared to other models in catchments with low mean annual runoff or runoff ratio. This improvement, however, decreases when the fraction of precipitation falling as snow increases. This study offers a novel perspective on understanding water partitioning patterns in natural catchments by leveraging principles from human water resources management.

This paper addresses the question of how catchment-scale water and energy balances have responded to climatic and land surface changes over the last 70 years in the federal state of Saxony in eastern Germany. Therefore, observational data of hydrological and meteorological monitoring sites from 1951 to 2020 across 71 catchments are examined in a relative water- and energy-partitioning framework to put the recent drought-induced changes into a historical perspective. A comprehensive visualization method is used to analyze the observed time series. The study focuses on changes on a decadal timescale and finds the largest decline in observed runoff in the last decade (2011–2020). The observed decline can be explained by the significant increase in aridity, caused by the reduction in annual mean rainfall and a simultaneous increase in potential evaporation. In a few mainly forested headwater catchments, the observed decline in runoff was even stronger than predicted by climate conditions alone. These catchments are still recovering from past widespread forest damages sustained in the 1970s to 1980s, resulting in a continuous increase in actual evapotranspiration due to forest regrowth. On the contrary, runoff stayed almost constant in other catchments despite an increase in aridity. These results highlight that water budgets in Saxony are in an unstable, non-stationary regime due to significant climatic changes and the regional impacts of land surface changes such as forest health. The recent decreases in the mean annual runoff are substantial and must be taken into account by the authorities for freshwater management.

Understanding the influence of global warming on regional hydroclimatic extremes is challenging. To reduce the potential risk of extremes under future climate states, assessing the change in extreme climate events is important, especially in Asia, due to spatial variability of climate and its seasonal variability. Here, the changes in hydroclimatic extremes are assessed over the Asian monsoon region under global mean temperature warming targets of 1.5 and 2.0 ∘C above preindustrial levels based on representative concentration pathways (RCPs) 4.5 and 8.5. Analyses of the subregions classified using regional climate characteristics are performed based on the multimodel ensemble mean (MME) of five bias-corrected global climate models (GCMs). For runoff extremes, the hydrologic responses to 1.5 and 2.0 ∘C global warming targets are simulated based on the variable infiltration capacity (VIC) model. Changes in temperature extremes show increasing warm extremes and decreasing cold extremes in all climate zones with strong robustness under global warming conditions. However, the hottest extreme temperatures occur more frequently in low-latitude regions with tropical climates. Changes in mean annual precipitation and mean annual runoff and low-runoff extremes represent the large spatial variations with weak robustness based on intermodel agreements. Global warming is expected to consistently intensify maximum extreme precipitation events (usually exceeding a 10 % increase in intensity under 2.0 ∘C of warming) in all climate zones. The precipitation change patterns directly contribute to the spatial extent and magnitude of the high-runoff extremes. Regardless of regional climate characteristics and RCPs, this behavior is expected to be enhanced under the 2.0 ∘C (compared with the 1.5 ∘C) warming scenario and increase the likelihood of flood risk (up to 10 %). More importantly, an extra 0.5 ∘C of global warming under two RCPs will amplify the change in hydroclimatic extremes on temperature, precipitation, and runoff with strong robustness, especially in cold (and polar) climate zones. The results of this study clearly show the consistent changes in regional hydroclimatic extremes related to temperature and high precipitation and suggest that hydroclimatic sensitivities can differ based on regional climate characteristics and type of extreme variables under warmer conditions over Asia.

Future water availability is threatened by changes in both climate and water demand. Water rights with differing priorities are an important foundation of demand‐side tools (e.g., buyback, water pricing, and water market) to improve water use efficiency and reduce water scarcity, especially in highly regulated river systems. This paper assesses the impact of climate change on water yields from carryover storage with dual‐priority (high/low) water rights allocation systems using a simple and rapid analytical method. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and Cv). We evaluate this method against a water resource simulation model in the Goulburn River basin, Australia. In general, our analytical “dual‐priority” Gould‐Dincer model reproduces water allocation estimates from the simulation model. We further demonstrate this method across 12 Australian catchments to investigate the climate change impact on “dual‐priority” water rights yield at the catchment scale. The hydrological projections show decreasing mean annual runoff and increasing annual runoff variability, except for some catchments in northern Australia. Water yield for high‐priority water rights (HPWRs) and low‐priority water rights (LPWRs) decreases for most catchments except for some catchments in northern Australia. South Dandalup in the 2070s (RCP8.5) shows the largest percentage decrease in HPWR and LPWR yield (about −53.53% and −56.81%, respectively). Our results show that changes in mean annual inflow have a more significant influence on water yield of HPWR and LPWR than Cv. Overall, the simple method provides a rapid assessment of water yields with “dual‐priority” water rights which is applicable across multiple sites at regional or even global scale. Key Points A simple analytical method is used to evaluate the impact of climate change on the yield of “dual‐priority” water rights Water yield for both high‐ and low‐priority water rights generally decreases under future climate in Australia Future change in mean annual inflow has a more significant influence on water yield than change in annual flow variability