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Studying streamflow processes and controlling factors is crucial for sustainable water resource management. This study demonstrated the potential of integrating hydrological models with machine learning by constructing two machine learning methods, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), based on the input and output data from the Soil and Water Assessment Tool (SWAT) and comparing their streamflow simulation performances. The Shapley Additive exPlanations (SHAP) method identified the controlling factors and their interactions in streamflow variation, whereas scenario simulations quantified the relative contributions of climate and land use changes. The results showed that when integrated with the SWAT model, XGBoost demonstrated better streamflow simulation performance than RF. Among the key factors influencing streamflow variation, area was the most important, with precipitation having a stronger impact than temperature, positively affecting streamflow when exceeding 550 mm. Different land use types exerted nonlinear impacts on streamflow, with notable differences and threshold effects. Specifically, grassland, cropland, and forest positively contributed to streamflow when their proportions were below 50%, above 20%, and between 30% and 50%, respectively. Nonlinear interaction effects on streamflow between land use types resulted in positive or negative contributions at specific proportion thresholds. Furthermore, precipitation was not dominant in the interaction with land use. Streamflow changes were primarily driven by drastic land use changes, which contributed 55.71%, while climate change accounted for 44.27%. This integration of hydrological models with machine learning revealed the complex impacts of climate and land use changes on streamflow, offering scientific insights for watershed water resource management. Plain Language Summary As global water resources have increased in scarcity, it has become necessary to study the processes underlying streamflow variation and its controlling factors. In this study, it was revealed that combining hydrological models with machine learning methods is a promising approach for addressing this issue. When integrated with the SWAT model, XGBoost demonstrated better streamflow simulation performance than RF. Through the SHAP method, we identified the key factors influencing streamflow variation. Watershed area was the main factor, and precipitation exerted a greater impact on streamflow than temperature. Different land use types had nonlinear impacts on streamflow with significant threshold effects. The interaction effects between land use types revealed that different land use combinations played complex roles in regulating streamflow. Precipitation did not dominate the interaction effects with land use. Instead, drastic land use changes led to abrupt variations in watershed streamflow, although climate changes played contributing roles as well. Key Points Machine learning methods can be effectively integrated with hydrological models, resulting in superior streamflow simulation capabilities Different controlling factors exerted nonlinear influences on streamflow, with notable differences and threshold effects Precipitation does not dominate the interaction effects with land use, with land use change as the primary driver of streamflow variation

The great river systems originating from the Tibetan Plateau are pivotal for the wellbeing of more than half the global population. Our understanding of historical ranges and future changes in water availability for much of Southeast Asia is, however, limited by short observational records and complex environmental factors. Here we present annually resolved and absolutely dated tree ring-based streamflow reconstructions for the Mekong, Salween and Yarlung Tsangpo rivers since 1000 ce, which are supplemented by corresponding model projections until 2100 ce. We show a significant positive correlation between streamflow and dry season vegetation indices over the Indochinese Peninsula, revealing the importance of the Tibetan Water Tower for the functioning and productivity of ecological and societal systems in Southeast Asia. The streamflow variability is associated with low-frequency sea-surface temperature variability in the North Atlantic and North Pacific. We find that streamflow extremes coincide with distinct shifts in local populations that occurred during medieval times, including the occupation and subsequent collapse of Angkor Wat from the eleventh to the sixteenth century. Finally, our projections suggest that future streamflow changes will reach, or even exceed, historical ranges by the end of this century, posing unprecedented risks for Southeast Asia.Reconstructions of Tibetan Plateau streamflow over the last millennia reveal close associations with dry season vegetation and major population shifts in Southeast Asia.

Global warming is expected to intensify the Earth's hydrological cycle and increase flood and drought risks. Changes over the 21st century under two warming scenarios in different percentiles of the probability distribution of streamflow, and particularly of high and low streamflow extremes (95th and 5th percentiles), are analyzed using an ensemble of bias-corrected global climate model (GCM) fields fed into different global hydrological models (GHMs) provided by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to understand the changes in streamflow distribution and simultaneous vulnerability to different types of hydrological risk in different regions. In the multi-model mean under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, 37 % of global land areas experience an increase in magnitude of extremely high streamflow (with an average increase of 24.5 %), potentially increasing the chance of flooding in those regions. On the other hand, 43 % of global land areas show a decrease in the magnitude of extremely low streamflow (average decrease of 51.5 %), potentially increasing the chance of drought in those regions. About 10 % of the global land area is projected to face simultaneously increasing high extreme streamflow and decreasing low extreme streamflow, reflecting the potentially worsening hazard of both flood and drought; further, these regions tend to be highly populated parts of the globe, currently holding around 30 % of the world's population (over 2.1 billion people). In a world more than 4° warmer by the end of the 21st century compared to the pre-industrial era (RCP8.5 scenario), changes in magnitude of streamflow extremes are projected to be about twice as large as in a 2° warmer world (RCP2.6 scenario). Results also show that inter-GHM uncertainty in streamflow changes, due to representation of terrestrial hydrology, is greater than the inter-GCM uncertainty due to simulation of climate change. Under both forcing scenarios, there is high model agreement for increases in streamflow of the regions near and above the Arctic Circle, and consequent increases in the freshwater inflow to the Arctic Ocean, while subtropical arid areas experience a reduction in streamflow.

Reductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream‐aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision‐making process. Key priorities include (a) linking streamflow depletion to decision‐relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision‐making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision‐support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water‐connected disciplines, professions, governments, organizations, and communities. Plain Language Summary Pumping water from a well can reduce flow in surrounding streams, a phenomenon called “streamflow depletion.” It is important for water managers to know when, where, and how much streamflow depletion is occurring because it can affect the amount of water available for ecosystems and other water users. However, estimating streamflow depletion is challenging because weather and other factors affect streamflow, in addition to pumping. Here, we discuss important topics related to streamflow depletion that need further research. Most importantly, scientists need to move beyond estimating changes in flow caused by pumping, and also develop improved approaches to estimate the impacts of these streamflow changes on ecosystems and water users. Additionally, it will be important to develop improved tools for estimating streamflow depletion and linking those estimates to water management decisions. Making these advances will require basic scientific research and collaboration between hydrologists and other fields; these efforts should be prioritized because streamflow depletion is occurring at a rapid pace around the world. Key Points Changes in streamflow caused by groundwater pumping (“streamflow depletion”) are a link between basic and applied hydrologic science Streamflow depletion science is critical to support decision making and requires advances in hydrology and transdisciplinary collaboration We identify key priorities for streamflow depletion research to improve hydrological process understanding and support water management

Climate change has remarkable global impacts on hydrological systems, prompting the need to attribute past changes for better future risk estimation and adaptation planning. This study evaluates the differences in simulated discharge from hydrological models when driven by a set of factual and counterfactual climate data, obtained using the Inter-Sectoral Impact Model Intercomparison Project's recommended data and detrending method, for quantification of climate change impact attribution. The results reveal that climate change has substantially amplified streamflow trends in the Upper Yangtze and Upper Yellow basins from 1961 to 2019, aligning with precipitation patterns. Notably, decreasing trends of river flows under counterfactual climate have been reversed, resulting in significant increases. Climate change contributes to 13%, 15% and 8% increases of long-term mean annual discharge, Q10, and Q90 in the Upper Yangtze at Pingshan, and 11%, 10%, 10% in the Upper Yellow at Tangnaihai. The impact are more pronounced at headwater stations, particularly in the Upper Yangtze, where they are twice as high as at the Pingshan outlet. Climate change has a greater impact on Q10 than on Q90 in the Upper Yangtze, while the difference is smaller in the Upper Yellow. The impact of climate change on these flows has accelerated in the recent 30 years compared to the previous 29 years. The attribution of detected differences to climate change is more obvious for the Upper Yangtze than for the Upper Yellow.

The uncertainties around the hydrological and socio-economic implications of climate change pose a challenge for Nile River system management, especially with rapidly rising demands for river-system-related services and political tensions between the riparian countries. Cooperative adaptive management of the Nile can help alleviate some of these stressors and tensions. Here we present a planning framework for adaptive management of the Nile infrastructure system, combining climate projections; hydrological, river system and economy-wide simulators; and artificial intelligence multi-objective design and machine learning algorithms. We demonstrate the utility of the framework by designing a cooperative adaptive management policy for the Grand Ethiopian Renaissance Dam that balances the transboundary economic and biophysical interests of Ethiopia, Sudan and Egypt. This shows that if the three countries compromise cooperatively and adaptively in managing the dam, the national-level economic and resilience benefits are substantial, especially under climate projections with the most extreme streamflow changes.The Nile River system is faced with challenges including increasing water demands, political tensions, and future climate and socio-economic uncertainties. Cooperative adaptive management can help increase synergies, balance trade-offs and bring various benefits to riparian countries.

Changes in river discharge due to climate change are highly uncertain, and a recent study used a global streamflow dataset to assess whether such trends are detectable. Streamflow changes occurred more often in basins impacted by human disturbances than in pristine ones, and there was no clear signal from climate change alone.

Amid a heated debate on what are possible and what are plausible climate futures, ascertaining evident changes that are attributable to historical climate change can provide a clear understanding of how warmer climates will shape our future habitability. Hence, we detect changes in the streamflow simulated using three different datasets for the historical period (1901–2019) and analyze whether these changes can be attributed to observed climate change. For this, we first calibrate and validate the Soil and Water Integrated Model and then force it with factual (observed) and counterfactual (baseline) climates presented in the Inter-Sectoral Impact Model Intercomparison Project Phase 3a protocol. We assessed the differences in simulated streamflow driven by the factual and counterfactual climates by comparing their trend changes ascertained using the Modified Mann–Kendall test on monthly, seasonal, and annual timescales. In contrast to no trend for counterfactual climate, our results suggest that mean annual streamflow under factual climate features statistically significant decreasing trends, which are − 5.6, − 3.9, and − 1.9 m3s−1 for the 20CRv3-w5e5, 20CRv3, and GSWP3-w5e5 datasets, respectively. Such trends, which are more pronounced after the 1960s, for summer, and for high flows can be attributed to the weakening of the monsoonal precipitation regime in the factual climate. Further, discharge volumes in the recent factual climate dropped compared to the early twentieth-century climate, especially prominently during summer and mainly for high flows whereas earlier shifts found in the center of volume timings are due to early shifts in the nival regime. These findings clearly suggest a critical role of monsoonal precipitation in disrupting the hydrological regime of the Jhelum River basin in the future.

Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly sectoral water use data set (GMSWU), coupled with the dead capacity data of the large reservoirs within the LR basin and the Budyko hypothesis framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that started in October 2004. The annual streamflow and annual mean temperature time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant decreasing trend. (2) The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was due to HAs, such as the construction of hydropower stations within the study area. (3) The comparison with the other two methods showed that the CR of CC calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new framework proposed in this study were within 5 %. Therefore, the newly proposed framework, which considers the uncertainty of hydrological simulations, can accurately quantify the CR of CC and HAs to streamflow changes. (4) The quantitative results calculated by using the simulation results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other words, our novel framework could effectively solve the calculation errors caused by the “equifinality for different parameters” of hydrological simulations. (5) The results of this case study also showed that the reduction in the streamflow in June and November was mainly caused by decreased precipitation and increased evapotranspiration, while the changes in the streamflow in other months were mainly due to HAs such as the regulation of the constructed reservoirs. In general, the novel quantitative framework that considers the uncertainty of hydrological simulations presented in this study has validated an efficient alternative for quantifying the CR of CC and HAs to streamflow changes.

Human activities, as well as climate variability, have had increasing impacts on natural hydrological systems, particularly streamflow. However, quantitative assessments of these impacts are lacking on large scales. In this study, we use the simulations from six global hydrological models driven by three meteorological forcings to investigate direct human impact (DHI) and climate impact on streamflow in China. Results show that, in the sub-periods of 1971–1990 and 1991–2010, one-fifth to one-third of mean annual streamflow (MAF) was reduced due to DHI in northern basins, and much smaller (<4 %) MAF was reduced in southern basins. From 1971–1990 to 1991–2010, total MAF changes range from −13 % to 10 % across basins wherein the relative contributions of DHI change and climate variability show distinct spatial patterns. DHI change caused decreases in MAF in 70 % of river segments, but climate variability dominated the total MAF changes in 88 % of river segments of China. In most northern basins, climate variability results in changes of −9 % to 18 % in MAF, while DHI change results in decreases of 2 % to 8 % in MAF. In contrast with the climate variability that may increase or decrease streamflow, DHI change almost always contributes to decreases in MAF over time, with water withdrawals supposedly being the major impact on streamflow. This quantitative assessment can be a reference for attribution of streamflow changes at large scales, despite remaining uncertainty. We highlight the significant DHI in northern basins and the necessity to modulate DHI through improved water management towards a better adaptation to future climate change.