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The naturalized streamflow, i.e., streamflow without water management effects, in the Yellow River basin (YRB) has been significantly decreased at a rate of -3.71×108 m 3 yr -1 during 1982–2018, although annual precipitation experienced an insignificantly positive trend. Explicit detection and attribution of naturalized streamflow are critical to manage limited water resources for the sustainable development of ecosystems and socio-economic systems. The effects from temporally explicit changes of climate variables and underlying surfaces on the streamflow trend were assessed using the variable infiltration capacity (VIC) model prescribed with continuously dynamic leaf area index (LAI) and land cover. The results show a sharp increase of the LAI trend and land use change as a conversion of cropland into forest grass in the basin. The decrease in naturalized streamflow can primarily be attributed to the vegetation changes including an interannual LAI increase and intra-annual LAI temporal pattern change, which account for the streamflow reduction of 1.99×108 and 0.45×108 m 3 yr -1 , respectively. The impacts of the LAI change are largest at the subregion of Longmen–Huayuankou where the LAI increasing trend is high and land use change is substantial. Attribution based on simulations with multiyear average LAI changes obviously underestimates the impacts of the interannual LAI change and intra-annual LAI temporal change on the natural streamflow trend. Overall, the effect of climate variation on streamflow is slight because the positive effect from precipitation and wind speed changes was offset by the negative effect from increasing temperature. Although climate variation is decisive for streamflow change, this study suggests that change in underlying surfaces has imposed a substantial trend on naturalized streamflow. This study improves the understanding of the spatiotemporal patterns and the underlying mechanisms of natural streamflow reduction across the YRB between 1982 and 2018. [ABSTRACT FROM AUTHOR]

A warming climate will intensify the water cycle, resulting in an exacerbation of water resources crises and flooding risks in the Lancang–Mekong River Basin (LMRB). The mitigation of these risks requires accurate streamflow and flood simulations. Process-based and data-driven hydrological models are the two major approaches for streamflow simulations, while a hybrid of these two methods promises advantageous prediction accuracy. In this study, we developed a hybrid physics-data (HPD) methodology for streamflow and flood prediction under the physics-guided neural network modeling framework. The HPD methodology leveraged simulation information from a process-based model (i.e., VIC-CaMa-Flood) along with the meteorological forcing information (precipitation, maximum temperature, minimum temperature, and wind speed) to simulate the daily streamflow series and flood events, using a long short-term memory (LSTM) neural network. This HPD methodology outperformed the pure process-based VIC-CaMa-Flood model or the pure observational data driven LSTM model by a large margin, suggesting the usefulness of introducing physical regularization in data-driven modeling, and the necessity of observation-informed bias correction for process-based models. We further developed a gradient boosting tree method to measure the information contribution from the process-based model simulation and the meteorological forcing data in our HPD methodology. The results show that the process-based model simulation contributes about 30% to the HPD outcome, outweighing the information contribution from each of the meteorological forcing variables (<20%). Our HPD methodology inherited the physical mechanisms of the process-based model, and the high predictability capability of the LSTM model, offering a novel way for making use of incomplete physical understanding, and insufficient data, to enhance streamflow and flood predictions. [ABSTRACT FROM AUTHOR]

In this study, we assessed the impacts of climate variations on streamflow in 28 near-natural catchments in Iran. To this end, we analysed the trend of annual streamflow, precipitation, and temperatures using the Mann-Kendall test and Sen's slope between water years 1982 and 2011. We evaluated the frequency of precipitation in different classes, i.e. below 5, 5–10, 10–15, and above 15 mm/day. Our results indicate a decline in streamflow at 25 catchments, at a rate of −5.66 to −0.19 mm/year. The annual precipitation amount did not decrease significantly, while the frequency of light precipitation events (<5 mm/day) increased. About 60% of the upward trend was significant. Mean temperature increased at all studied catchments, at an average rate of 0.055°C/year. In short, our results indicate that increases in temperature and the frequency of light precipitation events are two leading factors in streamflow reduction across studied catchments. [ABSTRACT FROM AUTHOR]

Traditional impact assessments of future changes on flow regimes mainly focus on streamflow magnitude and static land use, which are insufficient to capture entire characteristics of flow regime variations and future land use change. In this study, 18 flow regime metrics are adopted to fully characterize the streamflow hydrograph. The future changes are considered, including land use scenario in 2025 predicted by the Cellular Automata‐Markov (CA‐Markov), and climate change scenarios under three representative concentration pathways (RCPs) (i.e., RCP2.6, RCP4.5, and RCP8.5) obtained from five general circulation models (GCMs) for the period 2021–2030. Regional impacts of future land use and climate changes on flow regimes in the Yellow River Source Region are simulated and identified using distributed hydrological modeling and spatial classification. Results show that the increases in unused land (14.16%), and decreases in grassland (2.54%), glacier and snow cover (62.85%) are remarkable for the period 1980–2025. The flow regimes will be highly impacted in the source region for the RCP2.6 and RCP8.5 scenarios, but in the middle stream and downstream regions for the RCP4.5 scenario. Both the future land use and climate changes will increase flow magnitude for most regions, but their impacts on other flow regime metrics are not homogeneous. The climate change will play the dominant role in the flow regime variations, while the land use change will highly affect mean pre‐flood runoff, frequency and duration of high flow events, and mean rates of positive and negative changes. Key Points: Based on CA‐Markov model, grassland, glacier and snow cover will be remarkably degraded in 2025Impacts of future land use and climate changes on flow regimes are clustered to three robust classesClimate change results in flow regime variations, while land use change mainly impacts flow variability, frequency, and duration of high flow [ABSTRACT FROM AUTHOR]

Besides climate change (i.e., change on an average condition), streamflow change is also affected by interannual variability of climate (i.e., change from year to year). To quantify the impact of interannual climatic variability, in this study, we first clarify the content and definition of interannual climate elasticity that represents the sensitivity of streamflow to climatic variability from year to year and equals to the ratio of proportional annual change in streamflow and a climatic variable. Then, we analytically derive the interannual climate (i.e., potential evapotranspiration and precipitation) elasticities of streamflow by integrating the annual water balance with a conceptual hydrologic model (i.e., extended Budyko framework). At interannual scale, the terrestrial water storage change (i.e., ∆S) is a substantial component of water balance equation. Consequently, the analytical solutions of the interannual climate elasticities are functions of precipitation, potential evapotranspiration, and ∆S. The newly derived interannual climate elasticities are applied to 335 Model Parameter Estimation Experiment watersheds as a case study to evaluate the hypotheses in this study and construct the regional map of the streamflow sensitivity to interannual variability of climate. Results show that the interannual precipitation elasticity is generally larger than the absolute interannual potential evapotranspiration elasticity across study watersheds. Spatially, streamflow in the arid region tends to be more sensitive to interannual variability of climate than that in humid region. This study may be useful for understanding the extreme events and annual streamflow change caused by the ∆S (e.g., groundwater abstraction) across watersheds or regions. Key Points: The analytical solutions of interannual climate elasticities of streamflow are derivedThe influence of terrestrial water storage change is integrated in the derivation of the interannual climate elasticitiesThe regional map of streamflow sensitivity to interannual climatic variability across the United States is provided [ABSTRACT FROM AUTHOR]

The precipitation elasticity of runoff, defined as the percentage change in mean annual runoff for a given percentage change in mean annual precipitation, has been widely used as a simple rule of thumb for estimating runoff sensitivity to changes in precipitation. Most applications assume the same runoff sensitivity for both an increase and a decrease in precipitation. This study examines this assumption using long historical annual precipitation and streamflow data from 353 watersheds across the United States. The results show that the assumption is acceptable in humid and subhumid regions (aridity index less than 1.0). However, in arid and semiarid regions (aridity index greater than 1.0), the sensitivity of runoff to precipitation is greater for a decrease in precipitation than for an increase in precipitation. Therefore, the use of precipitation elasticity of runoff estimated using the entire data set in semiarid and arid regions will result in an overestimation of runoff increase from precipitation increase and an underestimation of runoff decrease from a decrease in precipitation. These findings suggest that multiple precipitation elasticities of runoff may be needed to estimate nonlinear responses of streamflow to precipitation change in a changing climate, especially for persistent increase and decrease in precipitation. Key Points: Precipitation elasticity of runoff is generally assumed to be the same for an increase as well as a decrease in precipitationResults show that runoff change in semiarid and arid watersheds is more sensitive to precipitation decreases than precipitation increasesIt is useful to characterize different precipitation elasticity for reducing uncertainties in regional projection of runoff [ABSTRACT FROM AUTHOR]

In this study, we examine the spatial and temporal characteristics of water stress in China for the historical (1971–2010) and the future (2021–2050) periods using a multimodel simulation approach. Three water stress indices (WSIs), that is, the ratios of water withdrawals to locally generated runoff (WSIR), to natural streamflow (WSIQ), and to natural streamflow minus upstream consumptive water withdrawals (WSIC), are used for the assessment. At the basin level, WSIR estimates generally match the reported data and indicate severe water stress in most northern basins. At the grid cell level, the WSIs show distinct spatial patterns of water stress wherein WSIR (WSIQ) estimates higher (lower) water stress compared to WSIC. Based on the WSIC estimates, 368 million people (nearly one third of the total population) are affected by severe water stress annually during the historical period, while WSIR and WSIQ suggest 595 and 340 million, respectively. Future projections of WSIC indicate that more than 600 million people (43% of the total) might be affected by severe water stress, and half of China's land area would be exposed to stress. The found aggravating water stress conditions could be partly attributed to the elevated future water withdrawals. This study emphasizes the necessity of considering explicit upstream and downstream relations with respect to both water availability and water use in water stress assessment and calls for more attention to increasing levels of water stress in China in the coming decades. Plain Language Summary: Severe water stress in China has been widely reported, but its time evolution and spatial patterns are rarely assessed. We examine the spatial and temporal change patterns of water stress in China by using multimodel simulations and three different water stress indices (WSIs). Results suggest that different WSIs imply distinct spatial patterns of water stress over China. The WSIs indicate that water stress conditions in northern China are quite distinct from that in southern China. During the past decades (1971–2010), severe water stress is found in northern areas while little is found in southern areas. In the future (2021–2050), however, water‐stressed areas might expand in southern China and water stress levels might aggravate in urban areas, putting considerably more people exposed to severe water stress. This assessment provides useful information for regional water planning/management within the context of future climate change and socioeconomic development in China. Key Points: Water stress estimates can be much different with the consideration of upstream water withdrawal and consumptionSevere water stress conditions are found in northern China in the pastWater‐stressed areas significantly expand over southern China in the future [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A long-term consistent and comprehensive dataset of land surface hydrologic fluxes and states will greatly benefit the analysis of land surface variables, their changes and interactions, and the assessment of land-atmosphere parameterizations for climate models. While some offline model studies can provide balanced water and energy budgets at land surface, few of them have presented an evaluation of the long-term interaction of water balance components over China. Here, a consistent and comprehensive land surface hydrologic fluxes and states dataset for China using the Variable Infiltration Capacity (VIC) hydrologic model driven by long-term gridded observation-based meteorological forcings is developed. The hydrologic dataset covers China with a 0.25° spatial resolution and a 3-hourly time step for 1952-2012. In the dataset, the simulated streamflow matches well with the observed monthly streamflow at the large river basins in China. Given the water balance scheme in the VIC model, the overall success at runoff simulations suggests that the long-term mean evapotranspiration is also realistically estimated. The simulated soil moisture generally reproduces the seasonal variation of the observed soil moisture at the ground stations where long-term observations are available. The modeled snow cover patterns and monthly dynamics bear an overall resemblance to the Northern Hemisphere snow cover extent data from the National Snow and Ice Data Center. Compared with global product of a similar nature, the dataset can provide a more reliable estimate of land surface variables over China. The dataset, which will be publicly available via the Internet, may be useful for hydroclimatological studies in China. [ABSTRACT FROM AUTHOR]

The MODerate Resolution Imaging Spectroradiometer (MODIS)/Terra daily snow-cover 500 m resolution product (MOD10A1) is evaluated through comparisons with ground-snow observations over the western US. The misclassified fractions of MOD10A1 (most common misclassification of snow-covered as non-snow-covered pixels) are generally lower at high elevations than at low elevations, and are greatest during the ablation period and at the beginning of the snow-accumulation period. The MOD10A1 data are used to update the snow-cover status in the Variable Infiltration Capacity (VIC) hydrology model applied to the Feather River Basin, California over the 2000-2008 MODIS period. The effect of MODIS updating is evaluated both on retrospective simulations, and on forecasts made at twice-monthly intervals, both for lead times of 2 weeks, and for seasonal forecasts from winter and spring forecast dates through to the end of July. Evaluations of simulated streamflow using the calibrated standard VIC model and the calibrated VIC with snow-covered area (SCA) updating show that the MODIS updating tends to reduce simulated streamflow and does not necessarily reduce simulation errors. Results for retrospective forecasts showed that assimilation of MODIS data reduced the Mean Absolute Error (EMAE) of 2 week lead-time forecasts during 2000-2008 in the snow-ablation period. However, forecast accuracy for seasonal forecasts during the same period was not improved by assimilation of MODIS data. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas. [ABSTRACT FROM AUTHOR]

• Climatic and reservoir impacts were quantified using a hydrological-reservoir model. • Reservoir strongly alter the streamflow in the period of 2008–2016 in the LMRB. • Upstream reservoir mainly affected the streamflow of the midstream stations. • Reservoir operation has reduced the flood risk increased by climatic change. The Lancang-Mekong River Basin (LMRB) is one of the most important transboundary river basins in Asia. While climate change perturbs the streamflow and affects flood events, reservoir operation may mitigate or aggravate this impact. Therefore, quantitative assessment of the climate change impact and reservoir effect on the LMRB is a vital prerequisite for future hydropower development and environmental protection. This study aimed to estimate the variation of the streamflow and flood characteristics affected by climate change and reservoir operation within the LMRB. A reservoir module was incorporated into the Variable Infiltration Capacity (VIC) model to simulate the streamflow susceptible to the reservoirs. It was found that the reservoirs had a substantial influence on the streamflow during 2008–2016, when many reservoirs were constructed in the LMRB. The reservoirs across the Lancang River (the upper Mekong River located in China) reduced the annual average streamflow by 5% at Chiang Sean station (northern Thailand) in 2008–2016, whereas their influence became undetectable downstream of Vientiane station (northern Laos). The streamflow changes downstream of Mukdahan station at southern Laos (including the stations in Cambodia and southern Vietnam) were mainly attributed to the local reservoirs and climate change. Compared with the baseline period of 1985–2007, the upstream reservoir operation dramatically affected streamflow at the midstream stations with higher dry season streamflow (+15% to +37%), but lower wet season streamflow was less affected (−2% to −24%) in 2008–2016. Climate change increased the magnitude and frequency of the flood by up to 14% and 45%, respectively, whereas the reservoir operation reduced them by 16% and 36%, respectively. Our findings provide insights into the interaction between climate change and reservoir operation and their integrated effects on the streamflow, informing and supporting water management and hydropower development in the LMRB. [ABSTRACT FROM AUTHOR]

In this study, variation characteristics of hydrometeorological factors were explored based on observed time-series data between 1957 and 2010 in four subregions of the Yellow River Basin. For each region, precipitation–streamflow models at annual and flood-season scales were developed to quantify the impact of annual precipitation, temperature, percentage of flood-season precipitation, and anthropogenic interference. The sensitivities of annual streamflow to these three climatic factors were then calculated using a modified elasticity coefficient model. The results presented the following: (1) Annual streamflow exhibited a negative trend in all regions; (2) The reduction of annual streamflow was mainly caused by a precipitation decrease and temperature increase for all regions before 2000, whereas the contribution of anthropogenic interference increased significantly—more than 45%, except for Tang-Tou region after 2000. The percentage of flood-season precipitation variation can also be responsible for annual streamflow reduction with a range of 7.36% (Tang-Tou) to 21.88% (Source); (3) Annual streamflow was more sensitive to annual precipitation than temperature in the humid region, and the opposite situation was observed in the arid region. The sensitivities to intra-annual climate variation increased after 2000 for all regions, and the increase was more significant in Tou-Long and Long-Hua regions. [ABSTRACT FROM AUTHOR]

• Budyko-based framework is improved for attribution of streamflow signature changes. • Streamflow signatures show clearly nonlinear relationships with climate variables. • Human activities play dominant roles in decreasing overall streamflow signatures. Streamflow has been complexly altered by climate change and human activities in major rivers worldwide. The traditional Darwinian approach for impact assessment of climate change and human activities mainly focuses on variations in annual streamflow, but ignores other critical streamflow characteristics (e.g., high- and low-flow signatures). In our study, seven streamflow metrics were adopted to characterize the main streamflow signatures including average, low and high flows, and the Budyko-based method was extended based on the derived differential equation of streamflow signature change to separate individual impacts of climate change and human activities. The Yellow River Basin, China was selected as the study area because of its high intensity of human activities, and daily runoff observation series from 1960 to 2015 at four stations from the source region to the downstream region were used for the assessment. The assessment results were also compared with those estimated by the hydrological modeling method for validation. The results show that all streamflow signatures at the four stations have evidently declined after the 1980s, particularly for high-flow magnitudes. Both climate change and human activities decrease most of streamflow signatures, and human activities play major roles, which are gradually enhanced from the source region to the downstream region. The contributions of human activities are from −66.85% to −56.68% in the Tangnaihai controlled catchment, from −96.93% to −54.59% in the Qingtongxia controlled catchment, from −83.61% to −57.49% in the Longmen controlled catchment, and from −91.02% to −57.29% in the Lijin controlled catchment. The most impacted signature is the average flow in the non-flood season, followed by the annual maximum flow. The estimated contributions of climate change and human activities match well with those estimated by the hydrological modeling method, particularly for the high- and average-flow signatures with contribution deviations less than 10%. However, the estimated contributions to the low-flow signatures are quite different likely due to large uncertainty of low flow estimation. This study is expected to provide insights into the streamflow variations basin wide and provide scientific foundations for ecological protection and high-quality development of the Yellow River Basin, China. [ABSTRACT FROM AUTHOR]