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Inland lakes in arid Central Asia are particularly susceptible to the impact of climate change and human activities. Ebinur Lake, the largest salt inland lake in Xinjiang, Northwest China, has experienced rapid shrinkage, with human activities identified as the primary influencing factor. However, it remains unclear how human water use in different sectors, such as irrigation, husbandry, and industry, is responsible for the long-term change and seasonal variability of the lake area under changing climate. This study aims to address this knowledge gap by developing an integrated hydrological-socioeconomic-lake model that simulates the changes in the Ebinur Lake area during the period of 1950–2020. The simulated lake area changes under different model experiments were then compared with separate the impacts of climate change and human water use of different sectors. The results indicate that climate change, irrigation, husbandry, and industry water uses have contributed +5%, −79%, −25%, and −1% of the long-term change in the lake area, respectively. Despite climate change alone increasing the lake area, the increase in human water use has resulted in the persistent decline of Ebinur Lake. Notably, husbandry water use emerges as a significant contributor to lake shrinkage, accounting for approximately one-third of the impact of irrigation. Furthermore, irrigation water use has contributed to the enhanced seasonal variability, as indicated by the difference between the maximum and minimum monthly lake area in a year. This difference has increased from 120 km2in the 1950s to 183 km2in the 2010s.

Extreme precipitation events and associated hazards are becoming more frequent and intense due to climate change. On June 2021, a devastating flash flood occurred in the Melamchi River in Nepal causing immense destruction to lives and property. The flood event was cascading in nature and associated with multiple driving factors. This study evaluates the post-extreme flood conditions of the Melamchi Flood 2021 (MF21). Rainfall, streamflow (discharge), and gauge height (water level) data of the nearest stations were analyzed. Since there was a lack of reliable instantaneous river discharge during the disastrous MF21, this study developed the flow rating curves at hydrometric stations. Post-extreme flood was evaluated considering upstream catchment hydrology, glacial lake outbursts, landslide damming, and the hydrodynamic propagations using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model and gauge-to-gauge (G2G) correlation. The results were then compared to field observations. The HEC-RAS model was simulated for several flood frequency periods in different return periods (namely 2, 5, 10, 25, 50, 100, 200, 500, and 1000 years). Results showed that the average 24 ​h gauge height and discharge at Nakote station (a few km downstream of landslide damming) are 15.88 ​m and 169.18 ​m3/s, in which the model-based highest frequency of 1-h extreme flood and return period can reach 716 ​m3/s in 1000 years. During MF21, the discharge of the Nakote region at the 10 ​min intervals was estimated to be with a maximum of 7162.10 ​m3/s, an average of 2207.13 ​m3/s and a minimum of 237.44 ​m3/s.Additionally, the outburst discharge from glacial lake at high elevation was estimated to be 295.14 ​m3/s contributing to downstream flooding. The model-based discharge during the past flood period was also estimated in 9 cross-section points of the river in which discharge at the damming area was 63.81 ​m3/s and downstream Dumredovan was 1206.58 ​m3/s. This research provides valuable insights to post-extreme flood evaluation and the approach can be used for other watersheds in evaluating post flood conditions.

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research.

•Water security is under threat on the northern slope of the Tianshan Mountains.•Climate change and economic growth are responsible for increased water stress.•Balancing water for humans and nature is vital in achieving SDGs.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

Precipitation recycling ratio (i.e., evaporation-precipitation feedback strength) and water vapor sources are two key aspects of regional water cycle, and their quantification is essential for understanding water cycle processes and their changes. The results of existing studies on the precipitation recycling ratio and water vapor sources for the Tibetan Plateau were highly controversial. This article clarifies different frameworks for understanding the water cycle. It points out that (1) the ratio of evaporation to precipitation depends mainly on climate regimes, while the precipitation recycling ratio is closely related to both the climate regimes and the scale of the region of interest, and (2) the water vapor sources depend on the traced period (precipitating or non-precipitating period) and the degree of tracing. Within the same theoretical framework, there is no fundamental conflict among the results of different studies on the water cycle in the Tibetan Plateau.

Tracking and quantifying the moisture sources of precipitation in different drainage basins in the Tibetan Plateau (TP) help to reveal basin-scale hydrological cycle characteristics under the interactions between the westerlies and Indian summer monsoon (ISM) systems and to improve our understanding on the mechanisms of water resource changes in the ‘Asian Water Tower’ under climate changes. Based on a Eulerian moisture tracking model (WAM-2) and three atmospheric reanalysis products (ERA-I, MERRA-2, and JRA-55), the contributions of moisture sources to the precipitation in six major sub-basins in the TP were tracked during an approximately 35-year period (1979/1980–2015). The results showed that in the upper Indus (UI), upper Tarim River (UT), and Qaidam Basin (QB), the moisture sources mainly extended westward along the mid-latitude westerlies to the western part of the Eurasian continent. In contrast, in the Brahmaputra (BR), inner TP (ITP), and the source area of three eastern rivers (TER, including the Nujiang River, Lancang River, and Yangtze River), the moisture sources extended both westward and southward, but mainly southward along the ISM. In winter and spring, all of the sub-basins were dominated by western moisture sources. In summer, the western sources migrated northward with the zonal movement of the westerlies, and simultaneously the southern sources of the BR, ITP, and TER expanded largely toward the Indian Ocean along the ISM. In autumn, the moisture sources of the UI, UT, and QB shrank to the western sources, and the moisture sources of the BR, ITP, and TER shrank to the central-southern TP and the Indian subcontinent. By quantifying the moisture contributions from multiple sources, we found that the terrestrial moisture dominated in all of the sub-basins, particularly in the UT and QB (62–73%). The oceanic contributions were relatively high in the UI (38–42°%) and BR (38–41%). In winter, evaporation from the large western water bodies (such as the Mediterranean, Red Sea, and Persian Gulf) was significantly higher than that from the continental areas. This contributed to the peak (valley) values of the oceanic (terrestrial) moisture contributions to all of the sub-basins. In summer, the terrestrial moisture contributions to the UI, UT, and QB reached their annual maximum, but the abundant oceanic moisture transported by the ISM restrained the appearance of land source contribution peaks in the BR, ITP, and TER, resulting in almost equal moisture contributions in the BR from the ocean and land.

Previous studies have shown that regional deep learning (DL) models can improve runoff prediction by leveraging large hydrological datasets. However, training a DL regional model using all data without screening may degrade local performance. This study focuses on constructing enhanced local models through the utilization of cross‐basin data. To this end, we propose an approach that employs a novel training strategy to optimize DL model training for specific basins. The approach measures the impact of any one basin's gradient on the loss of the basin of interest, providing insights into the relationships between different basins. The approach was validated using 531 basins from the CAMELS dataset. Results suggest that local performance degradation is a common occurrence in regional models, and imbalanced data are likely to result in a specific pattern dominating the entire regional model. In comparison to a regional model simply trained with all basins, the median Nash‐Sutcliffe efficiency (NSE) for our models is 0.031 higher. In particular, the increase in NSE can exceed 0.2 for some dry basins. Our findings indicate that this novel DL strategy can significantly improve model performance in specific basins using large hydrological datasets, while mitigating local performance loss. In the realm of deep learning, incorporating more data into the training process typically results in a more potent model. Conventionally, large datasets have been employed to train regional models with the intention of predicting rainfall‐runoff processes across all basins. However, such regional models often encounter performance degradation when applied to local basins. This degradation can be attributed to the extraction of overly general features, leading to a loss of specificity. In this paper, our objective is to harness information from a large dataset to establish a more robust local model. We have introduced a method that autonomously learns the similarities in rainfall‐runoff behavior among basins. Subsequently, we utilize this learned similarity to selectively choose data that proves advantageous for training the local model. Our results demonstrate that this strategy can significantly enhance runoff prediction, particularly in arid basins. Training a DL regional model using all data without screening may degrade local performanceProper selection of training data is crucial for enhancing DL model training for individual basins, especially arid basinsData from different basins might act as mutual noise during the training process Training a DL regional model using all data without screening may degrade local performance Proper selection of training data is crucial for enhancing DL model training for individual basins, especially arid basins Data from different basins might act as mutual noise during the training process

Phosphorus (P) losses from fertilized croplands to inland water bodies cause serious environmental problems. During wet years, high precipitation disproportionately contributes to P losses. We combine simulations of a gridded crop model and outputs from a number of hydrological and climate models to assess global impacts of changes in precipitation regimes on P losses during the 21st century. Under the baseline climate during 1991–2010, median P losses are 2.7 ± 0.5 kg P ha–1year–1over global croplands of four major crops, while during wet years, P losses are 3.6 ± 0.7 kg P ha–1year–1. By the end of this century, P losses in wet years would reach 4.2 ± 1.0 (RCP2.6) and 4.7 ± 1.3 (RCP8.5) kg P ha–1year–1due to increases in high annual precipitation alone. The increases in P losses are the highest (up to 200%) in the arid regions of Middle East, Central Asia, and northern Africa. Consequently, in three quarters of the world’s river basins, representing about 40% of total global runoff and home up to 7 billion people, P dilution capacity of freshwater could be exceeded due to P losses from croplands by the end of this century.

Previous studies have estimated power plant cooling water consumption based on the long-term average cooling water consumption intensity (WI: water consumption per unit of electricity generation) at an annual scale. However, the impacts of the seasonality of WI and streamflow on electricity generation are less well understood. In this study, a risk assessment method is developed to explore the seasonal risk of water–electricity nexus based on the Integrated Environmental Control Model, which can simulate variable WIs in response to daily weather conditions and avoid underestimation in WIs as well as nexus risk during dry seasons. Three indicators, reliability, maximum time to recovery, and total power generation loss, are proposed to quantify the seasonal nexus risk under water consumption policy constraint represented by the allowed maximum percentage of water consumption to streamflow. The applications of the method in two representative watersheds demonstrate that the nexus risk is highly seasonal and is greatly impacted by the seasonal variability of streamflow rather than annual average water resources conditions on which most previous studies are based. The nexus is found more risky in the watershed with almost double mean annual streamflow and greater streamflow variability, compared with the watershed with less streamflow variability.

The endorheic Tibetan Plateau (ETP), which consists of all the endorheic basins of the Tibetan Plateau (TP), has exhibited an overall mass gain in recent decades. However, the role played by atmospheric water (AW) transport on the hydrological status over the ETP is poorly understood. In this study, the AW source to the ETP was tracked with the Water Accounting Model‐2 layers (WAM‐2) and AW transport to the ETP through its boundaries was quantified, with three reanalysis products (ERA‐I, MERRA‐2, and JRA‐55) during 1979/1980–2015. It is found that total AW input to the ETP is about 13–25%, 59–71%, 10–13%, and 3–7% of mean annual totals in spring, summer, autumn, and winter, respectively. At annual scales, the AW source from land (52–54%) dominates the AW contribution to the ETP, while local recycling of AW over the ETP accounts for about 17–22% of the mean annual total AW contribution. Increased precipitation over the ETP during 1979–2015 was mostly attributed to the significantly increased AW contribution from the Indian Ocean, especially from increased AW inputs transported from the western and southern boundaries in summer. Comparisons between the AW budget and terrestrial water storage changes indicate that the AW budget change over the ETP modulated the variations of terrestrial water storage change during 2002–2014 and annual lake mass change during 1989–2015. The AW source from land dominated the AW contribution to the ETP at annual scales during 1979–2015The increased AW contribution from the Indian Ocean favored increasing precipitation over the ETP from 1979 to 2015The change of AW budget modulated the variations of TWS change over the ETP during 2002–2014

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. 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

The Zoige peatlands are the largest peatland area in China, and the largest high‐altitude peatland in the world. As with many peatlands worldwide, degradation from land management and climate change mean that the intact Zoige peatland area has decreased, potentially reducing the carbon (C) sink function and ecosystem services. This review summarizes current knowledge of the impacts of land‐use and climate change on the Zoige peatland C cycle in a global perspective and identifies future research and management directions. The existing literature suggests that artificial drainage carried out to lower water tables and improve grazing has a significant impact on the peatland C cycle. Drained and degraded areas may act as a net C source, through increased CO2emissions, although the overall C balance of the Zoige peatlands is likely still a net C sink. Future climate change may also impact upon the peatland C cycle. Warming of 2°C may significantly reduce the strength of the C sink of intact peatland areas, which may shift the overall Zoige peatland C cycle balance to a net C source. The effect of warming on degraded Zoige peatlands is a major uncertainty, although the global literature suggests warming effects may be greater in degraded peatlands. Restoration of degraded peatlands (by blocking drains) may help reverse some of the impacts of degradation and gradually recover C sink function. However, there are fewer studies in Zoige peatlands than elsewhere. We conclude with several specific suggestions for future research on the peatland C cycle. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangeAssessing Impacts of Climate Change > Observed Impacts of Climate ChangeClimate, Ecology, and Conservation > Observed Ecological Changes Intact Zoige peatlands function as a net carbon sink, while artificial drainage may change such areas to a net carbon source. Climate warming may decrease the carbon sink strength of intact peatland areas, effects of warming on degraded Zoige peatlands are generally unknown.

Total evaporation from the vast terrain of the Tibetan Plateau (TP) may strongly influence downwind regions. However, the ultimate fate of this moisture remains unclear. This study tracked and quantified TP‐originating moisture using an extended Eulerian model. The findings reveal that the involvement of moisture from the TP in the downwind precipitation is most pronounced near the eastern boundary of the TP and gradually diminishes eastward. Consequently, the TP moisture ratio in precipitation reaches the highest of over 30% over the central‐eastern TP. 44.9%–46.7% of TP annual evaporation is recycled over the TP, and 65.1%–66.8% of the TP evaporation is reprecipitated over terrestrial China. Moisture recycling of TP origin shows strong seasonal variation, with seasonal patterns largely determined by precipitation, evaporation and wind fields. High levels of evaporation and precipitation over the TP in summer maximize local recycling intensity and recycling ratios. Annual precipitation of TP origin increased mainly around the northeastern TP during 2000–2020. This region consumed more than half of the increased TP evaporation. Further analyses showed that changes in reprecipitation of TP origin were consistent with precipitation trends in nearby downwind areas: when intensified TP evaporation meets intensified precipitation, more TP moisture is precipitated out. The model estimated an annual precipitation recycling ratio (PRR) of 26.9%–30.8% in forward moisture tracking. However, due to the non‐closure issue of the atmospheric moisture balance equation, the annual PRR in backward tracking can be ∼6% lower. The fate and changes of Tibetan Plateau (TP) evaporation were identified and quantified using an extended moisture tracking model44.9%–46.7% of TP evaporation was recycled locally and 65.1%–66.8% of it was reprecipitated over terrestrial ChinaIncreased TP evaporation was reprecipitated mainly around the northeast Plateau consistent with downwind precipitation changes The fate and changes of Tibetan Plateau (TP) evaporation were identified and quantified using an extended moisture tracking model 44.9%–46.7% of TP evaporation was recycled locally and 65.1%–66.8% of it was reprecipitated over terrestrial China Increased TP evaporation was reprecipitated mainly around the northeast Plateau consistent with downwind precipitation changes

We investigated the nonlinearity of runoff response to global mean temperature (GMT) change in the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at the river basin scale globally. Results show that changes in long‐term mean annual runoff are nonlinear with GMT rise over most extended subtropical basins, suggesting that estimation of future runoff change derived from the linear scaling relations would be biased. As for the interannual variability, nonlinearities are apparent mainly in central and western Asia, southern and western Africa, most of Europe, and Australia when GMT increases beyond 1.5°C. This suggests that impacts of climate change under 1.5°C GMT rise on runoff variability should not be simply scaled from that under a 2°C warming world. Our results highlight the contrasting response of areal runoff to GMT rise across global major river basins and reveal the threshold of GMT increment at which the nonlinear runoff response is projected to emerge. This paper addresses the nonlinear response of areal runoff to global warming at river basin scale globally and reveals the specific basins and the target global mean temperature rise where and when the nonlinearity is projected to occur. Furthermore, we highlight the possible mechanisms behind the revealed nonlinear response of river basin mean runoff to global mean temperature rise. These findings greatly differ from the global‐scale linear relation concluded from previous analyses. This will provide great scientific implications for the ongoing impact assessment of limiting global warming to 1.5° advocated by 2015 Paris agreement, and thus benefit local climate impact assessment, mitigation, and adaptation. Response of runoff to global warming varies across warming levels and river basinsRunoff in many subtropical river basins exhibits evident nonlinear response to warmingRunoff response could change substantially when global temperature rises beyond 1.5°C

In Northwest China, water originates from the mountain area and is largely used for irrigation agriculture in the middle reaches. This study investigates the local and remote impact of irrigation on regional climate in the Heihe River Basin, the second largest inland river basin in Northwest China. An irrigation scheme was developed and incorporated into the Weather Research and Forecasting (WRF) model with the Noah‐MP land surface scheme (WRF/Noah‐MP). The effects of irrigation is assessed by comparing the model simulations with and without consideration of irrigation (hereafter, IRRG and NATU simulations, respectively) for five growth seasons (May to September) from 2009 to 2013. As consequences of irrigation, daily mean temperature decreased by ~1.7°C and humidity increased by ~2.3 g kg−1(corresponding to ~38.5%) over irrigated area. The temperature and humidity of IRRG simulation matched well with the observations, whereas NATU simulation overestimated temperature and underestimated humidity over irrigated area. The effects on temperature and humidity are generally small outside the irrigated area. The cooling and wetting effects have opposing impacts on convective precipitation, resulting in a negligible change in localized precipitation over irrigated area. However, irrigation may induce water vapor convergence and enhance precipitation remotely in the southeastern portion of the Heihe River Basin. A new irrigation parameterization scheme mimicking the local irrigation activities was incorporated into WRF/Noah‐MP modelThe irrigation results in strongly cooling and wetting effects on surface, and hence, modeling warm and dry bias are largely reducedThe irrigation has not modified local precipitation while has remotely precipitation effects by modifying atmospheric moisture transportation

Extreme riverine floods and hydrodynamic response of downstream lakes are the main focus of catchment flood control, especially under climate change. In this study, future changes in the extreme floods and their hydrodynamic responses are projected by driving the external coupling of the hydrological and hydrodynamic models using the precipitation and temperature outputs of the general circulation models (GCMs) in seven emission scenarios, and the impact of climate change is evaluated. The Baiyangdian Lake and its controlled catchment are selected as our study area. Results show that: (a) both the daily runoff and the lake water levels are well simulated, (b) compared with the baseline scenario, extreme riverine floods are projected to increase evidently throughout the catchment with mean rates of 33.2%, 14.1% and 10.9% in the RCP2.6, 4.5 and 8.5 scenarios, respectively because of increasing precipitation; consequently, the total lake inflow also increases by 13.5%–150.9%, most of which are from Zhulong, Xiaoyi, Tanghe, and Fuhe Rivers, and (c) the water level of the Baiyangdian Lake increases temporally during the flood seasons in all the scenarios, and the increase in water level is 0.27–1.29 m greater than that in the baseline scenario; consequently, the water depth in 55.8% and 73.4% of lake area increases over 1.0 m in the future short‐term and long‐term scenarios, respectively; the inundation area also increases by 23.4%–26.7% in the future scenarios, except in the long‐term RCP4.5 scenario. The northeastern and south‐central regions of Baiyangdian, which are near Xiong'an New Area, have a relatively high inundation risk. In this study, the catchment‐waterbody relationship is revealed by externally coupling a distributed catchment hydrological model with a lake hydrodynamic model to simulate the flood changes throughout the catchment and the hydrodynamic responses of the downstream lake. Future changes in the extreme floods and their hydrodynamic responses are projected in seven emission scenarios, and the impact of climate change is evaluated. The Baiyangdian Lake and its controlled catchment are selected as our study area. Results show that: (a) both the daily runoff at 71.4% of total stations and the lake water levels are well simulated, (b) compared with the baseline scenario, extreme riverine floods are projected to increase evidently because of increasing precipitation, (c) the water level of the Baiyangdian Lake increases temporally during the flood season in all the scenarios; consequently, the water depth in 55.8% and 73.4% of lake area increases over 1.0 m in the future short‐term and long‐term scenarios. The inundation area increases by 23.4%–26.7% in the future scenarios, except in the long‐term RCP4.5 scenario. The northeastern and south‐central regions of Baiyangdian, which are near Xiong'an New Area, have a relatively high inundation risk. Future flood changes and their impacts on lake water depth and inundation area are projected using a coupled hydrological‐hydrodynamic modelExtreme riverine floods increase by 10.9%–33.2% in selected RCP scenarios due to increasing precipitationWater depth increases in 93.8%–97.3% of lake area, particularly in regions near Xiong'an New Area with an increase exceeding 1.0 m Future flood changes and their impacts on lake water depth and inundation area are projected using a coupled hydrological‐hydrodynamic model Extreme riverine floods increase by 10.9%–33.2% in selected RCP scenarios due to increasing precipitation Water depth increases in 93.8%–97.3% of lake area, particularly in regions near Xiong'an New Area with an increase exceeding 1.0 m

Many countries have experienced the increase of groundwater depletion rate (GWDR), posing great challenges to the environment and society. Given the slow variation of groundwater, long‐term estimation of GWDR at high‐resolution is critical for sustainable managements of regional water resources and risks of hydrological extremes in a changing climate. However, limited in‐situ and satellite observations and uncertainties from hydrological model simulations prohibit reliable estimation of the long‐term trend and inter‐annual variation of GWDR. Using survey‐based water withdrawal and water availability datasets, this study provided a spatially distributed estimation of annual GWDR over China at the 0.25° × 0.25° grid scale for the period 1965–2016 using a flux‐based approach. The estimated GWDR has higher spatial resolution and longer temporal coverage than previous products. With the newly compiled data, we found that groundwater over China was depleted at an average rate of 20.4 km3year−1during 1965–2016, especially in the arid and semi‐arid areas of northern China. The GWDR showed a significant increasing trend during 1965–2016, but such trend slowed down from 0.66 km3year−1during 1965–2001 to 0.06 km3year−1during 2001–2016, which was mainly attributed to the deceleration in human water withdrawal. Further analysis showed that over 40% of the inter‐annual variability of GWDR could be explained by precipitation variability, and the climate control increased over time. This study suggested a growing control of climate variations on GWDR over China as the human water withdrawal has been decelerating, and a need for climate change adaptation for a sustainable groundwater management. Groundwater serves as an important freshwater source, and is critical for human life and socioeconomic development. Yet unsustainable groundwater abstraction exceeds recharge from rain and rivers, leading to losses of groundwater storage and substantial drops in groundwater level, namely groundwater depletion. Groundwater depletion would result in significant environmental degradation and threaten food security, which has attracted much attention to long‐term estimation of groundwater depletion at high‐resolution. This study provided a spatially distributed estimation of groundwater depletion rate over China and further analyzed its changing patterns. We found that groundwater depletion in China increased continuously during 1965–2016, but this increasing trend slowed down after 2001 due to the deceleration in human water withdrawal. Results also suggested an increasing negative correlation between groundwater depletion rate and precipitation over time, and addressed the nonnegligible impacts of climate extremes on groundwater depletion. The findings have important implications for groundwater sustainability over water‐scare regions, and water managers should take into account both water demand and climate variations when designing and selecting robust adaptation strategies to cope with groundwater depletion under changing environment. Groundwater depletion rate (GWDR) in China was estimated by a flux‐based approach at high spatial resolution and long temporal coverageThe increasing trend of GWDR slowed down after 2001 due to the deceleration in human water withdrawalThere was a significant negative correlation between GWDR and precipitation, and the climate control increased over time Groundwater depletion rate (GWDR) in China was estimated by a flux‐based approach at high spatial resolution and long temporal coverage The increasing trend of GWDR slowed down after 2001 due to the deceleration in human water withdrawal There was a significant negative correlation between GWDR and precipitation, and the climate control increased over time

Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment, preparedness and mitigation. In recent decades, developments in ground and satellite measurements have made the hydrometeorological information readily available, and advances in information technology have facilitated the data analysis in a real-time manner. New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution. These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status, and earlier warning of extreme hydrological events such as droughts and floods. This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting, reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting, and briefly introduces the current monitoring and forecasting practices in China. The grand challenges and perspectives for the near future are also discussed, including acquiring and extracting reliable information for monitoring and forecasting, predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity, and filling the gap between numerical models and the end user. We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.

The complexity involved in accurate estimation and numerical simulation of regional evapotranspiration (ET) can lead to inconsistency among techniques, usually attributed to methodological deficiencies. Here we hypothesize instead that discrepancies in ET estimates should be expected in some cases and can be applied to measure the effect of anthropogenic influences in developed river basins. We compare an ensemble of corrected ET estimates from land surface models with Gravity Recovery and Climate Experiment and Moderate Resolution Imaging Spectroradiometer satellite‐based estimates in the intensively managed Colorado River Basin to contrast the roles of natural variability and human impacts. Satellite‐based approaches yield larger annual amplitudes in ET estimates than land surface model simulations, primarily during the growing season. We find a total satellite‐based ET flux of 142 ± 7 MAF yr−1(175 ± 8.63 km3yr−1), with 38% due to anthropogenic influences during summer months. We evaluate our estimates by comparison with reservoir storage and usage allotment components of the basin water management budget. Satellite observations used to infer role of water management in ET ratesSatellite estimates of ET are larger during summer months compared to model estimatesBasin‐wide observed ET is 142 MAF/yr (175 km3 yr−1) with 38% due to management impacts in summer

As a major agricultural country, China suffers from severe meteorological drought almost every year. Previous studies have applied a single threshold to identify the onset of drought events, which may cause problems to adequately characterize long-term patterns of droughts. This study analyzes meteorological droughts in China based on a set of daily gridded (0.5° × 0.5°) precipitation data from 1961 to 2014. By using a multi-threshold run theory approach to evaluate the monthly percentage of precipitation anomalies index (Pa), a drought events sequence was identified at each grid cell. The spatiotemporal variations of drought in China were further investigated based on statistics of the frequency, duration, severity, and intensity of all drought events. Analysis of the results show that China has five distinct meteorological drought-prone regions: the Huang-Huai-Hai Plain, Northeast China, Southwest China, South China coastal region, and Northwest China. Seasonal analysis further indicates that there are evident spatial variations in the seasonal contribution to regional drought. But overall, most contribution to annual drought events in China come from the winter. Decadal variation analysis suggests that most of China’s water resource regions have undergone an increase in drought frequency, especially in the Liaohe, Haihe, and Yellow River basins, although drought duration and severity clearly have decreased after the 1960s.

Five General Circulation Model (GCM) climate projections under the RCP8.5 emission scenario were used to drive the Variable Infiltration Capacity (VIC) hydrologic model to investigate the impacts of climate change on hydrologic cycle over continental China in the 21st century. The bias-corrected climatic variables were generated for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). Results showed much larger fractional changes of annual mean Evapotranspiration (ET) per unit warming than the corresponding fractional changes of Precipitation (P) per unit warming across the country, especially for South China, which led to a notable decrease of surface water variability (P-E). Specifically, negative trends for annual mean runoff up to −0.33%/year and soil moisture trends varying between −0.02% to −0.13%/year were found for most river basins across China. Coincidentally, interannual variability for both runoff and soil moisture exhibited significant positive trends for almost all river basins across China, implying an increase in extremes relative to the mean conditions. Noticeably, the largest positive trends for runoff variability and soil moisture variability, which were up to 0.41%/year and 0.90%/year, both occurred in Southwest China. In addition to the regional contrast, intra-seasonal variation was also large for the runoff mean and runoff variability changes, but small for the soil moisture mean and variability changes. Our results suggest that future climate change could further exacerbate existing water-related risks (e.g., floods and droughts) across China as indicated by the marked decrease of surface water amounts combined with a steady increase of interannual variability throughout the 21st century. This study highlights the regional contrast and intra-seasonal variations for the projected hydrologic changes and could provide a multi-scale guidance for assessing effective adaptation strategies for China on a river basin, regional, or as a whole.

We estimated runoff sensitivities to global mean temperature change using climate experiments archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and compared with the similar CMIP3 results. We also evaluated differences in runoff sensitivity for the Earth System Models (ESMs) and Climate System Models (CSMs), which were separately identified in CMIP5 for the first time. Our results show that runoff sensitivity is relatively independent of emission scenarios in CMIP5, as in CMIP3 results. Global mean runoff would increase about 2.9% per °C of global warming in CMIP5, as contrasted with 1.9% in CMIP3. Although global runoff sensitivity patterns for CMIP5 and CMIP3 are roughly similar, CMIP5 suggests significant declines (increases) in runoff sensitivity over 17% (25%) of the global land area relative to CMIP3. Globally, the ESMs and CSMs have about the same model spread in runoff change projections. CMIP5 results imply global runoff increases by 2.9% per degree C global warmingEarth System Models do not reduce model spread in runoff change projectionsRunoff sensitivities in CMIP5 models are consistent across RCP scenarios

Groundwater use underpins much economic production. The unsustainable use of groundwater threatens environmental flows in surface waters, sustainable development, and future food security. The connection between agricultural trade and groundwater depletion has been recently highlighted, but how groundwater depletion supports the production of industrial and tertiary goods, trade, and consumption remains less well understood. Here, we present the first analysis of groundwater use and depletion embedded in the complete supply chain of China (including primary, secondary, and tertiary products). We use a multiregion input‐output analysis coupled with the high‐resolution groundwater use modeling to track groundwater depletion from production to end consumer. Our modeling results show that groundwater depletion occurred primarily in water scarce North China for agricultural production, but the depleted resource was then incorporated throughout the supply chain and dispersed across Chinese and international consumers. ∼64 billion m3yr−1(±1 billion m3yr−1) groundwater was depleted in China, in which more than a half was from Xinjiang, Hebei, Henan and Heilongjiang Provinces. Approximately 40% of the groundwater depletion can be traced to interprovincial transfer (21 billion m3yr−1) and export (4.8 billion m3yr−1). The hot spots for final consumption of groundwater depletion were major cities in both North and South China. Importantly, over 60% of the groundwater depletion was embodied in industrial and tertiary products for final consumption, highlighting the importance of tracing groundwater through the full economy. Groundwater depletion represents a long‐term risk to supply chains, and policy‐makers can use this understanding to sustainably manage groundwater and diversify supply chains. Groundwater is a critical resource that is under increased pressure due to unsustainable exploitation. Groundwater resources underpin both national supply chains and global trade, but how groundwater supports the full supply chain of a nation remains unresolved. This study traces the groundwater depletion incorporated in the complete supply chain (including industrial and tertiary products, not just primary goods) of China through interprovincial transfers and international exports. The results show how groundwater depletion, which is concentrated in North China, is distributed to domestic and international consumers of final goods. International importers receive China's groundwater in the receipt of industrial products. We highlight that groundwater depletion represents a long‐term risk to supply chains. Groundwater depletion is traced using a multi‐region input‐output analysis coupled with the high‐resolution groundwater use modelingAbout 21 billion m3yr−1groundwater depletion, primarily from water scarce Northern‐China, is virtually transferred domesticallyGroundwater depletion supporting export to the Rest of the World is estimated to be 4.8 billion m3yr−1 Groundwater depletion is traced using a multi‐region input‐output analysis coupled with the high‐resolution groundwater use modeling About 21 billion m3yr−1groundwater depletion, primarily from water scarce Northern‐China, is virtually transferred domestically Groundwater depletion supporting export to the Rest of the World is estimated to be 4.8 billion m3yr−1

The past decade has seen an exceptional number of unprecedented summer extreme weather events in northern mid-latitudes, along with record declines in both summer Arctic sea ice and snow cover on high-latitude land. The underlying mechanisms that link the shrinking cryosphere with summer extreme weather, however, remain unclear. Here, we combine satellite observations of early summer snow cover and summer sea-ice extent with atmospheric reanalysis data to demonstrate associations between summer weather patterns in mid-latitudes and losses of snow and sea ice. Results suggest that the atmospheric circulation responds differently to changes in the ice and snow extents, with a stronger response to sea-ice loss, even though its reduction is half as large as that for the snow cover. Atmospheric changes associated with the combined snow/ice reductions reveal widespread upper-level height increases, weaker upper-level zonal winds at high latitudes, a more amplified upper-level pattern, and a general northward shift in the jet stream. More frequent extreme summer heat events over mid-latitude continents are linked with reduced sea ice and snow through these circulation changes.

Study on hydroclimatological changes in the mountainous river basins has attracted great interest in recent years. Changes in temperature, precipitation and river discharge pattern could be considered as indicators of hydroclimatological changes of the river basins. In this study, the temperatures (maximum and minimum), precipitation, and discharge data from 1980 to 2009 were used to detect the hydroclimatological changes in the Bagmati River Basin, Nepal. Simple linear regression and Mann-Kendall test statistic were used to examine the significant trend of temperature, precipitation, and discharge. Increasing trend of temperature was found in all seasons, although the change rate was different in different seasons for both minimum and maximum temperatures. However, stronger warming trend was found in maximum temperature in comparison to the minimum in the whole basin. Both precipitation and discharge trend were increasing in the pre-monsoon season, but decreasing in the post-monsoon season. The significant trend of precipitation could not be observed in winter, although discharge trend was decreasing. Furthermore, the intensity of peak discharge was increasing, though there was not an obvious change in the intensity of maximum precipitation events. It is expected that all these changes have effects on agriculture, hydropower plant, and natural biodiversity in the mountainous river basin of Nepal.

In northern Ethiopian highlands, climate variability and land use/land cover (LULC) change has been observed during the past decades. However, it remains unclear that to which extent and how these key drivers contributed to changes in hydrological extremes. As a case study, the respective impacts of LULC change and climate variability on hydrological extremes with respect to magnitude, duration, and frequency metrics were assessed in the Upper Tekeze basin, Ethiopia. A scenario‐based approach using LULC and hydro‐climatological data was developed and five simulation experiments were conducted using Soil and Water Assessment Tool model. Results showed that, during 1986–2015, low‐flow duration has decreased by 5.0 days/yr, and the magnitude and occurrence of high flow has increased by 5.23 m3/s/yr and 5.11 days/yr, respectively. Meanwhile, about 47.42% of the landscape has changed its category while no significant change was detected in annual rainfall during the past 31 yr. Particularly, rocky area increased by 82% which was mostly contributed by losses of bare, sand, and low biomass areas. Significant positive and negative contributions to the changes in high and low‐flow extremes, respectively, were detected from LULC change, while no significant contribution was detected from climate change. The increased rock area tends to be one of the major contributors to the increased high flow while climate change seemed to play a minor role. The potential mechanism is the expansion in rock area increased runoff and high flow, which may lead to significant land deterioration and loss of water storage capacity in the mountainous basin. Significant increase in high flow events and decrease in low flow events were detected in the Upper Tekeze River basin (UTB)Changes in the flow extremes was largely attributed to land use/land cover change and not significantly influenced by climate changePotential mechanism that increased land degradation led to more streamflow in the UTB Significant increase in high flow events and decrease in low flow events were detected in the Upper Tekeze River basin (UTB) Changes in the flow extremes was largely attributed to land use/land cover change and not significantly influenced by climate change Potential mechanism that increased land degradation led to more streamflow in the UTB

This study evaluates the validity of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) in scaling extreme precipitation with temperature (termed scaling factor, SF). To this end, we use Hadley‐Integrated Surface Database (HadISD) data set at 1‐hourly, 6‐hourly, and 24‐hourly scales between 2000 and 2020 over the contiguous United States (CONUS) as a reference data set. Our findings reveal that IMERG can capture changes in precipitation extremes with the temperature at 6‐hourly, and 24‐hourly scales, especially over dry regions. The difference between IMERG's SF and HadISD's SF at 6‐hourly scale is less than 0.3%/°C in dry areas. However, IMERG underestimates SF in wet regions, especially at 1‐hourly scale, up to ∼5%/°C. It is found that IMERG's performance is dependent on temperature, particularly at 1‐hourly scale. IMERG overestimates extreme precipitations when temperatures are below 5°–11°C while the opposite is true at higher temperatures, indicating the need for considering temperature in adjusting IMERG. Understanding the scaling rate of precipitation extremes with temperature can help to estimate precipitation extremes under global warming. However, we do not have in situ precipitation data in many parts of the world to monitor changes in precipitation extremes with temperature. The remotely sensed precipitation data sets that are available globally with high spatio‐temporal resolution might be excellent resources to use for this purpose. To discover that, in this study, we evaluated the performance of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) as one of the newest precipitation data sets for scaling extreme precipitation with temperature over the contiguous United States. We compared IMERG's results with Hadley‐Integrated Surface Database results at 1‐hourly, 6‐hourly, and 24‐hourly scales. Results indicate IMERG has a considerable performance at 6‐hourly and 24‐hourly time scales. Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG) represents well the relationship between precipitation extremes and temperature at 6‐hourly and daily scalesIMERG's scaling factors in dry areas are closer to the observations than in wet regionsIMERG overestimates (underestimates) hourly precipitation extremes at low (high) temperatures Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG) represents well the relationship between precipitation extremes and temperature at 6‐hourly and daily scales IMERG's scaling factors in dry areas are closer to the observations than in wet regions IMERG overestimates (underestimates) hourly precipitation extremes at low (high) temperatures

Hydropower dams are proliferating in the riparian countries of the Lancang‐Mekong River Basin (LMRB) driven by the pursuit of renewable electricity and societal resilience to flooding. However, the tradeoffs between hydropower production and flood control are unclear in a changing environment. Here, we use a hydrological variable infiltration capacity model combined with a reservoir module to quantify the relative effects of climate change and reservoir operation on flood and hydropower generation in LMRB. Results show that while climate change would increase flood magnitude and frequency, adaptive reservoir operation can reduce flood magnitude by 5.6%–6.4% and frequency by 17.1%–18.9% at the cost of 9.8%–14.4% of basin‐wide hydropower generation. Particularly, upstream reservoirs suffer more hydropower loss (5.4 times) than downstream ones when flood control is prioritized in reservoir regulation. Our findings have implications for integrated water and energy management at the transboundary river basin under climate change. Dams and reservoirs provide two important services, i.e., flood control and hydropower generation. In this study, we seek to understand the future tradeoff between these two services provided by reservoir regulation in the transboundary Lancang‐Mekong River Basin. Using a modeling based approach, we find that climate change will likely lead to more frequent and larger flood events, but reservoir operation, by regulating water discharges and levels in streams, can effectively reduce flood frequency and magnitude at the expense of hydropower generation. Our results highlight the importance of coordinating water and energy management across countries in this transboundary river basin. Reservoir can mitigate future flood risk from climate change at the cost of reducing hydropower generation in the Lancang‐Mekong River BasinReservoir regulation can delay the timing when flood risk exceeds the historical baseline by at least 20 years in Laos and ThailandFlood control measures would reduce hydropower at a magnitude of 5.4 times in China than downstream countries Reservoir can mitigate future flood risk from climate change at the cost of reducing hydropower generation in the Lancang‐Mekong River Basin Reservoir regulation can delay the timing when flood risk exceeds the historical baseline by at least 20 years in Laos and Thailand Flood control measures would reduce hydropower at a magnitude of 5.4 times in China than downstream countries

Daily floods including event, characteristic, extreme and inundation in the Lancang‐Mekong River Basin (LMRB), crucial for flood projection and forecasting, have not been adequately modeled. An improved hydrological‐hydrodynamic model (VIC and CaMa‐Flood) considering regional parameterization was developed to simulate the flood dynamics over the basin from 1967 to 2015. The flood elements were extracted from daily time series and evaluated at both local and regional scales using the data collected from in‐situ observations and remote sensing. The results show that the daily discharge and water level are both well simulated at selected stations with relative error (RE) less than 10% and Nash‐Sutcliffe efficiency coefficient (NSE) over 0.90. Half of the flood events have NSEexceeding 0.76. The peak time and flood volume are well reproduced while both peak discharge and water level are slightly underestimated. The results tend to worsen when the characteristics of flood events are extended to annual extremes. These extremes are generally underestimated with NSEless than 0.5 but REis within 20%. The simulated rainy season inundation area generally agrees with observations from remote sensing, with about 86.8% inundation occurrence frequency captured within the model capacity. Ignoring the regional parameterization and reservoir regulation can both deteriorate flood simulation performance at the local scale, resulting in lower NSE. Specifically, systematically higher water levels and up to 27% overestimation of peak discharge are found when ignoring regional parameterization, while ignoring reservoir regulation would cause up to 23% overestimation for flood extremes. It is expected that these findings would contribute to the regional flood forecasting and flood management. An improved hydrodynamic module with regional parameterization to enhance sub‐basin physical representation was developedDaily floods were modeled with relative error no more than 20% and NSEover 0.90 at selected stationsFlood simulation without regional parameterization or reservoir regulation shows less satisfactory performance with lower NSE An improved hydrodynamic module with regional parameterization to enhance sub‐basin physical representation was developed Daily floods were modeled with relative error no more than 20% and NSEover 0.90 at selected stations Flood simulation without regional parameterization or reservoir regulation shows less satisfactory performance with lower NSE

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. 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 Based on CA‐Markov model, grassland, glacier and snow cover will be remarkably degraded in 2025 Impacts of future land use and climate changes on flow regimes are clustered to three robust classes Climate change results in flow regime variations, while land use change mainly impacts flow variability, frequency, and duration of high flow

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. 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

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data

Evapotranspiration occurs via land‐atmosphere coupling. To understand the impacts of irrigation on local potential evapotranspiration, we carried out a couple of simulations with the Weather Research and Forecast (WRF) model, where an irrigation scheme was incorporated, for the irrigated area in the middle reaches of the Heihe River, Northwest China. By comparing the irrigation simulation (hereafter, the IRRG case) with the simulation excluding irrigation (hereafter, the NATU case), we found that irrigation may lead to a decrease in reference evapotranspiration (ET0) during the growing season (May to September), which is a metric of potential evapotranspiration, by approximately 1.05 mm d−1, accounting for approximately 19% of NATU ET0. The ET0in the IRRG is closer to the ground measurements than the NATU ET0, with a root‐mean‐square error of 0.75 mm d−1in the IRRG and 1.86 mm d−1in the NATU cases. Meanwhile, irrigation leads to an actual evapotranspiration (ETa) increase of approximately 2.1 mm d−1for croplands. Such an asymmetric relationship between ET0decrease and ETaincrease was found over the irrigated croplands. The ET0decrease was mainly induced by the moistening and cooling effects of irrigation through intensified evapotranspiration and reduced sensible heat. This study highlights the importance of considering land‐atmosphere coupling when assessing the impacts of climatic change on irrigation water requirements. Irrigation intensifies evapotranspiration and leads to cooling and moistening effects, as well as to potential evapotranspiration decreasesThe increase in irrigation‐induced actual evapotranspiration and the decrease in potential evapotranspiration follow the complementary principleIt is necessary to consider land‐atmosphere coupling when assessing the impacts of climate change on irrigation water requirements

Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO) algorithm. We use this framework to calibrate catchment‐specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters—the infiltration parameter (B) and two of the soil depth parameters (D1, D2)—are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash‐Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA‐based ASMO framework is an effective and efficient model‐optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions. We proposed a calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO)Calibrating unnecessary parameters (Type I error) and missing sensitive parameters (Type II error) were detected in default VIC calibrationThis SA‐based ASMO framework is an effective and efficient model‐optimization technique for streamflow simulations across China

Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments. Direct calibration of hydrological models solely against remotely sensed ET (RS‐ET) data is used to predict runoff in ungauged regionsThe RS‐ET runoff‐free calibration approach is tested on 222 catchments across AustraliaThe RS‐ET runoff‐free calibration can estimate mean annual runoff and monthly runoff series in wet catchments reasonably well

Identification of vegetation changes and economic valuation of natural resources are important to strengthen national economy and sustainable environment development. This study identified the changes in vegetation and ecosystem service values at the national and provincial scales in Nepal from 2000 to 2017. Mann Kendall test statistics and Sen's slope were computed for temporal and spatial normalized difference vegetation index (NDVI) values in each pixel having a spatial resolution of 250 m and at 16-days interval. Land cover types were defined based on the NDVI values and applied ecosystem service values (ESV) coefficient. Results show that NDVI has significantly increased in Nepal with an average trend of 0.0018 yr−1during 2000–2017. Except for Province 6, the NDVI has increased significantly in all the provinces. Results additionally suggested a 27.88% greening in Nepal. At the provincial scale, the highest (56.41%) and lowest (12.52%) greening were observed in Provinces 2 and 6, respectively. In 2017, the total ESV in Nepal was 21.88 billion USD which showed a 1.15 billion USD higher than in year 2000. The ESV has increased in forests but decreased in the croplands, grasslands and barren lands between 2000 and 2017. The ESV of national forest are estimated at 19.17 billion USD in 2017. The highest value of 4.17 billion USD and the lowest of 1.09 billion USD were found in Provinces 1 and 2, respectively. Meanwhile, the available ESV per capita was relatively higher in Province 6 and lower in the Province 2. As the ESV is important lifeline for the society, this study provides crucial information about how this important environmental parameter has changed over time in Nepal.