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Water scarcity is a critical threat in arid regions in China due to dry climate and rising human water demand. The sustainability of a recent wetter trend and its impact on future water security remain uncertain. This case study focuses on a hotspot region, the North Slope of the Tianshan Mountains (NSTM), to assess water scarcity in the coming decades (2030–2050) under two climate scenarios. To this end, we developed an integrated agro‐hydrological model to simulate historical and future hydrological processes and crop water dynamics in arid regions. Our results indicate nonsignificant increases in precipitation (around 3%) and evident rising temperatures (0.9–1.5°C) in the NSTM compared to the present‐day (2011–2020) climate. This translates to a projected increase in water availability (5.6%–11.2%) during 2030–2050, with slightly larger increases (6.3%–14%) in glacier runoff. However, the spatial mismatch between precipitation increases and water demand makes this potential gain largely offset by rising irrigation water demand (over 7%) if cropland remains constant from 2020 onwards. As a result, the current annual water deficit (3.3 km3) is likely to increase by 5%–11%, with 32% of NSTM basins facing persistent water scarcity. Most croplands are at high risk of groundwater depletion and 17%–34% of basins will experience intensified water scarcity. These findings highlight the urgent need for comprehensive water management strategies, including improved irrigation efficiency and exploration of alternative water sources, to ensure water security and sustainable development in arid China facing a changing climate. Plain Language Summary: Dry regions in China face a water shortage due to low rainfall and rising water use. Recently, even though more rainfall was observed in some arid regions, scientists aren't sure if this will continue. We focus on the hotspot region, the North Slope of the Tianshan Mountains (NSTM), to see how future climate change might affect water scarcity. We found slightly more rain and warmer temperatures in the coming decades (2030–2050) in the NSTM. This could lead to a small increase in available water. However, the need for irrigation water is expected to offset these gains. The current water deficit is likely to worsen, with some areas facing even greater scarcity and increased risk of groundwater depletion. This highlights the need for better irrigation practices and exploring alternative water sources to ensure water security and sustainable development in these regions under a changing climate. Key Points: Slight increases in precipitation and water availability are projected in the NSTMWater scarcity persists due to the spatial mismatch between increased water availability and high irrigation demandThe water deficit is likely to increase by 5%–11% from the current level of 3.3 km3 per year [ABSTRACT FROM AUTHOR]

We assessed how climate change may impact net primary production (NPP) and reference evapotranspiration (ET0) from the Zagros region of western Iran covered extensively by oak, -Quercus branti Lindl. forests. The daily meteorological parameters of temperature (T), precipitation (P), relative humidity (RH), wind speed (WS), and sunshine hours were obtained from 20 meteorological stations throughout the region over a 30-year period (1988-2017). Net primary production and ET0 were estimated by the synthetic and the FAO Penman-Monteith models, respectively. A non-dimensional relative sensitivity coefficient was used to examine the sensitivity of NPP and ET0 to changes in the meteorological parameters. The sensitivity analyses were carried out for T, P, and WS within a possible range of ± 10%, ± 30%, and ± 10%, respectively, from the long-term mean. Except for the P with no notable trend, other meteorological parameters exhibited upward or downward trends. The mean NPP and ET0 values were estimated to be 6.5 t ha-1 y-1 and 3.9 mm d-1 across the Zagros region, respectively. Net primary production was found to be more sensitive to precipitation with a sensitivity coefficient of 0.66 and less sensitive to temperature with a sensitivity coefficient of 0.40. The ET0 sensitivity coefficients in response to T changes doubled relative to WS (0.33 against 0.63). Identifying the contributing factors in NPP and ET0 trends is important for understanding the relative impacts of climate change and human activities in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

A favorable environment can induce vegetation overgrowth to exceed the ecosystem carrying capacity, exacerbating water resource depletion and increasing the risk of lagged effects on vegetation degradation. This phenomenon is defined as structural overshoot, which can lead to large‐scale forest mortality and grassland deterioration. However, the current understanding of structural overshoot remains incomplete due to the complex time‐varying interactions between vegetation and climate. Here, we used a dynamic learning algorithm to decompose the contributions of vegetation and climate to drought occurrence, trace the connection between antecedent and concurrent vegetation dynamics, thus effectively capturing structural overshoot. This study focused on the climate‐sensitive hotspot in Northwest China drylands, where significant vegetation greening induced by a warming and wetting climate was detected during 1982–2015, leading to soil moisture deficit and aggravating vegetation degradation risks during droughts. We found that during this period, structural overshoot induced approximately 34.6% of the drought events, and lagged effects accounted for 16.7% of the vegetation degradation for these overshoot drought events. The occurrence of overshoot droughts exhibited an increasing trend over time, which was primarily driven by vegetation overgrowth followed by precipitation variation. Although the severity of overshoot and non‐overshoot droughts were generally comparable in spatial distribution, the impact of overshoot droughts is still becoming increasingly obvious. Our results indicate that the expected intensified overshoot droughts cannot be ignored and emphasize the necessity of sustainable agroecosystem management strategies. Plain Language Summary: Structural overshoot droughts, defined as the vegetation growth that exceeds the ecosystem carrying capacity due to the favorable environments, have posed substantial threats to Northwest China drylands. Using a dynamic learning algorithm driven by satellite and reanalysis data, we conducted comprehensive assessments of the impact of structural overshoot on drought occurrence in the dryland ecosystem of Northwest China. Our results reveal that structural overshoot accounted for over one‐third of drought events, and demonstrated increasing trends from 1982 to 2015. The frequency and impact of structural overshoot droughts are expected to increase in a warming climate, which highlights the importance of sustainable land use management. Key Points: Structural overshoot accounted for over 1/3 drought events in Northwest China drylands during 1982–2015Nearly 20% vegetation declines were caused by vegetation lagged effects of overshoot droughtsStructural overshoot drought frequency increased due to vegetation overgrowth trailed by precipitation [ABSTRACT FROM AUTHOR]

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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Climate change was projected to have negative effects on water availability and consequently a serious constraint to food production in many areas of the world. However, such effects have not been well understood, particularly over rainfed croplands, partly because of the poor representation of green water in associated assessments. In this study, we develop an integrated agricultural water scarcity index (WSIag) that incorporates blue and green water components to examine the agricultural water scarcity in the baseline (1981–2005) and future (2026–2050) periods under the Representative Concentration Pathway (RCP)2.6 and RCP6.0 scenarios. Results show that ∼3.8 million km2 (∼39% of total) croplands experienced water scarcity in the baseline period and it would expand by more than 3% in the future. Under the two scenarios, WSIag projections are similar overall and are higher than that under the baseline in 83%–84% of global total croplands. Differences are found between the scenarios in Amazonia, Southern Africa, and South Asia. The increases in future WSIag are dominated by the decreased water availability in ∼60% of total croplands and ∼24% of which is dominated by the increased crop water requirement. Changes in green water availability have a significant contribution to changes in WSIag in 16% of global croplands, mostly in arid/semiarid regions (e.g., the South edge of the Sahel, Southern Africa, Northeast China, and Central America). This implies the important role of green water management for agriculture in these regions. The integrated assessment can help develop effective strategies for agricultural water management under climate change. Plain Language Summary: Future climate change may intensify water scarcity and adversely affect crop productivity in many regions of the world. Traditionally, water scarcity assessment focused on blue water (from rivers, reservoirs, and lakes) or green water (crop evapotranspiration from soil moisture) alone. In order to examine the impact of climate change on water scarcity in croplands, we develop an integrated agricultural water scarcity index (WSIag) that incorporates blue and green water components and calculate WSIag for both historical and future periods. The assessment suggests that nearly 40% of global croplands have experienced water scarcity in the past and the situation would worsen in the future. WSIag is projected to be higher than in the past in 83%–84% of global total croplands. The increased WSIag is mostly caused by less water availability and larger crop water requirement. It is noted that changes in green water availability could also be a dominant factor in some arid/semiarid regions in the South edge of the Sahel, Southern Africa, India, Northeast China, and Central America. Our study highlights the important role of green water for agriculture water management and is expected to provide useful information for agricultural adaptation to future climate change. Key Points: Agricultural water scarcity will intensify in more than 80% of global croplandsDecreased water availability dominates the aggravation of future water scarcity globallyGreen water have a significant contribution to future changes in water scarcity in 16% of global croplands [ABSTRACT FROM AUTHOR]

Over the past decades, water scarcity has become prevalent in many regions of the world. Previous water scarcity assessments largely considered "blue water" (surface and groundwater) and overlooked "green water" (soil moisture), despite its key role in supporting crop production. Moreover, the long‐term evolution of water scarcity remains unclear. Here, we propose an agricultural water scarcity index (WSIAW) that explicitly combines green and blue water. The WSIAW measures the degree to which both green and blue water availabilities can satisfy the agricultural water requirement. The WSIAW can directly indicate water scarcity in both rainfed and irrigated areas, which cannot be captured by the blue water scarcity index alone. Using the WSIAW, we assessed the evolution of water scarcity across regions of the world during 1971–2010 at a spatial resolution of 0.5 arc degree. We found that 22% of the 228 river basins with a WSIAW of >1 presented a much broader geographical coverage compared with estimates of water scarcity based on blue water only (8% of total basins). The WSIAW also exhibited a clear trend of increasing severity over the study period, particularly in major agricultural regions. Expanding cropland and increasing/competing water withdrawals by other sectors were the main reasons for the intensification of water scarcity, which was pronounced in some Asian and African river basins. The proposed WSIAW can be applied to different regions with various scales and is important for addressing global water scarcity issues. Key Points: We propose a new agricultural water scarcity index (WSIAW) by explicitly incorporating green and blue water for crop productionWSIAW indicated severe water scarcity over a much broader geographical coverage compared with water scarcity based on blue water onlyWSIAW exhibited an increasing trend in many regions, mainly due to expanding cropland and increasing water withdrawals by other sectors [ABSTRACT FROM AUTHOR]

Climate variability and underlying surface changes are strongly associated with runoff alterations. The Yarlung Zangbo River Basin (YZRB) is a typical alpine region located in the southeast Qinghai–Tibet Plateau, where runoff is particularly sensitive and vulnerable to climate and environmental changes. Here, we conducted a quantitative assessment of the contributions of climate variability and underlying surface changes to runoff alterations from 1966 to 2015 in the upper, middle, and lower regions of the YZRB. The year 1997 was identified as the runoff breakpoint in all three sub‐regions, which divided the runoff time series into the baseline period (1966–1997) and change period (1998–2015). An adjusted Budyko framework accounting for glacier runoff was developed to conduct a runoff alteration attribution analysis. The results indicated that the increase in runoff in the upper region was dominated by changes in the underlying surface and glacier runoff, whose contribution accounted for 59.61 and 49.18%, respectively. The runoff increase in the middle and lower regions was mainly attributed to the increase in precipitation, accounting for 39.36 and 129.21% of the total runoff alteration, respectively. Moreover, due to the little variation in vegetation and degradation of permafrost in the upper region, increases in runoff might be largely attributed to increases in subsurface runoff caused by the melting of permafrost. In the middle region, in addition to increased precipitation, vegetation degradation had positive effects on runoff increases. The lower region exhibited far higher water consumption rates due to its extensive and dense vegetation coverage accompanied by rising temperature, which resulted in a negative contribution (−58.74%) to runoff alteration. Our findings may therefore have important implications for water resource security and sustainable development in alpine regions. [ABSTRACT FROM AUTHOR]

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data. [ABSTRACT FROM AUTHOR]

We assessed how climate change may impact the canopy hydrology of individual Pinus eldarica, Pinus brutia, and Cupressus arizonica trees planted in arid and semi-arid climates in Iran. Precipitation that reaches the forest floor as throughfall (TF) represents a significant portion of individual precipitation events (Pg). We used a nondimensional relative sensitivity coefficient to examine the sensitivity of TF to changes in Pg. The Coupled Model Intercomparison Project phase 5 (CMIP5) HadGEM2-ES product was used under two emission scenarios (Representative Concentration Pathway (RCP) 2.6 and 8.5) to project yearly precipitation and Pg for the measurement sites from 2020–50. There was a strong linear relationship between TF and Pg at all sites [TF = 0.6 (Pg) − 0.2; R2 = 0.9]. The arid P. eldarica trees showed the highest sensitivity coefficient (5.3) compared with semi-arid C. arizonica trees (1.6). The sensitivity coefficient approximately doubled for individual P. eldarica and C. arizonica trees in the arid climate relative to P. brutia and C. arizonica trees in the semi-arid climate. This suggests that TF under trees in the arid climate were more affected by changing Pg than in the semi-arid climate. Pinus eldarica trees planted in the dry climate exhibited the largest sensitivity coefficient differences (3.4) between small and large Pg. Changes in precipitation and increased smaller storm frequency are expected for arid and semi-arid regions. To buffer expected shifts in storm size due to climate change, it may be suitable to incorporate TF sensitivity when selecting trees for landscaping and urban greening. [ABSTRACT FROM AUTHOR]

Investigation of the pressing impacts of climate change on drought is vital for sustainable societal and ecosystem functioning. The magnitude of how much the drought will change and the way how droughts would affect society and the environment are inadequately addressed over East Africa. This study aimed at assessing future drought changes using an ensemble of five Global Climate Models (GCMs) in the Coupled Model Intercomparison Project (CMIP5) over East Africa. To this end, drought characteristics were investigated under the Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 in the near term (the 2020s; 2011–2040), midcentury (2050s; 2041–2070), and end of century (2080s; 2071–2,100). The changes of the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) were first compared, and the SPEI was used for measuring future droughts as it showed stronger changes due to its inclusion of temperature effects. Drought area in East Africa is likely to increase at the end of the 21st century by 16%, 36%, and 54% under RCP 2.6, 4.5, and 8.5, respectively, with the areas affected by extreme drought increasing more rapidly than severe and moderate droughts. Spatially, drought event, duration, frequency and intensity would increase in Sudan, Tanzania, Somalia, and South Sudan, but generally decrease in Kenya, Uganda, and Ethiopian highlands. Results also confirm that drought changes over East Africa follow the "dry gets drier and wet gets wetter" paradigm. The findings provide important guidance for improving identification of causes, minimizing the impacts and enhancing the resilience to droughts in East Africa. Key Points: We examined the impact of changes in precipitation and temperature on drought characteristics over East Africa during 1981–2099Precipitation and temperature will increase over East Africa; however, the temperature impact is dominant leading to extreme droughtsDrought area in East Africa is likely to increase at the end of the 21st century by 16%, 37%, and 54% under RCP 2.6, 4.5, and 8.5, respectively [ABSTRACT FROM AUTHOR]

Drought affects many aspects of society and its impact is global. To this end, rendering recurrent drought occurrences is a key research focus. Recently, in addition to existing knowledge, progress in scientific advancements regarding drought have been observed on a regional and global scale. Here, we reviewed and outlined current and emerging scientific developments in drought, focusing on progress made in recent years. In the human‐modified world, the anthropogenic effects on drought were dominant and drought‐frequency showed a prominent increase. In this study, we have identified the development of drought concepts, types, and indices, and their indicators were developed as sector‐specific, comprehensive, and oriented towards multiple scales. Anthropogenic changes have enhanced hydrological processes and affected the development of drought. Urbanization, deforestation, and related human activities have aggravated drought. Climate change has had an exacerbating role in drought, which is expected to increase during the 21st century. Human communities, in particular, are undertaking activities that cause droughts; suffering from and coping with their impact. In addition, the direct and indirect effects of drought need reconsideration. As such, the health impact of droughts is a concern in drought‐vulnerable societies and its burden on public health is largely unknown. The need for drought recovery to aid in effective ecosystem functioning in the aftermath of the drought and the development of mitigation measures to alleviate recurrent drought is critical. Enhanced drought monitoring and management options are required under existing environmental and socio‐economic setups in the 21st century. This article is characterized under:Science of Water > Water ExtremesEngineering Water > Planning WaterWater and Life > Stresses and Pressures on Ecosystems [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]

The interaction between surface water and groundwater is an important aspect of hydrological processes. Despite its importance, groundwater is not well represented in many land surface models. In this study, a groundwater module with consideration of surface water and groundwater dynamic interactions is incorporated into the distributed biosphere hydrological (DBH) model in the upstream of the Yellow River basin, China. Two numerical experiments are conducted using the DBH model: one with groundwater module active, namely, DBH_GW and the other without, namely, DBH_NGW. Simulations by two experiments are compared with observed river discharge and terrestrial water storage (TWS) variation from the Gravity Recovery and Climate Experiment (GRACE). The results show that river discharge during the low flow season that is underestimated in the DBH_NGW has been improved by incorporating the groundwater scheme. As for the TWS, simulation in DBH_GW shows better agreement with GRACE data in terms of interannual and intraseasonal variations and annual changing trend. Furthermore, compared with DBH_GW, TWS simulated in DBH_NGW shows smaller decreases during autumn and smaller increases in spring. These results suggest that consideration of groundwater dynamics enables a more reasonable representation of TWS change by increasing TWS amplitudes and signals and as a consequence, improves river discharge simulation in the low flow seasons when groundwater is a major component in runoff. Additionally, incorporation of groundwater module also leads to wetter soil moisture and higher evapotranspiration, especially in the wet seasons. [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]

Abstract: 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. [ABSTRACT FROM AUTHOR]