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Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO 2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region., (© 2023. The Author(s).)

Water use efficiency (WUE) is a central parameter for linking carbon and water exchange processes in terrestrial ecosystems. The Beijing-Tianjin Sand Source Region (BTSSR) in China has undergone tremendous vegetation restoration and climate change. Understanding the WUE responses to climate change and human activity and their relative contributions to the trends and inter-annual variations (IAVs) in WUE is necessary to improve water use efficiency and strengthen water resource management. The evapotranspiration (ET) dataset based on the model tree ensemble (MTE) algorithm which was a machine learning approach using flux-tower ET measurements and the GLASS GPP dataset, as well as the variance decomposition method, were used to analyze the spatiotemporal changes in water use efficiency and inherent water use efficiency (IWUE) and the impacts of climate change and human activities. The results showed that the annual WUE and IWUE exhibited significantly increase in most regions of the BTSSR. The trend of human activity played the most important role in the increases of WUE and IWUE, with relative contributions of 88.2% and 85.9%, respectively, followed by the IAV of human activity for WUE (6.1%) and the trend of climate change (8.7%) for IWUE. The contribution of IAV to climate change was relatively small. Moreover, WUE and IWUE were all positively correlated with precipitation and temperature in most regions. Our results indicated that ecological restoration projects had significantly improved water use efficiency in BTSSR and may decrease the water burden in the BTSSR., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Excessive heat can cause discomfort, stress even mortality to humans. We investigate the multifaceted characteristics of summer heat and the affected population across China using a composite heat index (HI) based on meteorological observations and the Coupled Model Intercomparison Project Phase 6 climate models. We highlight that HI is only applicable when maximum air temperature is above 26.7 ℃. From 1961 to 2014, China has experienced increasing heat days (1.05 day/10a) and severity (0.15 ℃/10a) with more population influenced by expanding heat extent. Simultaneously, increases in the frequency (7.58–14.80 times), duration (0.46–1.23 days) and intensity (1.00–1.42 ℃/day) of heat events are detected with increased population exposure (9.33 × 105–5.59 × 106 times·persons). In the future, increases in heat severity, spatial extent, and the affected population would be aggravated from 1.5 to 2 to 3 ℃ warming. Dangerous heat events would experience increases in frequency (12.67–70.81 times), duration (0.85–7.21 days), intensity (1.78–8.57 ℃/day), and population exposure (2.06 × 106–3.18 × 107 times·persons) under a warming climate. Some regions over the Tibetan Plateau and southwest China would be affected by expanding cautionary and extremely cautionary heat. Northwest China would experience intensified dangerous heat events whereas southeastern China would face longer-lasting heat events with stronger intensity. Precautionary strategies are essential for these regions under risk. [ABSTRACT FROM AUTHOR]

Elucidating the impact of reservoir operation on hydrological signatures is crucial for the effective management of large rivers under the changing climate. This study first revised the reservoir operation scheme in the Soil and Water Assessment Tool (SWAT) model to improve its description of actual operation laws of reservoirs in the upper Yangtze River basin (UYRB). Then, we identified the reservoir-induced hydrological alteration through a hydrological index method driven by observed and simulated daily streamflow from 1960 to 2017. The results revealed the superiority of the revised reservoir algorithm in the SWAT model in simulating streamflow and floods at Cuntan and Yichang stations with the Nash-Sutcliffe efficiency (NSE) coefficient and the Kling-Gupta efficiency (KGE) coefficient improved from 0.01 to 0.08 and 0.01 to 0.05, respectively. Relative to the baseline period (1960–2002), the hydrological signatures in the impact period (2003–2017) changed substantially after 2003. Reservoirs induced a remarkable increase of 27.76% and 55.97% in streamflow from January to March, accompanied by a notable decrease of 6.95% and 20.92% in streamflow from September to October after 2003 at Cuntan and Yichang stations, respectively. Meanwhile, the annual streamflow range contracted, and the flow became more stable with a reduced variation in daily streamflow, extremely low flow spell duration, and extremely high flow spell duration. Consequently, our results improved the quantitative understanding of reservoir-induced alteration and informed the management and planning of reservoir construction in the UYRB under climate change. [ABSTRACT FROM AUTHOR]

The most basic features of climatological normals and variability are useful for describing observed or likely future climate fluctuations. Pan evaporation (Epan) is an important indicator of climate change; however, current research on Epan has focused on its change in mean rather than its variability. The variability of monthly Epan from 1961 to 2020 at 969 stations in China was analyzed using a theoretical framework that can distinguish changes in Epan variance between space and time. The Epan variance was decomposed into spatial and temporal components, and the temporal component was further decomposed into interannual and intra-annual components. The results show that the variance in Epan was mainly controlled by the temporal component. The time variance was mainly controlled by intra-annual variance, decreasing continuously in the first 30 years, and slightly increasing after the 1990s. This is mainly due to the fact that the decrease of wind speed and the increase of water vapor pressure deficit with the temperature increase offset each other and inhibit the variability of Epan. The variance decreased more in the northern region, whereas it exhibited a small decrease or slight increase in the southern region. The reduction in seasonality was dominated by spring, followed by summer. The differences in Epan variability in space and season were mainly caused by the differing rates of change in evaporation driving forces, such as a greater reduction in wind speed in the northern region and spring. Significance Statement: The purpose of this study is to better understand how the variability of evaporation changes rather than in mean under climate change. This is important because the variability is useful to describe the observed or likely future fluctuations, and a small fluctuation may have large impacts on water practices, such as agricultural production. Our findings showed that the temporal and spatial variability of evaporation decreased due to its drivers offsetting each other. However, because the drivers are numerous and continuously changing under climate change, it is necessary to pay attention to its mean and variability for serving water resources practice. [ABSTRACT FROM AUTHOR]

Drought has exerted significant impacts on socio-economic systems in China, yet accurately estimating its direct economic loss remains challenging. The main constraint lies in establishing a robust relationship between drought events and associated losses, compounded by insufficient loss data. To address this gap, we developed a set of annual gridded Gross Domestic Product (GDP) and applied to a process-based sigmoidal type of drought loss function. The pioneering effort produced the first gridded dataset of drought-induced economic losses for 1991 to 2016, offering valuable insights for the previously unrecorded period from 1991 to 2001. The sigmoidal curve fits well in most provinces, with R2 values exceeding 0.5, and higher accuracy observed before 2010, indicating a certain level of the model's credibility before 2002. The estimated losses ranged from 3.91 billion CNY in 1991 to 22.02 billion CNY in 1999, thereby filling the statistical gap and providing critical reference information for 1991 to 2001 in China. Spatial analysis revealed a pronounced pattern of elevated losses per unit area in southern and eastern China, aligns closely with GDP distribution and demarcated by the Hu Line. This correlation underscores the significant influence of economic development on drought losses. This study also highlights the necessity of integrating policy measures into drought loss assessments to account for the impacts of adaptation and mitigation strategies. This integration is essential for enhancing the accuracy of drought-related hazards assessments and facilitating more strategic adaptation and mitigation initiatives. • We developed the first spatially explicit drought losses dataset from 1991 to 2020. • Losses were 3.91–22.02 billion CNY for unrecorded period from 1991 to 2001. • Drought losses showed a clear regional pattern as delineated by the Hu Line. [ABSTRACT FROM AUTHOR]

Hot and dry extremes have co‐occurred with global warming, causing worse threats than single climate extremes. We analyse the compound hot and dry day (CHDD) and population exposure (Epop) across China based on observations and 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6. The bias‐corrected GCMs, particularly the two‐stage multivariate quantile mapping‐corrected ones, perform better than the raw ones in catching changes in CHDD but they all overestimate CHDD and Epop. More CHDDs occur in the southeast (SE), north (NC) and northwest (NW) China with increasing trends over regions stretching from the northeast (NE) to southwest (SW) China. Despite the declined CHDD, historical Epop has generally increased by the increased population. Future projections in the SSP5‐8.5 experiment indicate an overall increase of CHDD and Epop at 1.5°C warming and an increasing Epop to dangerous CHDD at 2°C warming. SW, NE and the Tibetan Plateau need effective measures of adaptation to reduce sufferings from CHDD under climate change. [ABSTRACT FROM AUTHOR]

Stationarity is an assumption that permeates training and practice in water-resource engineering. However, with global change, the validity of stationarity as well as uncertainty of nonstationarity in water-resource planning are being questioned; thus, it is critical to evaluate the stationarity of climate variables, especially precipitation. Based on the continuous observation data of precipitation from 1427 stations across China, 593 efficient grid cells (1° × 1°) are constructed, and the annual precipitation stationarities from 1959 to 2018 are analyzed. The evaluated autocorrelation stationarity indicates that 92.24%–96.12% of the grid cells for an autocorrelation coefficient of lag 1–8 years of precipitation are indistinguishable from 0 [90% confidence level (CL)]. The mean stationarity indicates that 97.47% of the grid cells have a stable mean for 30 years (90% CL); beyond the confidence limits, they are mainly located in the northwest of China, where annual precipitation is less, and the average exceeding range is ±3.78 mm. The long-term observation of annual precipitation in Beijing (1819–2018) and Shanghai (1879–2018) also yields autocorrelation and mean stationarities. There is no significant difference in the annual precipitations between the past 20 years (1999–2018) and the past 60 years (1959–2018) over China. Therefore, the annual precipitation in China exhibits a weak stationary behavior that is indistinguishable from the stationary stochastic process. The average variation in precipitation is ±9.55% between 30 successive years and 16.53% between 10 successive years. Therefore, it is valuable and feasible to utilize the historical data of annual precipitation as the basis of water-resources application. [ABSTRACT FROM AUTHOR]

• A framework for decomposing the contributing factors of extreme droughts is developed for China. • Extreme droughts attributed to rises in temperature, net radiation, precipitation variability. • A smaller rise in extreme droughts per Kelvin in high-latitude regions is due to their high sensitivity to precipitation. Climate change in mean-state or variability could lead to more/less frequency of extreme drought. In this study, we developed a new framework for decomposing the contributing factors of changes in future extreme drought frequency in China in accordance to the Palmer Drought Severity Index (PDSI). We performed a series of numerical experiments to ensure that the sensitivity of trend and variability in individual climatic variables can be distinguished. In addition, an extreme drought rate with warming (EDRW , unit: %·K−1) is defined to quantify the relative change in extreme drought frequency per Kelvin. Overall, the EDRW will increase dramatically by an average of 27.71%·K−1. The ascended EDRW are attributed to increases in temperature (+56.90%·K−1), net radiation (14.80%·K−1) and precipitation variability (+8.23%·K−1); a decrease in relative humidity (+4.61%·K−1), but is partly offset by an increase in precipitation (i.e., "wetting", −60.12%·K−1). A smaller increase in the frequency of extreme droughts is found in high-latitude regions due to their enhanced sensitivity to "wetting". [ABSTRACT FROM AUTHOR]

Declining pan evaporation (Epan) across many continents in the past decades despite global warming is gaining attention of hydrology and climate science communities. While Geo‐detector is considered useful for attributing the spatio‐temporal variation of a certain geography‐related element, it has not been applied for Epan attribution. Based on the Geo‐detector, we first attribute the spatio‐temporal variation of Epan across China in 1961–2001. The results show that relative humidity is the primary factor influencing the temporal dynamics of Epan, followed by sunshine duration, maximum air temperature and wind speed. In the northwest China (south China and east China), wind speed, air temperature and relative humidity (relative humidity, sunshine duration, wind speed and air temperature) are the dominant factors of Epan variations. The results obtained are comparable to the previous studies using other statistical methods (i.e., the partial differential method, Pearson correlation coefficient and multiple regression), which reveal the usefulness of Geo‐detector for attributing and diagnosing the spatio‐temporal variations of Epan. Moreover, more detailed spatial information and interactions among meteorological elements can be obtained using Geo‐detector. These findings should provide helpful references for understanding the mass and energy budgets at different spatial–temporal scales under global warming. [ABSTRACT FROM AUTHOR]

Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation. However, this scaling behavior is inappropriate for high temperatures and short-duration precipitation extremes. Here, daily data from 702 stations during 1951–2014 and hourly data from 8 stations during 2000–15 are used to examine and explain this behavior in China. Both daily and hourly precipitation extremes exhibit an increase in temperature dependency at lower temperatures. The CC scaling transitions from positive to negative rates with temperatures greater than 25°C. Unlike the increase in daily data, which is similar to single-CC (1CC) scaling, the increase in hourly data resembles super-CC (2CC) scaling for temperatures greater than 13°C. Results show that the precipitation extremes are controlled by water vapor for a given temperature. At lower temperatures, precipitation extremes exhibit a positive linear dependence on daily actual vapor pressure whose value is almost equal to the saturated vapor pressure at a given temperature. At higher temperatures, actual vapor pressure has difficulty maintaining a consistent increasing rate because of the exponential increasing of the saturated vapor pressure. Higher temperatures result in larger vapor pressure deficits, which lead to sharp decreases in precipitation extremes. Similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature increases from 17°C along the northwest inland area to 25°C along the southeast coast. These behaviors demonstrate that precipitation extremes are firmly linked to temperature when there is sufficient moisture at lower temperatures and limited by insufficient moisture at higher temperatures. Overall, precipitation extreme events require more attention in a warming climate. [ABSTRACT FROM AUTHOR]

Highlights • The ED approach can make robust improvement for the PD method. • The ED approach can better identify and quantify the contribution of meteorological observations. • The wind function: f q (u 2) = 3.977 × 10 - 8 (1 + 0.505 u 2) is recommended in the PenPan model in D20 E pan estimation. • The ED approach can be used in attribution analysis in complicated hydrological models. Abstract In predicting how droughts and hydrological cycles might change in a changing climate, change of pan evaporation (E pan) is one crucial element to be understood. The derived partial differential (PD) form of the PenPan equation is a prevailing attribution approach worldwide. However, small biases exist and the application of PD method is limited within the derivation of partial differential form of the equation, which impede the attribution analysis in hydrology. Here we designed a series of numerical experiments by detrending each climatic variable, i.e., an experimental detrending (ED) approach, to attribute changes of E pan over China. We compared the attribution results using these two methods and further analyzed the plausible advantages of ED approach. The comparison shows that both ED approach and PD method perform well in attributing changes of E pan and to the input meteorological variables in China over 1960–2017. The first advantage of ED approach is that it can help make robust adjustment for the PD method in attribution analysis. Another advantage lies in its ability to attribute to the observed meteorological variables in China, when the PD method fails to quantify the contribution of relative humidity and air temperature in net radiation in E pan attribution analysis. We highlight that the ED approach is recommended in attribution analysis for hydrologic research. Together with the adjusted PD method, both methods can assist a better understanding and prediction of water-energy cycles change in a changing climate. [ABSTRACT FROM AUTHOR]

Climate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited.Based on spatially distributed catchments inChina, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annualET is predictable in nonhumid regions but less predictable in humid regions, and annualQ is predictable in humid regions but less reliable in nonhumid regions. Themain cause of the varied predictability lies in the variation of water storage change DS in the water balance equation. It affects both the estimation and the variability ofQin nonhumid catchmentsmore than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and nonhumid catchments.Moreover, the differences between the controlling factors of ET variability in different regions add more differences in their predictability. The dominant control of precipitation makes it easy to predict annual ET in nonhumid regions. By contrast, precipitation, potential evaporation, and their covariance take considerable effort to determine annualETvariations,which leads to less reliableETestimation and predictability in humid catchments. Therefore, one can accurately predict annual ET in nonhumid catchments andQ in humid catchments based on commonly used hydrological models.With proper consideration of ΔS, the predictability of annual ET and Q in both humid and nonhumid catchments can be improved. [ABSTRACT FROM AUTHOR]

Abstract: Although gridded air temperature data sets share much of the same observations, different rates of warming can be detected due to different approaches employed for considering elevation signatures in the interpolation processes. Here we examine the influence of varying spatiotemporal distribution of sites on surface warming in the long‐term trend and over the recent warming hiatus period in China during 1951–2015. A suspicious cooling trend in raw interpolated air temperature time series is found in the 1950s, and 91% of which can be explained by the artificial elevation changes introduced by the interpolation process. We define the regression slope relating temperature difference and elevation difference as the bulk lapse rate of −5.6°C/km, which tends to be higher (−8.7°C/km) in dry regions but lower (−2.4°C/km) in wet regions. Compared to independent experimental observations, we find that the estimated monthly bulk lapse rates work well to capture the elevation bias. Significant improvement can be achieved in adjusting the interpolated original temperature time series using the bulk lapse rate. The results highlight that the developed bulk lapse rate is useful to account for the elevation signature in the interpolation of site‐based surface air temperature to gridded data sets and is necessary for avoiding elevation bias in climate change studies. [ABSTRACT FROM AUTHOR]

• Calibration with only runoff may underestimate SCA while overestimating snowpack. • Including snow in the calibration improved both snow and runoff simulations. • 15% to 30% decline in runoff was projected in 2085s compared with 1985s. • Future runoff decline is due to mild rise in rain not enough to mitigate larger AE. Snow-affected mountainous areas are highly vulnerable to climate changes in arid and semi-arid regions. Previous studies have put more emphasis on runoff, however, research about snow hydrological processes is still insufficient. Based on the optimization of calibration strategies, this study aims to provide a more realistic simulation of snow hydrology by coupling both a lumped rainfall-runoff model (GR6J) and a semi-distributed snow model (CemaNeige). Compared with calibration using only runoff, the multi-objective calibration with both runoff and snow not only significantly improved the KGE (Kling-Gupta efficiency) of snow from 0.34/0.47 to 0.67/0.69 during the calibration/validation period, but also slightly improved the KGE of runoff from 0.84/0.75 to 0.86/0.78, respectively. Additionally, we also found that the calibration using only runoff may underestimate snow cover (41%) and overestimate snowpack (20%), while there is little discrepancy in snowmelt and runoff. Snowmelt runoff is 57.7 mm accounting for 26% of the annual runoff. In the historical period (1980–2011), the runoff varied with a positive trend of 2.15 mm year−1 (p < 0.1), while its tendency became negative in the future. A 15% to 30% decline in runoff was projected for SSP370 (SSP3-RCP7) and SSP585 (SSP5-RCP8.5) in the 2085s (2070–2100) compared with that in the 1985s (1970–2000). Through further study on seasonal variations, we found that the future runoff decline is probably due to mild rise in rain not enough to alleviate the larger AE (actual evaporation), and for the runoff and AE we also found the variations in summer dominated the annual changes. In response to global warming, SCED (snow cover end date) advances while SCOD (snow cover onset date) recedes in all four scenarios as the year increases. Generally, coupling different hydrological models and multi-objective calibration strategies is vital for understanding the hydrological processes in snow-affected catchments. [ABSTRACT FROM AUTHOR]

We assess hydroclimatic projections for the Murray-Darling Basin (MDB) using an ensemble of 39 Intergovernmental Panel on Climate Change AR4 climate model runs based on the A1B emissions scenario. The raw model output for precipitation, P, was adjusted using a quantile-based bias correction approach. We found that the projected change, ?P, between two 30 year periods (2070-2099 less 1970-1999) was little affected by bias correction. The range for ΔP among models was large (∼±150 mm yr-1) with all-model run and all-model ensemble averages (4.9 and -8.1 mm yr-1) near zero, against a background climatological P of ∼500 mm yr-1. We found that the time series of actually observed annual P over the MDB was indistinguishable from that generated by a purely random process. Importantly, nearly all the model runs showed similar behavior. We used these facts to develop a new approach to understanding variability in projections of ΔP. By plotting ΔP versus the variance of the time series, we could easily identify model runs with projections for ΔP that were beyond the bounds expected from purely random variations. For the MDB, we anticipate that a purely random process could lead to differences of ±57 mm yr-1 (95% confidence) between successive 30 year periods. This is equivalent to ±11% of the climatological P and translates into variations in runoff of around ±29%. This sets a baseline for gauging modeled and/or observed changes. [ABSTRACT FROM AUTHOR]

Snow has been widely recognized as a crucial component of water resources and is expected to be vulnerable to climate change in cold and mountainous regions. Here we projected climate change impacts on snow hydrology in the upper Yellow River (UYR) basin through a distributed biosphere hydrological model with improved snow physics, forced with Inter‐Sectoral Impact Model Intercomparison Project climate model outputs under two emission scenarios (representative concentration pathways, RCP4.5 and RCP8.5) during the period of 1996–2025. The results indicated that the climate in the UYR basin is turning warmer and wetter. In this context, more precipitation would occur as rain (snow‐to‐precipitation ratio would decrease accordingly). The total runoff generation would increase slightly with increased precipitation. The simulated snow depth and snow water equivalent would decrease by 33% and 19% per 1 °C warming under the RCP4.5. These declined rates would further enlarge under the RCP8.5. Accordingly, the multimodel ensemble mean snowmelt‐to‐runoff ratio decreases by 9% and 7% per 1 °C warming under the two emission scenarios. The projected responses of snow hydrology (e.g., snow depth, snow water equivalent, and snowmelt) to the changing climate in the UYR basin are consistent among different climate models and RCP scenarios, which would provide valuable insights for water resources management, environmental protection, and climate change adaption in high mountainous basins and their downstream regions. Key Points: Snow hydrological responses to climate change were projected in the UYR basin through a climate‐land surface hydrologic modeling frameworkSnow depth and SWE in the UYR basin would decline with regional climate warming and wettingBiased ISI‐MIP‐GCMs‐outputted air temperature leads to impractical simulation of changes in snow seasonality [ABSTRACT FROM AUTHOR]

Abstract: In response to worldwide observations reporting a decline in pan evaporation over the last 30–50 years, we developed an instrumented US Class A pan that replicated an operational pan at Canberra Airport in Australia. The aim of the experimental facility was to investigate the physics of pan evaporation under non-steady state conditions. By monitoring the water level at 5-min intervals we were able to calculate the evaporation rate and thereby determine the short-term mass balance of the pan. Over the same time intervals, we also monitored (short- and long-wave) radiation, temperature (air, water surface, bulk water, inner and outer pan wall), atmospheric pressure as well as the air vapour pressure and the wind speed at a standard reference height (2m above ground level). The experimental pan was operated for three years (2007–2010). In this paper, we develop a framework for quantifying vapour transfer by coupling Fick''s First Law of Diffusion with boundary layer theory. This approach adequately represented pan evaporation measurements over short time intervals (half-hourly) under non-steady state conditions provided that surface temperature measurements, that account for the substantial cooling associated with evaporation, are available. It involved estimating the boundary layer thickness and other properties of air above the evaporating surface for a pan. Our results are consistent with the “envelope of theoretical curves” concept for the wind function introduced by . [Copyright &y& Elsevier]