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An understanding of climate variability, trends, and prediction for better water resource management and planning in a basin is very important. Since the water resources of the Lake Chad basin (LCB) are highly vulnerable to changing climate, in the present study, a combination of trend analysis methods was used to examine the climate variability and trends for the period of 1951–2015 using observed and Climate Research Unit (CRU) data, and a combination of spectral analysis techniques was used for the prediction of temperature and precipitation using CRU data. Eighty-four percent of the temperature time series indicated extremely strong signals of increasing trends (α = 0.001) and 25–38% of the precipitation time series indicated strong decreasing trends (α = 0.05). Temperature is expected to increase and precipitation is expected to decrease in the future. However, surprisingly, in some regions located in the South, the temperature was predicted to decrease slightly in 2021–2030 relative to 2006–2015. This decrease might occur because these regions are highly protected natural resource areas and forests are frequently present. On the whole, the temperature was predicted to increase by 0.65–1.6 °C and precipitation was predicted to decrease by 13–11% in the next two decades (i.e., 2016–2025 and 2026–2035) relative to 1961–1990. Periodic analysis showed a 20- to 25-year cycle in precipitation in all basins and a 40- to 45-year cycle in temperature but only in the Chari-Logone basin.

The Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau. Plain Language Summary The Three‐River Headwater Region, which is also known as the Sanjiangyuan in Chinese, is located in Qinghai Tibetan Plateau, China. It is considered the water tower of China because it is the source of three giant rivers the Yangtze, Yellow, and Lancang (Mekong). However, its water resources (hydrological cycle) are very sensitive and vulnerable to changing climate. Therefore, we assessed the potential impact of climate change on all the important hydrological components such as precipitation, streamflow, snow melt flow, surface flow, baseflow, soil moisture (SM), and changes in terrestrial water storage. Previous studies mainly focused on precipitation, streamflow, and SM. Global Climate Models (GCMs) are the main tool to assess the future changes in hydrological components under changing climate. Since GCMs have a coarse spatial resolution and biases in their outputs, a statistical downscaling model (SDSM) was applied to fix these issues and used to generate climate data (e.g., temperature and precipitation) for the future (2021–2100) under two scenarios (i.e., SSP2‐4.5 and SSP5‐8.5). These scenarios represent the global development and greenhouse gas emissions in the future. SSP2‐4.5 scenario typically involves moderate greenhouse gas emissions reduction efforts and some adaptation and mitigation measures to address climate change impacts, and SSP5‐8.5 represents high greenhouse gas emissions and limited efforts to mitigate climate change impacts. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The downscaled future climate data was used as input to run a hydrological model (HEC‐HMS) to generate hydrological components under both scenarios. The future changes in the hydrological components were obtained for 2021–2060 and 2061–2100 with respect to the baseline period 1981–2020. The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. Almost all components are expected to increase in the future under both scenarios. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the second half of the twenty‐first century (2061–2100). Peak flows are expected to be much higher than the present conditions and to shift forward. Snowmelt will start earlier in the future. This study will be very useful in understanding the hydrological cycle and can be used by policymakers, planners, and stakeholders for proactive adaptation strategies such as water resources planning and management, investments in water infrastructure, land use planning, ecosystem restoration, and community resilience‐building initiatives to mitigate potential risks. Key Points The region will be hotter and wetter, with intensive and frequent floods The hydrological components are expected to increase in the future Surface flow showed an unprecedented increase of 500%–1,000%

Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, six different models are constructed and evaluated in estimating global MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NNM) shows the best accuracy with higher R2 and lower NMAE and NRMSE in different spatial scales. The estimates achieved from the NNM model are consistent with census and survey data, and outperforms the existing global gridded MWW dataset. At last, the NNM model is applied to mapping global gridded MWW for the year 2015 at 0.1 × 0.1° resolution. The proposed method can be applied to a wider aggregated output learning problem and the high-resolution global gridded MWW data can be used in hydrological models and water resources management.

Recently, Goji berry ( Lycium barbarum L.) has been extensively cultivated to improve the fragile ecological environment and increase the income of residents in Qinghai Province, northwestern China. However, few studies have focused on the physiological responses of Goji berry under salt stress and alkali stress. Gas exchange, photosynthetic pigments, and chlorophyll fluorescence were evaluated in response to neutral (NaCl) and alkali (NaHCO 3 ) salt stresses. Nine irrigation treatments were applied over 30 days and included 0(Control group), 50, 100, 200, and 300 mM NaCl and NaHCO 3 . The results showed that salt and alkali stress reduced all the indicators and that alkali stress was more harmful to Goji berry than salt stress under the same solution concentrations. The salt tolerance and alkali resistance thresholds were identified when the index value exceeded the 50% standard of the control group, and threshold values of 246.3 ± 2.9 mM and 108.4.7 ± 2.1 mM, respectively, were determined by regression analysis. These results were used to identify the optimal water content for Goji berry. The minimum soil water content to cultivate Goji berry should be 16.22% and 23.37% under mild and moderate salt stress soils, respectively, and 29.10% and 42.68% under mild and moderate alkali stress soil, respectively.

The North China Plain is a crucial agricultural region in China, but irregular precipitation patterns have led to significant water shortages. To address this, analyzing the high-resolution dynamics of root-zone soil moisture transport is essential for optimizing irrigation strategies and improving water resource efficiency. The Richards equation is a robust model for describing soil moisture transport dynamics across multiple soil layers, yet its application at large spatial scales is hindered by its sensitivity to boundary conditions and model parameters. This study introduces a novel approach that, for the first time, employs a continuous time series of near-surface soil moisture as the upper boundary condition in the Richards equation to estimate high-resolution root-zone soil moisture in the North China Plain, thus enabling its large-scale application. Singular spectrum analysis (SSA) was first applied to reconstruct site-specific time series, filling in missing and singular values. Leveraging observational data from 617 monitoring sites across the North China Plain and multiple spatial covariates, we developed a machine learning model to estimate near-surface soil moisture at a 1 km resolution. This high-resolution, continuous near-surface soil moisture series then served as the upper boundary condition for the Richards equation, facilitating the estimation of root-zone soil moisture across the region. The results indicated that the machine learning model achieved a correlation coefficient (R) of 0.92 for estimating spatial near-surface soil moisture. Analysis of spatial covariates showed that atmospheric forcing factors, particularly temperature and evaporation, had the most substantial impact on model performance, followed by static factors such as latitude, longitude, and soil texture. With a continuous time series of near-surface soil moisture, the Richards equation method accurately predicted multi-layer soil moisture and demonstrated its applicability for large-scale spatial use. The model yielded R values of 0.97, 0.78, 0.618, and 0.43, with RMSEs of 0.024, 0.06, 0.08, and 0.11, respectively, for soil layers at depths of 10 cm, 20 cm, 40 cm, and 100 cm across the North China Plain.

Many studies such as climate variability, climate change, trend analysis, hydrological designs, agriculture decision-making etc. require long-term homogeneous datasets. Since homogeneous climate data is not available for climate analysis in Pakistan and India, the present study emphases on an extensive quality control and homogenization of daily maximum temperature, minimum temperature and precipitation data in the Jhelum River basin, Pakistan and India. A combination of different quality control methods and relative homogeneity tests were applied to achieve the objective of the study. To check the improvement after homogenization, correlation coefficients between the test and reference series calculated before and after the homogenization process were compared with each other. It was found that about 0.59%, 0.78% and 0.023% of the total data values are detected as outliers in maximum temperature, minimum temperature and precipitation data, respectively. About 32% of maximum temperature, 50% of minimum temperature and 7% of precipitation time series were inhomogeneous, in the Jhelum River basin. After the quality control and homogenization, 1% to 11% improvement was observed in the infected climate variables. This study concludes that precipitation daily time series are fairly homogeneous, except two stations （Naran and Gulmarg）, and of a good quality. However, maximum and minimum temperature datasets require an extensive quality control and homogeneity check before using them into climate analysis in the Jhelum River basin.

Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period.

Pakistan is one of the most highly water-stressed countries in the world and its water resources are greatly vulnerable to changing climatic conditions. The present study investigates the possible impacts of climate change on the water resources of the Kunhar River basin, Pakistan, under A2 and B2 scenarios of HadCM3, a global climate model. After successful development of the hydrological modeling system (HEC-HMS) for the basin, streamflow was simulated for three future periods (2011–2040, 2041–2070, and 2071–2099) and compared with the baseline period (1961–1990) to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both A2 and B2 scenarios. However, while summer and autumn showed a noticeable increase in streamflow, spring and winter showed decreased streamflow. High and median flows were predicted to increase, but low flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of flow is likely to be more in the future. It was also noted that peaks were predicted to shift from June to July in the future, and the center-of-volume date—the date at which half of the annual flow passes—will be delayed by about 9–17 days in the basin, under both A2 and B2 scenarios. On the whole, the Kunhar basin will face more floods and droughts in the future due to the projected increase in high flow and decrease in low flow and greater temporal and magnitudinal variations in peak flows. These results highlight how important it is to take cognizance of the impact of climate change on water resources in the basin and to formulate suitable policies for the proper utilization and management of these resources.

The drought episodes in the second half of the 20th century have profoundly modified the state of Lake Chad and investigation of its variations is necessary under the new circumstances. Multiple remote sensing observations were used in this paper to study its variation in the recent 25 years. Unlike previous studies, only the southern pool of Lake Chad (SPLC) was selected as our study area, because it is the only permanent open water area after the serious lake recession in 1973–1975. Four satellite altimetry products were used for water level retrieval and 904 Landsat TM/ETM+ images were used for lake surface area extraction. Based on the water level (L) and surface area (A) retrieved (with coinciding dates), linear regression method was used to retrieve the SPLC’s L-A curve, which was then integrated to estimate water volume variations ( Δ V ). The results show that the SPLC has been in a relatively stable phase, with a slight increasing trend from 1992 to 2016. On annual average scale, the increase rate of water level, surface area and water volume is 0.5 cm year−1, 0.14 km2 year−1 and 0.007 km3 year−1, respectively. As for the intra-annual variations of the SPLC, the seasonal variation amplitude of water level, lake area and water volume is 1.38 m, 38.08 km2 and 2.00 km3, respectively. The scatterplots between precipitation and Δ V indicate that there is a time lag of about one to two months in the response of water volume variations to precipitation, which makes it possible for us to predict Δ V . The water balance of the SPLC is significantly different from that of the entire Lake Chad. While evaporation accounts for 96% of the lake’s total water losses, only 16% of the SPLC’s losses are consumed by evaporation, with the other 84% offset by outflow.

The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data.