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Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

The projected climate change substantially impacts agricultural productivity and global food security. The cropping system models (CSM) can help estimate the effects of the changing climate on current and future crop production. The current study evaluated the impact of a projected climate change under shared socioeconomic pathways (SSPs) scenarios (SSP2-4.5 and SSP5-8.5) on the grain yield of winter wheat in the North China Plain by adopting the CSM-DSSAT CERES-Wheat model. The model was calibrated and evaluated using observed data of winter wheat experiments from 2015 to 2017 in which nitrogen fertigation was applied to various growth stages of winter wheat. Under the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) SSP2-4.5 and SSP5-8.5 scenarios, the future climate projections were based on five global climate models (GCMs) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The GCMs projected an increase in grain yield with increasing temperature and precipitation in the near-term, mid-term, and long-term projections. In the mid-term, 13% more winter wheat grain yield is predicted under 1.3 °C, and a 33 mm increase in temperature and precipitation, respectively, compared with the baseline period (1995–2014). The increasing CO2 concentration trends projected an increase in average grain yield from 4 to 6%, 4 to 14%, and 2 to 34% in the near-term, mid-term, and long-term projections, respectively, compared to the baseline. The adaptive strategies were also analyzed, including three irrigation levels (200, 260, and 320 mm), three nitrogen fertilizer rates (275, 330, and 385 kg ha−1), and four sowing times (September 13, September 23, October 3, and October 13). An adaptive strategy experiments indicated that sowing winter wheat on October 3 (traditional planting time) and applying 275 kg ha−1 nitrogen fertilizer and 260 mm irrigation water could positively affect the grain yield in the North China Plain. These findings are beneficial in decision making to adopt and implement the best management practices to mitigate future climate change impacts on wheat grain yields.

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

Focusing on the Lower Bari Doab Canal (LBDC) command area, characterized by its heavy reliance on agriculture, this study addresses the critical issue of groundwater table fluctuations in response to diverse pumping scenarios. Herein, we comprehensively evaluated the dynamic interplay between crop water requirements and groundwater pumping within the expansive canvas of the LBDC, which is facing water shortages. Using the Penman–Monteith equation, we calculated annual average evapotranspiration for major crops—wheat, maize, cotton, rice, and sugarcane. Three-dimensional MODFLOW-based numerical modeling was used to analyze the dynamics of groundwater regimes. MODFLOW was calibrated from 2010 to 2020. Thereafter, we simulated water table changes under a 20% increase and decrease in groundwater extraction up to 2040s. Results revealed significant variations in water demands among these crops, with sugarcane requiring the highest average annual evapotranspiration at 1281 mm. Spatiotemporal analysis revealed substantial declines in the water table in the tail-end command areas, particularly Sahiwal and Khanewal where the decline was 0.55 m/year between 2010 and 2020. The upper reaches, such as Balloki and Okara, experienced milder declines. In considering management scenarios, a 20% increase in groundwater extraction up to September 2040 was projected to raise pumping to 4650 MCM/year. and decrease the net water balance to −235 MCM/year. Alternatively, a 20% decrease in groundwater extraction up to September 2040 could reduce pumping to 4125 MCM/year and increase the net water balance to 291 MCM/year. This study sheds light on major crop water requirements, spatiotemporal groundwater dynamics, and the implications of groundwater extraction in the LBDC command area. Scenarios presented here, encompassing increased and decreased groundwater extraction, offer invaluable guidance for policymakers and stakeholders seeking a balance between agricultural productivity and long-term groundwater sustainability.

To reduce air pollution, China adopted rigorous control mechanisms and announced the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. Here, using OMI satellite, the NASA Socioeconomic Data and Application Center (SEDAC), and Fifth ECMWF (ERA5) data at a 0.25° × 0.25° resolution, we explored changes in NO2, PM, SO2, and O3 and climatology over China in response to the Action Plan between 2004 and 2021. This study attempts to investigate the long term trend analysis of air pollution and climatic variations during two scenarios before (2004–2013) and after (2013–2021) APPCAP. We investigated the climatic effects of air pollution in China before and after APPCAP adoption using geographically weighted regression (GWR) and differential models to assess the contribution of air pollution. The spatial representation analysis demonstrated how air pollution affected climatic factors before and after the APPCAP. Several important findings were derived: (1) the APPCAP significantly influenced air pollution reduction in China post-scenario (2013–2021); (2) the Mann Kendall test investigated that all pollutants showed an increasing trend pre-APPCAP, while they showed a decreasing trend, except for O3, post-APPCAP; (3) for climatic factors, the MK test showed an increasing trend of precipitation and mean minimum air temperature tmin post-APPCAP; (4) innovative trend analysis (ITA) showed a reduction in NO2, SO2, and PM, although O3 showed no trend post-APPCAP; and (5) pre-scenario, NO2 contributed to an increase in the mean maximum air temperature (tmax) by 0.62 °C, PM contributed to raising tmin by 0.41 °C, while O3 reduced the tmax(tmin) by 0.15 °C (0.05 °C). PM increased tmax and precipitation with a magnitude 0.38 °C (7.38 mm), and NO2 contributed to increasing tmin by (0.35 °C), respectively, post-scenario. In particular, post-scenario led to an increase in tmin and precipitation across China. The results and discussion presented in this study can be beneficial for policymakers in China to establish long-term management plans for air pollution and climatological changes.

IntroductionThe Arabian Gulf (Gulf) is a dynamic marine ecosystem in which phytoplankton productivity, indicated by Chlorophyll-a (Chl-a), is strongly affected by environmental and climatic variables. Understanding the spatiotemporal variability of Chl-a and its driving environmental factors is critical for assessing primary productivity and ecosystem dynamics of the Gulf.MethodsThis study investigated the long-term Chl-a variability and its dynamic response to environmental variables in the Gulf using two decades (2003 to 2023) of Chl-a data from merged multi-sensor Ocean Colour Climate Change Initiative. We adopted an integrated approach that includes climatology, multivariate statistical analysis, interannual variability and trend analysis to evaluate Chl-a variability and identify its dominant drivers.ResultsSeasonal climatology exhibited a marked winter bloom driven by convective mixing and nutrient replenishment, followed by a summer decline due to strong stratification. Box average analysis using correlogram and principal component analysis for selected regions revealed distinct regional patterns, with the northern and central Gulf showing higher variability. The results further highlighted sea surface temperature (SST), sea surface salinity, photosynthetically available radiation and wind speed as primary drivers of Chl-a variability in the Gulf. The interannual variability of Chl-a peaks along the central eastern Gulf in winter and central western Gulf during summer, highlighting regional heterogeneity in phytoplankton dynamics. Long-term spatial trend analysis of Chl-a, net primary productivity (NPP) and SST indicated overall decreasing trend in Chl-a and NPP, particularly along the north and eastern coasts of the Gulf; and warming SST in the northern and central Gulf.DiscussionThe results indicate the requirement of further research on the complex interplay between physical and biogeochemical factors, and anthropogenic influences on Chl-a distribution, which can help future monitoring and predictive ecosystem models for the Gulf under changing climate conditions.

Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level.

Droughts have an adverse influence on agriculture, the environment, water supplies, and the global economy. The drought risk was computed using an integrated prospective approach: drought hazard, exposure, and vulnerability based on biophysical and socio-economic conditions over Karachi, Pakistan during 2000-2019. Drought hazard map (DHM) was created using annual Palmer drought severity Index (PDSI). Drought exposure map (DEM) was derived using population density and gross domestic product (GDP), as well as land surface temperature (LST), Normal difference vegetation index (NDVI), Night light images (NTL), land use land cover (LULC), and Distance to water were used for drought vulnerability map (DVM). An estimation of drought Risk (EDR) was derived by integrating layers of DHM, DEM, and DVM. Results showed that Central, South, and East regions of Karachi were at high risk, whereas the North East and North were less affected by the drought. The estimated average drought hazard (EDH) was 0.84, with minimum (maximum) value of 0.68 (1). Similarly, the average estimated drought exposure (estimated drought vulnerability) for EDE (EDV) was 0.27 (0.42), with the maximum value of 0.55 (0.84) and the minimum value of 0 (0). The drought risk assessment map (DRAM) shows that the average risk values is 0.18 while highest value is 0.36.

The live storage of Pakistan’s major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R2 = 0.85, PBIAS = 11.2%, NSE = 0.84) and validation (R2 = 0.88, PBIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R2 = 0.88, PBIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield’s absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management.

In climate research, an important task is to characterise the relationships between Essential Climate Variables (ECVs). Here, satellite-derived data sets have been used to examine the seasonal cycle of phytoplankton (chlorophyll concentration) in the waters off Somalia, and its relationship to aerosols, winds and Sea Surface Temperature (SST). Chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), A° ngstro¨m Exponent (AE), Dust Optical Thickness (DOT), Sea Surface Temperature (SST) and sea-surface wind data for a 16-year period were assembled from various sources. The data were used to explore whether there is evidence in the data to show that dust aerosols enhance Chl-a concentration in the study area. The Cross 11 Correlation Function (CCF) showed highest positive correlation (r2=0.3) in the western Arabian Sea when AOT led Chl-a by 1 to 2 time steps (here, 1 time step is 8 days). A 2o□ 2obox off Somalia was selected for further investigations. The correlations of alongshore wind speed, Ekman Mass Transport (EMT) and SST with Chl-a were higher than that of AOT, for a lag of 8 days. When all four variables were considered together in a multiple linear regression, the increase in r2 associated with the AOT is only about 0.02, a consequence of covariance among AOT, SST, EMT and alongshore wind speed. The AOT data show presence of dust aerosols most frequently during the summer monsoon season (June- September). When the analyses were repeated for the dust aerosol events, the correlations were generally lower, but still significant. Again, the inclusion of DOT in the multiple linear regression increased the correlation coefficient by only 2%, indicating minor enhancement in Chl-a concentration. Interestingly, during summer monsoon season, there is a higher probability of finding more instances of positive changes in Chl-a after one time step, regardless of whether there is dust aerosol or not. On the other hand, during the winter monsoon season (November-December) and rest of the year, the probability of Chl-a enhancement is higher when dust aerosol is present than when dust aerosol is present than when it is absent. The phase relationship in the annual climatologies of Chl-a and AOT