An integrated modeling approach for estimating monthly global rainfall erosivity.

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis.

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB).

The growth of agricultural production systems is a major driver of groundwater depletion worldwide. Balancing groundwater supply and food production requires localized understanding of groundwater storage and depletion variations in response to diverse cropping systems and surface water availability for irrigation. While advances through Gravity Recovery and Climate Experiment (GRACE) have facilitated estimating the groundwater storage (GWS) changes in recent years, the coarse resolution of GRACE data hinders the characterization of GWS variation hotspots. Herein, we present a novel spatial water balance approach to improve the distributed estimation of groundwater storage and depletion changes at a spatial scale that can detect the hotspots of GWS variation. We used a mixed geographically weighted regression (MGWR) model to downscale GRACE Level-3 data from coarse resolution (1° × 1°) to fine scale (1 km × 1 km) based on high resolution environmental variables. We then combined the downscaled GRACE-based GWS variations with results from a calibrated Soil and Water Assessment Tool (SWAT) model. We demonstrate an application of the approach in the Irrigated Indus Basin (IIB). Between 2002 and 2019, total loss of groundwater reserves varied in the IIB's 55 canal command areas with the highest loss observed in Dehli Doab by >50 km3 followed by 7.8-49 km3 in the upstream, and 0.77-7.77 km3 in the downstream canal command areas. GWS declined by -325.55 mm/year at Dehli Doab, followed by -186.86 mm/year at BIST Doab, -119.20 mm/year at BARI Doab, and -100.82 mm/year at JECH Doab. The rate of groundwater depletion is increasing in the canal command areas of Delhi Doab and BIST Doab by 0.21-0.35 m/year. Larger groundwater depletion in some canal command areas (e.g., RACHNA, BIST Doab, and Delhi Doab) is associated with the rice-wheat cropping system, low rainfall, and low flows from tributaries.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

A modified approach to quantify aquifer vulnerability to pollution towards sustainable groundwater management in Irrigated Indus Basin.

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5-22% using traditional recharge maps, while those are 9-23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was>2500 mS/cm in the high groundwater vulnerability zones, while it was <1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices.

Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB).

Understanding the basin-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision, as well as high-resolution precipitation data. We have made an attempt to develop an Integrated Downscaling and Calibration (IDAC) framework to generate high-resolution (1 km × 1 km) gridded precipitation data. Traditionally, GWR (Geographical weighted regression) model has widely been applied to generate high-resolution precipitation data for regional scales. The GWR model generally assumes a spatially varied relationships between precipitation and its associated environmental variables, however, the relationships need to remain constant (fixed) for some variables over space. In this study, a Mixed Geographically Weighted Regression (MGWR) model, capable of dealing with the fixed and spatially varied environmental variables, is proposed to downscale the Original-TRMM precipitation data from a coarse resolution (0.25o × 0.25o) to a high-resolution (1 km × 1 km) for the period of 2000-2018 over the Upper Indus Basin (UIB). Additionally, accuracy of the downscaled precipitation data was further improved by merging it with the recorded data from rain gauge stations (RGS) using two calibration approaches such as Geographical Ratio Analysis (GRA) and Geographical Difference Analysis (GDA). We found MGWR to perform better given its higher R2 and lower RMSE and bias values (R2 = 0.96; RMSE = 56.01 mm, bias = 0.014) in comparison to the GWR model (R2 = 0.95; RMSE = 60.76 mm, bias = 0.094). It was observed that the GDA and GRA calibrated-downscaled precipitation datasets were superior to the Original-TRMM, yet GRA outperformed GDA. Annual precipitation from downscaled and calibrated-downscaled datasets was further temporally downscaled to obtain high-resolution monthly and daily precipitations. The results revealed that the monthly-downscaled precipitation (R2 = 0.82, bias = -0.02 and RMSE = 11.93 mm/month) and the calibrated-downscaled (R2 = 0.89, bias = -0.006 and RMSE = 9.19 mm/month) series outperformed the Original-TRMM (R2 = 0.72, bias = 0.14 and RMSE = 19.8 mm/month) as compared to the RGS observations. The results of daily calibrated-downscaled precipitation (R2 = 0.79, bias = 0.001 and RMSE = 1.7 mm/day) were better than the Original-TRMM (R2 = 0.64, bias = - 0.12 and RMSE = 6.82 mm/day). In general, the proposed IDAC approach is suitable for retrieving high spatial resolution gridded data for annual, monthly, and daily time scales over the UIB with varying climate and complex topography.