Monitoring surface water dynamics is critical for hydrologic assessment in drought prone and climate sensitive regions. This study evaluates differences between two widely used satellite-derived inundation products: the Landsat-based Global Surface Water dataset (30 m) with the Sentinel-based Dynamic World Global Surface Water dataset (10 m). Using the Colorado River Basin as a case study, basin-scale and stream-adjacent inundation patterns are compared to assess how differences in spatial resolution, observed frequency, and classification methodology influence mapped surface water extent. Results indicate that the Sentinel-derived product detects more than twice as many low-order streams segments than the Landsat-based product, with the largest discrepancies occurring in snowmelt-dominated headwater regions. While agreement between products is high for large, stable water bodies, divergence increases in narrow or fragmented channels. These findings demonstrate that commonly used inundation products can yield systematically different representations of stream connectivity and cumulative water extent. The results highlight the importance of understanding product-specific characteristics when interpreting long-term surface water patterns, particularly in semi-arid regions where small or ephemeral features are hydrologically significant.

Drought risk in groundwater reservoirs poses a critical threat to water security in arid and semi-arid regions, where reliable regulation of subsurface storage is essential for sustaining socio-economic and ecological systems. In this study, we develop a digital twin (DT) framework specifically targeted at proactive drought-risk mitigation in a karez-style underground reservoir system. Taking the Tailan River aquifer in Xinjiang as a case study, the framework integrates multi-source data fusion, dual-scale state prediction, and optimized scheduling via a B/S architecture. Its core innovations are: (1) a dual-scale prediction engine based on the Random Forest algorithm, achieving high-precision forecasting of total storage and multi-point water levels (R-2 = 0.9455, RMSE = 0.0088 m); and (2) a hierarchical collaborative optimization (HCO) method that integrates multiple linear regression, differential evolution, and gradient descent into a three-stage 'baseline construction-global search-local optimization' process to generate real-time, multi-objective mitigation strategies. Case validation demonstrates stable data connectivity with low latency and high prediction accuracy (MAE < 0.05 m). This research establishes a forward-looking paradigm for intelligent groundwater management. Nonetheless, the framework currently relies on simulation-driven training data and limited field validation, which are identified as key directions for future improvement.

Understanding drought response mechanisms is critical for high-efficiency maize production in the seasonally arid regions of Southwest China. A two-year field experiment (2022-2023) was conducted using a randomized block design with two deficit degrees (D25% and D50%) imposed at single or multi stages to investigate summer maize growth, yield and water use responses to stage-specific deficit irrigation (DI). The study site is located in a typical seasonally arid region with subtropical climate. Results indicated that DI during vegetative growth (VG) period significantly inhibited maize morpho-physiological traits (p 0.05) while enhancing water use efficiency (3.26%). We conclude that stage-specific DI serves as a practical, climate-adaptive tool for summer maize production in seasonally arid regions.

Accurate estimation of daily evapotranspiration (ET) at the field scale is essential for agricultural water management, particularly in arid and semi-arid regions, yet existing satellite products often suffer from spatiotemporal trade-offs. To overcome this limitation, we generated high-resolution daily ET data using two data fusion approaches based on the unbiased variant of Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ubESTARFM). In the first approach (LST-fused ET), ubESTARFM was used to generate high spatio-temporal resolution land surface temperature (LST) data, which served as the key input for the soil moisture-coupled Two-Source Energy Balance (TSEB-SM) model to estimate high-resolution daily ET. In the second approach (Fused ET), high-resolution daily ET data were directly generated by applying ubESTARFM to fuse ET products derived from MODIS and Landsat observations using the TSEB-SM model. Results showed that LST-fused ET agreed better with eddy covariance (EC) observations, yielding a lower RMSE of 0.469 mm/day (compared to 0.567 mm/day for Fused ET) and a significantly smaller systematic bias (-0.149 mm/day vs. -0.430 mm/day) at the relatively heterogeneous Boyagin site. Furthermore, LST-fused ET demonstrated superior spatial consistency with ECOSTRESS results as benchmarks over heterogeneous surfaces, achieving a significantly lower MAPE of 0.49% compared to 9.53% for Fused ET. This limitation of Fused ET, primarily attributed to pixel-matching biases, could be mitigated by incorporating dynamic, high-resolution LAI into the fusion process as a structural constraint, thereby improving accuracy while maintaining efficiency. Moving forward, improving the efficiency and accuracy of the Fused ET could provide a pragmatic and scalable pathway for large-area, field-scale daily ET mapping, supporting agricultural water-use monitoring and water resource management in arid and semi-arid regions.

The Guadalupe Valley Basin (GVB) in Baja California, Mexico, faces severe groundwater depletion from intensive agriculture, over-extraction, and declining precipitation. The water imbalance has been progressive, and an annual deficit of approximately 18 Mm3 was reported in 2024. In response, we identified suitable sites for Managed Aquifer Recharge (MAR) through an integrated geospatial and hydrogeological analysis. We combined aquifer data with surface datasets in a Spatial Multi-Criteria Decision Analysis (SMCDA) framework using the Analytical Hierarchy Process (AHP). The basin was stratified into two zones (valley aquifer and highlands) to account for differing recharge conditions. Sensitivity analysis and validation with observed groundwater level changes were performed to ensure model robustness. Three zones were identified as highly suitable for MAR: the central Calafia area, the Porvenir Wells area, and an upstream highland sub-basin. Areas within the 99th percentile of suitability covered approximately 0.62 km2 in the aquifer region and 1.75 km2 in the highlands. The analysis also identified areas prone to waterlogging, which may limit the feasibility of MAR projects. Our results provide a basis for guiding MAR implementation in this water-stressed basin and offer an applicable framework for similar semi-arid regions worldwide.

Previous research on water erosion has primarily focused on arid and semi-arid regions, with less attention given to tropical areas. The rapid expansion of rubber cultivation in tropical areas has significantly altered the vegetation structure, making the spatial-temporal evolution of soil erosion and its impact mechanisms unclear. Traditional evaluation methods often overlook how varying rainfall intensities impact vegetation across different periods. Therefore, this research applied the Chinese soil loss equation model, the optimal parameter geographical detector model, and the logarithmic mean divisia index model to calculate the soil erosion modulus of Hainan Island and determine the influence of various factors on soil water erosion. The soil erosion modulus in 68.89% of the regions exhibited an increasing trend, indicating a risk of soil erosion. Slope, elevation and the NDVI play key roles in determining the spatial variation of water erosion on Hainan Island. The shift from tropical rainforests to rubber plantations diminishes the soil conservation capacity of the understory vegetation, thereby exacerbating soil erosion. This study lays the groundwork for an understanding of soil erosion mechanisms associated with rubber forest expansion and soil protection and ecological conservation in Hainan Island, and offers valuable insights for future research in similar regions.

Increases in precipitation or humidity generally enhance the carbon sequestration capacity; however, this phenomenon has not been consistent worldwide. In particular, in water-limited arid and semi-arid regions, it remains uncertain whether additional precipitation can offset the soil moisture depletion induced by continuous vegetation greening, and thus sustain carbon sequestration. Using multi-source remote sensing data, we found that along the aridity gradient, interannual precipitation increased at a rate of 7.7 & times; 10(-)(2) mm yr(-)(1), whereas the trend of vegetation carbon sequestration initially rose and then declined, with a reversal occurring in the forest-grassland ecotone. This reversal was primarily attributed to the synergistic and tradeoff effects of soil moisture and precipitation. In arid zones, the concurrent increases in soil moisture and precipitation jointly accelerated carbon sequestration, while in semi-arid regions, soil moisture scarcity reduced the water use efficiency (WUE) of forests and grasslands, offsetting 46% and 24% of the precipitation-induced enhancement in carbon sequestration rates, respectively. Notably, vegetation physiological factors such as WUE and leaf area index (LAI) served as key mediators in the indirect regulation of carbon sequestration rates by external water conditions, with their indirect effects exceeding direct effects by 60%. These findings emphasize that, in order to minimize adverse impacts such as groundwater resource depletion caused by vegetation greening, it is crucial to adopt region-specific planting structure optimization strategies.

A study evaluated the stability of 16 grass pea genotypes across 12 diverse rainfed environments in western and southwestern Iran over three years (2017-2020) using nonparametric stability measures. Significant genotype-by-environment (G x E) interactions highlighted challenges in selecting stable, high-yielding genotypes. The grand mean yield was 1.39 t/ha. Negative correlations between yield-oriented measures (Kang's Yield-Stability Index, YS; Fox's TOP) and rank-consistency statistics (H & uuml;hn's S-1-S-6; Thennarasu's N-1-N-4) highlighted contrasting stability concepts. A sequential sieving procedure was therefore applied: genotypes with mean yield = population mean (5.44) and TOP >= 4; rank-consistency parameters characterized residual sensitivity without further elimination. This approach identified genotypes 3, 1, 4, and 6 as superior, combining high grain yield (>1.48 t/ha) with reasonable stability across variable rainfed conditions. These genotypes are recommended for breeding programs targeting climate-resilient cultivars in marginal environments. Integrating multiple nonparametric metrics with prioritized yield potential proved essential for robust selection, supporting sustainable agriculture and food security in arid and semi-arid regions.

Dual-purpose cowpea with improved grain and fodder quality has potential to enhance food and nutritional security. The objective of this study was to quantify the nutritive value and cooking time of dual-purpose cowpea cultivars (cv.). The study was conducted in 2021 and 2022, at Bambey, Senegal to evaluate 20 cowpea cultivars and two levels of fertilizer (zero and 9-30-15 kg NPK rates). Cowpea fodder crude protein concentration ranged from 16% with EBC4STR2 to 20% with forage-only cultivar 66-35 F. Cowpea fodder macronutrients K, Mg, and S differed up to 77%, 32%, and 33%, respectively, among cultivars. Grain P, K, Ca, Mg, and S concentrations also varied by up to 24%, 24%, 60%, 30%, and 21% among cultivars. Dual-purpose cultivars had lower Ca and Mg compared with grain-only cultivars. However, grain protein and other macronutrient concentrations in dual-purpose cultivars were similar to the grain-only cultivars. Cowpea grain cooking time ranged from 31 minutes for 66-35 F to 106 minutes for Bambey-21. In conclusion, dual-purpose cultivars, such as E-BC4STR11 and E-BC4STR2 with greater grain and fodder quality and less than an hour of grain cooking time, have the potential to improve productivity, quality, and economic value of cowpea in semi-arid regions of Sub-Saharan Africa.

Sorghum productivity on vertisols is constrained by phosphate fixation, salinity, and nutrient limitations, undermining food security in semi-arid regions. We evaluated the impact of indigenous arbuscular mycorrhizal fungi (AMF) inoculation on growth, yield, and phosphorus (P) dynamics of four Sudanese sorghum cultivars across two vertisol sites: Abassyia (salinity - sodicity stress) and Medani (P deficiency). Field inoculation with indigenous AMF consortia (Rhizophagus, Glomus, Claroideoglomus, Funneliformis, Entrophospora, Scutellospora, and Acaulospora) improved sorghum performance, increasing biomass, panicle length, grain yield, root colonization, spore density, and phosphorus acquisition efficiency (PAE). Inoculated plants achieved colonization levels of 52-81% and spore densities up to 4894 spores per 100 g soil, with PAE increasing to 132 mg P plant- 1. Shoot P concentration and plant height were unaffected; no conclusion can be drawn regarding P allocation. Cultivar- and site-specific responses identified Tabat as the most productive cultivar (36.6 g grain yield plant- 1), while Tetron showed superior PAE. Multivariate analyses confirmed strong associations between AMF colonization, biomass, and yield, with clustering of inoculated and uninoculated plants. These findings provide robust field evidence that leveraging indigenous AMF biodiversity enhances P-use efficiency and sorghum productivity on vertisols, supporting sustainable biofertilization in low-input systems in sub-Saharan Africa under climate-stressed environments.