Zia Ahmed

Grassland degradation in semi-arid regions is largely driven by complex climate-human interactions, but most assessments rely on a single remote-sensing indicator, limiting the detection of structural and functional shifts. To overcome this constraint, we established a comprehensive framework that combines multiple indicators and sensor datasets. This approach integrates fractional vegetation cover (FVC) and net primary productivity (NPP) spanning 2001–2022, along with aboveground biomass (AGB) data for 2001–2020, to systematically assess long-term grassland dynamics in the Ili region of northwestern China. Degradation classifications were validated using Landsat 8 and Sentinel-2 imagery with texture-enhanced Random Forest models. Among the three indicators, AGB exhibited the highest reliability (overall accuracy ≈ 0.95; user's accuracy > 0.70) and strongest temporal responsiveness to extreme events. Trend analysis revealed weak but consistent long-term declines across all indicators, with severe degradation peaking in 2008 (70.8%) and 2014 (65.9%), while partial recovery occurred in 2002 and 2016. Spatially, mountainous areas showed greater resilience, whereas lowland plains and agro-pastoral ecotones were more vulnerable. Using an XGBoost-SHAP framework, we quantified nonlinear driver contributions and identified temperature as the dominant long-term control, followed by anthropogenic factors, which had moderate influence (population 1.28, livestock 1.24, GDP 0.45). Integrating biomass-based structural metrics with greenness and productivity indicators improves the detection of degradation dynamics and clarifies climate-human interactions in fragile grassland ecosystems.

This study provides the first systematic comparison between the Chinese Precipitation Measurement Radar (PMR) and the international Dual-frequency Precipitation Radar (DPR), which mainly focuses on orbital characteristics, precipitation detection accuracy, classification of precipitation types, and vertical precipitation structures. Specifically, how these structures respond to topographic influences was also examined. Our findings indicate that the PMR, owing to its wider swath and higher sampling frequency, achieves substantially greater spatial coverage across mainland China when compared with DPR. Both radars show frequent overlapping scans at high latitudes, with up to eight overlaps within a single year, and a gradual decline in overlap frequency toward lower latitudes. In terms of detection accuracy, DPR generally surpasses PMR. As for identification of precipitation types, however, PMR demonstrates higher sensitivity and broader spatial representation, detecting nearly twice as many events as DPR, particularly excelling in the recognition of stratiform precipitation. In terms of vertical precipitation features, PMR exhibits better accuracy in capturing storm top heights (STH) of convective systems and shows stronger responsiveness to regional and orographic variations. Freezing level heights (FLH) from both instruments are largely consistent, displaying an overall increasing trend with terrain elevation. For bright-band height (BBH), although both radars experience considerable data gaps, PMR delivers more spatially uniform observations comparable to DPR at low and mid-elevation regions, but more physically realistic over high-elevation western areas. In summary, our results suggest that PMR generally offers greater adaptability than DPR in resolving vertical precipitation structures over complex terrain. This strengthens its value as a space-borne observing tool for improving satellite-based precipitation monitoring and deepening the understanding of orographic precipitation processes.

Lakes play a crucial role in supplying water resources, regulating regional climates, and supporting ecosystems. However, they are increasingly threatened by recurrent droughts. This study focuses on the Mu Us Sandy Land, the fourth largest desert in China, and presents an approach that combines machine-learning techniques with a newly constructed drought index—the detrended cumulative standardized precipitation-evapotranspiration index (DeCumSPEI)—to forecast monthly lake-area variations from 2001 to 2020. Remote sensing data, including lake-area measurements, were obtained using the Google Earth Engine platform. In addition, meteorological factors—such as precipitation, temperature, and actual evapotranspiration (ET_a) as well as anthropogenic variables, such as crop evapotranspiration, the normalized difference vegetation index, and land-use and land-cover change—were collected. The study assessed the performance of six machine-learning models using fivefold cross validation: gradient boosting decision tree, extra trees, random forest, adaptive boosting (AB), bootstrap aggregating (Bagging), and eXtreme gradient boosting. These models were evaluated for their ability to predict lake areas under both short-term (monthly) and long-term (annual) drought conditions. In addition, the influence of ET_a as an upper boundary condition was investigated. The results show that: First, all models, except for AB and Bagging, demonstrated strong predictive performance, achieving coefficients of determination (R²) as high as 0.833, and the lowest average root-mean-square error and standard deviation of 1.05 and 1.01 km². Second, incorporating the 12-month scale DeCumSPEI significantly enhanced model accuracy, with performance improvements of up to 32.01%. Third, comparing models with and without ET_a confirmed the critical role of ET_a in improving prediction accuracy. These findings offer valuable insights for future lake area forecasting in drought-affected regions and underscore the potential of machine-learning models in hydrological and drought response research.

Crop and drought monitoring are vital for sustainable agriculture, as they ensure optimal crop growth, identify stress factors, and enhance productivity, all of which contribute to food security. However, climate projections are equally important as they provide future adaptation scenarios for agriculture. This study aims to project climatic scenarios for northern Bangladesh, assess agricultural and meteorological drought, and estimate crop yield using remote sensing and field data. Our simulated data from 21 CMIP6 models (ensemble mean) under SSP2-4.5 scenarios reveal that maximum temperatures will rise in northwestern regions such as Nawabganj, Rajshahi, Naogaon, Dinajpur, and Joypurhat. By 2050 and 2060, the areas affected by rising mean temperatures are expected to expand into other districts. Additionally, projected rainfall is expected to increase in northern districts, particularly after 2040. Drought assessments using the TOTRAM model reveal a water deficit in many areas in 2022, with milder conditions in 2023. Applying SPEI (Hargreaves-Zamani method) from 1984 to 2024, Sirajganj faced moderately dry conditions (14.38%), while Dinajpur had near-normal conditions (63.33%). Our crop yield model, incorporating Leaf Area Index (LAI), NDVI, SAVI, and field data, demonstrates successful yield estimation, with a significant correlation between predicted and observed yields. The multiple linear regression model achieved a 0.82 goodness-of-fit value, indicating effective yield estimation. We emphasize that timely, accurate information dissemination and monitoring are often more effective in preventing agricultural losses than structural developments. This study encourages decision-makers to integrate geospatial technologies and share critical information with farmers to strengthen food security.