The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions.

High-resolution, satellite-retrieved precipitation products are useful input data for hydrological predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. This research explores the temporal variability of rainfall, crucial for understanding hydrology and water resource management, particularly in vulnerable regions like Bihar, India. satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) was evaluated using observed rainfall at meteorological station using key statistical parameters and also carried long-term trend analysis during 2000–2023 by applying Mann–Kendall Test and estimating Standardized Anomaly Index (SAI). The results reveal that, Rainfall was underestimated by satellite product and Before bias correction, TRMM data exhibited significant discrepancies in rainfall estimates, with varying biases and mean errors across grid points. After bias correction, the agreement between TRMM and observed rainfall significantly improved, with Pearson correlation coefficients stabilizing between 0.8 and 1.0, bias reduced to − 0.1 to 0.2, and mean errors minimized to − 0.1 to 0.1. Additionally, root mean square error (RMSE) improved, and R² values, indicating enhanced reliability of the corrected data. The analysis of annual rainfall and the Standardized Anomaly Index (SAI) indicates significant variability without a clear trend. This variability exposes the region to extreme weather, with flooding in wet years like 2007, 2008, 2011 and 2021 and droughts in dry years such as 2005 and 2019. Thus, there is an urgent need for adaptive water management and agricultural strategies.

Hydrological modeling is critical for assessing water availability and guiding sustainable resource management, particularly in monsoon-dependent, data-scarce basins such as the Chalakudy River Basin (CRB) in Kerala, India. This study integrated the Soil Conservation Service Curve Number (SCS-CN) method within the Google Earth Engine (GEE) platform, making novel use of multi-source, open access datasets (CHIRPS precipitation, MODIS land cover and evapotranspiration, and OpenLand soil data) to estimate spatially distributed long-term runoff (2001–2023). Model calibration against observed runoff showed strong performance (NSE = 0.86, KGE = 0.81, R² = 0.83, RMSE = 29.37 mm and ME = 13.48 mm), validating the approach. Over 75% of annual runoff occurs during the southwest monsoon (June–September), with July alone contributing 220.7 mm. Seasonal assessments highlighted monsoonal excesses and dry-season deficits, while water balance correlated strongly with rainfall (r = 0.93) and runoff (r = 0.94) but negatively with evapotranspiration (r = –0.87). Time-series analysis indicated a slight rise in rainfall, a decline in evapotranspiration, and a marginal improvement in water balance, implying gradual enhancement of regional water availability. Spatial analysis revealed a west–east gradient in precipitation, evapotranspiration, and water balance, producing surpluses in lowlands and deficits in highlands. These findings underscore the potential of cloud-based hydrological modeling to capture spatiotemporal dynamics of hydrological variables and support climate-resilient water management in monsoon-driven and data-scarce river basins.

Precision genome editing, particularly using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR‐associated protein 9 (Cas9), is advancing crop improvement by enabling targeted and efficient genetic modifications. Root and tuber crops such as potato, cassava, sweet potato, and yam are vital for global food and nutritional security but remain highly vulnerable to climate change, pests, diseases, and limited genetic diversity. Genome editing technologies facilitate the development of improved traits, including enhanced disease resistance, tolerance to abiotic stress, improved nutritional quality, and extended shelf life. This review synthesizes recent advances in genome editing for root and tuber crops across global production systems, including illustrative examples from Sub‐Saharan Africa, where active genome editing initiatives are being implemented. It further examines key technical constraints, such as low efficiency of plant transformation and regeneration, and highlights regulatory challenges arising from differing policy frameworks across countries. Emerging solutions are discussed, including genotype‐independent editing strategies and DNA‐free approaches that avoid the integration of foreign genetic material. Addressing these challenges will be critical for developing resilient and sustainable food systems. Unlike previous reviews, this study integrates mechanistic insights with cross‐crop synthesis and proposes next‐generation genome editing strategies for engineering complex traits in polyploid root and tuber crops.