Advances in near real-time rainstorm prediction using remote sensing have offered important opportunities for effective disaster management. However, this information is subject to several sources of systematic errors that need to be corrected. Temporal and spatial characteristics of both satellite and in-situ data can be combined to enhance the quality of storm estimates. In this study, we present a spatiotemporal object-based method to bias correct two sources of systematic error in satellites: displacement and volume. The method, Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction (ST-CORAbico), uses the spatiotemporal rainfall analysis ST-CORA incorporated with a multivariate kernel density storm segmentation for describing the main storm event characteristics (duration, spatial extension, volume, maximum intensity, centroid). Displacement and volume are corrected by adjusting the spatiotemporal structure and the intensity distribution, respectively. ST-CORAbico was applied to correct the early version of the Integrated Multi-satellite Retrievals for the Global Precipitation Mission (GPM-IMERG) over the Lower Mekong basin in Thailand during the monsoon season from 2014 to 2017. The performance of ST-CORABico is compared against the Distribution Transformation (DT) and Gamma Quantile Mapping (GQM) probabilistic methods. A total of 120 storm events identified over the study area were classified into short and long-lived storms by using a k-means cluster analysis method. Examples for both storm event types describe the error reduction due to location and magnitude by ST-CORAbico. The results showed that the displacement and magnitude correction made by ST-CORAbico considerably reduced RMSE and bias of GPM-IMERG. In both storm event types, this method showed a lower impact on the spatial correlation of the storm event. In comparison with DT and GQM, ST-CORAbico showed a superior performance, outperforming both approaches. This spatiotemporal bias correction method offers a new approach to enhance the accuracy of satellite-derived information for near real-time estimation of storm events.

Water shortage is a critical problem that affects the sustainability of the agricultural sector in Northeastern Thailand, especially areas located far from major rivers. This study developed an integrated QSWAT–WEAP modeling system combined with GIS-based spatial analysis for water shortage risk assessment at the sub-district level in Maha Sarakham Province, covering 5292 sq.km and 133 sub-districts. The system bridges the institutional gap between hydrological sub-watershed boundaries and administrative jurisdictions, enabling model outputs that directly support local governance decision-making. The QSWAT model simulated sub-watershed streamflow while the WEAP model evaluated water balance against water demand from five sectors. QSWAT validation (2017–2023) achieved R² = 0.72, NSE = 0.70, and RSR = 0.54, while WEAP verification yielded R² of 0.66–0.80 and NSE of 0.65–0.71. In the spatial analysis, 106 of 133 sub-districts (79.7%) experienced low water availability. Agriculture was the dominant sector in terms of water demand, which has increased at an average rate of 1.7% annually and accounted for 88.7% of total demand in the study period. According to the temporal analysis, the dry season was the most critical period, with peak water shortage of 99.2% in March. Overall, 105 of 133 sub-districts (78.9%) were classified as having water shortages of moderate to very high severity. These findings provide a quantitative basis for sustainable water resource management planning and drought mitigation, thus helping to enhance agricultural sustainability in Maha Sarakham Province.

Water shortage is a major problem that affects sustainability of agricultural sector in northeastern Thailand especially areas remote from a main river. This study aims at developing a system for water shortage risk assessment at sub-district level in Maha Sarakham Province. QSWAT model for sub-watershed streamflow simulation was integrated with WEAP model to analyze water balance and assess water shortage based on water demand from five sectors: non-irrigated agriculture, irrigated agriculture, consumption, services, and industry. The findings revealed that both models provided the results of calibration, and the validated results of streamflow analysis were at satisfactory to good level. From spatial analysis, distribution of provincial water resource was significantly different. Sub-districts located along the Chi River had high streamflow but low water shortage while those from central to southern part had limited streamflow although water demand was high especially for agricultural sector. According to temporal analysis, critical period of water shortage was found in the dry season and seasonal transition after post-harvest period. Integration of data on streamflow and water demand can be useful to divide subdistricts into three group: positive water balance, balance, and negative water balance. Most sub-districts were classified as negative water balance that needed urgent measures for water resource development. These findings have provided important data for planning water resource management locally and supporting development of drought mitigation measures for vulnerable areas in Maha Sarakham Province.

Accurate precipitation data is essential for hydrological modeling and water resource management, particularly in tropical regions with complex topography and limited ground-based observation networks. This study develops an integrated two-stage framework combining K-nearest neighbors (KNN) machine learning bias correction with Kalman filter blending to enhance Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily precipitation estimates across Thailand's diverse geographical and climatic conditions. The methodology utilized comprehensive meteorological data from 628 stations across Thailand spanning 44 years (1981-2024), with temporal partitioning into training (1981-2015) and validation (2016-2024) periods. The first stage implemented seasonal KNN bias correction using 11-dimensional feature vectors incorporating CHIRPS satellite precipitation, auxiliary meteorological variables (maximum/minimum temperature, relative humidity, evaporation), and station coordinates. The second stage applied adaptive Kalman filter blending with dual-update processing, combining raw CHIRPS data with KNN-corrected estimates. Results demonstrate exceptional performance improvements across both periods. Correlation coefficients increased dramatically from 0.42 to 0.94 during training (124% improvement) and from 0.41 to 0.91 during validation (122% improvement). Systematic bias correction transformed raw CHIRPS overestimation of 34.03% to controlled underestimation of -10.00% (BC CHIRPS) and -10.78% (BBC CHIRPS) during training, with similar validation patterns. Regional analysis revealed differential effectiveness across Thailand's climatic zones. The most challenging DJF dry season showed severe raw CHIRPS overestimation of 588.27% (training) and 167.12% (validation), reduced by 95-99% with both corrections. Spatial validation confirmed operational applicability, effectively eliminating widespread overestimation while preserving legitimate precipitation signals. The integrated framework successfully addresses systematic biases in satellite precipitation products while maintaining computational efficiency. This research demonstrates that sophisticated machine learning integrated with optimal filtering theory can significantly enhance satellite precipitation accuracy for operational applications in data-scarce tropical regions, with demonstrated effectiveness across Thailand's diverse conditions and strong potential for broader tropical applications.