Surajit Ghosh

Punsisi Rajakaruna, Surajit Ghosh, Bunyod Holmatov

This study presents a comprehensive remote sensing analysis of rainfall patterns and selected hydropower reservoir water extent in two tropical monsoon countries, Vietnam and Sri Lanka. The aim is to understand the relationship between remotely sensed rainfall data and the dynamic changes (monthly) in reservoir water extent. The analysis utilizes high-resolution optical imagery and Sentinel-1 Synthetic Aperture Radar (SAR) data to observe and monitor water bodies during different weather conditions, especially during the monsoon season. The average annual rainfall for both countries is determined, and spatiotemporal variations in monthly average rainfall are examined at regional and reservoir basin levels using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset from 1981 to 2022. Water extents are derived for selected reservoirs using Sentinel-1 SAR Ground Range Detected (GRD) images in Vietnam and Sri Lanka from 2017 to 2022. The images are pre-processed and corrected using terrain correction and refined Lee filter. An automated thresholding algorithm, OTSU, distinguishes water and land, taking advantage of both VV and VH polarization data. The connected pixel count threshold is applied to enhance result accuracy. The results indicate a clear relationship between rainfall patterns and reservoir water extent, with increased precipitation during the monsoon season leading to higher water extents in the later months. This study contributes to understanding how rainfall variability impacts reservoir water resources in tropical monsoon regions. The preliminary findings can inform water resource management strategies and support these countries' decision-making processes related to hydropower generation, flood management, and irrigation.

Surajit Ghosh, Archita Mallick, Anuva Chowdhury et al.

Geospatial sciences include a wide range of applications, from environmental monitoring transportation to infrastructure planning, as well as location-based analysis and services. Graph theory algorithms in mathematics have emerged as indispensable tools in these domains due to their capability to model and analyse spatial relationships efficiently. This article explores the applications of graph theory algorithms in geospatial sciences, highlighting their role in network analysis, spatial connectivity, geographic information systems, and various other spatial problem-solving scenarios like digital twin. The article provides a comprehensive idea about graph theory's key concepts and algorithms that assist the geospatial modelling processes and insights into real-world geospatial challenges and opportunities. It lists the extensive research, innovative technologies and methodologies implemented in this domain.

Surajit Ghosh, Arpan Dawn, Sneha Kour et al.

Floods are one of the most common disasters globally. Flood affects humans in many ways. Therefore, rapid assessment is needed to assess the effect of floods and to take early action to support the vulnerable community in time. Sentinel-1 is one such Earth Observation (EO) mission widely used for mapping the flooding conditions at a 10m scale. However, various preprocessing steps are involved before analyses of the Sentinel-1 data. Researchers sometimes avoid a few necessary corrections since it is time-consuming and complex. Standardization of the Sentinel-1 data is the need of the hour, specifically for supporting researchers to use the Standardized Analysis-Ready (STAR) data cube without experiencing the complexity of the Sentinel-1 data processing. In the present study, we proposed a workflow to use STAR in Google Earth Engine (GEE) environment. The Nigeria Flood of 2022 has been used as a case study for assessing the model performance.

Hugo Retief, Kayathri, Vigneswaran et al.

Reliable daily estimates of reservoir storage are pivotal for water allocation and drought response decisions in semiarid regions. Conventional rating curves at Loskop Dam, the primary storage on South Africa's Olifants River, have become increasingly uncertain owing to sedimentation and episodic drawdown. A 40 year Digital Earth Africa (DEA) surface area archive (1984-2024) fused with gauged water levels to develop data driven volume predictors that operate under a maximum 9.14%, a 90 day drawdown constraint. Four nested feature sets were examined: (i) raw water area, (ii) +a power law "calculated volume" proxy, (iii) +six river geometry metrics, and (iv) +full supply elevation. Five candidate algorithms, Gradient Boosting (GB), Random Forest (RF), Ridge (RI), Lasso (LA) and Elastic Net (EN), were tuned using a 20 draw random search and assessed with a five fold Timeseries Split to eliminate look ahead bias. Prediction errors were decomposed into two regimes: Low (<250 x 10^6 cubic meters) and High (>250 x 10^6 cubic meters) storage regimes. Ridge regression achieved the lowest cross validated RMSE (12.3 x 10^6 cubic meters), outperforming GB by 16% and RF by 7%. In regime terms, Ridge was superior in the Low band (18.0 ver. 22.7 MCM for GB) and tied RF in the High band (~12 MCM). In sample diagnostics showed GB's apparent dominance (6.8-5.4 MCM) to be an artefact of overfitting. A Ridge meta stacked ensemble combining GB, RF, and Ridge reduced full series RMSE to ~ 11 MCM (~ 3% of live capacity). We recommend (i) GB retrained daily for routine operations, (ii) Ridge for drought early warning, and (iii) the stacked blend for all weather dashboards. Quarterly rolling retraining and regime specific metrics are advised to maintain operational accuracy below the 5% threshold mandated by the Department of Water and Sanitation.