Techno-social flood assessment in a data-scarce region: a case of Tinau River, Nepal

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans.

The number of tidal flood events has been increasing in Indonesia in the last decade, especially along the north coast of Java. Hydrodynamic models in combination with Geographic Information System applications are used to assess the impact of high tide events upon the salt production in Cirebon, West Java. Two major flood events in June 2016 and May 2018 were selected for the simulation within inputs of tidal height records, national seamless digital elevation dataset of Indonesia (DEMNAS), Indonesian gridded national bathymetry (BATNAS), and wind data from OGIMET. We used a finite method on MIKE 21 to determine peak water levels, and validation for the velocity component using TPXO9 and Tidal Model Driver (TMD). The benchmark of the inundation is taken from the maximum water level of the simulation. This study utilized ArcGIS for the spatial analysis of tidal flood distribution upon solar salt production area, particularly where the tides are dominated by local factors. The results indicated that during the peak events in June 2016 and May 2018, about 83% to 84% of salt ponds were being inundated, respectively. The accurate identification of flooded areas also provided valuable information for tidal flood assessment of marginal agriculture in data-scarce region.

A flood assessment of data scarce region using an open-source 2D hydrodynamic modeling and Google Earth Image: a case of Sabarmati flood, India

Flooding is a recurring environmental hazard with devastating socio-economic and ecological impacts, especially in vulnerable regions like Bayelsa State, Nigeria. The state’s low-lying terrain, dense river networks, and poor drainage infrastructure exacerbate its flood susceptibility. This study employs morphometric analysis to assess flood-prone areas across major river basins using Shuttle Radar Topographic Mission (SRTM) data, Geographic Information Systems (GIS), and remote sensing techniques. Key morphometric parameters stream order, drainage density (2.41-3.57 km/km²), bifurcation ratio (1.84-2.84), relief ratio (0.03-0.15), stream frequency (5.00-11.71 streams/km²), infiltration number, and form factor (0.64-1.04) were extracted and analyzed using ArcGIS 10.5, Arc Hydro tools, and Python. Results indicate significant spatial heterogeneity in flood susceptibility. The Forcados River catchment recorded the highest flood risk, with a priority score of 3.4/5, a relief ratio of 0.15, drainage density of 3.57 km/km², and stream frequency of 11.71 streams/km². This aligns with 78% of historical flood event locations. Conversely, the Ekole and Seibri catchments exhibited the lowest susceptibility, with priority scores of 2.0-2.1, relief ratios below 0.05, and drainage densities under 0.9 km/km². The Nun River catchment showed moderate risk (priority score: 2.4), with a stream frequency of 3.2/km² and elongation ratio of 0.6. To enhance predictive capacity, machine learning models were employed. The Random Forest classifier achieved 89% accuracy and an AUC-ROC of 0.93, outperforming the Support Vector Machine model. This study offers a scalable flood assessment framework for data-scarce regions and recommends targeted structural interventions and nature-based solutions tailored to each catchment’s flood profile.