Hydraulic models are essential for simulating river water levels, but their results often deviate from reality due to systematic errors and modelling uncertainties. These are commonly addressed through roughness calibration, which can obscure underlying model issues and limit reliability under varying flow conditions. This study explores alternative strategies that align simulation results with measurements. We apply: (i) a mesh elevation modification technique to correct spatial representation errors, and (ii) an upstream boundary condition adjustment to refine flow inputs. The approach is tested on the Waal River in the Netherlands, with both adjustments implemented without roughness calibration. The model successfully reproduces observed water levels across the full discharge range, achieving a mean absolute error below 10 cm over two hydrologically distinct years. The findings demonstrate that targeted structural corrections and informed boundary adjustments can improve model performance, providing a complementary approach to traditional calibration-based methods.
The study aims to evaluate the suitability of gridded and observed rainfall data for detecting droughts in areas with limited rainfall information. We utilized a time-series historical rainfall dataset spanning from 1990 to 2022. To evaluate dry and wet conditions during this period, we utilized the Standardized Precipitation Index (SPI). The ARIMA model was employed to predict future drought trend in the study area. Analysis revealed a low bias of 3.89% between gridded and ground-based rainfall datasets. This corresponded to a strong coefficient of determination (R2 = 0.707) and correlation coefficient (r = 0.84), indicating robust agreement between the datasets. A 50% chance of near-normal drought is predicted to occur in the study area over the next decade. Policymakers in Bontanga should therefore prioritize rainwater harvesting, water-saving irrigation technologies, and drought-resilient crop varieties to strengthen food production resilience across northern Ghana.
Flood and drought disasters are often assessed independently, resulting in fragmented adaptation strategies and limited consideration of their interactions. This study presents an integrated drought-flood risk assessment framework that combines GIS-based multi-criteria evaluation (MCE) with hydrologically derived indicators to jointly assess both disasters. The framework integrates hazard, vulnerability, and exposure components, and introduces a transition layer to quantify the temporal linkage between drought and subsequent flood events. Applied to the Muda River Basin in Malaysia, the results identify the southwestern basin as a key hotspot driven by intensive land use activities and high population exposure. Incorporating the transition layer increases high-risk areas from 14.60% to 25.5% of the basin, highlighting the importance of sequential hazard dynamics. This framework addresses a critical gap in multi-hazard assessment and supports climate adaptation, spatial planning, and disaster risk reduction.
