Volume 2 - Issue 3, May - June 2026

Reservoir storage determination in ungauged basins is limited by the lack of long-term stream flow records required for hydrological analysis and reservoir design. This study developed a hydrological framework for estimating the storage capacity of the proposed Orle Reservoir using rainfall–runoff derived monthly discharge data. Historical and predictive 30-year rainfall datasets, together with observed rainfall and discharge records, were used to develop a nonlinear rainfall–runoff model based on the Gauss–Newton regression algorithm, with rainfall as the predictor and discharge as the response variable. The developed model showed strong predictive performance with a coefficient of determination R2 of 0.989, RMSE of 1.591 m³/s, and MAE of 1.399 m³/s, indicating close agreement between observed and predicted discharge values. The validated model was used to generate synthetic monthly discharge series for historical and predictive flow scenarios. The generated discharge data were converted into cumulative inflow volumes and analyzed using the mass curve method, yielding a gross reservoir storage capacity of approximately 1.04×108 m³. Seepage and evaporation losses were evaluated using the Fakhari and Gambari seepage model and the Linarce evaporation method, producing annual losses of 7.95×106m³ and 3.91×103 m³ respectively. After accounting for evaporation, seepage, and sedimentation losses, the effective reservoir storage capacity was estimated as approximately 9.08×107 m³. The study demonstrates that integrating nonlinear rainfall– runoff modeling with mass curve analysis provides a reliable framework for reservoir storage determination and water resources planning in data-scarce regions.

Luqman Muhammed Audu, Ibrahim Rasheed, Umar Isah Adam, Odion Ainakhuagbon, Isa Ainavberokhai, Sule Seghosimhe, Dirisu Braimah,"Development of Optimal Storage Capacity for Orle Reservoir Using Rainfall–Runoff Derived Monthly Flow Series" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(945-954) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.