Assessment of Rainfall-Runoff Dynamics and Reservoir Inflow Response under Rainfall Scenarios in the Yeleswaram Catchment Using HEC-HMS

Abstract

A continuous daily rainfall–runoff model of the Yeleswaram catchment (2378.03 km²), which drains into the Yeleru reservoir in Kakinada district, Andhra Pradesh, India, was developed using HEC-HMS version 4.2.1. Daily rainfall and meteorological data from NASA POWER, together with FAO-56 Penman–Monteith potential evapotranspiration, were used to drive the model for the period 2019–2024. The Soil Moisture Accounting (SMA) loss method and the Clark Unit Hydrograph transform were adopted, with model geometry and curve number characteristics derived from a Digital Elevation Model, land use/land cover and hydrologic soil group data in a GIS environment. The model was calibrated against observed daily reservoir inflow for 2019–2022 and validated for 2023–2024, achieving a Nash–Sutcliffe Efficiency (NSE) of 0.117 and 0.170, and a coefficient of determination (R²) of 0.287 and 0.286, respectively. The model reproduced the calibration-period peak reasonably well but underestimated the major validation-period peak and inflow depth, indicating limited predictive performance rather than strong forecasting skill. Five hypothetical rainfall scenarios, obtained by uniformly scaling the baseline daily rainfall by ?30%, ?15%, 0%, +15% and +30%, were simulated using the calibrated model to assess the sensitivity of reservoir inflow to rainfall variability. The results show a nonlinear response, with peak reservoir inflow decreasing by 54.51% under the ?30% scenario and increasing by 62.41% under the +30% scenario relative to the baseline peak of 723.9 m³/s, while the corresponding simulated inflow depth changed by ?84.98% and +203.03%. The study demonstrates a workflow for GIS-based continuous HEC-HMS modelling of a data-limited reservoir catchment and highlights the usefulness of rainfall-scenario simulation for examining potential changes in reservoir inflow relevant to water resources planning.

Introduction

The text presents a study on rainfall–runoff modelling and reservoir inflow prediction for the Yeleswaram catchment feeding the Yeleru Reservoir in Kakinada district, Andhra Pradesh. Reliable estimation of reservoir inflow is important for irrigation, drinking water supply, flood management, groundwater recharge, and overall reservoir operation, especially where long-term streamflow observations are limited.

Main purpose

The study uses the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) to develop a continuous daily rainfall–runoff model for the Yeleswaram catchment. The model combines GIS-based watershed information with rainfall and meteorological data to simulate reservoir inflow under historical and hypothetical rainfall conditions.

Objectives

The main objectives are to:

• Delineate and characterize the Yeleswaram catchment using GIS and ArcGIS.

• Develop a daily continuous HEC-HMS rainfall–runoff model.

• Calibrate the model using observed reservoir inflow data from 2019–2022 and validate it using data from 2023–2024.

• Examine reservoir inflow under rainfall-change scenarios of −30%, −15%, 0%, +15%, and +30%.

• Assess how these rainfall changes affect peak reservoir inflow and simulated inflow depth.

Study area

The Yeleswaram catchment drains into the Yeleru Reservoir near Yeleswaram village in Kakinada district, Andhra Pradesh. The GIS-derived catchment area is approximately 2,378 km². The region has a tropical monsoon climate, with rainfall mainly occurring during the southwest and northeast monsoons.

The reservoir is important for irrigation, drinking water, industrial water supply, and groundwater recharge. The study uses GIS-derived information on elevation, slope, land use/land cover, soil characteristics, curve number, and flow paths to represent the catchment.

Literature findings

Previous studies demonstrate that HEC-HMS is a flexible and widely used hydrological modelling tool for both event-based and continuous rainfall–runoff simulations. Research has shown that:

• HEC-HMS can effectively simulate watershed runoff and flood hydrographs.

• Continuous modelling is useful when long-term rainfall and streamflow data are available.

• Appropriate selection of loss, transform, and routing methods is important.

• Calibration against observed flow is essential for reliable model performance.

• GIS integration improves watershed characterization and model parameterization.

• Rainfall uncertainty and changes can substantially influence reservoir inflow predictions.

The review identifies a research gap: continuous, GIS-based HEC-HMS studies combined with rainfall-scenario analysis for reservoir catchments in coastal Andhra Pradesh are relatively limited.

Methodology

The study follows three major stages:

1. GIS-based catchment characterization

   • ArcGIS 10.8 is used to delineate the watershed.

   • Terrain, slope, land use/land cover, soil groups, curve number, and flow-path characteristics are derived.

2. Continuous HEC-HMS modelling

   • Daily rainfall and meteorological data are obtained from NASA POWER.

   • Potential evapotranspiration is estimated using the FAO-56 Penman–Monteith method.

   • The Soil Moisture Accounting (SMA) method is used to represent rainfall losses and soil-water processes.

   • The Clark Unit Hydrograph is used to transform excess rainfall into runoff.

   • The model is calibrated for 2019–2022 and validated for 2023–2024 against observed reservoir inflow.

3. Rainfall-scenario analysis

   • The calibrated model is subjected to rainfall changes of −30%, −15%, 0%, +15%, and +30%.

   • Changes in reservoir inflow depth and peak inflow are then evaluated.

Conclusion

A continuous daily rainfall–runoff model of the Yeleswaram catchment (2378.03 km²) was developed in HEC-HMS 4.2.1 using NASA POWER rainfall and meteorological data and FAO-56 Penman–Monteith potential evapotranspiration for 2019–2024, with the Soil Moisture Accounting loss method and Clark Unit Hydrograph transform, and GIS-derived terrain, land use/land cover and hydrologic soil group data used to characterise the catchment and assign a composite curve number of 89.46. The model was calibrated for 2019–2022 and validated for 2023–2024, achieving NSE values of 0.117 and 0.170 and R² values of 0.287 and 0.286, respectively. The model reproduced the calibration-period peak inflow reasonably closely in magnitude and timing but substantially underestimated the major validation-period peak and inflow depth; the model should therefore be characterised as having limited predictive performance for the largest observed events, rather than as a highly accurate reservoir-inflow forecasting tool in its current form. The rainfall-scenario simulations demonstrated a clear, nonlinear sensitivity of reservoir inflow to rainfall magnitude, with peak reservoir inflow ranging from 329.3 m³/s under a ?30% rainfall scenario to 1175.7 m³/s under a +30% scenario, compared with a baseline peak of 723.9 m³/s, and an even larger relative change in simulated inflow depth. This scenario analysis illustrates the practical usefulness of the calibrated model for exploring the potential effects of rainfall variability on reservoir inflow, even though further refinement of the calibration would be needed before the model is relied upon for detailed flood-peak forecasting at the Yeleru reservoir.

References

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Copyright

Copyright © 2026 Mamidivalasa Deepika, Dr. Vemu Sreenivasulu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.