Applicability of Lumped Hydrological Models in a Data-Constrained River Basin of Asia
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 8
Abstract
A representative of meteorological data-constrained basin, Ayeyarwady, in Myanmar, Southeast Asia, is set for flow simulation and forecasting at 15 locations using a range of hydrological modeling approaches: conceptual lumped (GR4J), hybrid-lumped [Identification of unit Hydrographs And Component flows from Rainfall Evapotranspiration and Streamflow Catchment Wetness Index (IHACRES CWI)], semidistributed [Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS)], and relatively distributed [Soil and Water Assessment Tool (SWAT)]. Using daily rainfall data from 51 surface rainfall stations (over an area of approximately ) and coarse monthly evaporation inputs from global sources, the models are calibrated (validated) against observed flows for 2001–2009 (2010–2014) using the performance indicators Nash-Sutcliffe efficiency (NSE), percentage bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and volumetric efficiency . The developed models were then integrated with rainfall forecasts from the Weather Research and Forecasting Model for 2015–2018 and assessed for biases against observed flows. The NSE values were favorable for GR4J (median ), followed by IHACRES (), SWAT () and HEC-HMS () during calibration and GR4J () and the latter three () during validation. Lumped models were found to have comparable, albeit better in simulating low, median, and high quantiles of flows during both calibration and validation periods, compared to other models of varying complexity set for the study basin. The hydrometeorological coupling also revealed that GR4J yielded the least while HEC-HMS yielded the highest biases (up to 30-fold at some stations) in daily flow forecasting. The analysis suggested that while process-based and relatively complex models may exhibit better performance in data-rich basins, simple conceptual models like GR4J are useful for daily flow simulation and forecasting in data-constrained basins of the region.
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Data Availability Statement
Some or all data, models or code that support the findings of this study are available from the corresponding author upon reasonable request. Model scripts of GR4J and IHACRES are customized in R platform and HEC-HMS and SWAT models are set using their desktop versions. These models can be requested from the corresponding author. The meteorological and hydrological data used in the study can be accessed from the Department of Meteorology and Hydrology, Myanmar following their standard protocol of data request.
Acknowledgments
The required rainfall and discharge data for Ayeyarwady was acquired from the Regional Integrated Multi-Hazard Early Warning System (RIMES) project on the Development and Implementation of an End-to-End Flood Forecasting and Decision Support System for Myanmar. This manuscript forms part of the detailed development of robust flood forecasting system in Myanmar by RIMES. The authors acknowledge the Department of Meteorology and Hydrology, Myanmar, for providing the data and sharing insights regarding the challenges in hydrological modeling in Myanmar.
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Journal of Hydrologic Engineering
Volume 25 • Issue 8 • August 2020
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©2020 American Society of Civil Engineers.
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Received: Oct 4, 2019
Accepted: Feb 14, 2020
Published online: Jun 5, 2020
Published in print: Aug 1, 2020
Discussion open until: Nov 5, 2020
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