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 400,000km2400,000  km2) 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 NSE=0.9NSE=0.9), followed by IHACRES (NSE=0.86NSE=0.86), SWAT (NSE=0.81NSE=0.81) and HEC-HMS (NSE=0.77NSE=0.77) during calibration and GR4J (NSE=0.87NSE=0.87) and the latter three (NSE=0.83NSE=0.83) 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.