Abstract
Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.
Highlights
In an effort to advance hydrologic modeling and forecasting capabilities, the development and implementation of physically based, spatially distributed hydrologic models has proliferated in the hydrologic literature, supported by readily available geographic information system (GIS) data and rapidly increasing computational power
This study addresses the above research challenges by focusing on the following four questions: (1) how does calibration procedure for using multisite data affect the accuracy and uncertainty of distributed models used for streamflow predictions at ungaged sites; (2) what effects does increased parameter complexity have on distributed model calibration and prediction; (3) how much degradation in model accuracy and uncertainty can be expected for interior flow estimation based on a calibration procedure using only the basin outlet; and (4) how do different calibration formulations for a distributed model alter projections of streamflow at ungaged sites under climate change conditions? These questions are considered in an application of a distributed version of the daily HYMOD hydrologic model to the Kabul River basin in Afghanistan and Pakistan
The comparison between different calibration strategies is based on the model performance evaluated with the Nash–Sutcliffe efficiency (NSE), as well as an alternative metric, the Kling–Gupta efficiency (KGE)
Summary
In an effort to advance hydrologic modeling and forecasting capabilities, the development and implementation of physically based, spatially distributed hydrologic models has proliferated in the hydrologic literature, supported by readily available geographic information system (GIS) data and rapidly increasing computational power. Distributed hydrologic models can account for spatially variable physiographic properties and meteorological forcing (Beven, 2012), improving simulations compared to conceptual, lumped models for basins where spatial rainfall variability effects are significant (Ajami et al, 2004; Koren et al, 2004; Reed et al, 2004; Khakbaz et al, 2012; Smith et al, 2012) and for nested basins (Bandaragoda et al, 2004; Brath et al, 2004; Koren et al, 2004; Safari et al, 2012; Smith et al, 2012). S. Wi et al.: Implication for streamflow projections under climate change ical response at interior ungaged sites, a benefit not afforded by lumped models. The use of distributed hydrologic modeling for interior point streamflow estimation is relevant for poorly gaged river basins in developing countries, where reliable predictions at interior sites are often required to inform water infrastructure investments. As international development agencies begin to integrate climate change considerations into their decision-making processes (e.g., Yu et al, 2013), these investments need to be robust under both current climate conditions and possible future climate regimes
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