Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

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.

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

“…Migliaccio and Chaubey (2007) recommend the use of SC technique for calibrating nested sub-basins with hydrologic connections. Contrary to the SC technique used by Wi et al (2015), Shrestha et al (2016) and Leta et al (2017), whereby the calibrated and optimized parameter set obtained in the upstream gauge is fixed while calibrating the downstream counterpart, that approach was not adopted in this study because of the hydrological connection between upstream and downstream hydrometric stations. Note that the same number of parameters and their ranges were used to initiate each calibration.…”

“…However, calibration of CHMs to determine a suitable set of parameter values that can describe the hydrology of the catchment is not always an easy task . Studies have shown that the parameter set used to calibrate CHMs against flow measured only at the catchment outlet (single-site calibration (SSC)) may not produce similar results at other internal hydrometric stations within the catchment (Wang et al 2012;Wi et al 2015;Leta et al 2017). Furthermore, several researchers have demonstrated the effectiveness of calibrating CHMs with data from different parts of the catchment using simultaneous multi-site calibration (SMSC) over SSC (Wang et al 2012;Wi et al 2015;Chaibou Begou et al 2016).…”

Assessing Impacts of Land Use Changes on the Hydrology of a Lowland Rainforest Catchment in Ghana, West Africa

“…Shortcomings of this study also include the use of single streamflow gauge, as well as few rain gauges for ACRU model simulation. Model calibration and validation would have been more robust if several streamflow [50] and rainfall gauges were available, as the streamflows in the Bonsa catchment are more variable at the subcatchment than at the catchment scale. The abovementioned uncertainties were minimized by using physically meaningful sensitivity analysis of the ACRU model parameters in a companion paper [37] for the Bonsa catchment.…”

Future Water Availability from Hindukush-Karakoram-Himalaya upper Indus Basin under Conflicting Climate Change Scenarios

“…The UIB is located within the geographical range of [31][32][33][34][35][36][37] • N and 72-82 • E, covering an area of 162,393 km 2 up to the Besham Qila gauging site (Figure 1). The basin hosts three gigantic massifs-western Himalaya at lower latitudes of the basin, Karakoram Range in further north (Trans-Himalayas), and Hindu Kush Range in the west of Karakoram-Himalaya ranges.…”