Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa

Abstract. A parameter regionalization scheme to transfer parameter values from gaged to ungaged areas for a monthly water balance model (MWBM) was developed and tested for the conterminous United States (CONUS). The Fourier Amplitude Sensitivity Test, a global-sensitivity algorithm, was implemented on a MWBM to generate parameter sensitivities on a set of 109 951 hydrologic response units (HRUs) across the CONUS. The HRUs were grouped into 110 calibration regions based on similar parameter sensitivities. Subsequently, measured runoff from 1575 streamgages within the calibration regions were used to calibrate the MWBM parameters to produce parameter sets for each calibration region. Measured and simulated runoff at the 1575 streamgages showed good correspondence for the majority of the CONUS, with a median computed Nash-Sutcliffe efficiency coefficient of 0.76 over all streamgages. These methods maximize the use of available runoff information, resulting in a calibrated CONUS-wide application of the MWBM suitable for providing estimates of water availability at the HRU resolution for both gaged and ungaged areas of the CONUS.

Section: Parameter Sensitivitiesmentioning

confidence: 99%

Parameter regionalization of a monthly water balance model for the conterminous United States

Bock

Hay

McCabe

et al. 2016

“…McIntyre et al (2005) presented a GLUE-type approach consisting of transferring ensembles of parameter sets obtained on donor (gauged) catchments to target (ungauged) catchments. More recently, a framework based on constrained parameter sets was applied in several studies (Yadav et al, 2007;Zhang et al, 2008;Winsemius et al, 2009;Kapangaziwiri et al, 2012). It is a two-step procedure.…”

Section: Predicting Streamflow In Ungauged Catchments With Uncertaintmentioning

confidence: 99%

Transferring global uncertainty estimates from gauged to ungauged catchments

Bourgin

Andréassian

Perrin

et al. 2015

Abstract. Predicting streamflow hydrographs in ungauged catchments is challenging, and accompanying the estimates with realistic uncertainty bounds is an even more complex task. In this paper, we present a method to transfer global uncertainty estimates from gauged to ungauged catchments and we test it over a set of 907 catchments located in France, using two rainfall-runoff models. We evaluate the quality of the uncertainty estimates based on three expected qualities: reliability, sharpness, and overall skill. The robustness of the method to the availability of information on gauged catchments was also evaluated using a hydrometrical desert approach. Our results show that the method presents advantageous perspectives, providing reliable and sharp uncertainty bounds at ungauged locations in a majority of cases.

“…For example, Zhang et al (2008), imposed a set of three constraints to infer the runoff characteristics of catchments, following the concept of regionalization of hydrological signatures (initially developed by Yadav et al, 2007). Recently, Kapangaziwiri et al (2012) constrained the Pitman monthly rainfall runoff model (Hughes et al, 2006) based on a regionalization of runoff signatures. Perrin et al (2008) proposed a method called discrete parameterization based on the use of parameter sets compiled a priori via calibration to other catchments.…”

Section: S Gharari Et Al: Constraint-based Parameter Identificationmentioning

confidence: 99%

A constraint-based search algorithm for parameter identification of environmental models

Gharari

Shafiei

Hrachowitz

et al. 2014

Abstract. Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, we provide an alternative approach for parameter identification using constraints based on two types of restrictions derived from prior (or expert) knowledge. The first, called parameter constraints, restricts the solution space based on realistic relationships that must hold between the different model parameters while the second, called process constraints requires that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a search algorithm for finding parameter sets that simultaneously satisfy such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler's intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.