Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States

428

455

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small-to mediumsized basins across the contiguous United States (median basin size of 336 km 2 ) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980-2010 was generated for three basin spatial configurations -basin mean, hydrologic response units (HRUs) and elevation bands -by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90 % of the basins have NashSutcliffe efficiency scores ≥ 0.55 for the calibration period and 34 % ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

Section: Limitations and Uncertaintiesmentioning

confidence: 99%

Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

Newman

Clark

Sampson

et al. 2015

428

455

“…on the parameters) has the potential to result in more realistic models (Martinez and Gupta, 2011) that in several cases has practical benefits.…”

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.

“…Alternatively, ET can be used as a calibration target along with streamflow within a multi-objective setting (Zhang et al, 2009). There has also been a recent drive towards structurally flexible models that are able to both better characterize the uncertainty associated with model structure and use additional information to help reduce such uncertainty (Wagener et al, 2001;Marshall et al, 2006;Clark et al, 2008Clark et al, , 2015Savenije, 2010;Schaefli et al, 2011;Fenicia et al, 2008aFenicia et al, , b, 2011Bulygina and Gupta, 2009, 2010Martinez and Gupta, 2011;Nearing, 2013;Nearing and Gupta, 2015).…”

Section: T Roy Et Al: Evapotranspiration Estimates To Improve the Smentioning

confidence: 99%

Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

Roy

Gupta

Serrat-Capdevila

et al. 2017

Self Cite

Abstract. Daily, quasi-global (50 • N-S and 180 • W-E), satellite-based estimates of actual evapotranspiration at 0.25 • spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchmentscale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making "diagnostic structural improvements" to hydrologic models whenever possible.