Disinformative data in large-scale hydrological modelling

429

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: Error Characteristicsmentioning

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

429

455

“…Together with the largely inevitable errors introduced by data uncertainty (e.g. Beven and Westerberg, 2011;Renard et al, 2011;Beven, 2013;McMillan et al, 2012;Kauffeldt et al, 2013;McMillan and Westerberg, 2015;Coxon et al, 2015) and insufficient model evaluation and testing (cf. Klemeš, 1986;Wagener, 2003;Clark et al, 2008;Gupta et al, 2008Gupta et al, , 2012Andréassian et al, 2009), models then often experience substantial performance decreases when used to predict the hydrological response for time periods they were not calibrated for (e.g.…”

Section: Introductionmentioning

confidence: 99%

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

Hrachowitz

Clark

2017

181

130

Abstract. In hydrology, two somewhat competing philosophies form the basis of most process-based models. At one endpoint of this continuum are detailed, high-resolution descriptions of small-scale processes that are numerically integrated to larger scales (e.g. catchments). At the other endpoint of the continuum are spatially lumped representations of the system that express the hydrological response via, in the extreme case, a single linear transfer function. Many other models, developed starting from these two contrasting endpoints, plot along this continuum with different degrees of spatial resolutions and process complexities. A better understanding of the respective basis as well as the respective shortcomings of different modelling philosophies has the potential to improve our models. In this paper we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the seemingly competing modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy, although some models may be more suitable for specific applications than others and vice versa, but rather on the way a model is implemented. Based on the premises that any model can be implemented at any desired degree of detail and that any type of model remains to some degree conceptual, we argue that a convergence of modelling strategies may hold some value for advancing the development of hydrological models.

“…The scales of experimental data, variables appearing in equations, and computed quantities must be the same if they are to be compared in any meaningful way. As a prerequisite for this to happen, data generated by any of the methods must be consistent across the range of scales considered (Ly et al, 2013;Kauffeldt et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

On the consistency of scale among experiments, theory, and simulation

McClure

Dye

Miller

et al. 2017

Abstract. As a tool for addressing problems of scale, we consider an evolving approach known as the thermodynamically constrained averaging theory (TCAT), which has broad applicability to hydrology. We consider the case of modeling of two-fluid-phase flow in porous media, and we focus on issues of scale as they relate to various measures of pressure, capillary pressure, and state equations needed to produce solvable models. We apply TCAT to perform physicsbased data assimilation to understand how the internal behavior influences the macroscale state of two-fluid porous medium systems. A microfluidic experimental method and a lattice Boltzmann simulation method are used to examine a key deficiency associated with standard approaches. In a hydrologic process such as evaporation, the water content will ultimately be reduced below the irreducible wetting-phase saturation determined from experiments. This is problematic since the derived closure relationships cannot predict the associated capillary pressures for these states. We demonstrate that the irreducible wetting-phase saturation is an artifact of the experimental design, caused by the fact that the boundary pressure difference does not approximate the true capillary pressure. Using averaging methods, we compute the true capillary pressure for fluid configurations at and below the irreducible wetting-phase saturation. Results of our analysis include a state function for the capillary pressure expressed as a function of fluid saturation and interfacial area.