In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics

Abstract

Conventional basin‐by‐basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present‐day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid‐cell using machine learning‐based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid‐cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small‐to‐medium‐sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small‐to‐medium‐sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash‐Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof‐of‐concept for a new promising approach for fine‐scale hydrologic model calibrations.

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

“…It is to be noted that for our purposes, parameter value options have been limited to produce a more systematic experimentation rather than 10,000 random iterations. Other studies often test bi from 0.001 to 0.8, usoilD from 0.1 to 2.0 m, Dsmax from 0 to 40 mm/d, and Ds from 0.01 to 1.0 [10][11][12][13][14]. Our parameter value selections align with these values, even bi, which we have spatially varied so that when the median bi = 0.4, some locations have bi = 0.8, as explained below.…”

“…Four VIC runoff parameters are calibrated: the variable infiltration curve parameter (b i ), the upper soil layer depth (usoilD), the maximum velocity of baseflow (Dsmax), and the fraction of Dsmax where nonlinear baseflow begins (Ds) ( Table 1). Akin to [14], b i , usoilD, and Ds were selected as suitable parameters for calibration with the addition of Dsmax according to previous sensitivity analyses with VIC model parameters [10,11,13]. Although we only calibrate four parameters, these parameters alone alter model behavior significantly.…”

“…Satellite remote sensing has been utilized to quantify and assess water security [4], and measurements can be coupled with hydrologic models to improve overall discharge estimation [5][6][7][8][9]. In many modeling applications, calibration methods are used to optimize model parameters for estimating river discharge [10][11][12][13][14]. These techniques, data assimilation and calibration, are valuable tools used in efforts focused on understanding river discharge dynamics.…”

Impacts of Urbanization on Watershed Water Balances Across the Conterminous United States

“…Extensive model validation with streamflow measurements at 717 gaging stations that included both references and nonreferenced watershed offered a few insights on large scale hydrologic modeling. First, spatial patterns of the accuracy of the uncalibrated WaSSI model was comparable to other calibrated, physically based models that require more climate and parameter data such as the Variable Infiltration Capacity (VIC) model (Yang et al, 2019). WaSSI model tended to overestimate water yield in the Midwest dry regions in general, but performed better in the wet southeastern United States (precipitation > 1,200 mm; Q > 500 mm) than in dry regions (Q < 500 mm) as judged by R 2 and NSE ( Figure 2).…”

A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations

“…The same applies to global scale hydrologic modelling, where often creative calibration targets are used (e.g. Yang et al, 2019). Another example is a calibration of stream flow in the signature domain instead of the time domain, assuming this make the calibration more robust to objective function or data deficiencies (Fenicia et al, 2018;Westerberg and McMillan, 2015).…”