Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests

Abstract

Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. This study attacks the prediction in ungauged catchment problem by exploiting advances in flow index selection and regionalization in Bayesian inference and by developing new statistical tests of model performance in ungauged catchments. First, an extensive set of available flow indices is reduced using principal component (PC) analysis to a compact orthogonal set of “flow index PCs.” These flow index PCs are regionalized under minimal assumptions using random forests regression augmented with a residual error model and used to condition hydrological model parameters using a Bayesian scheme. Second, “adequacy” tests are proposed to evaluate a priori the hydrological and regionalization model performance in the space of flow index PCs. The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is shown that (1) a small number of PCs capture approximately 87% of variability in the flow indices and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series and are hence proposed as a prerequisite for flow prediction in ungauged catchments. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. Hence, a focus on improving hydrological model structure and input data (the effects of which are not disaggregated in this work) is recommended.

Towards Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

“…Related to the third conclusion, the challenge going forward is about how to extract the useful information from catchment attributes data for regional modeling. One of the historical reasons why this has been a hard problem is because the usual strategy is to use observable catchment attributes or characteristics to identify or 'regionalize' parameters of conceptual or process-based simulation models (e.g., Prieto, Le Vine, Kavetski, Garca, & Medina, 2019;Razavi & Coulibaly, 2012). This is hard because of strong interactions in high-dimensional parameter spaces.…”

Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

“…Catchment characteristics (e.g., basin surface, soil type, topography, and land use) and meteorological data (e.g., precipitation, air temperature, solar radiation, relative humidity, and wind speed) are commonly adopted as attributes to represent the physical similarity [12,[23][24][25][26][27][28][29]. The classical physical similarity technique assumes that the similarity in the input attribute is fully transferred to the output (i.e., the runoff).…”

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

“…The quality of parameter regionalization is dependent on many factors, including model structure, the quality of observations used for regionalization, hydrological variables of interest, calibration techniques, and regression methods. However, large uncertainty and the consequential limited reliability prevent model regionalization from being widely employed in hydrological analyses and design [16,[20][21][22].…”

“…There are many sources that introduce uncertainty in the regionalization process whose effects have been extensively discussed in literature [20][21][22][23][24]. However, the extent of uncertainty remains unclear even though many methods have been proposed to estimate the uncertainty in hydrological modeling [2,21,22,25,26].…”

“…There are many sources that introduce uncertainty in the regionalization process whose effects have been extensively discussed in literature [20][21][22][23][24]. However, the extent of uncertainty remains unclear even though many methods have been proposed to estimate the uncertainty in hydrological modeling [2,21,22,25,26]. Calibration can be a major underlying uncertainty source because calibrated parameter values are directly related to the selected watershed characteristics in a regionalization approach [8,19,20,27,28].…”