Exploring the physical controls of regional patterns of flow duration curves – Part 3: A catchment classification system based on regime curve indicators

Abstract

Abstract. Predictions of hydrological responses in ungauged catchments can benefit from a classification scheme that can organize and pool together catchments that exhibit a level of hydrologic similarity, especially similarity in some key variable or signature of interest. Since catchments are complex systems with a level of self-organization arising from co-evolution of climate and landscape properties, including vegetation, there is much to be gained from developing a classification system based on a comparative study of a population of catchments across climatic and landscape gradients. The focus of this paper is on climate seasonality and seasonal runoff regime, as characterized by the ensemble mean of within-year variation of climate and runoff. The work on regime behavior is part of an overall study of the physical controls on regional patterns of flow duration curves (FDCs), motivated by the fact that regime behavior leaves a major imprint upon the shape of FDCs, especially the slope of the FDCs. As an exercise in comparative hydrology, the paper seeks to assess the regime behavior of 428 catchments from the MOPEX database simultaneously, classifying and regionalizing them into homogeneous or hydrologically similar groups. A decision tree is developed on the basis of a metric chosen to characterize similarity of regime behavior, using a variant of the Iterative Dichotomiser 3 (ID3) algorithm to form a classification tree and associated catchment classes. In this way, several classes of catchments are distinguished, in which the connection between the five catchments' regime behavior and climate and catchment properties becomes clearer. Only four similarity indices are entered into the algorithm, all of which are obtained from smoothed daily regime curves of climatic variables and runoff. Results demonstrate that climate seasonality plays the most significant role in the classification of US catchments, with rainfall timing and climatic aridity index playing somewhat secondary roles in the organization of the catchments. In spite of the tremendous heterogeneity of climate, topography, and runoff behavior across the continental United States, 331 of the 428 catchments studied are seen to fall into only six dominant classes.

Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

“…Second, the predictions at these locations are improved using machine learning techniques for error correction. Third, the classification system proposed by Coopersmith et al (2012) is used to generalize the parameters calibrated at each location, enabling its application at other sites characterized by the same hydroclimatic class. Fourth, sites are examined for edaphic (soil property) similarity in addition to hydroclimates.…”

“…If the hypothesis is correct, then the first type, when x and y fall within the same hydroclimatic class, should display limited losses in predictive power. The second type, when x and y fall within a "similar" hydroclimatic class (two classes differing by a single division of the classification tree developed in Coopersmith et al, 2012), should display greater losses of predictive power. Finally, the third type, when x and y fall in two unrelated classes, should display the largest loss of predictive power.…”

“…This step tests the hypothesis that the classification system by Coopersmith et al (2012) can be used to generalize the calibrated parameters for the diagnostic soil moisture equation using hydroclimatic similarity. If two locations are assigned the same hydroclimatic classification, then the calibrated parameters from one SCAN sensor within that class will be assumed to perform well at another.…”

“…The proposed solution involves calibrating the Pan (2012) diagnostic soil moisture equation (model) at gauged sites and then extrapolating the calibrated model to ungauged sites by invoking similarity. Similarity here is defined on the basis of hydroclimatic characteristics, using a classification system developed by Coopersmith et al (2012), as well as edaphic (soil) properties. The proposed new scheme maintains the advantage of the parsimonious soil moisture model of Pan et al (2003) in that it does not require specification of initial soil moisture condition, and also there is no need to recalibrate periodically.…”

Scaling, similarity, and the fourth paradigm for hydrology

“…Recent similarity studies have systematically analyzed large numbers of catchments focusing on streamfloworiented signatures such as the runoff coefficient, baseflow index, and slope of the flow duration curve and have then explored relationships between these signatures and model process timescales . Coopersmith et al (2012) generalized this work with four nearly orthogonal signatures that included aridity, seasonality of rainfall, peak rainfall, and peak streamflow and demonstrated that 77 % of MOPEX catchments can be described by only six classes, which are themselves defined by combinations of the four signatures. Clearly there is information contained in these catchment databases about not just the coevolution of climate (forcing) and landscape properties (parameters), but also the physics of the catchment responses.…”