Abstract
Abstract. Prediction of streamflow at ungauged catchments requires transfer of hydrologic information (e.g., model parameters, hydrologic indices, streamflow values) from gauged (donor) to ungauged (receiver) catchments. A common metric used for the selection of ideal donor catchments is the spatial proximity between donor and receiver catchments. However, it is not clear whether information transfer among nearby catchments is suitable across a wide range of climatic and geographic regions. We examine this issue using the data from 756 catchments within the continental United States. Each catchment is considered ungauged in turn and daily streamflow is simulated through distance-based interpolation of streamflows from neighboring catchments. Results show that distinct geographic regions exist in US where transfer of streamflow values from nearby catchments is useful for retrospective prediction of daily streamflow at ungauged catchments. Specifically, the high predictability catchments (Nash-Sutcliffe efficiency NS > 0.7) are confined to the Appalachian Mountains in eastern US, the Rocky Mountains, and the Cascade Mountains in the Pacific Northwest. Low predictability catchments (NS < 0.3) are located mostly in the drier regions west of Mississippi river, which demonstrates the limited utility of gauged catchments in those regions for predicting at ungauged basins. The results suggest that high streamflow similarity among nearby catchments (and therefore, good predictability at ungauged catchments) is more likely in humid runoff-dominated regions than in dry evapotranspiration-dominated regions. We further find that higher density and/or closer distance of gauged catchments near an ungauged catchment does not necessarily guarantee good predictability at an ungauged catchment.
Highlights
Long-term measurements of river streamflow are essential for a number of applications in water resources, such as, planning of water supply and irrigation projects (Dunne and Leopold, 1978; Jain and Singh, 2003), delineation of river floodplains (Merwade et al, 2008; Tate et al, 2002), dayto-day management of dams and canals (Hirsch and Costa, 2004), to name a few
This study examined whether identification of hydrologic similarity based on spatial proximity measures is suitable for prediction at ungauged catchments across multiple environments
High predictability catchments are located along the Appalachian Mountains in eastern US, the Rocky Mountains, and the Cascade Mountains in the Pacific Northwest, whereas the low predictability catchments are located in the drier regions west of Mississippi river
Summary
Long-term measurements of river streamflow are essential for a number of applications in water resources, such as, planning of water supply and irrigation projects (Dunne and Leopold, 1978; Jain and Singh, 2003), delineation of river floodplains (Merwade et al, 2008; Tate et al, 2002), dayto-day management of dams and canals (Hirsch and Costa, 2004), to name a few. Streamflow measurements are important for characterizing the hydrologic behavior of river basins within modeling frameworks, so that future assessments of hydrologic behavior in response to climate and/or land-use change can be obtained. Developing strategies for prediction at ungauged basins (PUB; Sivapalan et al, 2003) is required for the above practical applications, and for advancing our process understanding of the controls on regional variability in landscape hydrologic response (Patil and Stieglitz, 2011; Wagener et al, 2004). Prediction of streamflow at ungauged catchments involves the following steps: (1) identify the gauged (donor) catchments that are most likely to be hydrologically similar, i.e., have similar streamflow response, to the ungauged (receiver) catchments, and (2) transfer the relevant hydrologic information (model parameters or streamflow values) from the gauged to ungauged catchments. Studies focusing on the transfer of parameters of rainfallrunoff models have typically used either physical proximity measures (e.g., topography, soil type, land cover) or Published by Copernicus Publications on behalf of the European Geosciences Union
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