Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment

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 distancebased 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.

Complex networks for streamflow dynamics

“…During the past few decades, a large number of studies have investigated the above streamflow data set (or a part or variant of it) in many different contexts (e.g., Slack and Landwehr, 1992;Kahya and Dracup, 1993;Vogel and Sankarasubramanian, 2000;Sivakumar, 2003;Tootle and Piechota, 2006;Patil and Stieglitz, 2012;Sivakumar and Singh, 2012;Kiang et al, 2013). Some of these studies have explicitly addressed the connections of streamflow between the stations, including in the context of data correlations, catchment similarities, and other measures; see, Patil and Stieglitz (2012) and Kiang et al (2013) for some recent studies. Many studies have explored the connections of streamflow with large-scale climatic patterns and relevant indexes, including El Niño, La Niña, Southern Oscillation Index, Pacific North America Index, and Pacific Decadal Oscillation.…”

Catchment classification based on characterisation of streamflow and precipitation time series

“…In fact such a contiguous area is certainly characterised by similar climate, topography and geology, and all other characteristics deriving from them, such as soil type, vegetation, etc. (Merz and Bloeschl, 2005;Patil et al, 2012).…”

Parameter regionalization for a process-oriented distributed model dedicated to flash floods

“…Three types of data are used to derive input maps for MARINE model ( Gathering appropriate attributes to characterize catchments properties and unicity is an important step for regionalization purpose. For example the UK Flood Estimation Handbook (IH, 1999) Patil and Stieglitz (2012) 756 US catchments, regionalization of a multiple drainage-area ration method based on donor-receptor proximity. Detection of hydrologic regions, low predictability for drier regions…”