Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks

Abstract

Estimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.

A multidecadal oscillation in precipitation and temperature series is pronounced in low flow series from Puget Sound streams

“…It was assumed that climatic and runoff patterns across the Puget Sound watershed were similar enough to be treated as a single response type for these purposes. This was supported by a classification of runoff response types across the western United States (U.S.) (Konrad & Rumsey, 2019), in which 1019 gages were clustered, based on the timing of low flows. Gages in the Puget Sound watershed comprised one of six principal clusters representing regional runoff types.…”

“…For many streams in the Pacific Northwest (PNW), analyses that assume monotonic change show summer low flows to have declined over recent decades (e.g., Kormos et al, 2016; Luce & Holden, 2009). Causes are not clear, partly because low flows are largely baseflows, the component of streamflow contributed by groundwater (Konrad & Rumsey, 2019). Many natural and human‐related factors affect groundwater recharge (reviewed by Price, 2011), but unlike surface flows, groundwater dynamics are hard to observe.…”

Distinguishing climate change impacts from development impacts on summer low flows in Puget Sound streams

“…To provide an impression of how low flow trends varied in each stream over time, splines were fit to the anomaly series for each gage using JMP software (v. 15.0, SAS Institute Inc., Cary, NC, 1989-2019. A smoothing factor (lambda) equal to 1 was used in all cases.…”

“…In many streams in the Pacific Northwest, summer low flows have declined over recent decades (Kormos et al., 2016; Luce & Holden, 2009; Rosburg et al., 2017). Since low flows are comprised largely of baseflows (the component of streamflow contributed by groundwater; Konrad & Rumsey, 2019), trends reflect the net effects of many natural and human‐related factors that increase or decrease groundwater recharge (reviewed by Price, 2011). Concerns that human impacts may be responsible for declining low flows have focused on two main causes: anthropogenic climate warming (AW), and various types of development, including urbanization, and, in rural areas, abstraction of groundwater via permit‐exempt wells.…”

Aqua temporaria incognita

“…Background elevation data from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-andderived-products. The stream network was obtained from http://www.sandre.eaufrance.fr/ Hydrological models (Gutiérrez-Jurado, Partington, Batelaan, Cook, & Shanafield, 2019;Ward, Schmadel, & Wondzell, 2018;Williamson, Agouridis, Barton, Villines, & Lant, 2015;Yu, Bond, Bunn, Xu, & Kennard, 2018), topographic data (Prancevic & Kirchner, 2019) and statistical approaches (Beaufort, Carreau, & Sauquet, 2019;Durighetto, Vingiani, Bertassello, Camporese, & Botter, 2020;González-Ferreras & Barquín, 2017;Jaeger, Sando, et al, 2019;Konrad & Rumsey, 2019;Russell, Gale, Muñoz, Dorney, & Rubino, 2015;Snelder et al, 2013) have been used to predict where streams are temporary and can be used to determine where additional data on the state of temporary streams may be most useful. However, to train and validate these models, more observations of the state of temporary streams and stream network dynamics are needed.…”