Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

Abstract

[1] Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km 2 ) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R 2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

Derivation of Flow Duration Curves to Estimate Hydropower Generation Potential in Data-Scarce Regions

“…Since the base flow index shows the ratio between groundwater flows and total streamflow, it is comparable to the parameter PSUB. The value of 0.6 for PSUB matches the calculated base flow index for the studied region as part of the Global Streamflow Characteristics Dataset (GSCD) [20], which was 0.597, quite well. The calculated FDC is shown in Figure 7.…”

Streamflow recession patterns can help unravel the role of climate and humans in landscape co-evolution

“…Other studies investigated how recession parameters a and b change with geology (Tague and Grant, 2004), scale (Clark et al, 2009), andclimate (van Dijk, 2010) or combinations thereof (Farmer et al, 2003;Beck et al, 2013;McMillan et al, 2014). At least for some catchments recession intercept a appears to vary throughout the year, in response to catchment-scale water storage in conjunction with spatial heterogeneity (Shaw and Riha, 2012;Shaw et al, 2013;Lyon et al, 2015).…”

Streamflow sensitivity to water storage changes across Europe

“…Although precipitation intensity controlled runoff-generating processes that can be observed during periods of rainfall [Dunne, 1983], for many catchments subsurface water storage is for the majority of time the main driver of streamflow response [Spence, 2010;McNamara et al, 2011;Riegger and Tourian, 2014]. There are regional differences in the estimated volume and timescale of the subsurface contributions to streamflow [Beck et al, 2013]. Both climatic changes and variations, and human groundwater abstractions are affecting groundwater storage around the globe [Green et al, 2011;Taylor et al, 2013;Döll et al, 2014;Richey et al, 2015;Gleeson et al, 2015], but a theory that exposes the sensitivity of flow to storage changes across diverse landscapes and spatial scales is currently not exploited.…”