Assessing baseflow index vulnerability to variation in dry spell length for a range of catchment and climate properties

Abstract

Baseflow index (BFI) prediction in ungauged basins has largely been based on the use of catchment physiographic attributes as dominant variables. In a context where changes in climate are increasingly evident, it is also important to study how the slow component of flow is potentially affected by climate. The aim of this study was to illustrate the impact of climate variability on the baseflow process based on analysis of daily rainfall characteristics and hydrological modelling simulation exercises validated with observed data. Ten catchments were analysed that span southern to northern Europe and range from arid Mediterranean to maritime temperate climate conditions. Additionally, more than 2,000 virtual catchments were modelled that cover an extended gradient of physiographic and climate properties. The relative amounts of baseflow were summarized by the BFI. The catchment slow response delay time (Ks) was assumed to be a measure of catchment effects, and the impact of climate properties was investigated with the dry spell length (d). Well‐drained and poorly‐drained groups were identified based on Ks and d, and their response to an increase or decrease in dry spell length was analysed. Overall, for either well‐ or poorly‐drained groups, an extension in dry spell length appeared to have minor effects on the baseflow compared with a decrease in dry spell length. Under the same dry spell variation, the BFI vulnerability appeared higher for catchments characterized by large initial d values in combination with poorly‐drained systems, but attributing an equal weight to the variations in d both in the case of dry and wet initial conditions, it is in the end concluded that the BFI vulnerability appears higher for systems laying in the transition zone between well‐ and poorly‐drained systems.

Drought Propagation in Contiguous U.S. Watersheds: A Process‐Based Understanding of the Role of Climate and Watershed Properties

“…Modeling‐based studies utilize hydrologic modeling for understanding the physical mechanisms of drought propagation under different climate and watershed properties (Apurv et al, 2017; Gevaert et al, 2018; Longobardi & Van Loon, 2018; Melsen & Guse, 2019; Staudinger et al, 2015; Tallaksen et al, 2009; Van Lanen et al, 2013; Van Loon et al, 2014; Van Loon & Van Lanen, 2012). The main drawbacks of the modeling approach lie in the limitations of hydrologic models in representing the complexities of real‐world watersheds and the uncertainties in model parameters and outputs.…”

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

“…It has further been suggested to calculate the BFI separately for different seasons using different N values to avoid identifying minima during seasons with a deviating runoff response (to that of rainfall), such as a spring flood due to snowmelt (Tallaksen, 1987). Aksoy et al (2008Aksoy et al ( , 2009 adapted the IH-UK method for perennial and intermittent streams accounting for the sensitivity of BFI to different block sizes N. They also compared the IH-UK method to other filter separation methods such as the recursive digital filter method (Lyne and Hollick, 1979) and were amongst others aware of the sensitivity of BFI to different block sizes N (Miller et al, 2015;Piggott et al, 2005). However, to our knowledge, a comprehensive analysis of the sensitivity in BFI to different block sizes N is still missing.…”

“…In the past, two-component hydrograph separation such as recursive digital filtering (Lyne and Hollick, 1979;Nathan and McMahon, 1992) or separation based on progressively identified streamflow minima in the IH-UK (Institute of Hydrology, UK) baseflow separation method (Gustard et al, 1992;Natural Environment Research Council, 1980) have proven a simple and practical way of indexing catchment response. Both methods were developed in regional studies (e.g.…”

Beyond binary baseflow separation: delayed flow index as a fresh perspective on streamflow contributions

“…Andermann et al (2012) found the time lag between precipitation and discharge with hysteresis loops in high-alpine catchments in central Himalaya to have a characteristic response time of 45 days. In a pan-European modelling study, Longobardi and Van Loon (2018) separated response patterns of catchments into poorly-drained (BFI < 0.5) and well-drained (BFI > 0.5) catchments and assigned estimated delay times of the slowest storage in the model 470 to be 48 ± 14 days and 126 ± 47, respectively. This gives some evidence that for mostly groundwater-dominated catchments, Nmax may be set to a larger value (here 60 days) to better capture small variations in year-to-year low flow magnitude.…”