How is Baseflow Index (BFI) impacted by water resource management practices?

Nans Addor,Benjamin P. Marchant,Gemma Coxon,John P. Bloomfield,Mengyi Gong

doi:10.5194/hess-25-5355-2021

Abstract

Abstract. Water resource management (WRM) practices, such as groundwater and surface water abstractions and effluent discharges, may impact baseflow. Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on Baseflow Index (BFI) using statistical models of 429 catchments from Great Britain. Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates). The LR and RF models show good agreement between explanatory covariates. In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography, and aridity are significant or important natural covariates in explaining BFI. When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes, and effluent discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models. Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model. Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis. Inclusion of WRM terms improves the performance of some models in specific catchments. The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high effluent discharges. However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models. These observations are discussed within a conceptual framework for baseflow generation that incorporates WRM practices. A wide range of schemes and measures are used to manage water resources in the UK. These include conjunctive-use and low-flow alleviation schemes and hands-off flow measures. Systematic information on such schemes is currently unavailable in CAMELS-GB, and their specific effects on BFI cannot be constrained by the current study. Given the significance or importance of WRM terms in the models, it is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological datasets and in the analysis and prediction of BFI and other measures of baseflow.

Highlights

IntroductionBaseflow, defined as streamflow fed from the deep subsurface and shallow subsurface storage between precipitation and/or snowmelt events (Tallaksen, 1995; Price, 2011; Zhang et al, 2017; Singh et al, 2019; Gnann et al, 2019), is a hydrological phenomenon that represents a whole catchment response to meteorological and other environmental signals (Bloomfield et al, 2011)
Modelling is used in this study not for predictive purposes but to explore model structures and performance to assess the evidence for the relative importance of Water resource management (WRM) practices in influencing Baseflow Index (BFI)
The purpose of the present modelling was not to develop models capable of predicting BFI, it is interesting to note that there have been clear benefits in applying both simple statistical models (LR models) and more flexible machine learning (ML) approaches (RF models) to the same parameter space to explore common model structures and covariates of interest, and the results have provided evidence to extend current process understanding of baseflow based beyond individual linear regression (LR) (Bloomfield at al., 2009; Carlier et al, 2018; Zhang et al, 2020) and random forest scheme (RF) (Mazvimavi et al, 2005; Addor et al, 2018; Huang et al, 2021) studies

Summary

Introduction

Baseflow, defined as streamflow fed from the deep subsurface and shallow subsurface storage between precipitation and/or snowmelt events (Tallaksen, 1995; Price, 2011; Zhang et al, 2017; Singh et al, 2019; Gnann et al, 2019), is a hydrological phenomenon that represents a whole catchment response to meteorological and other environmental signals (Bloomfield et al, 2011). Tains surface flows during relatively dry periods and droughts (Smakhtin, 2001; Miller et al, 2016) because it supports ecological flows and ecosystem functioning (Poff et al, 1997; Boulton, 2003) and is a factor in regulating streamflow quality and temperature (Jordan et al, 1997; GomezVelez et al, 2015; Hare et al, 2021) It integrates the outcomes of a wide range of natural and human-influenced surface and subsurface catchment processes (Price et al, 2011; Gnann et al, 2019) that include geomorphological controls related to surface topography (Santhi et al, 2008) and soil processes (Vivoni et al, 2007; Price et al, 2011; Singh et al, 2019; Yao et al, 2021) and (hydro)geological processes that control baseflow (Longobardi and Villani, 2008; Bloomfield et al, 2009; Kuentz et al, 2017; Carlier et al, 2018). There is growing evidence for the potential impact of climate change on baseflow across a variety of climate and catchment settings (Wang et al, 2014; Ficklin et al, 2016; Ahiabalme et al, 2017; Zhang et al, 2019), and it has been proposed that this should be viewed in the context of increasing sensitivity of changes in droughts and low flows to wider anthropogenic influences (Van Loon et al, 2016; Sankarasubramanian et al, 2020)

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