The hydrological impact of low-flow alleviation measures

Clayton, H.; Morris, Scott E.; McIntyre, Neil; Greaves, M.P.

doi:10.1680/wama.2008.161.4.171

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…3c) confirms the common observation that different techniques used for baseflow separation influence the estimated indices (Nathan and McMahon, 1990;Eckhardt, 2008;Beck et al, 2013;Addor et al, 2017). There are often large uncertainties in the underlying streamflow data used to estimate BFI (Coxon et al, 2015), but these are difficult to characterize across large samples of catchments, and uncertainty estimates are not available for all the CAMELS-GB catchments (Coxon et al, 2020b). However, BFI typically has lower uncertainty compared with other hydrological signatures, as it is based on temporal averaging (Westerberg and McMillan, 2015), and only typically small differences in the BFI estimates are observed in the present study based on the two methods of estimate (Fig.…”

Section: Datasupporting

confidence: 71%

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

Bloomfield

Gong

Marchant

et al. 2021

Hydrol. Earth Syst. Sci.

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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.

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Section: Datasupporting

confidence: 71%

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

Bloomfield

Gong

Marchant

et al. 2021

Hydrol. Earth Syst. Sci.

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“…The drought of 1988-1992 resulted in low groundwater levels across the unconfined Chalk of the Thames Basin, and the environmental regular at the time, the National Rivers Authority, identified five groundwaterdominated sub-catchments in the Chalk where over-abstraction had contributed to extreme low flows during this drought (National Rivers Authority, 1993). Low flow alleviation schemes were subsequently put in place (Clayton et al, 2008). Since 2000 across the Basin total abstraction has fallen, Figure 13 (Environment Agency, 2015), mainly due to a reduction in abstraction from surface waters, although during the recent drought of 2011-12 the relative proportion of groundwater abstraction was markedly increased (Figure 13).…”

Section: Nutrientsmentioning

confidence: 99%

A conceptual model for the analysis of multi-stressors in linked groundwater–surface water systems

Kaandorp

Molina‐Navarro

Andersen

et al. 2018

Science of The Total Environment

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Groundwater and surface water are often closely coupled and are both under the influence of multiple stressors. Stressed groundwater systems may lead to a poor ecological status of surface waters but to date no conceptual framework to analyse linked multi-stressed groundwater - surface water systems has been developed. In this paper, a framework is proposed showing the effect of groundwater on surface waters in multiple stressed systems. This framework will be illustrated by applying it to four European catchments, the Odense, Denmark, the Regge and Dinkel, Netherlands, and the Thames, UK, and by assessing its utility in analysing the propagation or buffering of multi-stressors through groundwater to surface waters in these catchments. It is shown that groundwater affects surface water flow, nutrients and temperature, and can both propagate stressors towards surface waters and buffer the effect of stressors in space and time. The effect of groundwater on drivers and states depends on catchment characteristics, stressor combinations, scale and management practises. The proposed framework shows how groundwater in lowland catchments acts as a bridge between stressors and their effects within surface waters. It shows water managers how their management areas might be influenced by groundwater, and helps them to include this important, but often overlooked part of the water cycle in their basin management plans. The analysis of the study catchments also revealed a lack of data on the temperature of both groundwater and surface water, while it is an important parameter considering future climate warming.

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