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

Bloomfield, John P. ORCID: https://orcid.org/0000-0002-5730-1723; Gong, Mengyi; Marchant, Benjamin P.; Coxon, Gemma; Addor, Nans. 2021 How is Baseflow Index (BFI) impacted by water resource management practices? Hydrology and Earth System Sciences, 25 (10). https://doi.org/10.5194/hess-25-5355-2021

Before downloading, please read NORA policies.

Preview

Text (Open Access Paper)
hess-25-5355-2021.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (7MB) | Preview

Abstract/Summary

Water resource management (WRM) practices, such as abstractions and 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 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 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 used to formulate a conceptual framework for baseflow generation that incorporates WRM practices. It is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological data sets and in the analysis and prediction of BFI and other measures of baseflow.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/hess-25-5355-2021
ISSN:	1027-5606
Additional Keywords:	GroundwaterBGS, Groundwater
Date made live:	28 Jun 2021 13:34 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530574

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.5194/hess-25-5355-2021

ISSN:

1027-5606

Additional Keywords:

GroundwaterBGS, Groundwater

Date made live:

28 Jun 2021 13:34 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/530574