How is Baseflow Index (BFI) impacted by water resource management practices?
How is Baseflow Index (BFI) impacted by water resource management practices?
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DOI：
10.5194/hess-25-5355-2021
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发表时间：
2021-10-05
影响因子：
6.3
通讯作者：
Addor, Nans
中科院分区：
文献类型：
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作者：
Bloomfield, John P.;Gong, Mengyi;Addor, Nans
水资源管理做法，如抽取地下水和地表水以及污水排放，可能会影响基流。在这里，CAMELS-GB大样本水文数据集被用来评估这种做法对基流指数（BFI）的影响，使用统计模型的429集水区从英国。两个互补的建模方案，多元线性回归（LR）和机器学习（随机森林，RF），用于调查BFI和两组协变量（仅自然协变量和一组组合的自然和水资源管理协变量）之间的关系。LR和RF模型显示解释协变量之间的良好一致性。在所有的模型中，断裂含水层，粘土，非含水层的程度，在集水区，集水地形和干旱的作物覆盖是显着的或重要的自然协变量在解释BFI。当水资源管理条款包括在内，地下水抽取是显着的或最重要的水资源管理协变量在两个建模方案，和污水排放到河流也被确定为显着的或有影响力的，虽然自然协变量仍然提供了主要的解释力的模型。地表水抽取量是LR模型中的重要协变量，但在RF模型中的重要性较小。对于这种大样本分析，在LR和RF模型中，水库蓄水量协变量不显著或不重要。纳入水资源管理条款，提高了某些模型在特定流域的性能。具有相对较高地下水抽取水平的高BFI集水区的LR模型显示出最大的改进，并且有一些证据表明具有中度至高度污水排放的集水区的LR模型有所改进。然而，没有证据表明，列入水资源管理协变量提高了高地表水抽取集水区的LR模型的性能，或者它们提高了RF模型的性能。这些意见进行了讨论的概念框架内基流发电，结合水资源管理的做法。联合王国采用了各种各样的计划和措施来管理水资源。这些措施包括联合使用和低流量缓解计划以及不干涉流量措施。目前CAMELS-GB中还没有关于此类计划的系统信息，其对BFI的具体影响不能受到当前研究的限制。考虑到水资源管理在模型中的重要性，建议在未来的大样本水文数据集以及BFI和基流其他措施的分析和预测中，尽可能包括关于水资源管理的信息，特别是地下水抽取。
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.