Editorial: Broadening the Use of Machine Learning in Hydrology

Laloy, Eric

doi:10.3389/frwa.2021.681023

Cited by 63 publications

(40 citation statements)

References 30 publications

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“…5,7,and 8). Water resources in England have been well-regulated within the context of the European Water Framework Directive and Daughter Directives (European Commission, 2000), and a wide range of sophisticated schemes and measures are used to manage low flow and drought, including conjunctiveuse schemes, low-flow alleviation schemes, and hands-off flow measures (Clayton et al, 2008;Shepley et al, 2009;Agnew et al, 2000;Hutchinson et al, 2012;Wendt et al, 2020Wendt et al, , 2021. Conjunctive-use schemes use combined management of groundwater and surface water abstractions to maintain ecological flows, while low-flow alleviation schemes and hands-off flow measures are used in England to constrain the amount of water that is abstracted from groundwater and rivers, with abstractions being reduced or stopped at a given low-flow trigger level.…”

Section: Impacts Of Wrm Practices On Bfimentioning

confidence: 99%

“…Now that the correlations between WRM covariates and BFI have been identified, future predictive models of BFI that take account of WRM practices could be developed using a refinement of the conceptual model (Fig. 4) to constrain a combination of multiple targeted statistical (LR) and multiple knowledge-guided ML models (Shen et al, 2021) deployed with appropriate cross-validation schemes.…”

Section: Implications For Future Researchmentioning

confidence: 99%

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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: Impacts Of Wrm Practices On Bfimentioning

confidence: 99%

Section: Implications For Future Researchmentioning

confidence: 99%

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 ways remote sensing, geospatial modeling, and/or machine learning are used in hydrologic studies depends on the question being addressed; the spatial and temporal scale of the question; and the type, amount, and quality of the available data [24][25][26]. Nevertheless, these tools have been incorporated into strategies to forecast groundwater levels [27][28][29][30], groundwater quality [31][32][33], saltwater intrusion and groundwater salinity [34], and groundwater resource availability [35,36].…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

et al. 2021

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We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

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“…Regarding the former, the field of multivariate data assimilation is ripe (Piazzi et al, 2018), although scaling up these approaches across the landscape may come with significant computational requirements. Regarding new assimilation data, statistical learning is making promising steps towards mining new information from traditional, sometimes even sparse and fuzzy data across geosciences (Avanzi et al, 2019;Ghanjkhanlo et al, 2020;Dramsch, 2020;Grossi et al, 2021;Maurer et al, 2021;Shen et al, 2021;Mosaffa et al, 2022). Meanwhile, Cluzet et al (2020) have shown some degree of success in assimilating satellite reflectance into snowpack simulations as a way to better capture snow microstructure and so the energy balance.…”

Section: Applicability and Future Developmentsmentioning

confidence: 99%

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

Avanzi

Gabellani²,

Delogu³

et al. 2022

Geosci. Model Dev.

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Abstract. By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries.

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Editorial: Broadening the Use of Machine Learning in Hydrology

Cited by 63 publications

References 30 publications

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

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

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt

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