http://ir.yic.ac.cn/bitstream/133337/24105/1/Influence%20of%20coastal%20groundwater%20salinization%20on%20the%20distribution%20and%20risks%20of%20heavy%20metals.pdf

Influence of coastal groundwater salinization on the distribution and risks of heavy metals

Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR

https://bura.brunel.ac.uk/bitstream/2438/23027/1/FullText.pdf

Nature-based solutions as enablers of circularity in water systems: A review on assessment methodologies, tools and indicators.

. Machine learning provides great potential for modelling
hydrological variables at a spatial resolution beyond the capabilities of
physically based modelling. This study features an application of random
forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at a 50 m resolution over a 15 000 km 2 domain in
Denmark. In Denmark, the shallow groundwater poses severe risks with respect to groundwater-induced flood events, affecting both urban and agricultural
areas. The risk is especially critical in wintertime, when the shallow
groundwater is close to terrain. In order to advance modelling capabilities
of the shallow groundwater system and to provide estimates at the scales
required for decision-making, this study introduces a simple method to unify
RF and physically based modelling. Results from the national water resources
model in Denmark (DK-model) at a 500 m resolution are employed as covariates in
the RF model. Thus, RF ensures physical consistency at a coarse scale and
fully exhausts high-resolution information from readily available
environmental variables. The vertical distance to the nearest water body was
rated as the most important covariate in the trained RF model followed by the
DK-model. The evaluation test of the trained RF model was very satisfying
with a mean absolute error of 76 cm and a coefficient of determination of
0.56. The resulting map underlines the severity of groundwater flooding risk
in Denmark, as the average depth to the shallow groundwater is 1.9 m and
approximately 29 % of the area is characterized as having a depth of less than 1 m during a typical wintertime minimum event. This study brings forward a
novel method for assessing the spatial patterns of covariate importance of the
RF predictions that contributes to an increased interpretability of the RF
model. Quantifying the uncertainty of RF models is still rare for hydrological
applications. Two approaches, namely random forests regression kriging
(RFRK) and quantile regression forests (QRF), were tested to estimate
uncertainties related to the predicted groundwater levels.

/pdf/modelling-of-the-shallow-water-table-at-high-spatial-1ibdd2tlys.pdf

R. Jones

Papers

Accounting for groundwater in future city visions