High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model

Koch, Julian; Gotfredsen, Jane; Schneider, Raphaël; Troldborg, Lars; Stisen, Simon; Henriksen, Hans Jørgen

doi:10.3389/frwa.2021.701726

Cited by 32 publications

(25 citation statements)

References 52 publications

(62 reference statements)

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“…The importance of the location of the shallow groundwater table, and it being controlled by small-scale variations in topography and geology, is also one of the main motivations for the creation of the finer 100 m resolution DK-model HIP (see also the 10 m resolution map of average groundwater tables developed in the same project by Koch et al, 2021).…”

Section: Dk-model Hipmentioning

confidence: 99%

“…More extreme and higher groundwater levels in the future pose significant challenges for infrastructure, agriculture, and ecosystems (Halsnaes et al, 2022). Due to the considerable small-scale variability of shallow groundwater levels (Koch et al, 2021), which are mainly controlled by topographic variability and hydrogeology, high-resolution information is required for purposeful groundwater management and climate adaptation.…”

Section: Introductionmentioning

confidence: 99%

“…This is a relatively new field with a few applications so far. For example, Koch et al (2021) used HM outputs as covariates in ML algorithms predicting groundwater table depth at high spatial resolution, or Zhang et al (2021) downscaled GRACE total water storage data with the help of HM output from the Global Land Data Assimilation System (GLDAS). Here, the presented research is contributing to the further integration of physically based HMs with ML techniques.…”

Section: Introductionmentioning

confidence: 99%

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Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth

Schneider

Koch

Troldborg

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. There is an urgent demand for assessments of climate change impacts on the hydrological cycle at high spatial resolutions. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, have major implications for agriculture, adaptation, and urban planning. Predicting such hydrological impacts is typically performed using physically based hydrological models (HMs). However, such models are computationally expensive, especially at high spatial resolutions. This study is based on the Danish national groundwater model, set up as a distributed, integrated surface–subsurface model at a 500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created, amongst others, to better represent the uppermost groundwater table and to meet end-user demands for water management and climate adaptation. However, the increase in resolution of the hydrological model also increases computational bottleneck. To evaluate climate change impacts, a large ensemble of climate models was run with the 500 m hydrological model, while performing the same ensemble run with the 100 m resolution nationwide model was deemed infeasible. The desired outputs at the 100 m resolution were produced by developing a novel, hybrid downscaling method based on machine learning (ML). Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range of hydrogeological settings, were run at 100 m resolutions with forcings from a reduced ensemble of climate models. Random forest (RF) algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100 m resolution from those submodels as training data. The trained downscaling algorithms were then applied to create nationwide maps of climate-change-induced impacts on the shallow groundwater table at 100 m resolutions. These downscaled maps were successfully validated against results from a validation submodel at a 100 m resolution excluded from training the algorithms, and compared to the impact signals from the 500 m HM across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential for further applications where, for example, computational limitations inhibit running distributed HMs at fine resolutions.

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Section: Dk-model Hipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth

Schneider

Koch

Troldborg

et al. 2022

Hydrol. Earth Syst. Sci.

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“…GeoAI has shown the potential for accurate hydrological modeling, such as for rainfallrunoff, river discharge, soil moisture dynamics, and groundwater table fluctuation [95,103,104]. The non-linear nature of these processes is challenging to model with simple empirical and physical-based models.…”

Section: Hydrological Process Modelingmentioning

confidence: 99%

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Gonzales‐Inca

Calle

Croghan

et al. 2022

Water

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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

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“…ML can be used for any spatial-and temporal-scale study, as long as there are sufficient data available for training and validation. Besides using local observations and remote sensing information, an up-coming trend is also to incorporate knowledge-based learning (Koch et al, 2021), where ML models are also trained with information provided by physically based models or in hybrid model set-ups. ML has not only shown to be promising in simulating hydrological variables such as discharge and groundwater levels but also in contributing to operational water management.…”

Section: Introductionmentioning

confidence: 99%

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

Hauswirth

Bierkens

Beijk³

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

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High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model

Cited by 32 publications

References 52 publications

Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth

Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

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