Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin

Rusli, Steven Reinaldo; Weerts, Albrecht; Taufiq, Ahmad; Bense, Victor

doi:10.1016/j.ejrh.2021.100911

Cited by 18 publications

(29 citation statements)

References 75 publications

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“…Several recent applications have already used HydroMT. It has been used to build instances of the hydrological Wflow model (Imhoff et al, 2020;Verseveld et al, 2022) for climate change impact studies (Sperna Weiland et al, 2021); for a large sample assessment of spatial scales in hydrological modeling (Aerts et al, 2021); to estimate water balance components based on global data in a data-scarce area (Rusli et al, 2021); and to build instances of the hydrodynamic SFINCS model (Leijnse et al, 2021) to simulate compound flood hazard from (global) datasets in any coastal delta globally (Eilander et al, 2022). While the package is sufficiently general to support model software outside the domain of hydro models, the focus of many workflows is on typical hydrological data and parameters and hence hydro models benefit most from the tool.…”

Section: Hydromt (Hydro Model Toolsmentioning

confidence: 99%

HydroMT: Automated and reproducible model building and analysis

Eilander¹,

Boisgontier²,

Bouaziz³

et al. 2023

JOSS

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Many impact assessment studies rely on hydrological and hydrodynamic (hydro) models. These models typically require a large set of parameters derived from different datasets and hence manual setup can be time consuming and hard to reproduce. HydroMT (Hydro Model Tools) is an open-source Python package that aims to make the process of building model instances and analyzing model results automated and reproducible. The package provides a common interface to data and model instances, workflows to transform data into models based on (hydrological) GIS and statistical methods, and various methods to analyze model results. The user can describe a full model instance including its forcing in a single configuration file based on a sequence of workflows, making the process reproducible, fast, and modular. The package has been designed with an iterative, data-centered modeling process in mind. First-order model schematizations can be built for any location in the world by leveraging open global datasets. These can later be improved by updating the input datasets with detailed local datasets. This iterative process enables the user to quickly get an initial result to then make informed decisions about the most relevant improvements and/or required data collection and to kick-start discussions with stakeholders. Furthermore, model parameter maps or forcing data can easily be modified for sensitivity analysis or calibration to support these robust modeling practices. HydroMT is successfully being used for several model software through a plugin infrastructure that allow for model specific functionality, such as readers and writers of model data formats, and can easily be extended to new model software. Currently supported model software include the distributed rainfall-runoff model Wflow, the hydrodynamic flood model SFINCS, the water quality models D-Water Quality and D-Emissions and the flood impact model Delft-FIAT.

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Section: Hydromt (Hydro Model Toolsmentioning

confidence: 99%

HydroMT: Automated and reproducible model building and analysis

Eilander¹,

Boisgontier²,

Bouaziz³

et al. 2023

JOSS

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“…Several regional scale studies have used globally or provincially available gridded precipitation data products. For example, the Tropical Rainfall Measuring Mission (TRMM) uses active and passive microwave instruments, for regional-scale water budgets in areas with heavy to moderate precipitation over tropical and subtropical areas (Armanios & Fisher, 2014;Moreira et al, 2019;Rusli et al, 2021;Soltani et al, 2020;D. Zhang et al, 2016).…”

Section: Precipitation Datamentioning

confidence: 99%

A Decade of Data‐Driven Water Budgets: Synthesis and Bibliometric Review

Moyers,

Sabie,

Waring

et al. 2023

Water Resources Research

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Scarce water resources across the globe have prompted the development of data‐driven water budgets to account for and distribute limited water more effectively across various land uses and purposes. Data‐driven approaches for estimating individual water budget components have been extensively developed and subsequently reviewed (e.g., evapotranspiration, precipitation, groundwater, surface water, runoff), but the state of the art of data‐driven approaches for estimating and integrating complete water budgets has not been the subject of a review paper to our knowledge. In this review, we fill this void by reviewing 81 systematically identified publications from the last decade (2012–2022) on data‐driven water budget approaches. We describe the current state of measurements and data products for data‐driven water budgets for various spatiotemporal scales. Our analysis suggests that spatiotemporal parameters drive the approach for data‐driven water budgets, with larger spatiotemporal scales relying more on satellite remote sensing data products and smaller spatiotemporal scales relying more on ground‐based monitoring. The incorporation of satellite remote sensing data products and ground‐based monitoring was common across various spatiotemporal scales and enabled the estimation of complete water budgets in areas of limited data availability. We conclude that improved reporting of simplifying assumptions, uncertainty analysis methods, and data sources are required for the alignment of water budget estimations between resource managers at varied spatiotemporal scales. Our review calls for the standardization of data‐driven water budget reporting protocols to improve the interpretability of data‐driven water budgets across decision‐makers working at various spatiotemporal scales.

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“…Wflow_sbm has been applied in various catchments around the world showing satisfactory (0.4 ≥ KGE < 0.7) to good (KGE ≥ 0.7) performance (e.g., López López et al, 2016;Hassaballah et al, 2017;Giardino et al, 2019;Gebremicael et al, 2019;Imhoff et al, 2020;Laverde-Barajas et al, 2020;Wannasin et al, 2021a, b;Rusli et al, 2021;Meijer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications

Verseveld¹,

Weerts²,

Visser³

et al. 2022

Preprint

Self Cite

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Abstract. The wflow_sbm hydrologic model, recently released by Deltares, as part of the Wflow.jl (v0.6.1) modelling framework is being used to better understand and potentially address multiple operational and water resources planning challenges from catchment scale, national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrologic modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalitities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrologic models. Most wflow_sbm parameters are based on physical characteristics or processes and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally results in a satisfactory (0.4 ≥ Kling-Gupta Efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance a-priori (without further tuning). Wflow_sbm includes relevant hydrologic processes as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resources modelling, the addition of a deep groundwater concept and distributed computing with a focus on multi-node parallelism.

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Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin

Cited by 18 publications

References 75 publications

HydroMT: Automated and reproducible model building and analysis

HydroMT: Automated and reproducible model building and analysis

A Decade of Data‐Driven Water Budgets: Synthesis and Bibliometric Review

Wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications

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