Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment

Tubini, Niccolo'; Rigon, Riccardo

doi:10.5194/gmd-15-75-2022

Cited by 3 publications

(3 citation statements)

References 93 publications

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“…A few books indeed cover the issues for scientific computing, such as Rouson et al (2014) for Fortran 95 and Gardner and Manduchi (2007) for Java. Another step forward can be made by using a certain degree of abstraction in software, as proposed, for instance, by Berti (2000), with examples in C++, and used in Tubini and Rigon (2022), with Java. Abstraction, one of the basic rules of design pattern (i.e., "program to interface and not to objects"), is important in that it allows the software "not to be modified" but "extended", thereby enormously increasing the flexibility of a project and its lifetime.…”

Section: How To Write and Manage Modelsmentioning

confidence: 99%

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HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

Rigon¹,

Formetta²,

Bancheri³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. The “Digital Earth” (DE) metaphor is very useful for both end users and hydrological modelers (i.e., the coders). In this opinion paper, we analyze different categories of models with the view of making them part of Digital eARth Twin Hydrology systems (DARTHs). We stress the idea that DARTHs are not models, rather they are an appropriate infrastructure that hosts (certain types of) models and provides some basic services for connecting to input data. We also argue that a modeling-by-component strategy is the right one for accomplishing the requirements of the DE. Five technological steps are envisioned to move from the current state of the art of modeling. In step 1, models are decomposed into interacting modules with, for instance, the agnostic parts dealing with inputs and outputs separated from the model-specific parts that contain the algorithms. In steps 2 to 4, the appropriate software layers are added to gain transparent model execution in the cloud, independently of the hardware and the operating system of computer, without human intervention. Finally, step 5 allows models to be selected as if they were interchangeable with others without giving deceptive answers. This step includes the use of hypothesis testing, the inclusion of error of estimates, the adoption of literate programming and guidelines to obtain informative clean code. The urgency for DARTHs to be open source is supported here in light of the open-science movement and its ideas. Therefore, it is argued that DARTHs must promote a new participatory way of performing hydrological science, in which researchers can contribute cooperatively to characterize and control model outcomes in various territories. Finally, three enabling technologies are also discussed in the context of DARTHs – Earth observations (EOs), high-performance computing (HPC) and machine learning (ML) – as well as how these technologies can be integrated in the overall system to both boost the research activity of scientists and generate knowledge.

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Section: How To Write and Manage Modelsmentioning

confidence: 99%

“…identify possible directions and hints, based on more than a decade of experience in deploying hydrological models (Rigon et al, 2006;Formetta et al, 2014;Tubini and Rigon, 2022) and contributing to geographic information system (GIS) developments (e.g., JGrass, uDig and gvSIG);…”

Section: Introductionmentioning

confidence: 99%

HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

Rigon¹,

Formetta²,

Bancheri³

et al. 2022

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

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“…Since these models seldom produce analytical solutions (for instance, see [ 7 , 8 , 9 ]), numerical methods and solutions are generally still proposed in the literature, sometimes highlighting features in heterogeneous soils [ 10 , 11 , 12 , 13 ], sometimes the proper treatment of nonlinear issues (e.g., [ 14 , 15 , 16 ]) and sometimes elegant techniques for dealing with time or space discretization (e.g., [ 17 , 18 ]). More refined models and software are able to couple moisture content dynamics with the heat equation (as in [ 19 ]). On the other hand, the use of physics-based models requires a deep the knowledge of soil characteristics and significant laboratory efforts for assessing hydraulic parameters (for example, see [ 20 , 21 ]), the measurement of which can be expensive and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework

Giorgio¹,

Buono

Berardi

et al. 2022

Sensors

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Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments. The forecasting was performed based on (i) soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, (ii) hourly atmospheric data and (iii) daily irrigation amounts. Exploratory data analysis and data pre-processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results.

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A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)

Ireson

Spiteri²,

Clark³

et al. 2023

Geosci. Model Dev.

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Abstract. A simple numerical solution procedure – namely the method of lines combined with an off-the-shelf ordinary differential equation (ODE) solver – was shown in previous work to provide efficient, mass-conservative solutions to the pressure-head form of Richards' equation. We implement such a solution in our model openRE. We developed a novel method to quantify the boundary fluxes that reduce water balance errors without negative impacts on model runtimes – the solver flux output method (SFOM). We compare this solution with alternatives, including the classic modified Picard iteration method and the Hydrus 1D model. We reproduce a set of benchmark solutions with all models. We find that Celia's solution has the best water balance, but it can incur significant truncation errors in the simulated boundary fluxes, depending on the time steps used. Our solution has comparable runtimes to Hydrus and better water balance performance (though both models have excellent water balance closure for all the problems we considered). Our solution can be implemented in an interpreted language, such as MATLAB or Python, making use of off-the-shelf ODE solvers. We evaluated alternative SciPy ODE solvers that are available in Python and make practical recommendations about the best way to implement them for Richards' equation. There are two advantages of our approach: (i) the code is concise, making it ideal for teaching purposes; and (ii) the method can be easily extended to represent alternative properties (e.g., novel ways to parameterize the K(ψ) relationship) and processes (e.g., it is straightforward to couple heat or solute transport), making it ideal for testing alternative hypotheses.

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Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment

Cited by 3 publications

References 93 publications

HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework

A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)

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