A dynamic model for water management at the farm level integrating strategic, tactical and operational decisions

Robert, Marion; Thomas, Alban; Sekhar, M.; Raynal, Hélène; Casellas, Éric; Casel, Pierre; Chabrier, Patrick; Joannon, Alexandre; Bergez, Jacques-Éric

doi:10.1016/j.envsoft.2017.11.013

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…The model is based on the groundwater flow equation solved numerically, using the finite-difference explicit scheme [55]. The implementation of various AMBHAS versions in India was shown to be highly successful in simulating groundwater levels across areas of Karnataka [56], in the Barembadi catchment [57,58] and an idealised system based on the Ganges River [10]. Additionally, de Bruin et al (2012) [59] utilised the results generated from AMBHAS-1D to guide the set of SWAT groundwater parameters for use in the Jaldhaka Basin.…”

Section: Representing Groundwater Processesmentioning

confidence: 99%

Extending a Large-scale Model to Better Represent Water Resources without Increasing the Model Complexity

Horan¹,

Rickards²,

Kaelin³

et al. 2021

Preprint

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A robust hydrological assessment is challenging in regions where human interference, within all aspects of the hydrological system, significantly alters the flow regime of rivers. The challenge was to extend a large-scale water resources model, GWAVA, to better represent water resources without increasing the model complexity. A groundwater and a regulated reservoir routine were incorporated into GWAVA using modifications of the existing AMBHAS-1D and Hanasaki methodologies, respectively. The groundwater routine can be varied in complexity when sufficient input data is available but fundamentally is driven by three input parameters. The reservoir routine was extended to account for the presence of large, regulated reservoirs using two calibratable parameters. The additional groundwater processes and reservoir regulation was tested in two highly anthropogenically influenced basins in India: the Cauvery and Narmada. The inclusion of the revised groundwater routine improved the simulation of streamflow in the headwater catchments and was successful in improving the representation of the baseflow component. In addition, the model was able to produce a time series of daily groundwater levels, recharge to groundwater and groundwater abstraction. The regulated reservoir routine improved the simulation of streamflow in catchments downstream of major reservoirs, where the streamflow was largely reflective of reservoir releases, when calibrated using downstream observed streamflow records. The model was able to provide a more robust representation of the annual volume and daily outflow released from the major reservoirs and simulate the major reservoir storages adequately. The addition of one-dimensional groundwater processes and a regulated reservoir routine proved successful in improving the model performance and traceability of water balance components, without excessively increasing the model complexity and input data requirements.

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Section: Representing Groundwater Processesmentioning

confidence: 99%

Extending a Large-scale Model to Better Represent Water Resources without Increasing the Model Complexity

Horan¹,

Rickards²,

Kaelin³

et al. 2021

Preprint

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“…This approach is useful for assessing farming practices and systems across temporal and spatial scales and thus for informing stakeholders engaged in the sustainability transition. For example, simulation modeling can assess irrigation practices at field and farm levels (MODERATO: [66]; Namaste: [67]). It can also assess impacts (e.g., on water management, nitrogen cycling, economics) of innovative practices (use of cover crops, crop diversification, reduced soil tillage, etc.)…”

Section: Overview and Corresponding Stances Objects And Scalesmentioning

confidence: 99%

How to Address the Sustainability Transition of Farming Systems? A Conceptual Framework to Organize Research

et al. 2018

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Stakeholders from academic, political, and social spheres encourage the development of more sustainable forms of agriculture. Given its scale and scope, the sustainability transition is a challenge to the entire agricultural sector. The main question is, how to support the transition process? In this article, we explore how agricultural science can address the sustainability transition of farming systems to understand and support transition processes. We discuss the potential for articulating three research approaches: comprehensive analysis, co-design, and simulation modeling. Comprehensive analysis of the sustainability transition provides perspectives on the interplay between resources, resource management, and related performances of farming systems on the one hand and technical, economic, and sociocultural dimensions of change on the other. Co-design of the sustainability transition stimulates local-scale transition experiments in the real world and identification of alternatives for change. Simulation modeling explores future-oriented scenarios of management at multiple levels and assesses their impacts. We illustrate the articulation of research approaches with two examples of research applied to agricultural water management and autonomy in crop-livestock systems. The resulting conceptual framework is the first one developed to organize research to understand and support the sustainability transition of farming systems.

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“…In the environmental sector, modelling serves a variety of interrelated purposes, including decision support, scientific discovery, and social learning (Badham et al, 2019;Gober 2018). In the water sector, for example, it supports a range of water management decisions, including infrastructure construction and operations, flood control and drought management, harvesting and storing water above and below ground, maintaining healthy ecosystems, and allocation of water for agriculture, energy production, cities, and environmental uses (Loucks, et al 2005;Mulligan and Ahlfeld 2016;Snow et al, 2016;Sharvelle et al, 2017;Robert et al 2018). Modelling also enables scientific discovery, for example, about anticipated impacts of climate change on regional hydrological systems (Cook et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A framework for characterising and evaluating the effectiveness of environmental modelling

Hamilton

Guillaume

et al. 2019

Environmental Modelling & Software

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Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures.

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A dynamic model for water management at the farm level integrating strategic, tactical and operational decisions

Cited by 14 publications

References 37 publications

Extending a Large-scale Model to Better Represent Water Resources without Increasing the Model Complexity

Extending a Large-scale Model to Better Represent Water Resources without Increasing the Model Complexity

How to Address the Sustainability Transition of Farming Systems? A Conceptual Framework to Organize Research

A framework for characterising and evaluating the effectiveness of environmental modelling

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