Aplicación de un modelo dinámico de multisimulación de cultivos para la evaluación de las necesidades hídricas en las zonas de La Mancha Oriental (Júcar) y La Crau (Ródano)
[EN] The European research project AGADAPT (Climate-KIC) intended to do the evaluation of model water resources in the context of climate change. Combining innovative technologies based on modelling and knowledge, AGADAPT demonstrated the added value offered by the state of the art research results...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/75158 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/75158 |
| Access Level: | acceso abierto |
| Palabra clave: | Climatic change Multisimulation Agricultura Cambio climático STICS Multisimulación Agriculture INGENIERIA HIDRAULICA Máster Universitario en Ingeniería Hidráulica y Medio Ambiente-Màster Universitari en Enginyeria Hidràulica i Medi Ambient |
| Sumario: | [EN] The European research project AGADAPT (Climate-KIC) intended to do the evaluation of model water resources in the context of climate change. Combining innovative technologies based on modelling and knowledge, AGADAPT demonstrated the added value offered by the state of the art research results for the management of water resources in agriculture. The project was divided into three working groups. The second working group of the project (WP2) aimed to test different models to assess water resources at river basin level. Therefore, they were evaluated and implemented various models for forecasting water resources, considering the drivers of global change (climate change and land use). For each model, was required data description and evaluation of the various pilot sites and provided simulation scenarios. WP2 dealt with water resources at the regional scale in the global context of climate change, using 2001-2010 as the current scenario and the prospects for change in the future scenario 2037-2065. The project demonstrated the potential of the state of the art of hydrometeorological models combined with models of water demand, for prospective studies on available water resources for agriculture. The initial activities were devoted to the study of the evolution of the models and an inventory of existing modelling framework. This allowed having an opinion about them and emphasizing the added value of the proposed model compared with the existing approaches. Thus several models and pilot sites were considered. INRA led the implementation of STICS model with the multi-simulation module in the areas of La Crau and La Mancha Oriental. STICS is a daily crop model, developed by INRA since 1996 (N. Brisson et al., 2003) with input variables related to climate, soil, crops and agronomic techniques, and output variables related to yield in terms quantity and quality and to the environment and in terms of drainage and nitrate leaching. STICS is applied to crop parcel scale. The specific objectives of this Master’s Final Work can be shown in the following contributions to the project with the work developed at INRA for the simulations with the STICS model: (1) Direct participation in the work of WP2, developing the database required for the pilot study area in the Júcar river basin, with particular interest in the details needed in La Mancha Oriental area where simulations were implemented. The database elaborated contains information on climate, soil and land use, crop species and agronomic techniques used in the study area. This database will be used by different models to evaluate in the project. Reviews and corrections were also needed in the database of La Crau area where simulations were also implemented. (2) Preparation and configuration of input variables needed in the simulation process using the STICS model and implementation of the model simulations on the Crau aquifer belonging to Rhone basin (Albert Olioso et al., 2013) and the area of La Mancha Oriental in the Jucar river basin. As a new point it was evaluated, reviewed and applied the STICS model of the study areas using Multisim STICS, one of its latest developments executing simultaneous and spatialized simulation in all the plots. This process of Multi-simulation also provides the ability to run simulations using combinations of input variables, such as variation in the sowing date or the amount of water used in irrigation stages. With these combinations it was possible to obtain an aggregation of results that fit in better as the reality of the process. (3) It is intended as the ultimate objective, to demonstrate the validity of the model in different places of study, obtaining reasonable results with available data. Then this model was applied in the context of future climate change. Implementation of the model over the Crau was more accurate because in this area there was a lot of information available and it was done lot of work by INRA in previous projects. Therefore the model had been calibrated before in this area and it was possible to obtain good indicators for assessing the tool concerning aquifer piezometry, water consumption for irrigation and agricultural production. These indicators provided satisfactory results, reflecting an adaptation of the model to the reality of the process showing a decrease in groundwater levels and drainage in years of drought and some increase in irrigations in those years to obtain an optimum crop production. For the future climate scenarios although rainfall was overestimated, a slight decrease in the recharges to the aquifer and increase of crop water needs were observed. The Implementation of the model over La Mancha Oriental area was a simple approximation, but results have shown that with simple assumption and without any calibration we arrive in first attempt to interesting results on water increasing needs for irrigation in dry years and good yield results in the study area comparing with statistics based on real data. |
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