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2. Evaluation of Statistical and Deep Learning Methods for Short-Term Weather Forecasting in Semi-arid Regions

Author

El Hachimi, Chouaib, Belaqziz, Salwa, Khabba, Saïd, Chehbouni, Abdelghani, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, Gawad, Iman O., Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Chenchouni, Haroun, editor, Zhang, Zhihua, editor, Bisht, Deepak Singh, editor, Gentilucci, Matteo, editor, Chen, Mingjie, editor, Chaminé, Helder I., editor, Barbieri, Maurizio, editor, Jat, Mahesh Kumar, editor, Rodrigo-Comino, Jesús, editor, Panagoulia, Dionysia, editor, Kallel, Amjad, editor, Biswas, Arkoprovo, editor, Turan, Veysel, editor, Knight, Jasper, editor, Çiner, Attila, editor, Candeias, Carla, editor, and Ergüler, Zeynal Abiddin, editor

Published
2024
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3. Topography-Based Estimation of Evapotranspiration at High Altitudes in Semi-arid Regions

Author

Sebbar, Badreddine, Merlin, Olivier, Khabba, Saïd, Simonneaux, Vincent, Bouchet, Marine, Chehbouni, Abdelghani, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, Gawad, Iman O., Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Bezzeghoud, Mourad, editor, Ergüler, Zeynal Abiddin, editor, Rodrigo-Comino, Jesús, editor, Jat, Mahesh Kumar, editor, Kalatehjari, Roohollah, editor, Bisht, Deepak Singh, editor, Biswas, Arkoprovo, editor, Chaminé, Helder I., editor, Shah, Afroz Ahmad, editor, Radwan, Ahmed E., editor, Knight, Jasper, editor, Panagoulia, Dionysia, editor, Kallel, Amjad, editor, Turan, Veysel, editor, Chenchouni, Haroun, editor, Ciner, Attila, editor, and Gentilucci, Matteo, editor

Published
2024
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8. Synergy of Sentinel-1 and Sentinel-2 Satellites for Surface Soil Moisture Retrieval Over Wheat Crops in Semi-arid Areas

Author

Ouaadi, Nadia, Ezzahar, Jamal, Jarlan, Lionel, Khabba, Saïd, Frison, Pierre Luis, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Bekkay, Hajji, editor, Mellit, Adel, editor, Gagliano, Antonio, editor, Rabhi, Abdelhamid, editor, and Amine Koulali, Mohammed, editor

Published
2023
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10. Snow hydrology in the Moroccan Atlas Mountains

Author

Hanich, Lahoucine, Chehbouni, Abdelghani, Gascoin, Simon, Boudhar, Abdelghani, Jarlan, Lionel, Tramblay, Yves, Boulet, Gilles, Marchane, Ahmed, Baba, Mohamed Wassim, Kinnard, Christophe, Simonneaux, Vincent, Fakir, Younes, Bouchaou, Lhoussaine, Leblanc, Marc, Le Page, Michel, Bouamri, Hafsa, Er-Raki, Salah, and Khabba, Saïd

Published
2022
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12. Estimation of the Evapotranspiration over Heterogeneous Region Using Shuttleworth-Wallace Model

Author

Elfarkh, Jamal, Er-Raki, Salah, Ezzahar, Jamal, Jarlan, Lionel, Khabba, Said, Chehbouni, Abdelghani, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, El Moussati, Ali, editor, Kpalma, Kidiyo, editor, Ghaouth Belkasmi, Mohammed, editor, Saber, Mohammed, editor, and Guégan, Sylvain, editor

Published
2020
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13. Inter-comparison Between Different Techniques for Evapotranspiration Partitioning: Eddy Covariance-, Sap Flow-, Lysimeter- and FAO-Based Methods

Author

Rafi, Zoubair, Merlin, Olivier, Le Dantec, Valérie, Khabba, Saïd, Er-Raki, Salah, Mordelet, Patrick, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, El Moussati, Ali, editor, Kpalma, Kidiyo, editor, Ghaouth Belkasmi, Mohammed, editor, Saber, Mohammed, editor, and Guégan, Sylvain, editor

Published
2020
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29. Assessment of crop evapotranspiration and deep percolation in a commercial irrigated citrus orchard under semi-arid climate: Combined Eddy-Covariance measurement and soil water balance-based approach

Author

Abou Ali, Asma, primary, Bouchaou, Lhoussaine, additional, Er-Raki, Salah, additional, Hssaissoune, Mohammed, additional, Brouziyne, Youssef, additional, Ezzahar, Jamal, additional, Khabba, Saïd, additional, Chakir, Adnane, additional, Labbaci, Adnane, additional, and Chehbouni, Abdelghani, additional

Published
2023
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34. Investigation of the diurnal cycles of interferometric coherence and backscattering coefficient on irrigated wheat in the South Mediterranean

Author

Ouaadi, Nadia, Villard, Ludovic, Khabba, Saïd, Frison, Pierre Louis, Chakir, Adnane, Ezzahar, Jamal, Fanise, Pascal, Kasbani, Mohamed, Le Dantec, Valérie, Jarlan, Lionel, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Cadi Ayyad [Marrakech] (UCA), and Université Gustave Eiffel

Subjects
[PHYS]Physics [physics], [SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2022

35. Hydro Statistical Assessment of TRMM and GPM Precipitation Products against Ground Precipitation over a Mediterranean Mountainous Watershed (in the Moroccan High Atlas)

Author

European Commission, Agencia Estatal de Investigación (España), Hera Portillo, África de la [0000-0001-7821-1834], Benkirane, Myriam, Laftouhi, Nour-Eddine, Khabba, Saïd, Hera Portillo, África de la, European Commission, Agencia Estatal de Investigación (España), Hera Portillo, África de la [0000-0001-7821-1834], Benkirane, Myriam, Laftouhi, Nour-Eddine, Khabba, Saïd, and Hera Portillo, África de la

Abstract

[EN] The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V05 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R > 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash–Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions.

Published
2022

37. In situ radar measurements for monitoring the physiological functioning of wheat crops in the semi-arid area

Author

Ouaadi, Nadia, primary, Villard, Ludovic, additional, Khabba, Saïd, additional, Frison, Pierre-Louis, additional, Ezzahar, Jamal, additional, Kasbani, Mohamed, additional, Fanise, Pascal, additional, Chakir, Adnane, additional, Le Dantec, Valerie, additional, Zribi, Mehrez, additional, Er-Raki, Salah, additional, and Jarlan, Lionel, additional

Published
2022
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40. Towards sustainable irrigation water management in a Semi-arid Climate: Quantifying Evapotranspiration and Crop coefficient for Citrus orchards Based on Eddy Covariance measurements

Author

ABOU ALI, Asma, primary, BOUCHAOU, Lhoussaine, additional, ER-RAKI, Salah, additional, EZZAHAR, Jamal, additional, KHABBA, Saïd, additional, BROUZIYNE, Youssef, additional, HSSAISSOUNE, Mohammed, additional, CHAKIR, Adnane, additional, LABBACI, ADNANE, additional, and CHEHBOUNI, Ghani, additional

Published
2022
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41. Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture.

Author

Hachimi, Chouaib El, Belaqziz, Salwa, Khabba, Saïd, Sebbar, Badreddine, Dhiba, Driss, and Chehbouni, Abdelghani

Subjects
DEEP learning, DATABASE management, ARTIFICIAL intelligence, BIG data, PRECISION farming, STANDARD deviations
Abstract

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R2) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET0) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R2 = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

Author

Bouras, El houssaine, Jarlan, Lionel, Er-Raki, Salah, Balaghi, Riad, Amazirh, Abdelhakim, Richard, Bastien, Khabba, Saïd, Université Cadi Ayyad [Marrakech] (UCA), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national de la recherche agronomique [Maroc] (INRA Maroc), Université Mohammed VI Polytechnique [Ben Guerir] (UM6P), Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ARTS program from IRD, France, H2020 PRIMA ALTOS project, MISTRALS/SICMED2, PHC Toubkal - 39064WG/2018, PRIMA-IDEWA project, CHAAMS project - ERANETMED03-62, ACCWA project - 823965, SAGESSE - PPR/2015/48, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Mohammed VI Polytechnic University [Marocco] (UM6P), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
machine learning, Remote sensing drought indices, Science, [SDE]Environmental Sciences, Crop yield forecasting, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, [INFO]Computer Science [cs], Climate indices, Semiarid region, weather data
Abstract

International audience; Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

Published
2021
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43. Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

Author

Ouaadi, Nadia, Jarlan, Lionel, Khabba, Saïd, Ezzahar, Jamal, Le Page, Michel, Merlin, Olivier, Université Cadi Ayyad [Marrakech] (UCA), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FAO-56, Science, particle filters, irrigation timing and amounts, Sentinel-1, Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture irrigation timing and amounts, semi-arid Mediterranean region, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, surface soil moisture, data assimilation, winter wheat
Abstract

International audience; Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas

Published
2021
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44. L'eau en milieu agricole

Author

Alcazar, Charlotte, Allain, Sandrine, Augeard, Bénédicte, Baillieux, Antoine, Ballot, Rémy, Barataud, Fabienne, Barone, Sylvain, Barreteau, Olivier, Bergez, Jacques-Éric, Bertuzzi, Patrick, Biarnès, Anne, Bonifazi, Mathilde, Bourrié, Guilhem, Bousquet, François, Chanzy, André, Chery, Jean-Pierre, Cognard-Plank, Anne-Laure, Coudercy, Laurent, Courault, Dominique, Cousin, Isabelle, Debril, Thomas, Destandau, François, Durand, Patrick, Echeverria, Javier, Étienne, Michel, Ferrand, Nils, Ferrané, Claudine, Garcia, Serge, Garin, Patrice, Gaudin, Alexandre, Gillon, Marina, Guichard, Laurence, Hassenforder, Émeline, Huard, Frédéric, Jarlan, Lionel, Jeuffroy, Marie-Hélène, Khabba, Saïd, Leenhardt, Delphine, Le Page, Christophe, Le Page, Michel, Magand, Claire, Meynard, Jean-Marc, Mignolet, Catherine, Millair, Laurent, Molénat, Jérôme, Murgue, Clément, Olioso, Albert, Petit, Olivier, Prost, Lorène, Reau, Raymond, Richer-de-Forges, Anne C., Rivière-Honegger, Anne, Ruy, Stéphane, Souchère, Véronique, Therond, Olivier, Thomas, Alban, Thoyer, Sophie, Trolard, Fabienne, Voltz, Marc, Leenhardt, Delphine, Voltz, Marc, and Barreteau, Olivier

Subjects
eau douce, Economics, Environmental Studies, pisciculture, santé, écosystème
Abstract

L’eau est un enjeu majeur dans de nombreuses régions du monde du fait de besoins en forte croissance, mais aussi de la rareté et de la dégradation des ressources disponibles. L’agriculture étant le premier usager de la ressource en eau, la durabilité de la gestion de l’eau en milieu rural est cruciale. Elle nécessite de dépasser les approches sectorielles, encore largement en cours malgré divers courants appelant à une gestion intégrée, territoriale ou adaptative de la ressource. Prenant acte que la mise en œuvre des concepts proposés par ces différents courants est rarement effective et qu’il est plus pragmatique de rechercher la meilleure coordination possible entre les parties prenantes de la gestion de l’eau, cet ouvrage collectif analyse les enjeux, outils et démarches permettant de faciliter et d’accompagner cette coordination. En effet, une coordination effective et acceptée nécessite des méthodes et outils pour informer sur l’état de la ressource et des usages, rendre visible l’importance des enjeux, évaluer les effets et les expliquer, structurer les échanges, et élaborer et discuter des actions de gestion. L’ouvrage aborde la gestion de l’eau en milieu rural en termes d’objectifs et de démarche, puis fait un panorama large des outils, des méthodes et des données actuellement disponibles. Enfin, il décrit des exemples de mises en œuvre dans différents cas d’étude. Cet ouvrage de synthèse s’adresse à un public large allant des chercheurs, étudiants et enseignants en gestion de l’eau aux professionnels concernés par sa mise en œuvre.

Published
2021

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