Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-897-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-897-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2019
Research article |
| 18 Feb 2019
| 18 Feb 2019
Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data
Felix Zaussinger
CORRESPONDING AUTHOR
CLIMERS – Research Group Climate and Environmental Remote Sensing, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Wouter Dorigo
CORRESPONDING AUTHOR
CLIMERS – Research Group Climate and Environmental Remote Sensing, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Alexander Gruber
CLIMERS – Research Group Climate and Environmental Remote Sensing, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Angelica Tarpanelli
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Paolo Filippucci
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Luca Brocca
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
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Jaime Gaona, Davide Bavera, Guido Fioravanti, Sebastian Hahn, Pietro Stradiotti, Paolo Filippucci, Stefania Camici, Luca Ciabatta, Hamidreza Mosaffa, Silvia Puca, Nicoletta Roberto, and Luca Brocca
Hydrol. Earth Syst. Sci., 29, 3865–3888, https://doi.org/10.5194/hess-29-3865-2025, https://doi.org/10.5194/hess-29-3865-2025, 2025
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Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Martin Hirschi, Pietro Stradiotti, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
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We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
Ling Zhang, Yanhua Xie, Xiufang Zhu, Qimin Ma, and Luca Brocca
Earth Syst. Sci. Data, 16, 5207–5226, https://doi.org/10.5194/essd-16-5207-2024, https://doi.org/10.5194/essd-16-5207-2024, 2024
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This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
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VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Jacopo Dari, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 28, 2651–2659, https://doi.org/10.5194/hess-28-2651-2024, https://doi.org/10.5194/hess-28-2651-2024, 2024
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We have developed the first operational system (10 d latency) for estimating irrigation water use from accessible satellite and reanalysis data. As a proof of concept, the method has been implemented over an irrigated area fed by the Kakhovka Reservoir, in Ukraine, which collapsed on June 6, 2023. Estimates for the period 2015–2023 reveal that, as expected, the irrigation season of 2023 was characterized by the lowest amounts of irrigation.
Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, https://doi.org/10.5194/hess-28-441-2024, 2024
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This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show that the synergy from using two equally important components in a joint approach within a baseline framework yields better irrigation estimates.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
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We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
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Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
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We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
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Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
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Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Kunlong He, Wei Zhao, Luca Brocca, and Pere Quintana-Seguí
Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, https://doi.org/10.5194/hess-27-169-2023, 2023
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In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the GPM daily precipitation product by exploiting the connection between surface soil moisture and precipitation according to the soil water balance equation. Based on this physical method, the spatial resolution of the daily precipitation product was downscaled to 1 km and the SMPD method shows good potential for the development of the high-resolution precipitation product.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
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The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
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The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
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Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
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A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
Hydrol. Earth Syst. Sci., 26, 841–860, https://doi.org/10.5194/hess-26-841-2022, https://doi.org/10.5194/hess-26-841-2022, 2022
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Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
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Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Cited articles
Allan, R. P. and Soden, B. J.: Atmospheric warming and the amplification of
precipitation extremes, Science, 321, 1481–1484, 2008. a
Ambika, A. K., Wardlow, B., and Mishra, V.: Remotely sensed high resolution
irrigated area mapping in India for 2000 to 2015, Scientific data, 3,
160118, https://doi.org/10.1038/sdata.2016.118, 2016. a, b
Bastiaanssen, W. G., Menenti, M., Feddes, R., and Holtslag, A.: A remote
sensing surface energy balance algorithm for land (SEBAL). 1. Formulation,
J. Hydrol., 212, 198–212, 1998. a
Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T.,
Modanesi, S., Ciabatta, L., Massari, C., Brocca, L., and Wagner, W.: Soil
Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with
Temporal Filtering, Remote Sensing, 10, 1030, https://doi.org/10.3390/rs10071030,
2018. a
Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard,
F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G., Bontemps, S., Defourny, P.,
Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M.,
Brockmann, C., Kirches, G., Zulkhe, M., Kalogirou, V., Seifert, F. M., and Arino, O.: Consistent
global land cover maps for climate modelling communities: current
achievements of the ESA's land cover CCI, in: Proceedings of the ESA Living
Planet Symposium, Edimburgh, 9–13, 2013. a, b, c
Boucher, O., Myhre, G., and Myhre, A.: Direct human influence of irrigation on
atmospheric water vapour and climate, Clim. Dynam., 22, 597–603, 2004. a
Brocca, L., Melone, F., Moramarco, T., Wagner, W., and Albergel, C.: Scaling
and filtering approaches for the use of satellite soil moisture observations,
in: Remote Sensing of Energy Fluxes and Soil Moisture Content, 415–430,
CRC Press, Boca Raton, 2013. a
Chan, S. K., Bindlish, R., O'Neill, P., Jackson, T., Njoku, E., Dunbar, S.,
Chaubell, J., Piepmeier, J., Yueh, S., Entekhabi, D., Colliander, A., Chen,
F., Cosh, M. H., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault,
M., Martínez-Fernández, J., Uldall, F., Seyfried, M., Bosch, D.,
Starks, P., Holifield Collins, C., Prueger, J., van der Velde, R., Asanuma,
J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T., and Kerr,
Y.: Development and
assessment of the SMAP enhanced passive soil moisture product, Remote Sens.
Environ., 204, 931–941, 2018. a
Chen, M., Shi, W., Xie, P., Silva, V., Kousky, V. E., Wayne Higgins, R., and
Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of
global daily precipitation, J. Geophys. Res.-Atmos.,
113, D04110, https://doi.org/10.1029/2007JD009132, 2008. a
Chew, C. and Small, E.: Soil moisture sensing using spaceborne GNSS
reflections: Comparison of CYGNSS reflectivity to SMAP soil moisture,
Geophys. Res. Lett., 45, 4049–4057, https://doi.org/10.1029/2018GL077905, 2018. a
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S. B.,
Cosh, M. H., Dunbar, R. S., Dang, L., Pashaian, L., Asanuma, J., Aida, K.,
Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D.,
al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J.,
González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A., Moghaddam,
M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J.,
Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M., Starks, P., Su, Z.,
Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M.,
Walker, J. P., Wu, X., Monerris, A., O'Neill, P. E., Entekhabi, D., Njoku, E.
G., and Yueh, S.: Validation of SMAP surface
soil moisture products with core validation sites, Remote Sens.
Environ., 191, 215–231, 2017. a
Daughtry, C., Ranson, K., and Biehl, L.: C-band backscattering from corn
canopies, Int. J. Remote Sens., 12, 1097–1109, 1991. a
der Schalie, R., Kerr, Y., Wigneron, J., Rodríguez-Fernández, N.,
Al-Yaari, A., and Jeu, R.: Global SMOS Soil Moisture Retrievals from The Land
Parameter Retrieval Model, Int. J. Appl. Earth Obs., 45, 125–134,
https://doi.org/10.1016/j.jag.2015.08.005, 2016. a
Döll, P. and Siebert, S.: Global modeling of irrigation water requirements,
Water Resour. Res., 38, 8-1–8-10, https://doi.org/10.1029/2001WR000355, 2002. a
Dorigo, W., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiova, A.,
Sanchis-Dufau, A., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global automated quality control of in situ soil moisture data from the
International Soil Moisture Network, Vadose Zone J., 12, https://doi.org/10.2136/vzj2012.0097, 2013. a
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi,
M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D.,
Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C.,
van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA
CCI Soil Moisture for improved Earth system understanding: State-of-the art
and future directions, Remote Sens. Environ., 203, 185–215,
https://doi.org/10.1016/j.rse.2017.07.001, 2017. a, b, c
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The soil moisture active
passive (SMAP) mission, P. IEEE, 98, 704–716, 2010. a, b, c
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S.,
Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer,
C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S.,
Rockström, J., Sheehan, J., Siebert, S., Tilman, D., and Zaks, D. P. M.:
Solutions for a cultivated planet, Nature, 478, 337–342, 2011. a
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple collocation-based
merging of satellite soil moisture retrievals, IEEE T.
Geosci. Remote, 55, 6780–6792, 2017. a
Hahn, S., Reimer, C., Vreugdenhil, M., Melzer, T., and Wagner, W.: Dynamic
characterization of the incidence angle dependence of backscatter using metop
ASCAT, IEEE J. Sel. Top. Appl., 10, 2348–2359, 2017. a
Hain, C. R., Crow, W. T., Anderson, M. C., and Yilmaz, M. T.: Diagnosing
neglected soil moisture source–sink processes via a thermal infrared–based
two-source energy balance model, J. Hydrometeorol., 16, 1070–1086,
2015. a
Howitt, R.: Preliminary Analysis: 2015 Drought Economic Impact Study, Tech.
rep., California Department of Food and Agriculture, 2015. a
Imaoka, K., Kachi, M., Fujii, H., Murakami, H., Hori, M., Ono, A., Igarashi,
T., Nakagawa, K., Oki, T., Honda, Y., and Shimoda, H.: Global Change
Observation
Mission (GCOM) for monitoring carbon, water cycles, and climate change,
P. IEEE, 98, 717–734, 2010. a
Jackson, T. E. A.: Soil Moisture Active Passive (SMAP) Project: Calibration and
Validation for the L2/3_SM_P Version 5 and L2/3_SM_P_E Version 5 Data
Products, NASA, 2018. a
Joseph, A., van der Velde, R., O'neill, P., Lang, R., and Gish, T.: Effects of
corn on C-and L-band radar backscatter: A correction method for soil moisture
retrieval, Remote Sens. Environ., 114, 2417–2430, 2010. a
Kebede, H., Fisher, D. K., Sui, R., Reddy, K. N.: Irrigation methods
and scheduling in the delta region of Mississippi: Current status and
strategies to improve irrigation efficiency, American Journal of Plant
Sciences, 5, 50005, https://doi.org/10.4236/ajps.2014.520307, 2014. a
Kueppers, L. M., Snyder, M. A., and Sloan, L. C.: Irrigation cooling effect:
Regional climate forcing by land-use change, Geophys. Res. Lett.,
34, L03703, https://doi.org/10.1029/2006GL028679, 2007. a, b, c
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H.,
Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating
the utility of satellite soil moisture retrievals over irrigated areas and
the ability of land data assimilation methods to correct for unmodeled
processes, Hydrol. Earth Syst. Sci., 19, 4463–4478,
https://doi.org/10.5194/hess-19-4463-2015, 2015. a, b, c
Kumar, S. V., Jasinski, M., Mocko, D., Rodell, M., Borak, J., Li, B.,
Kato Beaudoing, H., and Peters-Lidard, C. D.: NCA-LDAS land analysis:
Development and performance of a multisensor, multivariate land data
assimilation system for the National Climate Assessment, J.
Hydrometeorol., https://doi.org/10.1175/JHM-D-17-0125.1, 2018. a, b
Kummu, M., Guillaume, J., De Moel, H., Eisner, S., Flörke, M., Porkka, M.,
Siebert, S., Veldkamp, T., and Ward, P.: The world's road to water scarcity:
shortage and stress in the 20th century and pathways towards sustainability,
Sci. Rep.-UK, 6, 38495, https://doi.org/10.1038/srep38495, 2016. a
Le Toan, T., Ribbes, F., Wang, L.-F., Floury, N., Ding, K.-H., Kong, J. A.,
Fujita, M., and Kurosu, T.: Rice crop mapping and monitoring using ERS-1 data
based on experiment and modeling results, IEEE T. Geosci.
Remote, 35, 41–56, 1997. a
Linquist, B., Snyder, R., Anderson, F., Espino, L., Inglese, G., Marras, S.,
Moratiel, R., Mutters, R., Nicolosi, P., Rejmanek, H., Russo, A., Shapland,
T., Song, Z., Swelam, A., Tindula, G., and Hill, J.: Water balances
and evapotranspiration in water-and dry-seeded rice systems, Irrigation
Sci., 33, 375–385, 2015. a, b
Lobell, D. B., Bala, G., and Duffy, P. B.: Biogeophysical impacts of cropland
management changes on climate, Geophys. Res. Lett., 33, l06708,
https://doi.org/10.1029/2005GL025492, 2006. a, b, c
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J.
Remote Sens., 21, 1303–1330, 2000. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R.,
Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E.,
Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini,
K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G.,
Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu,
A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G.,
Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B.,
Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform
for coupled or offline simulation of earth surface variables and fluxes,
Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013,
2013. a
McColl, K. A., Alemohammad, S. H., Akbar, R., Konings, A. G., Yueh, S., and
Entekhabi, D.: The global distribution and dynamics of surface soil moisture,
Nat. Geosci., 10, 100–104, https://doi.org/10.1038/ngeo2868,
2017. a
Meier, J., Zabel, F., and Mauser, W.: A global approach to estimate irrigated
areas – a comparison between different data and statistics, Hydrol. Earth
Syst. Sci., 22, 1119–1133, https://doi.org/10.5194/hess-22-1119-2018, 2018. a
Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S.,
and Wagner, W.: An
improved soil moisture retrieval algorithm for ERS and METOP scatterometer
observations, IEEE T. Geosci. Remote, 47,
1999–2013, 2009. a
NASS, U.: Usual planting and harvesting dates for US field crops, Tech. rep.,
NASS, USDA, 2010. a
Nguyen, D. B., Clauss, K., Cao, S., Naeimi, V., Kuenzer, C., and Wagner, W.:
Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM
Data, Remote Sensing, 7, 15868–15893, https://doi.org/10.3390/rs71215808,
2015. a, b, c
Nguyen, D. B., Gruber, A., and Wagner, W.: Mapping rice extent and cropping
scheme in the Mekong Delta using Sentinel-1A data, Remote Sens. Lett., 7,
1209–1218, 2016. a
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., and Bindlish, R.: SMAP
L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture, Version 5, [Indicate
subset used], Boulder, Colorado USA, NASA National Snow and Ice Data Center
Distributed Active Archive Center, https://doi.org/10.5067/SODMLCE6LGLL, 2018. a
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys.
Res.-Earth, 113, F01002, https://doi.org/10.1029/2007JF000769, 2008. a
Ozdogan, M. and Gutman, G.: A new methodology to map irrigated areas using
multi-temporal MODIS and ancillary data: An application example in the
continental US, Remote Sens. Environ., 112, 3520–3537,
https://doi.org/10.1016/j.rse.2008.04.010, 2008. a
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the
Effects of Irrigation over the United States in a Land Surface Model Based on
Satellite-Derived Agricultural Data, J. Hydrometeorol., 11,
171–184, https://doi.org/10.1175/2009jhm1116.1, 2010a. a
Ozdogan, M., Yang, Y., Allez, G., and Cervantes, C.: Remote Sensing of
Irrigated Agriculture: Opportunities and Challenges, Remote Sensing, 2,
2274–2304, https://doi.org/10.3390/rs2092274, 2010b. a
Pereira, L. S., Oweis, T., and Zairi, A.: Irrigation management under water
scarcity, Agr. Water Manage., 57, 175–206, 2002. a
Pervez, M. S. and Brown, J. F.: Mapping Irrigated Lands at 250-m Scale by
Merging MODIS Data and National Agricultural Statistics, Remote Sensing, 2,
2388–2412, https://doi.org/10.3390/rs2102388, 2010. a, b, c, d
Pervez, S., Brown, J. F., and Maxwell, S.: Evaluation of remote sensing-based
irrigated area map for the Conterminous United States, Proceedings of the
ASPRS Pecora, 17, https://www.asprs.org/a/publications/proceedings/pecora17/0027.pdf (last access: 10 May 2018), 2008. a
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modeling, Global Biogeochem.
Cy., 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010. a, b
Pun, M., Mutiibwa, D., and Li, R.: Land Use Classification: A Surface Energy
Balance and Vegetation Index Application to Map and Monitor Irrigated Lands,
Remote Sensing, 9, 1256, https://doi.org/10.3390/rs9121256, 2017. a
Qiu, J., Gao, Q., Wang, S., and Su, Z.: Comparison of temporal trends from
multiple soil moisture data sets and precipitation: The implication of
irrigation on regional soil moisture trend, Int. J. Appl.
Earth Obs., 48, 17–27, 2016. a
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster,
R. D., and De Lannoy, G. J.: Assessment of MERRA-2 land surface hydrology
estimates, J. Climate, 30, 2937–2960,
https://doi.org/10.1175/JCLI-D-16-0720.1,
2017a. a
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster,
R. D., and De Lannoy, G. J.: Assessment of MERRA-2 land surface hydrology
estimates, J. Climate, 30, 2937–2960, 2017b. a
Reichle, R. H., Liu, Q., Koster, R. D., Draper, C. S., Mahanama, S. P., and
Partyka, G. S.: Land surface precipitation in MERRA-2, J. Climate,
30, 1643–1664, https://doi.org/10.1175/JCLI-D-16-0570.1,
2017c. a
Rockström, J., Falkenmark, M., Lannerstad, M., and Karlberg, L.: The
planetary water drama: Dual task of feeding humanity and curbing climate
change, Geophys. Res. Lett., 39, L15401, https://doi.org/10.1029/2012GL051688, 2012. a
Rosas, J., Houborg, R., and McCabe, M. F.: Sensitivity of Landsat 8 Surface
Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in
Dryland Irrigated Systems, Remote Sensing, 9, 988, https://doi.org/10.3390/rs9100988, 2017. a
Roseta-Palma, C., Iglesias, E., and Koppl-Turyna, M.: Illegal groundwater
pumping, in: 5th World Congress of Environmental and Resource Economists,
Istanbul, Turkey, paper, vol. 863, 2014. a
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., and Helkowski, J. H.:
Effects of global irrigation on the near-surface climate, Clim. Dynam.,
33, 159–175, https://doi.org/10.1007/s00382-008-0445-z, 2009. a, b, c
Saffi, M. and Cheddadi, A.: Identification of illegal groundwater pumping in
semi-confined aquifers, Hydrolog. Sci. J., 55, 1348–1356, 2010. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125 – 161,
https://doi.org/10.1016/j.earscirev.2010.02.004,
2010. a
Shiklomanov, I. A.: Appraisal and assessment of world water resources, Water
Int., 25, 11–32, 2000. a
Siebert, S., Döll, P., Hoogeveen, J., Faures, J.-M., Frenken, K., and
Feick, S.: Development and validation of the global map of irrigation areas,
Hydrol. Earth Syst. Sci., 9, 535–547,
https://doi.org/10.5194/hess-9-535-2005, 2005. a, b, c
Siebert, S., Döll, P., Feick, S., Hoogeveen, J., and Frenken, K.: Global
map of irrigation areas version 4.0. 1, Johann Wolfgang Goethe University,
Frankfurt am Main, Germany/Food and Agriculture Organization of the United
Nations, Rome, Italy, 2007. a
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll,
P., and Portmann, F. T.: Groundwater use for irrigation – a global
inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880,
https://doi.org/10.5194/hess-14-1863-2010, 2010. a, b
Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and
Scanlon, B. R.: A global data set of the extent of irrigated land from 1900
to 2005, Hydrol. Earth Syst. Sci., 19, 1521–1545,
https://doi.org/10.5194/hess-19-1521-2015, 2015. a
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., van Beek, R., Wada, Y.,
Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow, L.,
Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., MacDonald, A.,
Fan, Y., Maxwell, R. M., Yechieli, Y., Gurdak, J. J., Allen, D. M.,
Shamsudduha, M., Hiscock, K., Yeh, P. J. F., Holman, I., and Treidel, H.:
Ground water and climate change, Nat. Clim. Change, 3, 322–329,
https://doi.org/10.1038/nclimate1744, 2012. a
Teluguntla, P., Thenkabail, P. S., Xiong, J., Gumma, M. K., Congalton, R. G.,
Oliphant, A., Poehnelt, J., Yadav, K., Rao, M., and Massey, R.: Spectral
matching techniques (SMTs) and automated cropland classification algorithms
(ACCAs) for mapping croplands of Australia using MODIS 250-m time-series
(2000–2015) data, Int. J. Digit. Earth, 10, 944–977, 2017. a
Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y.,
Velpuri, M., Gumma, M., Gangalakunta, O. R. P., Turral, H., Cai, X.,
Vithanage, J., Schull, M. A., and Dutta, R.:
Global irrigated area map (GIAM), derived from remote sensing, for the end of
the last millennium, Int. J. Remote Sens., 30, 3679–3733,
2009. a
Thiery, W., Davin, E. L., Lawrence, D. M., Hirsch, A. L., Hauser, M., and
Seneviratne, S. I.: Present-day irrigation mitigates heat extremes, J.
Geophys. Res.-Atmos., 122, 1403–1422, 2017. a
Tuinenburg, O. and Vries, J.: Irrigation Patterns Resemble ERA-Interim
Reanalysis Soil Moisture Additions, Geophys. Res. Lett., 44, 10341–10348,
https://doi.org/10.1002/2017GL074884, 2017. a, b
Vreugdenhil, M., Dorigo, W. A., Wagner, W., De Jeu, R. A., Hahn, S., and
Van Marle, M. J.: Analyzing the vegetation parameterization in the TU-Wien
ASCAT soil moisture retrieval, IEEE T. Geosci. Remote, 54, 3513–3531, 2016. a
Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture
from ERS scatterometer and soil data, Remote Sens. Environ., 70,
191–207, 1999. a
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J.,
Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U.,
Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., and
Steinnocher, K.: The
ASCAT soil moisture product: A review of its specifications, validation
results, and emerging applications, Meteorol. Z., 22, 5–33,
2013. a
Wei, J., Dirmeyer, P. A., Wisser, D., Bosilovich, M. G., and Mocko, D. M.:
Where Does the Irrigation Water Go? An Estimate of the Contribution of
Irrigation to Precipitation Using MERRA, J. Hydrometeorol., 14,
275–289, https://doi.org/10.1175/jhm-d-12-079.1, 2013. a, b
Xie, P., Chen, M., and Shi, W.: CPC unified gauge-based analysis of global
daily precipitation, in: Preprints, 24th Conf. on Hydrology, Atlanta, GA,
Amer. Meteor. Soc, vol. 2,
available at: https://ams.confex.com/ams/90annual/techprogram/paper_163676.htm (last access: 5 June 2018), 2010. a
Short summary
About 70 % of global freshwater is consumed by irrigation. Yet, policy-relevant estimates of irrigation water use (IWU) are virtually lacking at regional to global scales. To bridge this gap, we develop a method for quantifying IWU from a combination of state-of-the-art remotely sensed and modeled soil moisture products and apply it over the United States for the period 2013–2016. Overall, our estimates agree well with reference data on irrigated area and irrigation water withdrawals.
About 70 % of global freshwater is consumed by irrigation. Yet, policy-relevant estimates of...
