Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-169-2023
© Author(s) 2023. 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-27-169-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jan 2023
Research article |
| 10 Jan 2023
| 10 Jan 2023
SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation
Kunlong He
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences, Chengdu 610299, China
School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences, Chengdu 610299, China
Luca Brocca
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Pere Quintana-Seguí
Ebro Observatory (OE), Ramon Llull University – CSIC, Roquetes,
Spain
Related authors
No articles found.
Julia Pfeffer, Benoît Meyssignac, Rory Bingham, Alejandro Blazquez, Marie Bouih, Carla Braitenberg, Luca Brocca, Henryk Dobslaw, Ramiro Ferrari, Ehsan Forootan, Helena Gerdener, Muhammad Tahir Javed, Laura Jensen, Volker Klemann, Anna Kremer, Jürgen Kusche, Gilles Larnicol, Muhammad Usman Liaqat, Francesco Leopardi, Elisavet-Maria Mamagiannou, Gerardo Maurizio, Roland Pail, Isabelle Panet, Thomas Papanikolaou, Peyman Saemian, Ingo Sasgen, Maike Schumacher, Marius Schlaak, Linus Shihora, Alireza Sobouti, Nico Sneeuw, Mohammad Javad Tourian, Dimitrios Tsoulis, Georgios Vergos, Bert Wouters, Fan Yang, and Ilias Daras
EGUsphere, https://doi.org/10.5194/egusphere-2026-4642, https://doi.org/10.5194/egusphere-2026-4642, 2026
This preprint is open for discussion and under review for Earth Observation (EO).
Short summary
Short summary
The ESA SING project demonstrates how the future satellite gravity missions NGGM and MAGIC will improve observations of continental water storage, oceans, glaciers, sea level, earthquakes, and climate change. Their more accurate, higher-resolution gravity measurements will enhance Earth system monitoring, improve climate and hazard assessments, and strengthen operational services for water management, disaster preparedness, and environmental decision-making.
Muhammad Usman Liaqat, Stefania Camici, Francesco Leopardi, Jaime Gaona, and Luca Brocca
Hydrol. Earth Syst. Sci., 30, 4969–4983, https://doi.org/10.5194/hess-30-4969-2026, https://doi.org/10.5194/hess-30-4969-2026, 2026
Short summary
Short summary
Tracking land water storage helps examine extreme events and manage water resources. GRACE (Gravity Recovery and Climate Experiment) missions observe changes, but coarse resolution in space and time challenges effective management. New gravity mission called MAGIC (Mass Change And Geosciences International Constellation) can offers better accuracy. This study tested MAGIC’s potential to estimate precipitation using the SM2RAIN approach. Results show SM2RAIN works well with frequent, accurate data but deteriorates with noisy/sparse data, emphasizing the need for precise gravity missions.
Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Gabrielle De Lannoy, Pia Langhans, Sara Modanesi, Christian Massari, Luca Brocca, Carla Saltalippi, Renato Morbidelli, Clément Albergel, and Wouter Dorigo
Earth Syst. Sci. Data, 18, 4833–4853, https://doi.org/10.5194/essd-18-4833-2026, https://doi.org/10.5194/essd-18-4833-2026, 2026
Short summary
Short summary
We developed a long-term dataset collection of irrigation water use based on about two decades of satellite observations, three distinct approaches, and many input datasets. The collection provides monthly estimates for major agricultural regions and helps describe how irrigation varies across locations, seasons, and years. It offers a foundation for improving how irrigation is quantified, compared across methods, and integrated into large-scale hydrological and climate studies.
Francesco Avanzi, Hans Lievens, Michael Matiu, Paolo Filippucci, Oscar M. Baez Villanueva, Simone Gabellani, Fabio Delogu, Lorenzo Alfieri, Andrea Libertino, Pere Quintana-Seguì, Diego G. Miralles, Luca Brocca, Christian Massari, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2026-2851, https://doi.org/10.5194/egusphere-2026-2851, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We developed a new system to map and monitor snow and stored water across large mountain regions using satellite data and computer simulations. Tested in four major European river basins, the system reproduced snow conditions with high accuracy and realistically captured how snow changes with elevation. Because it does not rely on ground measurements, it can help provide consistent information on snow-water resources worldwide, supporting water management and climate adaptation.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, JongCheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Jackson Tan, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 30, 3399–3423, https://doi.org/10.5194/hess-30-3399-2026, https://doi.org/10.5194/hess-30-3399-2026, 2026
Short summary
Short summary
Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Peyman Afrasiabikia, Atefeh Parvaresh Rizi, and Luca Brocca
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W8-2025, 33–38, https://doi.org/10.5194/isprs-annals-X-4-W8-2025-33-2026, https://doi.org/10.5194/isprs-annals-X-4-W8-2025-33-2026, 2026
Ehsan Modiri, Oldrich Rakovec, Pallav Kumar Shrestha, Almudena García-García, Leandro Avila, Katie Blackford, Elizabeth Cooper, Bram Droppers, Paolo Filippucci, Milan Fischer, Matěj Orság, Pietro Stradiotti, Luca Brocca, Douglas B. Clark, Wouter Dorigo, Stefan Kollet, Jian Peng, Niko Wanders, and Luis Samaniego
EGUsphere, https://doi.org/10.5194/egusphere-2026-1012, https://doi.org/10.5194/egusphere-2026-1012, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Drought impacts water supply, agriculture, and ecosystems, yet hydrological models often disagree on when and where drought occurs. This study tested whether satellite observations can improve how models represent soil moisture drought in the Rhine River basin. Using several models and major drought events, we show that satellite data improve spatial realism and reveal important differences among models, helping to better understand uncertainty in drought monitoring and early warning.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, https://doi.org/10.5194/essd-17-5221-2025, 2025
Short summary
Short summary
Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2022. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
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
Short summary
Short summary
Soil moisture is crucial for the water cycle since it is at the front line of drought. Satellite, model and in situ data help identify soil moisture stress but are challenged by data uncertainties. This study evaluates trends and data coherence of common active/passive microwave sensors and model-based soil moisture data against in situ stations across Europe from 2007 to 2022. Data reliability is increasing, but combining data types improves soil moisture monitoring capabilities.
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
Short summary
Short summary
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.
Pierre Laluet, Luis Olivera-Guerra, Víctor Altés, Vincent Rivalland, Alexis Jeantet, Julien Tournebize, Omar Cenobio-Cruz, Anaïs Barella-Ortiz, Pere Quintana-Seguí, Josep Maria Villar, and Olivier Merlin
Hydrol. Earth Syst. Sci., 28, 3695–3716, https://doi.org/10.5194/hess-28-3695-2024, https://doi.org/10.5194/hess-28-3695-2024, 2024
Short summary
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
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
Short summary
Short summary
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.
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-426, https://doi.org/10.5194/essd-2022-426, 2023
Preprint withdrawn
Short summary
Short summary
A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
Jaime Gaona, Pere Quintana-Seguí, María José Escorihuela, Aaron Boone, and María Carmen Llasat
Nat. Hazards Earth Syst. Sci., 22, 3461–3485, https://doi.org/10.5194/nhess-22-3461-2022, https://doi.org/10.5194/nhess-22-3461-2022, 2022
Short summary
Short summary
Droughts represent a particularly complex natural hazard and require explorations of their multiple causes. Part of the complexity has roots in the interaction between the continuous changes in and deviation from normal conditions of the atmosphere and the land surface. The exchange between the atmospheric and surface conditions defines feedback towards dry or wet conditions. In semi-arid environments, energy seems to exceed water in its impact over the evolution of conditions, favoring drought.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter M. Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-137, https://doi.org/10.5194/essd-2022-137, 2022
Preprint withdrawn
Short summary
Short summary
A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
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
Short summary
Short summary
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.
Yves Tramblay and Pere Quintana Seguí
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, https://doi.org/10.5194/nhess-22-1325-2022, https://doi.org/10.5194/nhess-22-1325-2022, 2022
Short summary
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
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
Short summary
Short summary
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
Short summary
Short summary
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
Abdollahipour, A., Ahmadi, H., and Aminnejad, B.: A review of downscaling
methods of satellite-based precipitation estimates, Earth. Sci. Inform., 15, 1–20, https://doi.org/10.1007/s12145-021-00669-4, 2021.
Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Beck, H. E., McNamara, I.,
Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J. D., and
Thinh, N. X.: RF-MEP: A novel Random Forest method for merging gridded
precipitation products and ground-based measurements, Remote Sens. Environ.,
239, 111606, https://doi.org/10.1016/j.rse.2019.111606, 2020.
Bezak, N., Borrelli, P., and Panagos, P.: Exploring the possible role of
satellite-based rainfall data in estimating inter- and intra-annual global
rainfall erosivity, Hydrol. Earth Syst. Sci., 26, 1907–1924, https://doi.org/10.5194/hess-26-1907-2022, 2022.
Birtwistle, A. N., Laituri, M., Bledsoe, B., and Friedman, J. M.: Using NDVI
to measure precipitation in semi-arid landscapes, J. Arid Environ., 131, 15–24, https://doi.org/10.1016/j.jaridenv.2016.04.004, 2016.
Brocca, L., Moramarco, T., Melone, F., and Wagner, W.: A new method for
rainfall estimation through soil moisture observations, Geophys. Res. Lett.,
40, 853–858, https://doi.org/10.1002/grl.50173, 2013.
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural
rain gauge: Estimating global rainfall from satellite soil moisture data, J.
Geophys. Res.-Atmos., 119, 5128–5141, https://doi.org/10.1002/2014JD021489, 2014.
Brocca, L., Massari, C., Ciabatta, L., Moramarco, T., Penna, D., Zuecco, G.,
Pianezzola, L., Borga, M., Matgen, P., and Martínez-Fernández, J.:
Rainfall estimation from in situ soil moisture observations at several sites
in Europe: an evaluation of the SM2RAIN algorithm, J. Hydrol. Hydromech., 63, 201–209, https://doi.org/10.1515/johh-2015-0016, 2015.
Brocca, L., Pellarin, T., Crow, W. T., Ciabatta, L., Massari, C., Ryu, D.,
Su, C. H., Rüdiger, C., and Kerr, Y.: Rainfall estimation by inverting
SMOS soil moisture estimates: A comparison of different methods over Australia, J. Geophys. Res.-Atmos., 121, 12062–12079, https://doi.org/10.1002/2016JD025382, 2016.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S.,
Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018):
global daily satellite rainfall data from ASCAT soil moisture observations,
Earth Syst. Sci. Data., 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019.
Carpintero, E., Mateos, L., Andreu, A., and González-Dugo, M. P.: Effect
of the differences in spectral response of Mediterranean tree canopies on
the estimation of evapotranspiration using vegetation index-based crop
coefficients, Agr. Water Manage., 238, 106201, https://doi.org/10.1016/j.agwat.2020.106201, 2020.
Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., and Yu, Z.: Geographically
weighted regression based methods for merging satellite and gauge
precipitation, J. Hydrol., 558, 275–289, https://doi.org/10.1016/j.jhydrol.2018.01.042, 2018.
Chen, F., Crow, W., and Holmes, T. R.: Improving long-term, retrospective
precipitation datasets using satellite-based surface soil moisture retrievals and the soil moisture analysis rainfall tool, J. Appl. Remote Sens., 6, 063604, https://doi.org/10.1117/1.JRS.6.063604, 2012.
Chen, S., Xiong, L., Ma, Q., Kim, J.-S., Chen, J., and Xu, C.-Y.: Improving
daily spatial precipitation estimates by merging gauge observation with
multiple satellite-based precipitation products based on the geographically
weighted ridge regression method, J. Hydrol., 589, 125156, https://doi.org/10.1016/j.jhydrol.2020.125156, 2020.
Chen, Y., Huang, J., Sheng, S., Mansaray, L. R., Liu, Z., Wu, H., and Wang,
X.: A new downscaling-integration framework for high-resolution monthly
precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data, Remote Sens. Environ., 214, 154–172, https://doi.org/10.1016/j.rse.2018.05.021, 2018.
Ciabatta, L., Marra, A. C., Panegrossi, G., Casella, D., Sanò, P., Dietrich, S., Massari, C., and Brocca, L.: Daily precipitation estimation
through different microwave sensors: Verification study over Italy, J. Hydrol., 545, 436–450, https://doi.org/10.1016/j.jhydrol.2016.12.057, 2017.
Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S.,
Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global
long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst.
Sci. Data., 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, 2018.
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S.,
Cosh, M., Dunbar, R., Dang, L., and Pashaian, L.: Validation of SMAP surface
soil moisture products with core validation sites, Remote Sens. Environ., 191, 215–231, https://doi.org/10.1016/j.rse.2017.01.021, 2017.
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.
Duan, Z. and Bastiaanssen, W.: First results from Version 7 TRMM 3B43
precipitation product in combination with a new downscaling–calibration
procedure, Remote Sens. Environ., 131, 1–13, https://doi.org/10.1016/j.rse.2012.12.002, 2013.
Ebrahimy, H. and Azadbakht, M.: Downscaling MODIS land surface temperature
over a heterogeneous area: An investigation of machine learning techniques,
feature selection, and impacts of mixed pixels, Comput. Geosci.-UK, 124,
93–102, https://doi.org/10.1016/j.cageo.2019.01.004, 2019.
Famiglietti, J. S. and Wood, E. F.: Multiscale modeling of spatially variable water and energy balance processes, Water Resour. Res., 30, 3061–3078, https://doi.org/10.1029/94WR01498, 1994.
Fan, Y., Ma, Z., Ma, Y., Ma, W., Xie, Z., Ding, L., Han, Y., Hu, W., and Su,
R.: Respective Advantages of “Top-Down” Based GPM IMERG and “Bottom-Up”
Based SM2RAIN-ASCAT Precipitation Products Over the Tibetan Plateau, J.
Geophys. Res.-Atmos., 126, e2020JD033946, https://doi.org/10.1029/2020JD033946, 2021.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.:
Evolution of the ESA CCI Soil Moisture climate data records and their
underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739,
https://doi.org/10.5194/essd-11-717-2019, 2019.
Guo, X., Guo, Cui, P., Chen, X., Li, Y., Zhang, J., and Sun, Y.: Spatial
uncertainty of rainfall and its impact on hydrological hazard forecasting in
a small semiarid mountainous watershed, J. Hydrol., 595, 126049, https://doi.org/10.1016/j.jhydrol.2021.126049, 2021.
Haylock, M. R., Cawley, G. C., Harpham, C., Wilby, R. L., and Goodess, C. M.: Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios, Int. J. Climatol., 26, 1397–1415, https://doi.org/10.1002/joc.1318, 2006.
He, K.: High-resolution soil moisture data (1 km) [Data set]. In Hydrology and Earth System Sciences (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.7451422, 2022.
He, X., Chaney, N. W., Schleiss, M., and Sheffield, J.: Spatial downscaling
of precipitation using adaptable random forests, Water Resour. Res., 52,
8217–8237, https://doi.org/10.1002/2016WR019034, 2016.
Hong, Z., Han, Z., Li, X., Long, D., Tang, G., and Wang, J.: Generation of
an improved precipitation dataset from multisource information over the
Tibetan Plateau, J. Hydrometeorol., 22, 1275–1295, https://doi.org/10.1175/JHM-D-20-0252.1, 2021.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation
Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014.
Huffman, G. J., Adler, R. F., Arkin, P., Chang, A., Ferraro, R., Gruber, A.,
Janowiak, J., McNab, A., Rudolf, B., and Schneider, U.: The global precipitation climatology project (GPCP) combined precipitation dataset, B.
Am. Meteorol. Soc., 78, 5–20, https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2, 1997.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P.,
and Yoo, S.-H.: NASA global precipitation measurement (GPM) integrated
multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis
Document (ATBD) Version 4, National Aeronautics and Space Administration, 26 pp., https://gpm.nasa.gov/resources/documents/algorithm-information/IMERG-V06-ATBD (last access: 9 January 2023), 2020a.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J.,
Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., and Tan, J.:
Integrated multi-satellite retrievals for the Global Precipitation
Measurement (GPM) mission (IMERG), in: Satellite precipitation measurement,
Springer, Cham, 343–353, https://doi.org/10.1007/978-3-030-24568-9_19, 2020b.
Hutengs, C. and Vohland, M.: Downscaling land surface temperatures at regional scales with random forest regression, Remote Sens. Environ., 178,
127–141, https://doi.org/10.1016/j.rse.2016.03.006, 2016.
Immerzeel, W. W., Rutten, M. M., and Droogers, P.: Spatial downscaling of
TRMM precipitation using vegetative response on the Iberian Peninsula, Remote Sens. Environ., 113, 362–370, https://doi.org/10.1016/j.rse.2008.10.004, 2009.
Jadidoleslam, N., Mantilla, R., Krajewski, W. F., and Goska, R.: Investigating the role of antecedent SMAP satellite soil moisture, radar rainfall and MODIS vegetation on runoff production in an agricultural
region, J. Hydrol., 579, 124210, https://doi.org/10.1016/j.jhydrol.2019.124210, 2019.
Jia, S., Zhu, W., Lű, A., and Yan, T.: A statistical spatial downscaling
algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of
China, Remote Sens. Environ., 115, 3069–3079, https://doi.org/10.1016/j.rse.2011.06.009, 2011.
Jing, W., Yang, Y., Yue, X., and Zhao, X.: A Spatial Downscaling Algorithm
for Satellite-Based Precipitation over the Tibetan Plateau Based on NDVI,
DEM, and Land Surface Temperature, Remote Sens., 8, 655, https://doi.org/10.3390/rs8080655, 2016a.
Jing, W., Yang, Y., Yue, X., and Zhao, X.: A Comparison of Different
Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation
over North China, Remote Sens., 8, 1–17, https://doi.org/10.3390/rs8100835, 2016b.
Jing, Y., Lin, L., Li, X., Li, T., and Shen, H.: An attention mechanism
based convolutional network for satellite precipitation downscaling over China, J. Hydrol., 613, 128388, https://doi.org/10.1016/j.jhydrol.2022.128388, 2022.
Joiner, J., Yoshida, Y., Anderson, M., Holmes, T., Hain, C., Reichle, R.,
Koster, R., Middleton, E., and Zeng, F.-W.: Global relationships among
traditional reflectance vegetation indices (NDVI and NDII),
evapotranspiration (ET), and soil moisture variability on weekly timescales,
Remote Sens. Environ., 219, 339–352, https://doi.org/10.1016/j.rse.2018.10.020, 2018.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method
that produces global precipitation estimates from passive microwave and
infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5,
487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Kubota, T., Shige, S., Hashizume, H., Aonashi, K., Takahashi, N., Seto, S.,
Hirose, M., Takayabu, Y. N., Ushio, T., and Nakagawa, K.: Global
precipitation map using satellite-borne microwave radiometers by the GSMaP
project: Production and validation, IEEE T. Geosci. Remote, 45, 2259–2275, https://doi.org/10.1109/TGRS.2007.895337, 2007.
Li, X. and Long, D.: An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach, Remote Sens. Environ., 248, 111966, https://doi.org/10.1016/j.rse.2020.111966, 2020.
Lin, A. and Wang, X. L.: An algorithm for blending multiple satellite precipitation estimates with in situ precipitation measurements in Canada,
J. Geophys. Res.-Atmos., 116, D21111, https://doi.org/10.1029/2011JD016359, 2011.
Long, D., Bai, L., Yan, L., Zhang, C., Yang, W., Lei, H., Quan, J., Meng,
X., and Shi, C.: Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution, Remote Sens. Environ., 233, 111364, https://doi.org/10.1016/j.rse.2019.111364, 2019.
Long, Y., Zhang, Y., and Ma, Q.: A Merging Framework for Rainfall Estimation
at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a
Data-Scarce Area, Remote Sens., 8, 599, https://doi.org/10.3390/rs8070599, 2016.
Lu, X., Tang, G., Wang, X., Liu, Y., Jia, L., Xie, G., Li, S., and Zhang, Y.: Correcting GPM IMERG precipitation data over the Tianshan Mountains in China, J. Hydrol., 575, 1239–1252, https://doi.org/10.1016/j.jhydrol.2019.06.019, 2019.
Lu, X., Chen, Y., Tang, G., Wang, X., Liu, Y., and Wei, M.: Quantitative
estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging, J. Mt.
Sci., 19, 58–72, https://doi.org/10.1007/s11629-021-6901-5, 2022.
Ma, Y., Sun, X., Chen, H., Hong, Y., and Zhang, Y.: A two-stage blending
approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau, Hydrol. Earth Syst. Sci., 25, 359–374, https://doi.org/10.5194/hess-25-359-2021, 2021.
Ma, Z., Zhou, Y., Hu, B., Liang, Z., and Shi, Z.: Downscaling annual
precipitation with TMPA and land surface characteristics in China, Int. J.
Climatol., 37, 5107–5119, https://doi.org/10.1002/joc.5148, 2017a.
Ma, Z., Shi, Z., Zhou, Y., Xu, J., Yu, W., and Yang, Y.: A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet
Plateau with the effects of systematic anomalies removed, Remote Sens. Environ., 200, 378–395, https://doi.org/10.1016/j.rse.2017.08.023, 2017b.
Ma, Z., He, K., Tan, X., Liu, Y., Lu, H., and Shi, Z.: A new approach for
obtaining precipitation estimates with a finer spatial resolution on a daily
scale based on TMPA V7 data over the Tibetan Plateau, Int. J. Remote Sens.,
40, 8465–8483, https://doi.org/10.1080/01431161.2019.1612118, 2019a.
Ma, Z., Ghent, D., Tan, X., He, K., Li, H., Han, X., Huang, Q., and Peng, J.: Long-Term Precipitation Estimates Generated by a Downscaling-Calibration Procedure Over the Tibetan Plateau From 1983 to 2015, Earth Space Sci., 6,
2180–2199, https://doi.org/10.1029/2019EA000657, 2019b.
Ma, Z., Xu, J., He, K., Han, X., Ji, Q., Wang, T., Xiong, W., and Hong, Y.:
An updated moving window algorithm for hourly-scale satellite precipitation
downscaling: A case study in the Southeast Coast of China, J. Hydrol., 581,
124378, https://doi.org/10.1016/j.jhydrol.2019.124378, 2020a.
Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., and Hong, Y.: AIMERG: a new Asian precipitation dataset (0.1∘/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE, Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, 2020b.
Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., and Xu, X.:
AERA5-Asia: A Long-Term Asian Precipitation Dataset (0.1∘, 1-hourly, 1951–2015, Asia) Anchoring the ERA5-Land under the Total Volume Control by APHRODITE, B. Am. Meteorol. Soc., 103, E1146–E1171, https://doi.org/10.1175/BAMS-D-20-0328.1, 2022.
Mao, Y., Crow, W. T., and Nijssen, B.: A Framework for Diagnosing Factors
Degrading the Streamflow Performance of a Soil Moisture Data Assimilation
System, J. Hydrometeorol., 20, 79–97, https://doi.org/10.1175/JHM-D-18-0115.1, 2019.
Maraun, D., Wetterhall, F., Ireson, A., Chandler, R., Kendon, E., Widmann, M., Brienen, S., Rust, H., Sauter, T., and Themeßl, M.: Precipitation
downscaling under climate change: Recent developments to bridge the gap
between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Maselli, F., Chiesi, M., Angeli, L., Fibbi, L., Rapi, B., Romani, M., Sabatini, F., and Battista, P.: An improved NDVI-based method to predict
actual evapotranspiration of irrigated grasses and crops, Agr. Water
Manage., 233, 106077, https://doi.org/10.1016/j.agwat.2020.106077, 2020.
Massari, C., Brocca, L., Moramarco, T., Tramblay, Y., and Didon Lescot, J.-F.: Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall, Adv. Water Resour., 74, 44–53, https://doi.org/10.1016/j.advwatres.2014.08.004, 2014.
McNally, A., Shukla, S., Arsenault, K. R., Wang, S., Peters-Lidard, C. D.,
and Verdin, J. P.: Evaluating ESA CCI soil moisture in East Africa, Int. J.
Appl. Earth Obs. Geoinf., 48, 96–109, https://doi.org/10.1016/j.jag.2016.01.001, 2016.
Mei, Y., Maggioni, V., Houser, P., Xue, Y., and Rouf, T.: A nonparametric
statistical technique for spatial downscaling of precipitation over High
Mountain Asia, Water Resour. Res., 56, e2020WR027472, https://doi.org/10.1029/2020WR027472, 2020.
Merlin, O., Walker, J. P., Chehbouni, A., and Kerr, Y.: Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency, Remote Sens. Environ., 112, 3935–3946, https://doi.org/10.1016/j.rse.2008.06.012, 2008.
Min, X., Ma, Z., Xu, J., He, K., Wang, Z., Huang, Q., and Li, J.: Spatially
Downscaling IMERG at Daily Scale Using Machine Learning Approaches Over Zhejiang, Southeastern China, Front. Earth Sci., 8, 146, https://doi.org/10.3389/feart.2020.00146, 2020.
Mishra, V., Ellenburg, W. L., Griffin, R. E., Mecikalski, J. R., Cruise, J.
F., Hain, C. R., and Anderson, M. C.: An initial assessment of a SMAP soil
moisture disaggregation scheme using TIR surface evaporation data over the
continental United States, Int. J. Appl. Earth Obs. Geoinf., 68, 92–104,
https://doi.org/10.1016/j.jag.2018.02.005, 2018.
Mu, Q., Jones, L. A., Kimball, J. S., McDonald, K. C., and Running, S. W.:
Satellite assessment of land surface evapotranspiration for the pan-Arctic
domain, Water Resour. Res., 45, W09420, https://doi.org/10.1029/2008WR007189, 2009.
Muelchi, R., Rssler, O., Schwanbeck, J., Weingartner, R., and Martius, O.:
An ensemble of daily simulated runoff data (1981–2099) under climate change
conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble),
Geosci. Data. J., 9, 46–57, https://doi.org/10.1002/gdj3.117, 2022.
Munsi, A., Kesarkar, A., Bhate, J., Panchal, A., Singh, K., Kutty, G., and
Giri, R.: Rapidly intensified, long duration North Indian Ocean tropical
cyclones: Mesoscale downscaling and validation, Atmos. Res., 259, 105678,
https://doi.org/10.1016/j.atmosres.2021.105678, 2021.
Nagler, P. L., Cleverly, J., Glenn, E., Lampkin, D., Huete, A., and Wan, Z.:
Predicting riparian evapotranspiration from MODIS vegetation indices and
meteorological data, Remote Sens. Environ., 94, 17–30, https://doi.org/10.1016/j.rse.2004.08.009, 2005a.
Nagler, P. L., Scott, R. L., Westenburg, C., Cleverly, J. R., Glenn, E. P.,
and Huete, A. R.: Evapotranspiration on western U.S. rivers estimated using
the Enhanced Vegetation Index from MODIS and data from eddy covariance and
Bowen ratio flux towers, Remote Sens. Environ., 97, 337–351, https://doi.org/10.1016/j.rse.2005.05.011, 2005b.
Neinavaz, E., Skidmore, A. K., and Darvishzadeh, R.: Effects of prediction
accuracy of the proportion of vegetation cover on land surface emissivity
and temperature using the NDVI threshold method, Int. J. Appl. Earth Obs.
Geoinf., 85, 101984, https://doi.org/10.1016/j.jag.2019.101984, 2020.
NOAA: Global Surface Summary of the Day – GSOD, https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day, last access: 6 January 2023.
Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y.: Mapping Winter Crops Using
a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and
Google Earth Engine, Remote Sens., 13, 2510, https://doi.org/10.3390/rs13132510, 2021.
Peng, J., Loew, A., Zhang, S., Wang, J., and Niesel, J.: Spatial Downscaling
of Satellite Soil Moisture Data Using a Vegetation Temperature Condition
Index, IEEE T. Geosci. Remote, 54, 558–566, https://doi.org/10.1109/TGRS.2015.2462074, 2016.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H.,
Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de
Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H.,
Lovergine, F., Mahecha, M. D., Marzahn, P., Mattia, F., Musial, J. P.,
Preuschmann, S., Reichle, R. H., Satalino, G., Silgram, M., van Bodegom, P.
M., Verhoest, N. E. C., Wagner, W., Walker, J. P., Wegmüller, U., and
Loew, A.: A roadmap for high-resolution satellite soil moisture applications
– confronting product characteristics with user requirements, Remote Sens.
Environ., 252, 112162, https://doi.org/10.1016/j.rse.2020.112162, 2021.
Piles, M., Sanchez, N., Vall-llossera, M., Camps, A., Martinez-Fernandez, J., Martinez, J., and Gonzalez-Gambau, V.: A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 7, 3845–3857, https://doi.org/10.1109/JSTARS.2014.2325398, 2014.
Prakash, S., Mitra, A. K., Pai, D. S., and AghaKouchak, A.: From TRMM to GPM: How well can heavy rainfall be detected from space?, Adv. Water Resour., 88, 1–7, https://doi.org/10.1016/j.advwatres.2015.11.008, 2016.
Quiroz, R., Yarlequé, C., Posadas, A., Mares, V., and Immerzeel, W. W.:
Improving daily rainfall estimation from NDVI using a wavelet transform,
Environ. Model. Softw., 26, 201–209, https://doi.org/10.1016/j.envsoft.2010.07.006, 2011.
Rockel, B.: The regional downscaling approach: a brief history and recent
advances, Curr. Clim. Change. Rep., 1, 22–29, https://doi.org/10.1007/s40641-014-0001-3, 2015.
Rozante, J. R., Gutierrez, E. R., Fernandes, A. d. A., and Vila, D. A.:
Performance of precipitation products obtained from combinations of satellite and surface observations, Int. J. Remote Sens., 41, 7585–7604,
https://doi.org/10.1080/01431161.2020.1763504, 2020.
Sabaghy, S., Walker, J. P., Renzullo, L. J., Akbar, R., Chan, S., Chaubell,
J., Das, N., Dunbar, R. S., Entekhabi, D., Gevaert, A., Jackson, T. J., Loew, A., Merlin, O., Moghaddam, M., Peng, J., Peng, J., Piepmeier, J., Rüdiger, C., Stefan, V., Wu, X., Ye, N., and Yueh, S.: Comprehensive analysis of alternative downscaled soil moisture products, Remote Sens.
Environ., 239, 111586, https://doi.org/10.1016/j.rse.2019.111586, 2020.
Salzmann, M.: Global warming without global mean precipitation increase?, Sci. Adv., 2, e1501572, https://doi.org/10.1126/sciadv.1501572, 2016.
Senanayake, I. P., Yeo, I. Y., Willgoose, G. R., and Hancock, G. R.:
Disaggregating satellite soil moisture products based on soil thermal inertia: A comparison of a downscaling model built at two spatial scales, J.
Hydrol., 594, 125894, https://doi.org/10.1016/j.jhydrol.2020.125894, 2021.
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.
Sheffield, J., Ferguson, C. R., Troy, T. J., Wood, E. F., and McCabe, M. F.:
Closing the terrestrial water budget from satellite remote sensing, Geophys.
Res. Lett., 36, L07403, https://doi.org/10.1029/2009GL037338, 2009.
Shen, Y., Xiong, A., Hong, Y., Yu, J., Pan, Y., Chen, Z., and Saharia, M.:
Uncertainty analysis of five satellite-based precipitation products and
evaluation of three optimally merged multi-algorithm products over the
Tibetan Plateau, Int. J. Climatol., 35, 6843–6858, https://doi.org/10.1080/01431161.2014.960612, 2014.
Song, S., Brocca, L., Wang, W., and Cui, W.: Testing the potential of soil
moisture observations to estimate rainfall in a soil tank experiment, J.
Hydrol., 581, 124368, https://doi.org/10.1016/j.jhydrol.2019.124368, 2020.
Sorooshian, S., Hsu, K.-L., Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D.: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall, B. Am. Meteorol. Soc., 81, 2035–2046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2, 2000.
Spötl, C., Koltai, G., Jarosch, A., and Cheng, H.: Increased autumn and
winter precipitation during the Last Glacial Maximum in the European Alps,
Nat. Commun., 12, 1–9, https://doi.org/10.1038/s41467-021-22090-7, 2021.
Tagesson, T., Horion, S., Nieto, H., Zaldo Fornies, V., Mendiguren González, G., Bulgin, C. E., Ghent, D., and Fensholt, R.: Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters, Remote Sens. Environ., 206, 424–441, https://doi.org/10.1016/j.rse.2017.12.036, 2018.
Tang, G., Behrangi, A., Long, D., Li, C., and Hong, Y.: Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded
precipitation products, J. Hydrol., 559, 294–306, https://doi.org/10.1016/j.jhydrol.2018.02.057, 2018.
Tang, J., Niu, X., Wang, S., Gao, H., Wang, X., and Wu, J.: Statistical
downscaling and dynamical downscaling of regional climate in China: Present
climate evaluations and future climate projections, J. Geophys. Res.-Atmos., 121, 2110–2129, https://doi.org/10.1002/2015JD023977, 2016.
Wackernagel, H.: Ordinary kriging, in: Multivariate geostatistics, Springer,
79–88, https://doi.org/10.1007/978-3-662-03098-1_11, 2003.
Wehbe, Y., Ghebreyesus, D., Temimi, M., Milewski, A., and Al Mandous, A.:
Assessment of the consistency among global precipitation products over the
United Arab Emirates, Hydrol. Reg. Stud., 12, 122–135, https://doi.org/10.1016/j.ejrh.2017.05.002, 2017.
Wehbe, Y., Temimi, M., and Adler, R. F.: Enhancing precipitation estimates
through the fusion of weather radar, satellite retrievals, and surface parameters, Remote Sens., 12, 1342, https://doi.org/10.3390/rs12081342, 2020.
Wei, K., Ouyang, C., Duan, H., Li, Y., Chen, M., Ma, J., An, H., and Zhou, S.: Reflections on the Catastrophic 2020 Yangtze River Basin Flooding in
Southern China, Innovation, 1, 100038, https://doi.org/10.1016/j.xinn.2020.100038, 2020.
Wen, F., Zhao, W., Wang, Q., and Sánchez, N.: A Value-Consistent Method
for Downscaling SMAP Passive Soil Moisture With MODIS Products Using Self-Adaptive Window, IEEE T. Geosci. Remote, 58, 913–924,
https://doi.org/10.1109/TGRS.2019.2941696, 2020.
Xia, T., Wang, Z.-J., and Zheng, H.: Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China, Atmosphere, 6, 983–1005, https://doi.org/10.3390/atmos6080983, 2015.
Xu, J., Ma, Z., Yan, S., and Peng, J.: Do ERA5 and ERA5-land precipitation
estimates outperform satellite-based precipitation products? A comprehensive
comparison between state-of-the-art model-based and satellite-based
precipitation products over mainland China, J. Hydrol., 605, 127353,
https://doi.org/10.1016/j.jhydrol.2021.127353, 2022.
Xu, S., Wu, C., Wang, L., Gonsamo, A., Shen, Y., and Niu, Z.: A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics, Remote Sens. Environ., 162, 119–140, https://doi.org/10.1016/j.rse.2015.02.024, 2015.
Yan, X., Chen, H., Tian, B., Sheng, S., and Kim, J. S.: A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation
Estimates Based on Random Forest and Cokriging, Remote Sens., 13, 2040,
https://doi.org/10.3390/rs13112040, 2021.
Yang, X. and Huang, P.: Restored relationship between ENSO and Indian summer
monsoon rainfall around 1999/2000, Innovation, 2, 100102, https://doi.org/10.1016/j.xinn.2021.100102, 2021.
Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.:
Temporal and spatial evaluation of satellite-based rainfall estimates across
the complex topographical and climatic gradients of Chile, Hydrol. Earth
Syst. Sci., 21, 1295–1320, https://doi.org/10.5194/hess-21-1295-2017, 2017.
Zeng, Z., Chen, H., Shi, Q., and Li, J.: Spatial Downscaling of IMERG
Considering Vegetation Index Based on Adaptive Lag Phase, IEEE T. Geosci. Remote, 60, 4201415, https://doi.org/10.1109/TGRS.2021.3070417, 2021.
Zhan, C., Han, J., Hu, S., Liu, L., and Dong, Y.: Spatial Downscaling of GPM
Annual and Monthly Precipitation Using Regression-Based Algorithms in a
Mountainous Area, Adv. Meteorol., 2018, 1506017, https://doi.org/10.1155/2018/1506017, 2018.
Zhang, H., Ma, J., Chen, C., and Tian, X.: NDVI-Net: A fusion network for
generating high-resolution normalized difference vegetation index in remote
sensing, ISPRS J. Photogram. Remote Sens., 168, 182–196, https://doi.org/10.1016/j.isprsjprs.2020.08.010, 2020.
Zhang, L., Ren, D., Nan, Z., Wang, W., Zhao, Y., Zhao, Y., Ma, Q., and Wu,
X.: Interpolated or satellite-based precipitation? Implications for hydrological modeling in a meso-scale mountainous watershed on the Qinghai-Tibet Plateau, J. Hydrol., 583, 124629, https://doi.org/10.1016/j.jhydrol.2020.124629, 2020.
Zhao, W., Sánchez, N., Lu, H., and Li, A.: A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression, J. Hydrol., 563, 1009–1024, https://doi.org/10.1016/j.jhydrol.2018.06.081, 2018.
Zhao, W., Wen, F., Wang, Q., Sanchez, N., and Piles, M.: Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products, J. Hydrol., 603, 126930, https://doi.org/10.1016/j.jhydrol.2021.126930, 2021.
Short summary
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.
In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for...
