Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
Abstract
:1. Introduction
2. The Water Cycle
2.1. Observations
- Mediterranean basin. In depth studies would be beneficial for filling the gaps in our understanding of the common characteristics of Mediterranean-type climates around the world and their variability and change [56]. Specifically, observational datasets [49,50] are providing new insights on long-term changes in the Mediterranean basin, in support of model projections predicting increasing temperatures and decreasing evapotranspiration and precipitation over the region by the middle of this century [57,58]. The most recent datasets are contributing to addressing the contribution of the Mediterranean Sea to climatological precipitation on one side, and extreme precipitation on the other [59].
- Arctic. Gaps in observations are particularly evident in the Arctic, where rapid changes in the hydrological cycle challenge our process understanding. Observations show that runoff is systematically larger (smaller) than precipitation increases (decreases), and thus, that quality observations need to resolve changes in evapotranspiration, and groundwater and permafrost storage [60]. Enhanced process understanding and modeling capabilities are starting to be able to better quantify the role of the atmosphere in the Arctic water cycle changes [61]. Uncertainties are still high in the determination of the large-scale freshwater cycle because of the sparseness of hydrographic data and insufficient information on sea-ice volume [62], as well as inadequate monitoring of precipitation, evapotranspiration, and river discharge fluxes [60,63]. Coordinated efforts in monitoring, modeling, and process studies on various scales are thus desirable at the interface between hydrology, atmosphere, ecology, resources, and oceans [64].
- High mountains. The melting of glaciers, and consequent intensification of the water cycle with greening ecosystems and increasing frequency of hazards, is closely linked to recent warming, especially over the Asian Third Pole, requiring investigations of every major component in the system, especially through improved observations [65]. Recent research efforts have attempted to evaluate the uncertainty of terrestrial water budget components over High Mountain Asia, which is significantly impacted by the uncertainty on the driving meteorology [66], and is of the utmost importance for the assimilation of the frozen components in land surface models [67].
2.2. Modeling the Processes
3. Satellite Measurement of Precipitation
- Raingauges are not evenly distributed, and cover a very limited portion of the Earth [100]. However, global gridded products are available from a variety of sources, such as, for example, the GPCC [1], the Global Historical Climatology Network (GHCN, [101]), and the recent Rainfall Estimates on a Gridded Network (REGEN, [102]).
- Radar networks are generally deployed by developed countries (http://wrd.mgm.gov.tr/default.aspx?l=en, last accessed 21 August 2019). Datasets for water cycle studies are becoming available over limited areas, such as the Multi-Radar/Multi-Sensor System (MRMS; https://www.nssl.noaa.gov/projects/mrms/, last accessed 24 September 2019) developed by the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) [103], and the Nimrod data system for UK and Western Europe (https://catalogue.ceda.ac.uk/uuid/82adec1f896af6169112d09cc1174499, last accessed 25 September 2019) developed by the UK Met Office.
- Oceans are not fully covered, apart from scattered ship observations, buoys, and radars on small islands which have been made available through the International Comprehensive Ocean-Atmosphere Data Set (ICOADS, [104]), the Global Summary of the Day (GSOD, [105]), the Pacific Rainfall Database (PACRAIN, [106,107]), and ship-based measurement campaigns, such as the Ocean Rain And Ice-phase precipitation measurement Network (OceanRAIN, [108]).
3.1. Science and Technology Advances
3.1.1. Synergy of Sensors for Precipitation Estimates
3.1.2. Precipitation Products
3.1.3. Smallsat Sensor Constellations
3.1.4. Evolution of Heritage Missions
3.1.5. Observing Precipitation through Other Water Cycle Components
3.1.6. Future Observations of the Water Cycle as a Whole
3.2. Scientific and Technological Challenges
3.2.1. Observational Grand Challenges
3.2.2. Observing Snow and Ice
3.2.3. Land Surface Emission
3.2.4. Precipitation over the Ocean
3.2.5. Orographic Enhancement of Precipitation
3.2.6. Observing Extremes
4. Applications Related to the Water Cycle
4.1. Assimilation and Validation in NWP Models
4.2. Nowcasting
4.3. Analysis of Precipitation Climatological Patterns
4.4. Hydrology and Water Management
4.5. Hydrogeology
4.6. Food Security
4.7. Public Health
5. Outlook
- the better quantification of high-latitude precipitation including snowfall;
- the improved accuracy in precipitation detection and intensity retrievals;
- the definition of error models for each satellite product;
- the creation of multi-satellite and multi-source global precipitation products.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Acronyms
AR | Atmospheric River |
AR4 | IPCC 4th Assessment Report |
AR5 | IPCC 5th Assessment Report |
ARC2 | Africa Rainfall Climatology 2.0 |
ASCAT | Advanced SCATterometer |
CALIPSO | Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations |
CAMELS-CL | Catchment Attributes and MEteorology for Large-sample Studies-Chile |
CC | Clausius-Clapeyron temperature scaling |
CCI | Climate Change Initiative |
CDR | Climate Data Record |
CGMS | Coordination Group for Meteorological Satellites |
CHIRPS | Climate Hazards Center’s Infrared Precipitation with Stations |
CHIRTS | Climate Hazards Center Infrared Temperature with Stations |
CIMR | Copernicus Imaging Microwave Radiometry |
CMAP | CPC Merged Analysis of Precipitation |
CMIP5 | Coupled Model Intercomparison Project phase 5 |
CMORPH | CPC MORPHing technique |
CPC | Climate Prediction Center |
CPP | Cloud and Precipitation Process mission |
DDWW | Dry regions to become Drier and Wet regions to become Wetter paradigm |
DFPSCAT | Dual-Frequency Polarized SCATterometer |
DNN | Deep Neural Networks |
DOLCE | Derived Optimal Linear Combination Evapotranspiration |
DPR | Dual-frequency Precipitation Radar |
EarthCARE | Earth Clouds, Aerosol and Radiation Explorer |
EC | European Commission |
ECE | Extreme Climatic Event |
ECMWF | European Centre for Medium-range Weather Forecasts |
ECV | Essential Climate Variable |
EDO | European Drought Observatory |
ENACTS | Enhancing National Climate Services |
EPE | Extreme Precipitation Event |
EPS-SG | EUMETSAT Polar System-Second Generation |
ESA | European Space Agency |
eTRaP | ensemble Tropical Rainfall Potential |
EU | European Union |
EUMETSAT | European Organization for the Exploitation of Meteorological Satellites |
FEWS NET | Famine Early Warning System NETwork |
FPIR | Fully-Polarized Interferometric synthetic aperture microwave Radiometer |
GCOS | Global Climate Observing System |
GEO | Geosynchronous Earth Orbit |
GHCN | Global Historical Climatology Network |
GLACE | Global Land-Atmosphere Climate Experiment |
GLC | Global Landslide Catalog |
GLDAS | Global Land Data Assimilation System |
GLEAM | Global Land Evaporation Amsterdam Model |
GMI | GPM Microwave Imager |
GOOS | Global Ocean Observing System |
GPCC | Global Precipitation Climatology Center |
GPCP | Global Precipitation Climatology Project |
GPM | Global Precipitation Measurement mission |
GRACE | Gravity Recovery and Climate Experiment |
GSMaP | Global Satellite Map** of Precipitation |
GSOD | Global Summary of the Day |
H-E | Hydro-Estimator |
ICI | Ice Cloud Imager |
ICOADS | International Comprehensive Ocean-Atmosphere Data Set |
IDF | Intensity-Duration-Frequency curves |
IMERG | Integrated Multi-satellitE Retrievals for GPM |
IPCC | International Panel on Climate Change |
IPWG | International Precipitation Working Group |
IR | InfraRed |
IRI | International Research Institute for Climate and Society |
IWSSM | International Workshop on Space-based Snowfall Measurement |
JMA | Japan Meteorological Agency |
JPI | Joint Programming Initiative |
JPL | Jet Propulsion Laboratory |
LEO | Low Earth Orbit |
LORA | Linear Optimal Runoff Aggregate |
LSE | Land Surface Emissivity |
MiRS | Microwave Integrated Retrieval System |
MRMS | Multi-Radar/Multi-Sensor System |
MSWEP | Multi-Source Weighted-Ensemble Precipitation |
MW | MicroWave |
NASA | National Aeronautics and Space Administration |
NCEP | National Centers for Environmental Prediction |
NHyFAS | NASA Hydrological Forecasting and Analysis System |
NOAA | National Oceanic and Atmospheric Administration |
NSSL | National Severe Storms Laboratory |
NWP | Numerical Weather Prediction |
NWS | National Weather Service |
OceanRAIN | Ocean Rainfall And Ice-phase precipitation measurement Network |
OLR | Outgoing Longwave Radiation |
OSCAR | Observing Systems Capability Analysis and Review |
PACRAIN | Pacific Rainfall Database |
PERSIANN | Precipitation Estimation from Remotely Sensed Information using Artificial Neural |
Networks | |
PMI | Polarized Microwave radiometric Imager |
PMW | Passive MW |
PR | Precipitation Radar |
QPE | Quantitative Precipitation Estimates |
REGEN | Rainfall Estimates on a Gridded Network |
SCaMPR | Self-Calibrating Multivariate Precipitation Retrieval |
SMAP | Soil Moisture Active Passive |
SMOS | Soil Moisture Ocean Salinity |
SM2RAIN | Soil Moisture to Rain method |
SPoRT | Short-term Prediction Research and Transition |
SWOT | Surface Water and Ocean Topography mission |
SWR | Short Wave Radiation |
TAMSAT | Tropical Applications of Meteorology using SATellite data and ground-based observations |
TAPEER | Tropical Amount of Rainfall with Estimation of Errors |
TC | Tropical Cyclone |
TELSEM | Tool to Estimate Land-Surface Emissivities at Microwave frequencies |
TEMPEST | Temporal Experiment for Storms and Tropical Systems |
TMPA | TRMM Multi-satellite Precipitation Analysis |
TRMM | Tropical Rainfall Measuring Mission |
TROPICS | Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats |
VIS | Visible |
WCOM | Water Cycle Observation Mission |
WCRP | World Climate Research Program |
WIVERN | Wind Velocity Radar Nephoscope |
WMO | World Meteorological Organization |
20CR | Twentieth Century Reanalysis |
References
- Schneider, U.; Finger, P.; Meyer-Christoffer, A.; Rustemeier, E.; Ziese, M.; Becker, A. Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC). Atmosphere 2017, 8, 52. [Google Scholar] [CrossRef]
- Oki, T.; Kanae, S. Global hydrological cycles and world water resources. Science 2006, 313, 1068–1072. [Google Scholar] [CrossRef] [PubMed]
- Trenberth, K.E.; Smith, L.; Qian, T.; Dai, A.; Fasullo, J. Estimates of the global water budget and its annual cycle using observational and model data. J. Hydrometeorol. 2007, 8, 758–769. [Google Scholar] [CrossRef]
- Abbott, B.W.; Bishop, K.; Zarnetske, J.P.; Hannah, D.M.; Frei, R.J.; Minaudo, C.; Chapin, F.S., III; Krause, S.; Conner, L.; Ellison, D.; et al. A water cycle for the Anthropocene. Hydrol. Proc. 2019. [Google Scholar] [CrossRef]
- Denman, K.L.; Brasseur, G.; Chidthaisong, A.; Ciais, P.; Cox, P.M.; Dickinson, R.E.; Hauglustaine, D.; Heinze, C.; Holland, E.; Jacob, D.; et al. Couplings Between Changes in the Climate System and Biogeochemistry. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., Eds.; Cambridge Univ. Press: Cambridge, UK; New York, NY, USA, 2007; Available online: https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter7-1.pdf (accessed on 28 August 2019).
- Trenberth, K.E.; Fasullo, J.T.; Kiehl, J. Earth’s global energy budget. Bull. Am. Meteorol. Soc. 2009, 90, 311–324. [Google Scholar] [CrossRef]
- Loeb, N.G.; Wielicki, B.A.; Doelling, D.R.; Smith, G.L.; Keyes, D.F.; Kato, S.; Manalo-Smith, N.; Wong, T. Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Clim. 2009, 22, 748–766. [Google Scholar] [CrossRef]
- Siler, N.; Roe, G.H.; Armour, K.C.; Feldl, N. Revisiting the surface-energy-flux perspective on the sensitivity of global precipitation to climate change. Clim. Dyn. 2019, 53, 3983. [Google Scholar] [CrossRef]
- Ramanathan, V.; Crutzen, P.J.; Kiehl, J.T.; Rosenfeld, D. Aerosols, climate and the hydrological cycle. Science 2001, 294, 2119–2124. [Google Scholar] [CrossRef] [PubMed]
- Mercado-Bettín, D.; Salazar, J.F.; Villegas, J.C. Long-term water balance partitioning explained by physical and ecological characteristics in world river basins. Echohydrolgy 2019, 12, 2072. [Google Scholar] [CrossRef]
- Vergopolan, N.; Fisher, J.B. The impact of deforestation on the hydrological cycle in Amazonia as observed from remote sensing. Int. J. Remote Sens. 2016, 37, 5412–5430. [Google Scholar] [CrossRef]
- Ciemer, C.; Boers, N.; Hirota, M.; Kurths, J.; Müller-Hansen, F.; Oliveira, R.S.; Winkelmann, R. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 2019, 12, 174–179. [Google Scholar] [CrossRef]
- Bonnesoeur, V.; Locatelli, B.; Guariguata, M.R.; Ochoa-Tocachi, B.F.; Vanacker, V.; Mao, Z.; Stokes, A.; Mathez-Stiefel, S.-L. Impacts of forests and forestation on hydrological services in the Andes: A systematic review. For. Ecol. Manag. 2019, 433, 569–584. [Google Scholar] [CrossRef]
- Ellison, D. From Myth to Concept and Beyond–The BioGeoPhysical Revolution and the Forest-Water Paradigm; UNFF 13; UN: Geneva, Switzerland, 2018; p. 45. [Google Scholar] [CrossRef]
- Ellison, D.; Morris, C.E.; Locatelli, B.; Sheil, D.; Cohen, J.; Murdiyarso, D.; Gutierrez, V.; van Noordwijk, M.; Creed, I.F.; Pokorny, J.; et al. Trees, forests and water: Cool insights for a hot world. Glob. Environ. Chang. 2017, 43, 51–61. [Google Scholar] [CrossRef]
- Häder, D.-P.; Barnes, P.W. Comparing the impacts of climate change on the responses and linkages between terrestrial and aquatic ecosystems. Sci. Total Environ. 2019, 682, 239–246. [Google Scholar] [CrossRef] [PubMed]
- Korenaga, J.; Planavsky, N.J.; Evans, D.A.D. Global water cycle and the coevolution of the Earth’s interior and surface environment. Philos. Trans. R. Soc. A 2017, 375, 0393. [Google Scholar] [CrossRef] [PubMed]
- Gleeson, T.; Zipper, S.C.; Erlandsson, L.W.; Porkka, M.; Jaramillo, F.; Gerten, D.; Fetzer, I.; Cornell, S.E.; Piemontese, L.; Gordon, L.; et al. The water planetary boundary: A roadmap to illuminate water cycle modifications in the Anthropocene. Earth Ar** of Precipitation. IEEE Trans. Geosci. Remote Sens. 2019, 57, 1928–1935. [Google Scholar] [CrossRef]
- Maidment, R.I.; Grimes, D.; Black, E.; Tarnavsky, E.; Young, M.; Greatrex, H.; Allan, R.P.; Stein, T.; Nkonde, E.; Senkunda, S.; et al. A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Sci. Data 2017, 4, 63. [Google Scholar] [CrossRef]
- Novella, N.S.; Thiaw, W.M. African Rainfall climatology version 2 for famine early warning systems. J. Appl. Meteorol. Climatol. 2013, 52, 588–606. [Google Scholar] [CrossRef]
- Roca, R.; Taburet, N.; Lorant, E.; Chambon, P.; Alcoba, M.; Brogniez, E.; Cloché, S.; Dufour, C.; Gosset, M.; Guilloteau, C. Quantifying the contribution of the Megha-Tropiques mission to the estimation of daily accumulated rainfall in the Tropics. Q. J. R. Meteorol. Soc. 2018, 144, 49–63. [Google Scholar] [CrossRef]
- Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J.; Wolff, D.B.; Adler, R.F.; Gu, G.; Hong, Y.; Bowman, K.P.; Stocker, E.F. The TRMM Multi-satellite Precipitation Analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeorol. 2007, 8, 38–55. [Google Scholar] [CrossRef]
- Huffman, G.J.; Bolvin, D.T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Nelkin, E.J.; Sorooshian, S.; Tan, J.; ** intensity-duration-frequency (IDF) curves from satellite-based precipitation: Methodology and evaluation. Water Resour. Res. 2018, 54, 7752–7766. [Google Scholar] [CrossRef]
- Ferraro, R.R.; Nelson, B.R.; Smith, T.; Prat, O.P. The AMSU-based hydrological bundle climate data record—Description and comparison with other data sets. Remote Sens. 2018, 10, 1640. [Google Scholar] [CrossRef]
- Hong, Y.; Adler, R.F.; Huffman, G.J. Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys. Res. Lett. 2006, 33, L22402. [Google Scholar] [CrossRef]
- Hong, Y.; Adler, R.F.; Huffman, G.J. Use of satellite remote sensing data in the map** of global landslide susceptibility. Nat. Hazards 2007, 43, 245–256. [Google Scholar] [CrossRef] [Green Version]
- Kirschbaum, D.B.; Adler, R.F.; Hong, Y.; Lerner-Lam, A. Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories. Nat. Hazards Earth Syst. Sci. 2009, 9, 673–686. [Google Scholar] [CrossRef] [Green Version]
- Kirschbaum, D.B.; Adler, R.F.; Hong, Y.; Hill, S.; Lerner-Lam, A. A global landslide catalog for hazard applications: Method, results, and limitations. Nat. Hazards 2010, 52, 561–575. [Google Scholar] [CrossRef]
- Kirschbaum, D.B.; Adler, R.F.; Adler, D.; Peters-Lidard, C.D.; Huffman, G.J. Global distribution of extreme precipitation and high-impact landslides in 2010 relative to previous years. J. Hydrometeorol. 2012, 13, 1536–1551. [Google Scholar] [CrossRef]
- Kirschbaum, D.; Watson, C.S.; Rounce, D.R.; Shugar, D.H.; Kargel, J.S.; Haritashya, U.K.; Amatya, P.; Shean, D.; Anderson, E.R.; Jo, M. The state of remote sensing capabilities of cascading hazards over High Mountain Asia. Front. Earth Sci. 2019, 7, 197. [Google Scholar] [CrossRef]
- Vrieling, A.; Sterk, G.; de Jong, S.M. Satellite-based estimation of rainfall erosivity for Africa. J. Hydrol. 2010, 395, 235–241. [Google Scholar] [CrossRef]
- Funk, C.; Shukla, S.; Thiaw, W.M.; Rowland, J.; Hoell, A.; McNally, A.; Husak, G.; Novella, N.; Budde, M.; Peters-Lidard, C.; et al. Recognizing the Famine Early Warning System NETwork–Over 30 years of drought early warning science advances and partnerships promoting global food security. Bull. Am. Meteorol. Soc. 2019, 100, 1011–1027. [Google Scholar] [CrossRef]
- Dinku, T.; Thomson, M.C.; Cousin, R.; del Corral, J.; Ceccato, P.; Hansen, J.; Connor, S.J. Enhancing National Climate Services (ENACTS) for development in Africa. Clim. Dev. 2018, 10, 664–672. [Google Scholar] [CrossRef]
- Shukla, S.; Arsenault, K.R.; Hazra, A.; Peters-Lidard, C.; Koster, R.D.; Davenport, F.; Magadzire, T.; Funk, C.; Kumar, S.; McNally, A.; et al. Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products. Nat. Hazards Earth Syst. Sci. Discuss. 2019. in review. [Google Scholar] [CrossRef]
- Vogt, J. The European Drought Observatory. In Proceedings of the IEEE 2011 GEOSS Workshop XL-Managing Drought through Earth Observation, Sydney, Australia, 10 April 2011. [Google Scholar] [CrossRef]
- Anyamba, A.; Small, J.L.; Britch, S.C.; Tucker, C.J.; Pak, E.W.; Reynods, C.A.; Crutchfield, J.; Linthicum, K.J. Recent weather extremes and impacts on agricultural production and vector-borne disease outbreak patterns. PLoS ONE 2014, 9, e92538. [Google Scholar] [CrossRef]
- Moore, S.M.; Azman, A.S.; Zaitchik, B.F.; Mintz, E.D.; Brunkard, J.; Legros, D.; Hill, A.; McKay, H.; Luquero, F.J.; Olson, D.; et al. El Niño and the shifting geography of cholera in Africa. Proc. Natl. Acad. Sci. USA 2017, 114, 4436–4441. [Google Scholar] [CrossRef]
- Watts, N.; Adger, W.N.; Agnolucci, P.; Blackstock, J.; Byass, P.; Cai, W.; Chaytor, S.; Colbourn, T.; Collins, M.; Cooper, A.; et al. Health and climate change: Policy responses to protect public health. Lancet 2015, 386, 1861–1914. [Google Scholar] [CrossRef]
- Parshley, L. Catching fever–Climate change is accelerating the spread of disease-and making it much harder to predict outbreaks. Sci. Am. 2018, 5, 58–61. [Google Scholar] [CrossRef]
- Trenberth, K.E. Changes in precipitation with climate change. Clim. Res. 2011, 47, 123–138. [Google Scholar] [CrossRef] [Green Version]
- Blöschl, G.; Hall, J.; Viglione, A.; Perdigão, R.A.P.; Parajka, J.; Merz, B.; Lun, D.; Arheimer, B.; Aronica, G.T.; Bilibashi, A.; et al. Changing climate both increases and decreases European river floods. Nature 2019, 573, 108–111. [Google Scholar] [CrossRef]
- van der Ent, R.J.; Savenije, H.H.G.; Schaefli, B.; Steele-Dunne, S.C. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 2010, 46. [Google Scholar] [CrossRef] [Green Version]
- Zhou, S.; Williams, A.P.; Berg, A.M.; Cook, B.I.; Zhang, Y.; Hagemann, S.; Lorenz, R.; Seneviratne, S.I.; Gentine, P. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl. Acad. Sci. USA 2019. [Google Scholar] [CrossRef]
- Taylor, C.M.; de Jeu, R.A.M.; Guichard, F.; Harris, P.P.; Dorigo, W.A. Afternoon rain more likely over drier soils. Nature 2012, 489, 423–426. [Google Scholar] [CrossRef] [Green Version]
- Guillod, B.P.; Orlowsky, B.; Miralles, D.G.; Teuling, A.J.; Seneviratne, S.I. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun. 2015, 6, 6443. [Google Scholar] [CrossRef]
- Schellekens, J.; Dutra, E.; Martínez-de la Torre, A.; Balsamo, G.; van Dijk, A.; Sperna Weiland, F.; Minvielle, M.; Calvet, J.-C.; Decharme, B.; Eisner, S.; et al. A global water resources ensemble of hydrological models: The eartH2Observe Tier-1 dataset. Earth Syst. Sci. Data 2017, 9, 389–413. [Google Scholar] [CrossRef]
- Velázquez, J.A.; Schmid, J.; Ricard, S.; Muerth, M.J.; Gauvin St-Denis, B.; Minville, M.; Chaumont, D.; Caya, D.; Ludwig, R.; Turcotte, R. An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources. Hydrol. Earth Syst. Sci. 2013, 17, 565–578. [Google Scholar] [CrossRef]
- Bhuiyan, M.A.E.; Nikolopoulos, E.I.; Anagnostou, E.N.; Polcher, J.; Albergel, C.; Dutra, E.; Fink, G.; Martínez-de la Torre, A.; Munier, S. Assessment of precipitation error propagation in multi-model global water resource reanalysis. Hydrol. Sci. J. 2019, 23, 1973–1994. [Google Scholar] [CrossRef] [Green Version]
- Krysanova, V.; Donnelly, C.; Gelfan, A.; Gerten, D.; Arheimer, B.; Hattermann, F.; Kundzewicz, Z.W. How the performance of hydrological models relates to credibility of projections under climate change. Hydrol. Sci. J. 2018, 63, 696–720. [Google Scholar] [CrossRef]
- Mazzoleni, M.; Brandimarte, L.; Amaranto, A. Evaluating precipitation datasets for large-scale distributed hydrological modelling. J. Hydrol. 2019, 578, 124076. [Google Scholar] [CrossRef] [Green Version]
- Ahi, G.O.; **, S. Hydrologic mass changes and their implications in Mediterranean-climate Turkey from GRACE measurements. Remote Sens. 2019, 11, 120. [Google Scholar] [CrossRef]
- Schumacher, D.L.; Keune, J.; van Heerwaarden, C.C.; Vilà-Guerau de Arellano, J.; Teuling, A.J.; Miralles, D.G. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 2019, 12, 712–717. [Google Scholar] [CrossRef]
- Funk, C.; Peterson, P.; Peterson, S.; Shukla, S.; Davenport, F.; Michaelsen, J.; Knapp, K.R.; Landsfeld, M.; Husak, G.; Harrison, L.; et al. A High-resolution 1983–2016 Tmax climate data record based on infrared temperatures and stations by the Climate Hazard Center. J. Clim. 2019, 32, 5639–5658. [Google Scholar] [CrossRef]
- Gebrechorkos, S.H.; Hülsmann, S.; Bernhofer, C. Statistically downscaled climate dataset for East Africa. Sci. Data 2019, 6, 31. [Google Scholar] [CrossRef]
- Bui, H.T.; Ishidaira, H.; Shaowei, N. Evaluation of the use of global satellite–gauge and satellite-only precipitation products in stream flow simulations. Appl. Water Sci. 2019, 9, 53. [Google Scholar] [CrossRef]
- Park, K.J.; Yoshimura, K.; Kim, H.; Oki, T. Chronological development of terrestrial mean precipitation. Bull. Am. Meteorol. Soc. 2017, 98, 2411–2428. [Google Scholar] [CrossRef]
- Hasan, E.; Tarhule, A.; Zume, J.T.; Kirstetter, P.-E. +50 years of terrestrial hydroclimatic variability if Africa’s transboundary waters. Sci Rep. 2019, 9, 12327. [Google Scholar] [CrossRef]
- Hasan, E.; Tarhule, A.; Hong, Y.; Moore, B., III. Assessment of physical water scarcity in Africa using GRACE and TRMM satellite data. Remote Sens. 2019, 11, 904. [Google Scholar] [CrossRef]
- Biswas, A.K.; Tortajada, C. Water crisis and water wars: Myths and realities. Int. J. Water Resour. Dev. 2019, 35, 727–731. [Google Scholar] [CrossRef]
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Levizzani, V.; Cattani, E. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate. Remote Sens. 2019, 11, 2301. https://doi.org/10.3390/rs11192301
Levizzani V, Cattani E. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate. Remote Sensing. 2019; 11(19):2301. https://doi.org/10.3390/rs11192301
Chicago/Turabian StyleLevizzani, Vincenzo, and Elsa Cattani. 2019. "Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate" Remote Sensing 11, no. 19: 2301. https://doi.org/10.3390/rs11192301