2019
DOI: 10.1201/9781003000389
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The Value of Using Hydrological Datasets for Water Allocation Decisions: Earth Observations, Hydrological Models, and Seasonal Forecasts

Abstract: Over the past five years I have been working towards the completion of this dissertation. Back in 2014, I was working in Costa Rica after my MSc studies in IHE-Delft. Micha suggested to start a full-time PhD research, which meant that I had to come back to the Netherlands. I resigned to my job and I accepted the PhD challenge. It has been the biggest challenge until now. Now that it is over, I want to thank many people that helped and supported me along the way. Certainly, without them, this work would have be… Show more

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Cited by 2 publications
(1 citation statement)
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“…These datasets are derived from missions such as the European Space Agency's Soil Moisture Ocean Salinity (SMOS) [39], NASA's Soil Moisture Active Passive ((SMAP) [40]), and the European Space Agency Sentinel-1 [41], which were specifically designed to measure SM. Beck et al [42] conducted an assessment of different satellite and model-based SM products and found that SMAP outperformed other datasets when compared with in situ SM measurements. Zhang et al [17] assimilated SMAP SM data into a terrestrial carbon cycle model and found that it helped reduce uncertainty in carbon flux estimates.…”
Section: Introductionmentioning
confidence: 99%
“…These datasets are derived from missions such as the European Space Agency's Soil Moisture Ocean Salinity (SMOS) [39], NASA's Soil Moisture Active Passive ((SMAP) [40]), and the European Space Agency Sentinel-1 [41], which were specifically designed to measure SM. Beck et al [42] conducted an assessment of different satellite and model-based SM products and found that SMAP outperformed other datasets when compared with in situ SM measurements. Zhang et al [17] assimilated SMAP SM data into a terrestrial carbon cycle model and found that it helped reduce uncertainty in carbon flux estimates.…”
Section: Introductionmentioning
confidence: 99%