Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations

Serrat-Capdevila, Aleix; Valdés, Juan B.; Stakhiv, Eugene Z.

doi:10.1111/jawr.12140

Cited by 74 publications

(44 citation statements)

References 71 publications

(115 reference statements)

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“…3) correspond well with those obtained by Tian et al (2007) and Ebert et al (2007). Figure 4 presents the difference in 3-day rainfall correlation coefficients between TMPA 3B42RT and ERA-Interim, highlighting the complementary performance of satellite and reanalysis P datasets reported in previous large-scale studies (Janowiak, 1992;Huffman et al, 1995;Arkin, 1996, 1997;Xie and Joyce, 2014;Adler et al, 2001;Ebert et al, 2007;Serrat-Capdevila et al, 2013;Peña Arancibia et al, 2013). Based on these results, we decided to use CMORPH, GSMaP-MVK, TMPA 3B42RT, ERA-Interim, and JRA-55 for determining the rainfall variability for ungauged regions in MSWEP.…”

Section: Gauge-based Evaluation Of Gridded P Datasetssupporting

confidence: 81%

“…E. • global precipitation Pozzi et al, 2013;Serrat-Capdevila et al, 2013;Lettenmaier et al, 2015). However, P is also one of the meteorological variables that is most difficult to estimate, due to its high spatiotemporal heterogeneity (Daly et al, 1994;Herold et al, 2015).…”

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confidence: 99%

“…With the exception of CMAP, all datasets listed in Table 1 employ only one or two of the main data sources -either gauge and satellite, or gauge and reanalysisand thus do not take full advantage of the complementary nature of satellite and reanalysis data identified in previous studies (e.g., Janowiak, 1992;Huffman et al, 1995;Arkin, 1996, 1997;Xie and Joyce, 2014;Adler et al, 2001;Ebert et al, 2007;Serrat-Capdevila et al, 2013;Peña Arancibia et al, 2013); 2. Many of the listed datasets do not explicitly and fully account for gauge under-catch and/or orographic effects, and consequently underestimate P in many regions around the globe (e.g., Zaitchik et al, 2010;Kauffeldt et al, 2013;Beck et al, 2015Beck et al, , 2016aPrein and Gobiet, 2016); 3.…”

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confidence: 99%

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MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

848

640

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Abstract. Current global precipitation (P ) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3-hourly temporal and 0.25 • spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km 2 ) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

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Section: Gauge-based Evaluation Of Gridded P Datasetssupporting

confidence: 81%

mentioning

confidence: 99%

mentioning

confidence: 99%

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MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

848

640

View full text Add to dashboard Cite

show abstract

“…Satellite precipitation data offers an attractive alternative to supplement in situ precipitation measurements in hydrological modelling, particularly in poorly gauged basins [29,63] [64]. In a similar study for Australia and Southeast Asia, TRMM and CMORPH outperformed, and an ensemble precipitation product was suggested as a reduction of system-specific and random errors [65].…”

Section: Precipitationmentioning

confidence: 96%

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

Bastiaanssen

Griensven

et al. 2018

Water

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Abstract:In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km 2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R 2 = 0.71, Nash-Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R 2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R 2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.

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“…They discovered that, in a relatively flat basin, gridded precipitation datasets estimated runoff better than the in situ station data within the SWAT model. Over the last decade, several additional studies [9][10][11] positively reviewed the efficacy of the use of satellite-derived products within hydrologic models, but there was unanimous consensus that continued studies are necessary. Moreover, remote-sensing based or land-surface model precipitation estimates do not consistently outperform in situ data [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data

Pricope

2017

Water

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Hydrologic models will be an increasingly important tool for water resource managers as water availability dwindles and water security concerns become more pertinent in data-scarce regions. Fortunately, newly available satellite remote sensing technology provides an opportunity for improving the spatial resolution and quality of input data to hydrologic models in such regions. In particular, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset provides quasi-global high resolution precipitation information derived from a blend of in situ and active and passive remote sensing data sources. We piloted the incorporation of the CHIRPS dataset into the Soil and Water Assessment Tool (SWAT), a hydrologic model. Comparisons of results between estimation of streamflow using in situ rainfall gauge station data, the Climate Forecast System Reanalysis (CFSR) dataset, and the CHIRPS dataset in the data-scarce Nzoia Basin in western Kenya over the temporal range 1990-2000 were reported. Simulated streamflow estimates were poor with rainfall gauge station data but improved significantly with the CFSR and CHIRPS datasets. However, the use of the CHIRPS dataset in comparison with the CFSR dataset provided an improved statistical performance following model calibration with the exception of one streamflow gauge station in higher elevation regions. Overall, the use of the CHIRPS dataset had the greatest linear correlation, relative variability, and normalized bias despite overall average Nash-Sutcliffe Efficiency (NSE) and R 2 values.

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Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations

Cited by 74 publications

References 71 publications

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data

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