Assessment of satellite rainfall products over the Andean plateau

Satgé, Frederic; Bonnet, Marie‐Paule; Gosset, Marielle; Molina, Jorge; Lima, Wilson Hernan Yuque; Zolá, Ramiro Pillco; Timouk, Franck; Garnier, Jérémie

doi:10.1016/j.atmosres.2015.07.012

Cited by 70 publications

(86 citation statements)

References 59 publications

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“…During wet seasons for all considered regions, TMPA presents CC higher than 0.7, RMSE close to 50% and Bias values into the −10%-10% intervals (Table 2; Figure 3). These specific values were previously defined as objective values to ensure the good performance of SREs at monthly scale [9,10,19,30]. Results over the TDPS and for TMPA are in line with a previous study of the [2005][2006][2007] period [9] with similar CC, RMSE and Bias values.…”

Section: Annual Scalesupporting

confidence: 72%

“…According to this characterization, several statistical parameters can be computed: the Probability of Detection (POD), the False Alarm Ratio (FAR), the Critical Success Index (CSI) and the Bias (B) (Equations (1)- (4)) [9,[18][19][20][21][22][23].…”

Section: Comparison Methodologymentioning

confidence: 99%

“…The climate throughout the region is semi-arid with mean rainfall of 350 mm (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). According to SRTM and TMPA data [9,12], the Amazon region is located at a mean elevation of 680 m with a mean slope value of 5.3 • and a mean annual rainfall of 1550 mm (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Finally, the La Plata watershed presents a mean elevation of 1350 m for a mean slope value of 7 • and a mean annual rainfall of 850 mm (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015).…”

Section: Introductionmentioning

confidence: 99%

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Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

et al. 2017

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Abstract:The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1 • , these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25 • ) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.

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Section: Annual Scalesupporting

confidence: 72%

Section: Comparison Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

et al. 2017

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“…For the quantitative statistics, mean error (ME) or bias, and root mean squared error (RMSE) were calculated based on Wilks (2006), and Nash Sutcliffe efficiency (NSE) 10 coefficient based on Nash and Sutcliffe (1970) was used as well. Additionally and similar to Blacutt et al (2015) and Satgé et al (2016), we used the Spearman rank correlation to estimate the goodness of fit to observations, the bias to show the degree of over-or underestimation (Duan et al, 2015), the root mean square error (RMSE) to compute the average magnitude of the estimated errors (values closer to zero generally indicate smaller magnitude of error), and finally the Nash Sutcliffe Efficiency coefficient that evaluates the prediction accuracy compared to observations (1 corresponds to a perfect match between gauge 15 observation and satellite-based estimate and zero indicates that the satellite estimations are as accurate as the mean of the observed data; negative values indicate that the observed mean is better than satellite-based estimate, see Nash and Sutcliffe (1970) for more details). To evaluate results, correlation coefficients larger or equal to 0.7 with a significance level of 0.01 were considered as reliable (Satgé et al, 2016;Condom et al, 2011 (Bartholmes et al, 2009;Ochoa et al, 2014;Satgé et al, 2016).…”

Section: Validation Of Satellite Rainfall Product Using Gauge-measurementioning

confidence: 49%

“…The satellite rain data was tested with gauged precipitation using the same methodology employed (for comparison reasons) in Blacutt et al (2015) and Satgé et al (2016) who did a similar analysis for Boliva but with different satellite imagery products. We discuss in the next sections our methodology employed to empirically combine the climate, vegetation, and crop data to derive risk based crop distributions including ENSO 5 effects.…”

Section: Methodsmentioning

confidence: 99%

Early warning and drought risk assessment for the Bolivian Altiplano agriculture using high resolution satellite imagery data

Rosso

Hochrainer‐Stigler

Pflug

et al. 2018

Preprint

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Abstract. Implementation of agriculturally related early warning systems is fundamental for the management of droughts.Additionally, risk-based approaches are superior in tackling future drought hazards. Due to data-scarcity in many regions, high resolution satellite imagery data are becoming widely used. Focusing on ENSO warm and cold phases, we employ a risk-based 15 approach for drought assessment in the Bolivian Altiplano using satellite imagery data and application of an early warning system. We use a newly established high resolution satellite dataset and test its accuracy as well as performance to similar (but with less resolution) datasets available for the Bolivian Altiplano. It is shown that during the El Niño years (warm ENSO phase), the result is great difference in risk and crop yield. Furthermore, the Normalized Difference Vegetation Index (NDVI) can be used to target specific hot spots on a very local scale. As a consequence, ENSO early warning forecasts as well as 20 possible magnitudes of crop deficits could be established by the government, including an identification of possible hotspots during the growing season. Our approach therefore should not only help in determining the magnitude of assistance needed for farmers on the local scale but also enable a pro-active approach to disaster risk management against droughts that can include economic-related instruments such as insurance as well as risk reduction instruments such as irrigation.

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Multiscale assessment of spatial precipitation variability over complex mountain terrain using a high‐resolution spatiotemporal wavelet reconstruction method

et al. 2016

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Studying precipitation variability in the Peruvian Andes is a challenge given the high topographic variability and the scarcity of weather stations. Yet previous research has shown that a near‐linear relationship exists between precipitation and vegetation in the semiarid central Andes. We exploit this relationship by developing a new, spatially highly resolved spatiotemporal precipitation reconstruction method, using daily precipitation time series from in situ weather stations, and dekadal (10 calendar days) normalized difference vegetation index (NDVI) fields. The two data sets are combined through a wavelet decomposition method. A 4° × 4° region around Quelccaya ice cap (QIC), the world's largest tropical ice cap located in the central Peruvian Andes, was selected as study area, due to its importance for climatic, glaciologic, and paleoclimatic research. The reconstructed end product, a ~1 km2 gridded precipitation data set at dekadal temporal resolution, was validated against independent rain gauge data and compared with the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 product. This validation showed a better overall performance of our own reconstruction than the TRMM data. Additionally, a comparison of our precipitation product with snowfall measurements at the QIC summit (5670 m) shows a regionally coherent signal at the dekadal scale, suggesting that the precipitation falling at QIC is driven by regional‐ rather than local‐scale convective activity. We anticipate that this methodology and the type of data generated in this study will be useful for hydrological and glaciological studies, as well as for validation of high‐resolution downscaling products in mountain regions.

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Assessment of satellite rainfall products over the Andean plateau

Cited by 70 publications

References 59 publications

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

Early warning and drought risk assessment for the Bolivian Altiplano agriculture using high resolution satellite imagery data

Multiscale assessment of spatial precipitation variability over complex mountain terrain using a high‐resolution spatiotemporal wavelet reconstruction method

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