Validation of high‐resolution satellite rainfall products over complex terrain

Dinku, Tufa; Chidzambwa, S.; Ceccato, Pietro; Connor, Stephen J.; Ropelewski, Chester F.

doi:10.1080/01431160701772526

Cited by 369 publications

(279 citation statements)

References 13 publications

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“…It is noteworthy that topography and climate conditions clearly influence the accuracy of retrieval results [38,39]. Our results also supported this view.…”

Section: Discussionsupporting

confidence: 80%

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Ning

Wang

Jin

et al. 2016

Water

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Abstract:The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial-temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1 • and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

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“…It is noteworthy that topography and climate conditions clearly influence the accuracy of retrieval results [38,39]. Our results also supported this view.…”

Section: Discussionsupporting

confidence: 80%

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Ning

Wang

Jin

et al. 2016

Water

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“…HOSCILO et al data (at 0.25 ∘ spatial resolution) estimated rainfall particularly well for Zimbabwe and Ethiopia (r 2 = 0.7). The RFE 1.0 product performed better than RFE 2.0 over Ethiopia, however RFE 2.0 was slightly better than RFE 1.0 over Zimbabwe (Dinku et al, 2008). A study by Beyene and Meissner (2010) also showed a good correlation between monthly RFE 1.0 and RFE 2.0 data (January to December 1996December -2006 and weather station rainfall records in Ethiopia (r 2 > 0.5 and 0.75 depending on the month).…”

Section: Rainfall Datamentioning

confidence: 73%

A conceptual model for assessing rainfall and vegetation trends in sub‐Saharan Africa from satellite data

Hościło

Balzter

Bartholomé³

et al. 2014

Int. J. Climatol

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Policymakers, governments and aid agencies require operational environmental monitoring in support of evidence-based policy-making and resource deployment in crisis situations. For Africa, this is only feasible at sub-continental scale with a large network of automated meteorological stations, a large number of highly coordinated field observers or with satellite remote sensing. The challenge with satellite data lies in the derivation of meaningful environmental indicators. This article describes a conceptual framework for understanding satellite-derived indicators of rainfall and vegetation greenness trends over Africa. It attributes observed vegetation changes to climatic (i.e. rainfall linked) and non-climatic drivers. A decade of annual rainfall and vegetation data over sub-Saharan Africa was analysed using satellite-based rainfall estimates [Famine Early Warning System Rainfall Estimation 2.0 (FEWSNET RFE 2.0)] from National Oceanic and Atmospheric Administration's (NOAA's) Climate Prediction Centre and the Normalized Difference Vegetation Index (NDVI) obtained from the Satellite Pour l'Observation de la Terre Vegetation (SPOT-VGT) sensor. Rainfall and vegetation greenness trends were analysed for 759 administrative regions of sub-Saharan Africa to identify those regions that have experienced a negative, positive or stable rainfall/vegetation trend over the period [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. The character of the relationship between the annual rainfall and max NDVI trends were examined to identify areas where the changes in greenness could be attributed to climatic (rainfall) and non-climatic (human land use or ecological disturbance) changes. Regions where increasing rainfall was associated with vegetation greening were found in West Africa, Central African Republic, West Cameroon and northeastern part of South Africa, whereas areas with evidence of 'climatic vegetation degradation' were located in Southern Madagascar, Nigeria, Kenya and the Garden Route region of South Africa.

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“…However, RFE underestimates rainfall during peak rainy seasons and overestimates in other seasons (Laws et al, 2004), with an average bias of −0.15 mm day −1 (NOAA/CPC, 2002). Few validation studies indicate estimates of errors for different locations in Africa and over different time periods (Laws et al, 2004;Dinku et al, 2008). However, we cannot extrapolate 8 N. M. Velpuri: A multi-source satellite data approach for modelling Lake Turkana water level these errors for the Lake Turkana basin due to its complexity.…”

Section: Uncertainties In Llm Approachmentioning

confidence: 99%

“…Errors in other parameters such as rainfall, runoff, and ET would critically affect modelling results. Validation of RFE rainfall over the Ethiopian highlands using gauge data suggested that RFE can be reliably used for early warning systems to empower the decision making (Dinku et al, 2008). However, RFE underestimates rainfall during peak rainy seasons and overestimates in other seasons (Laws et al, 2004), with an average bias of −0.15 mm day −1 (NOAA/CPC, 2002).…”

Section: Uncertainties In Llm Approachmentioning

confidence: 99%

A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data

Velpuri

Senay

Asante³

2012

Hydrol. Earth Syst. Sci.

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Abstract. Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of interand intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellitedriven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004)(2005)(2006)(2007)(2008)(2009). Model error estimates were within 10 % of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30 % of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1-2 m. The lake level fluctuated in the range up to 4 m between the years 1998-2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance model for (i) quantitative assessment of the impact of basin developmental activities on lake levels and for (ii) forecasting lake level changes and their impact on fisheries. From this study, we suggest that globally available satellite altimetry data provide a unique opportunity for calibration and validation of hydrologic models in ungauged basins.

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Validation of high‐resolution satellite rainfall products over complex terrain

Cited by 369 publications

References 13 publications

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

A conceptual model for assessing rainfall and vegetation trends in sub‐Saharan Africa from satellite data

A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data

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