Reduced precipitation over large water bodies in the Brazilian Amazon shown from TRMM data

Paiva, Rodrigo Cauduro Dias de; Buarque, Diogo Costa; Clarke, Robin T.; Collischonn, Walter; Allasia, Daniel

doi:10.1029/2010gl045277

Cited by 40 publications

(51 citation statements)

References 20 publications

(36 reference statements)

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“…Previous studies reported that satellite precipitation estimates could overestimate precipitation over inland waterbodies due to deficiencies in emissivity characterization used in the passive microwave-based rainfall retrieval algorithms [73,74]. However, the opposite finding was reported by [75], who found underestimation by satellite precipitation. These contrasting findings indeed highlight the need for more studies on this topic in future studies.…”

Section: Limitations and Recommendations For Future Studycontrasting

confidence: 41%

Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data

Duan

Gao

2018

Remote Sensing

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Lake Tana is the largest lake in Ethiopia, and its lake outflow is the source of the Blue Nile River that provides vital water resources for many livelihoods and downstream/international stakeholders. Therefore, it is essential to quantify and monitor the water balance of Lake Tana. However, Lake Tana is poorly gauged, with more than 50% of Lake Tana Basin being ungauged from in-situ measurements, making it difficult to quantify the lake inflow from surrounding basins. The lack of in-situ measurements highlights the need for the innovative application of satellite remote sensing. This study explores how freely accessible satellite remote sensing can be used to complement routine weather data to quantify the water balance of Lake Tana and its surrounding catchments. This study particularly investigates whether the outflow from Lake Tana can be estimated with sufficient accuracy as the residual of the lake water balance. Monthly inflow into lake was computed as the total runoff from the surrounding catchments; the runoff was estimated as the residual of the land-based catchment water balance using satellite precipitation improved with an integrated downscaling-calibration procedure, satellite evapotranspiration, and a correction term for changes in land total storage (soil moisture storage and deep percolation). The outflow from Lake Tana was estimated as the residual of lake water balance by combining satellite-based lake precipitation, changes in water storage, and lake inflow with estimated lake evaporation. Evaluation using limited available measurements showed that estimated annual runoff for two gauged subbasins agreed well with measurements, with differences within 4%. The estimated annual outflow from Lake Tana was also close to measured outflow, with a difference of 12%. However, the estimated monthly runoff from catchments and monthly lake outflow were unsatisfactory, with large errors.

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Section: Limitations and Recommendations For Future Studycontrasting

confidence: 41%

Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data

Duan

Gao

2018

Remote Sensing

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“…Moreover, Satyamurty et al [41] showed that an evapotranspiration rate of approximately 3.0 mm/day is responsible for over 30% of regional precipitation in the Amazon Basin. Many studies have been conducted in the Amazon [30,[42][43][44][45][46], especially in connection with deforestation [47], wind, humidity circulation [46] and river breeze circulation [30,48]. During the rainy season in the Amazon Basin, high amounts of precipitation are due to more frequent rather than higher intensity rain [30].…”

Section: Study Areamentioning

confidence: 99%

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

et al. 2015

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Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.

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“…Because of limited rain gauge data, the possibility of using remote-sensed estimates of rainfall, such as the Climate Prediction Center's CMORPH [Joyce et al, 2004] and the Tropical Rainfall Measurement Mission (TRMM) [Kummerow et al, 2000;Huffman et al, 2007], is attractive, and various aspects of such data sets in the Amazon have been explored and reported [Pereira Filho et al, 2006;Collischonn et al, 2008;Condom et al, 2011;Pereira Filho et al, 2010;Paiva et al, 2011;Buarque et al, 2011;Clarke et al, 2011;Getirana et al, 2010]. Rainfall estimation from satellite data continues to be explored ever more widely [Cheema and Bastiaanssen, 2012;Chokngamwong and Chiu, 2008;Heidinger et al, 2012;Nicholson et al, 2003].…”

Section: Introductionmentioning

confidence: 99%

“…[3] However, comparisons between rainfall characteristics derived from such data sets, with their values given by analyses of rain gauge data, show that considerable differences can exist [Adeyewa and Nakamura, 2003;Bell and Kundu, 2003;Condom et al, 2011;Paiva et al, 2011;Clarke et al, 2011;Buarque et al, 2011]. Furthermore, any comparison between indirect and direct measures of rainfall is complicated by high spatial and temporal variability, particularly in tropical rainfall 1 Instituto de Pesquisas Hidraulicas, Porto Alegre, Brazil.…”

Section: Introductionmentioning

confidence: 99%

Statistically combining rainfall characteristics estimated from remote‐sensed and rain gauge data sets in the Brazilian Amazon‐Tocantins Basins

Clarke

Buarque

2013

JGR Atmospheres

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[1] This paper explores the use of a parametric geostatistical model for combining rainfall characteristics derived from rain gauge data with the same characteristics derived from remote-sensed data sets. Hypotheses can then be tested about which predictors significantly increase precision of an estimated characteristic. Although applicable wherever ground-level data and remote-sensed data are to be combined, the statistical procedure set out in the paper is developed for two examples of rainfall characteristics: (i) G, the mean annual rainfall at an ungauged site, conditional on knowledge of two predictor variables T (the mean annual rainfall calculated from the TRMM 3B42 data set for 1998-2009), and C (mean annual rainfall derived from the CMORPH data set for 2003-2009); (ii) the mean annual maximum 1 day rainfall H, interpolated using the same modeling procedure with predictor variables T and C derived from annual maximum 1 day rainfalls in the same remote-sensed data sets. Prediction errors showed no bias, skewness of distribution, or spatial heterogeneity. The model's generality means that it could be used with any predictors other than T and C, possibly derived from other satellite data sets or radar. Provided that predictor variables are correlated with the variable to be predicted, it is not necessary for the model relating them to be fitted using data from identical periods nor for the grid spacing of T and C to be identical. Model performance was evaluated by using a "leave-one-site-out" procedure, which showed that the root mean square error (RMSE) of model predictions at omitted sites was smaller than RMSEs obtained from five other well-known spatial predictors.Citation: Clarke, R. T., and D. C. Buarque (2013), Statistically combining rainfall characteristics estimated from remotesensed and rain gauge data sets in the Brazilian Amazon-Tocantins Basins,

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Reduced precipitation over large water bodies in the Brazilian Amazon shown from TRMM data

Cited by 40 publications

References 20 publications

Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data

Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

Statistically combining rainfall characteristics estimated from remote‐sensed and rain gauge data sets in the Brazilian Amazon‐Tocantins Basins

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