Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco

Ouatiki, Hamza; Boudhar, Abdelghani; Tramblay, Yves; Jarlan, Lionel; Benabdelouhab, Tarik; Hanich, Lahoucine; Meslouhi, M. Rachid El; Chehbouni, Abdelghani

doi:10.3390/cli5010001

Cited by 78 publications

(50 citation statements)

References 50 publications

(63 reference statements)

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“…Jiang et al [19] analyzed the TRMM 3B42 product across a large region of China and obtained higher correlation coefficients in the wet part of the study area, whereas lower r values occurred mostly in relatively dry regions. Although the rainfall over the study area was better estimated in this work than in the studies by Ouatiki et al [41] and Yuan et al [20], the TRMM and Hydroe products showed high error values. Comparing satellite pixels with data from rain gauge stations involves a mismatched spatial scale because the satellite products are extracted from an area of approximately 625 km 2 for the TRMM products and of 25 km 2 for the Hydroe products, unlike the rain gauges that provide punctual rainfall measurements.…”

Section: Discussioncontrasting

confidence: 68%

“…Hence, the first validation was performed on the smallest possible spatial scale (point-to-pixel analysis) to understand the ability of the satellite products to estimate the daily rainfall at each point represented by the rain gauge stations. Ouatiki et al [41] found low values for the linear relationship (<0.38) at all 19 rain gauge stations across a mountainous watershed in Morocco and explained that the low efficiency of the TRMM 3B42 product was due to the rapid cloud dynamics and the stormy nature of rainfall events in the study region. The authors also observed that the TRMM product had difficulties in estimating rainfall levels over mountainous regions receiving snowfall [41].…”

Section: Discussionmentioning

confidence: 91%

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Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil

2017

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Abstract:Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse spatial distributions of traditional rain gauge stations. However, the accuracy of these satellite rainfall products over different spatial and temporal scales is unknown. In this study, we examined the potential of the TRMM 3B42 and Hydroe rainfall products to provide reliable rainfall estimates for a mountainous watershed in a humid subtropical climate region of Brazil. The purpose was to develop useful guidelines for future hydrologic studies on the potential and uncertainties of the rainfall products at different spatial and temporal resolutions. We compared the satellite products to reference rainfall data collected at 11 rain gauge stations irregularly distributed in the area. The results showed different levels of accuracy for each temporal scale evaluated. TRMM 3B42 performed better at the daily, monthly, and seasonal scales than Hydroe, while Hydroe presented a better correlation at the annual scale. In general, TRMM 3B42 overestimated the rainfall over the watershed at all evaluated temporal scales, whereas Hydroe underestimated it except for June-August at the seasonal scale. An evaluation based on contingency tables indicated that TRM 3B42 was better able to represent the local rainfall than Hydroe. The findings of this study indicate that satellite rainfall products are better suited for applications at the monthly and annual scales rather than the daily scale.

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Section: Discussioncontrasting

confidence: 68%

Section: Discussionmentioning

confidence: 91%

Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil

2017

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“…Meanwhile, a study carried out in Qinghai Lake Basin, China, shows that TRMM exhibits a poor performance between May and September, which is the dry season [19]. This is consistent with a study carried out in Morocco, which showed that July and August exhibit the lowest correlation coefficients [6]. A study in Circum-Bohai-Sea region, China, showed that the rainfall pattern is more effectively captured by TRMM for the wet region or season than for the dry region or season [20].…”

Section: Introductionsupporting

confidence: 86%

“…A study carried in Northeastern Iberia showed TRMM to exhibit a good performance in regions with minimum precipitation events and a low altitude, and shows a poor performance on the coastal areas and in complex terrains [5]. The impacts of altitude on the TRMM accuracy are consistent with a study carried out by Ouatiki et al (2017) in Morocco that showed that the TRMM product had problems in estimating rainfall rates over mountainous regions [6]. The difficulties are due to the ability of a Precipitation Radar (PR) to obtain the information about the rain event such as the intensity and distribution, the rain type, and other data [7].…”

Section: Introductionsupporting

confidence: 82%

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

2018

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Abstract:The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.

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“…The region frequently suffers from water scarcity and droughts and water availability is the main factor influencing socio-economic development, mostly driven by agriculture (Houdret, 2008). The studies of Tramblay et al (2012), Tramblay et al (2016) and Ouatiki et al (2017) are testimony to the relevance of this area. Therefore, developing new strategies to model this watershed is highly relevant to improve water management and assessment of the water availability within the basin.…”

Section: Introductionmentioning

confidence: 98%

Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

López

Sutanudjaja

Schellekens

et al. 2017

Hydrol. Earth Syst. Sci.

161

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Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydrometeorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5 × 5 arcmin are performed with varying parameters values for the 32-year period 1979-2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situobserved discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and MultiSource Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP).Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility of using globally available Earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture in large-scale hydrological models to estimate discharge at a river basin scale.

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Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco

Cited by 78 publications

References 50 publications

Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil

Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

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