Evaluation of TRMM 3B42V7 and CHIRPS Satellite Precipitation Products as an Input for Hydrological Model over Eastern Nile Basin

Abdelmoneim, Hadir; Soliman, M. R.; Moghazy, Hossam M.

doi:10.1007/s41748-020-00185-3

Cited by 40 publications

(13 citation statements)

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“…8.2a. (Kojiri et al 1998;Abdelmoneim et al 2020) Each mesh cell includes details such as surface runoff, land use, slope direction, and a channel's absence/presence (Saber 2010). Layer A represents the surface layer, while layers B to D represent the subsurface layers, as denoted in Fig.…”

Section: Gpcp-1dd Productmentioning

confidence: 99%

“…The model was confined to environments characterized by moist circumstances until the model was adjusted for simulations of flash flood events in arid wadis(Abdel-Fattah et al 2015;Saber et al 2013) and semiarid basins (Abdel-Fattah 2017; Saber and Yilmaz 2016). The model has also been successfully used in numerous hydrological studies under different climatic conditions (e.g., Abd-ElMoneim et al 2017;Saber and Yilmaz 2018;Abdelmoneim et al 2020). In this study, Hydro-BEAM is used to evaluate the simulated streamflow rates over the BNB based on six PESPs.…”

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confidence: 99%

“…Fig. 8.2 Conceptual representation of Hydro-BEAM: a the basic structure of Hydro-BEAM; b the kinematic wave model of the surface layer; c the subsurface storage tank layer representation; and d the reservoir modeling approach(Kojiri et al 1998;Abdelmoneim et al 2020) …”

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confidence: 99%

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Hydrologic Assessment of the Uncertainty of Six Remote Sensing Precipitation Estimates Driven by a Distributed Hydrologic Model in the Blue Nile Basin

Abdelmoneim

Soliman

Moghazy

2021

Natural Disaster Science and Mitigation Engineering: DPRI Reports

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Because of the sparseness of the ground monitoring network, precipitation estimations based on satellite products (PESPs) are currently requisite tools for hydrological simulation research and applications. The evaluation of six global high-resolution PESPs (TRMM 3B42V7, GPGP-1DD, TRMM 3B42RT, CMORPH-V1.0, PERSIANN, and PERSIANN-CDR) is the ultimate purpose of this research. Additionally, the distributed Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) is used to investigate their potential effects in streamflow predictions over the Blue Nile basin (BNB) during the period 2001 to 2007. The correctness of the studied PESPs is assessed by applying categorical criteria to appraise their performances in estimating and reproducing precipitation amounts, while statistical indicators are utilized to determine their rain detection capabilities. Our findings reveal that TRMM 3B42V7 outperforms the remaining product in both the estimation of precipitation and the hydrological simulation, as reflected in highest NSE and R2 values ranges from 0.85 to 0.94. Generally, the TRMM 3B42V7 precipitation product exhibits tremendous potential as a substitute for precipitation estimates in the BNB, which will provide powerful forcing input data for distributed hydrological models. Overall, this study will hopefully provide a better comprehension of the usefulness and uncertainties of various PESPs in streamflow simulations, particularly in this region.

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Section: Gpcp-1dd Productmentioning

confidence: 99%

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confidence: 99%

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Hydrologic Assessment of the Uncertainty of Six Remote Sensing Precipitation Estimates Driven by a Distributed Hydrologic Model in the Blue Nile Basin

Abdelmoneim

Soliman

Moghazy

2021

Natural Disaster Science and Mitigation Engineering: DPRI Reports

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“…The emergence of hyperspectral remote sensing technology is undoubtedly a breakthrough in the field of remote sensing [1][2][3]. Hyperspectral sensors with dozens or even hundreds of spectral bands may effectively capture abundant spectral and spatial information on the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

et al. 2021

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Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.

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“…The data are relatively accurate and are collected at relatively high frequencies [9]. However, it is more difficult to obtain representative and long-term precipitation data over complex terrain because of the inhomogeneous and sparse distribution of land gauges and the high costs to build new gauges [10][11][12]. By contrast, passive microwave observations from satellites take into account the interactions between the microwave radiation and the hydrometeors inside clouds and rain, allowing precipitation to be measured globally, extending the dataset collected by land stations.…”

Section: Introductionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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Evaluation of TRMM 3B42V7 and CHIRPS Satellite Precipitation Products as an Input for Hydrological Model over Eastern Nile Basin

Cited by 40 publications

References 49 publications

Hydrologic Assessment of the Uncertainty of Six Remote Sensing Precipitation Estimates Driven by a Distributed Hydrologic Model in the Blue Nile Basin

Hydrologic Assessment of the Uncertainty of Six Remote Sensing Precipitation Estimates Driven by a Distributed Hydrologic Model in the Blue Nile Basin

Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

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