Article
Environmental Sciences
Zhi-Weng Chua, Yuriy Kuleshov, Andrew B. Watkins, Suelynn Choy, Chayn Sun
Summary: This study presents an approach to develop a blended satellite-rainfall dataset over Australia. The blended dataset, which integrates Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates with station-based rain gauge data, exhibits improved performance compared to other non-gauge-based datasets. The proposed method effectively reduces biases and produces more realistic rainfall patterns.
Article
Environmental Sciences
Magdi S. A. Siddig, Salma Ibrahim, Qingchun Yu, Abdelmula Abdalla, Yahia Osman, Isameldin Abakar Atiem, Shindume Lomboleni Hamukwaya, Mazahir M. M. Taha
Summary: This study aims to minimize the uncertainties of satellite-based rainfall estimates (SREs) using a quantile mapping method. The adjusted estimates showed significant improvements in all statistical measures and evaluation of climatic zones.
Article
Multidisciplinary Sciences
Mojtaba Sadeghi, Phu Nguyen, Matin Rahnamay Naeini, Kuolin Hsu, Dan Braithwaite, Soroosh Sorooshian
Summary: Accurate long-term global precipitation estimates, especially for heavy precipitation rates, are essential for climatological studies. The PERSIANN-CCS-CDR dataset provides reliable precipitation estimates with high spatiotemporal resolution and a longer period of record, particularly for extreme events.
Article
Geosciences, Multidisciplinary
Dibesh Shrestha, Divas B. Basnyat, Januka Gyawali, Maggie J. Creed, Hugh D. Sinclair, Brian Golding, Manoranjan Muthusamy, Shankar Shrestha, C. Scott Watson, Divya L. Subedi, Rojina Haiju
Summary: This paper analyzes historical extreme rainfall patterns in Kathmandu and predicts future extreme rainfall events. The results show that the intensity of rainfall will increase significantly in the future, leading to an increased flood hazard. This analysis assists in assessing future flood-induced risks in Kathmandu.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Environmental Sciences
Sharon E. Nicholson, Douglas A. Klotter
Summary: This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. The diversity of estimates is generally large, with reanalysis products showing little relationship with observed rainfall or satellite estimates. The difficulty in assessing factors governing the interannual variability of rainfall in these regions is highlighted.
Article
Meteorology & Atmospheric Sciences
Geraldo Moura Ramos Filho, Victor Hugo Rabelo Coelho, Emerson da Silva Freitas, Yunqing Xuan, Luca Brocca, Cristiano das Neves Almeida
Summary: Gridded satellite-based rainfall products have been evaluated for flood hazards monitoring. The results show that these products tend to underestimate extreme rainfall events and the rainfall thresholds delineated by the satellite-based products also tend to be underestimated. However, in regions with low-density rain gauges, the satellite-based rainfall products can be used as an alternative data source to determine precipitation thresholds when considering multi-daily accumulated data.
ATMOSPHERIC RESEARCH
(2022)
Article
Environmental Sciences
Sheng Wang, Ke Zhang, Lijun Chao, Guoding Chen, Yi Xia, Chuntang Zhang
Summary: This study evaluates the applicability of three satellite rainfall data sets (CMORPH, GPM, and TRMM) for predicting flood and landslide hazards using a coupled hydrological-slope stability model. The results demonstrate that these satellite rainfall data sets can simulate the spatial distribution of flood and landslide events well, and the physically based slope stability model has higher global accuracy compared to the classical intensity-duration rainfall threshold method, with GPM rainfall providing the highest quality among the three data sets.
Article
Meteorology & Atmospheric Sciences
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, Alan J. P. Calheiros
Summary: This study presents Hydronn, a neural-network-based precipitation retrieval method that utilizes visible and infrared observations from geostationary satellites to provide near-real-time precipitation estimates for Brazil. The results show that Hydronn achieves high accuracy in precipitation estimation and detection compared to conventional methods. The study demonstrates the potential of deep-learning-based precipitation retrievals to improve precipitation estimates from satellite imagery.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2022)
Article
Meteorology & Atmospheric Sciences
Michael J. Erickson, Benjamin Albright, James A. Nelson
Summary: This study evaluates the verification of the Weather Prediction Center's Excessive Rainfall Outlook (ERO) and finds variations in the accuracy of its forecasts across different geographic regions and seasons. While ERO exhibits good reliability overall, some probabilistic categories may need more frequent issuances. Skill metrics such as AUC and BSS are useful, and verification against PP can provide more specific insights into the skillfulness of ERO.
WEATHER AND FORECASTING
(2021)
Article
Environmental Sciences
Girma Berhe Adane, Birtukan Abebe Hirpa, Chul-Hee Lim, Woo-Kyun Lee
Summary: This study evaluates and compares various satellite rainfall estimates with ground-observed data in central and northeastern parts of Ethiopia. The results show that different SREs exhibit varying trends and efficiencies under different elevations and terrains. Further analysis suggests that SREs can be alternative options for rainfall frequency, flood, and drought monitoring studies, with potential need for bias corrections to improve data quality.
Article
Engineering, Environmental
Punpim Puttaraksa Mapiam, Sikarin Sakulnurak, Monton Methaprayun, Choowit Makmee, Nat Marjang
Summary: This study investigated transformation equations to convert the calibrated daily Z-R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product. The results showed that combining the proposed 15-min Z-R scaling equation and the spatiotemporal scheme produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin.
WATER SCIENCE AND TECHNOLOGY
(2023)
Article
Geosciences, Multidisciplinary
J. L. Bytheway, E. J. Thompson, J. Yang, H. Chen
Summary: Passive aquatic listeners (PALs) have the potential to provide valuable reference data for evaluating satellite-based precipitation estimates (SPEs). In this study, we compare three SPE products (IMERG, CMORPH, and PDIR-Now) to PAL measurements over tropical, extratropical, and global oceans. We find that the SPEs have similar rain rate frequency distributions as PAL, but exhibit biases and performance differences depending on the region and time scale.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Astronomy & Astrophysics
Rocky Talchabhadel, Hajime Nakagawa, Kenji Kawaike, Kazuki Yamanoi, Herman Musumari, Tirtha Raj Adhikari, Rajaram Prajapati
Summary: This study investigated an extreme precipitation event in the West Rapti River Basin, Nepal in August 2014 and evaluated different satellite-based rainfall estimates (SREs) in capturing the event. While all SREs showed a similar pattern to gauge data on a daily scale, they were not able to replicate results accurately on a sub-daily scale. Overall, there is a significant challenge in using SREs for local flood simulations at high-temporal resolution in Nepal.
EARTH AND SPACE SCIENCE
(2021)
Article
Environmental Sciences
Nabila Siti Burnama, Faizal Immaddudin Wira Rohmat, Mohammad Farid, Arno Adi Kuntoro, Hadi Kardhana, Fauzan Ikhlas Wira Rohmat, Winda Wijayasari
Summary: This study presents a data-driven method for predicting flood inundation height across the Majalaya Watershed, by combining data from the HEC-RAS model, GSMaP satellite rainfall data, elevation, and other spatial data to build an artificial neural network model. The trained ANN model showed excellent validation performances and demonstrated the capability of predicting flood inundation height with unseen data, suggesting the potential of this approach in reducing flood risks.
Article
Engineering, Civil
Gilbert Hinge, Mohamed A. Hamouda, Di Long, Mohamed M. Mohamed
Summary: This work summarizes the lessons learned from using satellite precipitation products (SPPs) for flood simulation and prediction, and proposes future directions for research in this field. The study conducted a meta-analysis to review the impact of climate zones, topographical features, hydrological model selection, and calibration procedures on the performance of SPP-forced hydrological models. The results showed that SPPs performed better in temperate and tropical climates compared to dry climates. Areas with low lying and moderate elevations showed higher accuracy in performance compared to landscapes at higher latitudes. SPPs using microwave algorithms were found to outperform other methods. The best results in simulation and prediction were achieved after bias correction and model recalibration. Overall, SPPs offer great potential for flood simulation and prediction, but their performance needs improvement for operational purposes. The study also discusses bias correction techniques, model recalibration, the importance of interaction between different types of SPPs and hydrological models, as well as other lessons learned and future directions for using SPPs in flood applications.
JOURNAL OF HYDROLOGY
(2022)
