Evaluation of NCEP-GFS-based Rainfall forecasts over the Nagavali and Vamsadhara basins in India

Rao, G. Venkata; Reddy, K. Venkata; Sridhar, Venkataramana; Srinivasan, Raghavan; Umamahesh, N. V.; Pratap, Deva

doi:10.1016/j.atmosres.2022.106326

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…From the NCAR Research Data Archive, GFS forecasts were downloaded at a resolution of 0.25° × 0.25°. The detailed explanation about the evaluation of GFS rainfall forecasts over Nagavali and Vamsadhara basins can be found at Rao et al (2022).…”

Section: Gfs Forecasted Datamentioning

confidence: 99%

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Venkata Rao,

Nagireddy,

Keesara

et al. 2024

Nat Hazards

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Due to recent rainfall extremes and tropical cyclones that form over the Bay of Bengal during the pre- and post-monsoon seasons, the Nagavali and Vamsadhara basins in India experience frequent floods, causing significant loss of human life and damage to agricultural lands and infrastructure. This study provides an integrated hydrologic and hydraulic modeling system that is based on the Soil and Water Assessment Tool model and the 2-Dimensional Hydrological Engineering Centre-River Analysis System, which simulates floods using Global Forecasting System rainfall forecasts with a 48-h lead time. The integrated model was used to simulate the streamflow, flood area extent, and depth for the historical flood events (i.e., 1991–2018) with peak discharges of 1200 m3/s in the Nagavali basin and 1360 m3/s in the Vamsadhara basin. The integrated model predicted flood inundation depths that were in good agreement with observed inundation depths provided by the Central Water Commission. The inundation maps generated by the integrated modeling system with a 48-h lead time for tropical cyclone Titli demonstrated an accuracy of more than 75%. The insights gained from this study will help the public and government agencies make better decisions and deal with floods.

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Section: Gfs Forecasted Datamentioning

confidence: 99%

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Venkata Rao,

Nagireddy,

Keesara

et al. 2024

Nat Hazards

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“…Recent studies of precipitation quality suggest that in situ weather stations could be replaced by numerical models [16] or remotely sensed measures [11,13,29,44,59] in South America. Future phases could introduce the use of operational precipitation forecasts such as GFS [72], Weather Research Forecast (WRF) [73] and CHIRPS-GEFS [10].…”

Section: Future Outlookmentioning

confidence: 99%

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

Maragaño-Carmona,

Fustos Toribio,

Descote

et al. 2023

Water

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The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.

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Evaluation of NCEP-GFS-based Rainfall forecasts over the Nagavali and Vamsadhara basins in India

Cited by 2 publications

References 42 publications

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Real-time flood forecasting using an integrated hydrologic and hydraulic model for the Vamsadhara and Nagavali basins, Eastern India

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

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