Flash flood in the mountainous region of Rio de Janeiro state (Brazil) in 2011: part I—calibration watershed through hydrological SMAP model-Reference-Cited by-同舟云学术

Flash flood in the mountainous region of Rio de Janeiro state (Brazil) in 2011: part I—calibration watershed through hydrological SMAP model

Published:2020-05-02 Issue:3 Volume:102 Page:1117-1134
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Cavalcante Marianna Rodrigues Gullo^ORCID,da Cunha Luz Barcellos Priscila,Cataldi Marcio

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

https://link.springer.com/content/pdf/10.1007/s11069-020-03948-3.pdf

Reference54 articles.

1. Ahasan MN, Khan AQ (2013) Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan using WRF–ARW model. Nat Hazards 69:351–363

2. Amengual A, Homar V, Jaume O (2015) Potential of a probabilistic hydrometeorological forecasting approach for the 28 September 2012 extreme flash flood in Murcia, Spain. Atmos Res 166:10–23

3. Atencia A et al (2011) Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model. Hydrol Earth Syst Sci 15:3809–3827

4. Borges AC, Mediondo EM (2007) Comparison between the empirical equations for the estimation of reference evapotranspiration in the Jacupiranga river basin. Braz J Agric Environ Eng 11:293–300

5. Braga RS, Cataldi M, Oliveira HC (2009) Flow forecasting methodology in the Rio Grande basin. XVIII Brazilian Symposium on Water Resources. Brazilian Water Resources Association

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model;Natural Hazards;2024-05-26

2. Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System;Water;2024-02-16

3. Combining traditional hydrological models and machine learning for streamflow prediction;RBRH;2024

4. Development of a soil moisture forecasting method for a landslide early warning system (LEWS): Pilot cases in coastal regions of Brazil;Journal of South American Earth Sciences;2023-11

5. Evaluation of Extreme Hydroclimatic Trends in River Basins Located in the Northeast and South Regions of Brazil;Atmosphere;2023-09-02