scholarly journals FLOOD FORECAST IN MANAUS, AMAZONAS, BRAZIL

2020 ◽  
Author(s):  
JUSSARA SOCORRO CURY MACIEL ◽  
LUNA GRIPP SIMÕES ALVES ◽  
BRUNO GABRIEL DOS SANTOS CORRÊA ◽  
IRAÚNA MAICONÃ RODRIGUES DE CARVALHO ◽  
MARCO ANTÔNIO OLIVEIRA
Keyword(s):  
Flood Forecast

scholarly journals Research on flood forecast of dashimen reservoir in xinjiang based on melting snow and runoff yield under excess infiltration

2021 ◽  
Vol 826 (1) ◽  
pp. 012010
Author(s):  
Yi Liu ◽  
Fenglong Zhang ◽  
Haiyan Wu ◽  
Yongfeng Li ◽  
Zhiqiang Jiang ◽  
...  
Keyword(s):  
Flood Forecast ◽  
Melting Snow

On the need of ensemble flood forecast in India

2021 ◽  
Vol 12 ◽  
pp. 100086
Author(s):  
J.S. Nanditha ◽  
Vimal Mishra
Keyword(s):  
Flood Forecast

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Flood forecast skill for Early Action: Results and Learnings from the development of the Early-Action Protocol for Floods in Uganda

2021 ◽  
Author(s):  
Andrea Ficchì ◽  
Hannah Cloke ◽  
Linda Speight ◽  
Douglas Mulangwa ◽  
Irene Amuron ◽  
...  
Keyword(s):  
River Flow ◽  
National Level ◽  
Scale Up ◽  
Forecast Skill ◽  
Red Cross ◽  
Flood Forecast ◽  
Global Team ◽  
Early Action ◽  
Skill Scores ◽  
Local Risk

<p>Global flood forecasting systems are helpful in complementing local resources and in-country data to support humanitarians and trigger early action before an impactful flood occurs. Freely available global flood forecast information from the European Commission’s Global Flood Awareness System (GloFAS, a Copernicus EMS service) is being used by the Uganda Red Cross Society (URCS) alongside in-country knowledge to develop appropriate triggers for early actions for flood preparedness, within the Forecast-based Financing (FbF) initiative. To scale up the first FbF pilot to a national level, in 2020 URCS collaborated with several partners including the Red Cross Red Crescent Climate Centre (RCCC), the Uganda’s Ministry of Water and Environment, through the Directorate of Water Resources Management (DWRM), the Uganda National Meteorological Authority (UNMA), the 510 Global team and the University of Reading, through the UK-supported project Forecasts for Anticipatory Humanitarian Action (FATHUM). The new Early Action Protocol (EAP) for floods, submitted to the IFRC’s validation committee in September 2020, is now under review.</p><p>One of the aims of an EAP is to set the triggers for early action, based on forecast skill information, alongside providing a local risk analysis, and describing the early actions, operational procedures, and responsibilities. Working alongside our partners and practitioners in Uganda, we developed a methodology to tailor flood forecast skill analysis to EAP development, that could be potentially useful for humanitarians in other Countries and forecasters engaging with them. The key aim of the analysis is to identify skilful lead times and appropriate triggers for early action based on available operational forecasts, considering action parameters, such as an Action Lifetime of 30 days, and focusing on relevant flood thresholds and skill scores. We analysed the skill of probabilistic flood forecasts from the operational GloFAS (v2.1) system across Uganda against river flow observations and reanalysis data. One of the challenges was to combine operational needs with statistical robustness requirements, using relevant flood thresholds for action. Here we present the results from the analysis carried out for Uganda and the verification workflow, that we plan to make openly available to all practitioners and scientists working on the implementation of forecast-based actions.</p>

scholarly journals Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

2011 ◽  
Vol 29 ◽  
pp. 61-67 ◽  
Author(s):  
H.-J. Bao ◽  
L.-N. Zhao ◽  
Y. He ◽  
Z.-J. Li ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Grid Cell ◽  
Flood Forecasting ◽  
False Alarms ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Xinanjiang Model ◽  
Numerical Weather ◽  
Number Of False Alarms ◽  
Promising Tool

Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

Analysis of snowmelt model for flood forecast for water in arid zone: case of Tarim River in Northwest China

2011 ◽  
Vol 66 (5) ◽  
pp. 1423-1429 ◽  
Author(s):  
Mupenzi Jean de la Paix ◽  
Li Lanhai ◽  
Ge Jiwen ◽  
Habumugisha Jean de Dieu ◽  
Nzayisenga Theoneste
Keyword(s):  
Arid Zone ◽  
Northwest China ◽  
Tarim River ◽  
Flood Forecast ◽  
Snowmelt Model

Effective Representation of River Geometry in Hydraulic Flood Forecast Models

2018 ◽  
Vol 54 (2) ◽  
pp. 1031-1057 ◽  
Author(s):  
S. Grimaldi ◽  
Y. Li ◽  
J. P. Walker ◽  
V. R. N. Pauwels
Keyword(s):  
Flood Forecast ◽  
Forecast Models ◽  
Effective Representation
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