scholarly journals A pan-African Flood Forecasting System

2014 ◽  
Vol 11 (5) ◽  
pp. 5559-5597 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Lead Times ◽  
Flood Events ◽  
Pan African ◽  
Long Duration ◽  
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 of the ECMWF and critical hydrological thresholds. In this paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as with 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 "Save flooding" 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.

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.

scholarly journals Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

2021 ◽  
Vol 13 (21) ◽  
pp. 4459
Author(s):  
Aline Falck ◽  
Javier Tomasella ◽  
Fabrice Papa
Keyword(s):  
Data Collection ◽  
Lead Time ◽  
Flood Forecasting ◽  
Lead Times ◽  
Latency Time ◽  
Distributed Hydrological Model ◽  
Satellite Altimeter ◽  
Operational Characteristics ◽  
Potential Benefits ◽  
Forecasting System

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times.

scholarly journals Reply to “Comments on ‘Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence’”

2016 ◽  
Vol 31 (4) ◽  
pp. 1397-1405
Author(s):  
Weihong Qian ◽  
Ning Jiang ◽  
Jun Du
Keyword(s):  
Large Scale ◽  
Value Added ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Small Scale ◽  
Precipitation Efficiency ◽  
Long Duration ◽  
Advection Term ◽  
Rain Events ◽  
Mathematical Derivation

Abstract Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.

scholarly journals Implementation and validation of a coastal forecasting system for wind waves in the Mediterranean Sea

2012 ◽  
Vol 12 (2) ◽  
pp. 485-494 ◽  
Author(s):  
R. Inghilesi ◽  
F. Catini ◽  
G. Bellotti ◽  
L. Franco ◽  
A. Orasi ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Coastal Area ◽  
Large Scale ◽  
Wind Waves ◽  
Coastal Areas ◽  
Small Scale ◽  
Tyrrhenian Sea ◽  
Coastal System ◽  
Forecasting System ◽  
Propagation Of Waves

Abstract. A coastal forecasting system was implemented to provide wind wave forecasts over the whole Mediterranean Sea area, and with the added capability to focus on selected coastal areas. The goal of the system was to achieve a representation of the small-scale coastal processes influencing the propagation of waves towards the coasts. The system was based on a chain of nested wave models and adopted the WAve Model (WAM) to analyse the large-scale, deep-sea propagation of waves; and the Simulating WAves Nearshore (SWAN) to simulate waves in key coastal areas. Regional intermediate-scale WAM grids were introduced to bridge the gap between the large-scale and each coastal area. Even applying two consecutive nestings (Mediterranean grid → regional grid → coastal grid), a very high resolution was still required for the large scale WAM implementation in order to get a final resolution of about 400 m on the shores. In this study three regional areas in the Tyrrhenian Sea were selected, with a single coastal area embedded in each of them. The number of regional and coastal grids in the system could easily be modified without significantly affecting the efficiency of the system. The coastal system was tested in three Italian coastal regions in order to optimize the numerical parameters and to check the results in orographically complex zones for which wave records were available. Fifteen storm events in the period 2004–2009 were considered.

scholarly journals Sensitivity of Heavy Precipitation Forecasts to Small Modifications of Large-Scale Weather Patterns for the Elbe River

2010 ◽  
Vol 11 (3) ◽  
pp. 770-780 ◽  
Author(s):  
Ingo Schlüter ◽  
Gerd Schädler
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Synoptic Situation ◽  
Flood Events ◽  
Weather Patterns ◽  
Extreme Precipitation Events ◽  
The Impact ◽  
Precipitation Events

Abstract Extreme flood events are caused by long-lasting and/or intensive precipitation. The detailed knowledge of the distribution, intensity, and spatiotemporal variability of precipitation is, therefore, a prerequisite for hydrological flood modeling and flood risk management. For hydrological modeling, temporal and spatial high-resolution precipitation data can be provided by meteorological models. This study deals with the question of how small changes in the synoptic situation affect the characteristics of extreme forecasts. For that purpose, two historic extreme precipitation events were hindcasted using the Consortium for Small Scale Modeling (COSMO) model of the German Weather Service (DWD) with different grid resolutions (28, 7, and 2.8 km), where the domains with finer resolutions were nested into the ones with coarser resolution. The results show that the model is capable of simulating such extreme precipitation events in a satisfactory way. To assess the impact of small changes in the synoptic situations on extreme precipitation events, the large-scale atmospheric fields were shifted to north, south, east, and west with respect to the orography by about 28 and 56 km, respectively, in one series of runs while in another series, the relative humidity and temperature were increased to modify the amount of precipitable water. Both series were performed for the Elbe flood events in August 2002 and January 2003, corresponding to two very different synoptic situations. The results show that the modeled precipitation can be quite sensitive to small changes of the synoptic situation with changes in the order of 20% for the maximum daily precipitation and that the types of synoptic situations play an important role. While van Bebber weather conditions, of Mediterranean origin, were quite sensitive to modifications, more homogeneous weather patterns were less sensitive.

scholarly journals Thunderstorms Do Not Get Butterflies

2016 ◽  
Vol 97 (2) ◽  
pp. 237-243 ◽  
Author(s):  
Dale R. Durran ◽  
Jonathan A. Weyn
Keyword(s):  
Large Scale ◽  
Squall Line ◽  
Small Scale ◽  
Lead Times ◽  
Initial State ◽  
Weather Forecasts ◽  
Kinetic Energy Spectrum ◽  
Initial Errors ◽  
Squall Lines ◽  
Homogeneous Environment

Abstract One important limitation on the accuracy of weather forecasts is imposed by unavoidable errors in the specification of the atmosphere’s initial state. Much theoretical concern has been focused on the limits to predictability imposed by small-scale errors, potentially even those on the scale of a butterfly. Very modest errors at much larger scales may nevertheless pose a more important practical limitation. We demonstrate the importance of large-scale uncertainty by analyzing ensembles of idealized squall-line simulations. Our results imply that minimizing initial errors on scales around 100 km is more likely to extend the accuracy of forecasts at lead times longer than 3–4 h than efforts to minimize initial errors on much smaller scales. These simulations also demonstrate that squall lines, triggered in a horizontally homogeneous environment with no initial background circulations, can generate a background mesoscale kinetic energy spectrum roughly similar to that observed in the atmosphere.

scholarly journals An Attempt to Improve the Forecasting of Persistent Severe Rainfall Using the Spectral Nudging and Update Cycle Methods

2017 ◽  
Vol 32 (2) ◽  
pp. 713-723 ◽  
Author(s):  
Yanfeng Zhao ◽  
Donghai Wang ◽  
Jianjun Xu
Keyword(s):  
Large Scale ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Brier Score ◽  
Small Scale ◽  
Lead Times ◽  
Spectral Nudging ◽  
Numerical Predictions ◽  
Forecast Lead Time ◽  
Accumulated Rainfall

Abstract Using the interior spectral nudging and update cycle (SN+UIC) methods in the regional Weather Research and Forecasting (WRF) Model, the numerical predictions of four persistent severe rainfall (PSR) events during the preflood season in south China were investigated, based on the fact that the global model has an advantage in predicting the large-scale atmospheric variation and the regional model is better in terms of simulating small-scale changes. The simulation results clearly indicated that the SN+UIC improved the prediction of the PSR events’ daily precipitation for moderate, heavy, and torrential rains (10–100 mm day−1). It also improved the simulative forecasts of the two categories of rain with accumulated precipitation above 50 and 100 mm at lead times of 5–11 days. Moreover, the longer the forecast lead time is, the larger the decrease in the Brier score. Additionally, the SN+UIC method decreased the root-mean-square error for accumulated rainfall (6.2%) and relative humidity (5.67%).

scholarly journals On the use of weather regimes to forecast meteorological drought over Europe

2018 ◽  
Vol 18 (12) ◽  
pp. 3297-3309 ◽  
Author(s):  
Christophe Lavaysse ◽  
Jürgen Vogt ◽  
Andrea Toreti ◽  
Marco L. Carrera ◽  
Florian Pappenberger
Keyword(s):  
Large Scale ◽  
Warning System ◽  
Meteorological Drought ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
European Continent ◽  
Weather Regimes ◽  
The North ◽  
The One

Abstract. An early warning system for drought events can provide valuable information for decision makers dealing with water resources management and international aid. However, predicting such extreme events is still a big challenge. In this study, we compare two approaches for drought predictions based on forecasted precipitation derived from the Ensemble extended forecast model (ENS) of the ECMWF, and on forecasted monthly occurrence anomalies of weather regimes (MOAWRs), also derived from the ECMWF model. Results show that the MOAWRs approach outperforms the one based on forecasted precipitation in winter in the north-eastern parts of the European continent, where more than 65 % of droughts are detected 1 month in advance. The approach based on forecasted precipitation achieves better performance in predicting drought events in central and eastern Europe in both spring and summer, when the local atmospheric forcing could be the key driver of the precipitation. Sensitivity tests also reveal the challenges in predicting small-scale droughts and drought onsets at longer lead times. Finally, the results show that the ENS model of the ECMWF successfully represents most of the observed linkages between large-scale atmospheric patterns, depicted by the weather regimes and drought events over Europe.

Flood Inundation Simulations based on GSMaP Satellite Rainfall Data in Jakarta, Indonesia

2020 ◽  
Author(s):  
Bambang Adhi Priyambodoho ◽  
Shuichi Kure ◽  
Ryuusei Yagi ◽  
Nurul Fajar Januriyadi
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Observation Data ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Concentration Time ◽  
Gauge Data ◽  
Forecasting System

Abstract Jakarta is the capital of Indonesia and is considered as one of the most vulnerable cities to climate-related disasters, including flooding, sea-level rise, and storm surge, in the world. Therefore, the development of a flood-forecasting system for Jakarta is crucial. However, the accurate prediction of flooding in Jakarta is challenging because of the rapid flood-concentration time in highly urbanized basins and the shortage of rainfall data in poorly gauged areas. The aim of this study is to simulate flood inundation that occurred in recent years using global satellite mapping of precipitation (GSMaP) products. The GSMaP products (NRT and Gauge V7) were evaluated and compared with the observation data obtained hourly from five ground stations in the Ciliwung River Basin. In addition, a rainfall-runoff and flood inundation model were applied to the target basin. The results of the analysis showed that the GSMaP Gauge data were more accurate than the GSMaP NRT data. However, the GSMaP Gauge could not be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting. We conclude that the GSMaP Gauge is suitable for replicating past flood events, but it is challenging to use the GSMaP NRT for real-time flood forecasting in Jakarta.

scholarly journals Using a Tank Model to Determine Hydro-Meteorological Thresholds for Large-Scale Landslides in Taiwan

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 253
Author(s):  
Guan-Wei Lin ◽  
Hsien-Li Kuo ◽  
Chi-Wen Chen ◽  
Lun-Wei Wei ◽  
Jia-Ming Zhang
Keyword(s):  
Large Scale ◽  
Early Warning Systems ◽  
Small Scale ◽  
Multiple Sources ◽  
Tank Model ◽  
Essential Information ◽  
Water Index ◽  
Disturbed Area ◽  
Long Duration ◽  
Water Depths

Rainfall thresholds for slope failures are essential information for establishing early-warning systems and for disaster risk reduction. Studies on the thresholds for rainfall-induced landslides of different scales have been undertaken in recent decades. This study attempts to establish a warning threshold for large-scale landslides (LSLs), which are defined as landslides with a disturbed area more massive than 0.1 km2. The numerous landslides and extensive rainfall records make Taiwan an appropriate area to investigate the rainfall conditions that can result in LSLs. We used landslide information from multiple sources and rainfall data captured by 594 rain gauges to create a database of 83 rainfall events associated with LSLs in Taiwan between 2001 and 2016. The corresponding rainfall duration, cumulative event rainfall, and rainfall intensity for triggering LSLs were determined. This study adopted the tank model to estimate conceptual water depths (S1, S2, S3) in three-layer tanks and calculated the soil water index (SWI) by summing up the water depths in the three tanks. The empirical SWI and duration (SWI–D) threshold for triggering LSLs occurring during 2001–2013 in Taiwan is determined as SWI = 155.20 − 1.56D and D ≥ 24 h. The SWI–D threshold for LSLs is higher than that for small-scale landslides (SSLs), those with a disturbed area smaller than 0.1 km2. The LSLs that occurred during 2015–2016 support this finding. It is notable that when the SWI and S3 reached high values, the potential of LSLs increased significantly. The rainfall conditions for triggering LSLs gradually descend with increases in antecedent SWI. Unlike the rainfall conditions for triggering SSLs, those for triggering LSLs are related to the long duration–high intensity type of rainfall event.

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