scholarly journals The influence of antecedent conditions on flood risk in sub-Saharan Africa

2018 ◽  
Vol 18 (1) ◽  
pp. 271-285 ◽  
Author(s):  
Konstantinos Bischiniotis ◽  
Bart van den Hurk ◽  
Brenden Jongman ◽  
Erin Coughlan de Perez ◽  
Ted Veldkamp ◽  
...  
Keyword(s):  
Flood Risk ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Lead Times ◽  
Warning Systems ◽  
Pragmatic Approach ◽  
Physical Processes ◽  
Antecedent Conditions ◽  
Sub Saharan ◽  
Flood Generation

Abstract. Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydro-meteorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a) weather timescale (0–6 days) and (b) seasonal timescale conditions (up to 6 months) before the event. The 7-day precipitation preceding a flood event (PRE7) and the standardized precipitation evapotranspiration index (SPEI) are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet) values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.

scholarly journals The influence of antecedent conditions on flood risk in sub-Saharan Africa

2017 ◽  
Author(s):  
Konstantinos Bischiniotis ◽  
Bart van den Hurk ◽  
Brenden Jongman ◽  
Erin Coughlan de Perez ◽  
Ted Veldkamp ◽  
...  
Keyword(s):  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Lead Times ◽  
Warning Systems ◽  
Antecedent Conditions ◽  
Seasonal Scale ◽  
Additional Input ◽  
Sub Saharan ◽  
Flood Generation

Abstract. Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer – seasonal – time scales can also contribute to flood generation. In this study, the hydro-meteorological pre-conditions of 501 historical damaging flood events over the period 1980 to 2010 in sub-Saharan Africa are analyzed. These are separated into a) a short-term weather scale period (0–7 days) and b) a long-term seasonal-scale period (up to 6 months) before the flood event. Total 7-day precipitation is used to evaluate weather-scale conditions, while seasonal-scale conditions are reflected in the Standardized Precipitation Evapotranspiration Index (SPEI). Although the latter has been used for drought detection, because of its characteristics can also become a wetness monitoring tool. Results indicate that, although heavy 7-day lead precipitation is connected with the majority of the reported floods (72 %), more than 50 % of all floods exhibited higher than average antecedent conditions during the 6 preceding months. In case of extremely wet weather and seasonal scale conditions (SPEI > 2) the probability of flood is close to 50 %. The combined analysis of the two periods revealed that seasonal-scale information should not be neglected, and seasonal SPEI information could be a useful – additional – input to the weather-scale flood forecasts to improve flood preparedness.

scholarly journals A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0242883
Author(s):  
Shraddhanand Shukla ◽  
Greg Husak ◽  
William Turner ◽  
Frank Davenport ◽  
Chris Funk ◽  
...  
Keyword(s):  
Early Warning ◽  
Rainy Season ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Onset Date ◽  
Warning Systems ◽  
Seasonal Drought ◽  
Drought Conditions ◽  
Sub Saharan ◽  
Emergency Food Assistance

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the “Crisis” phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

scholarly journals Community Based Early Warning Systems for flood risk mitigation in Nepal

2016 ◽  
Author(s):  
Paul J. Smith ◽  
Sarah Brown ◽  
Sumit Dugar
Keyword(s):  
Early Warning ◽  
Flood Risk ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Current Status ◽  
Lead Times ◽  
Time Series Models ◽  
Warning Systems ◽  
Community Based ◽  
Probabilistic Forecasts

Abstract. This paper focuses on the use of Community Based Early Warning Systems for flood risk mitigation in Nepal. The first part of the work outlines the evolution and current status of these community based systems. A significant ongoing challenge faced by Community Based Early Warning Systems in Nepal is the short lead times available for early warning. The second part of the paper therefore focuses on the development of a robust operational flood forecasting methodology for use by the Department for Hydrology and Meteorology (DHM), Government of Nepal to compliment the community based systems. The resulting methodology uses data based physically interpretable time series models and data assimilation to generate probabilistic forecasts. The paper concludes with an example application to a flood prone catchment (Karnali Basin) in western Nepal.

scholarly journals A metric-based assessment of flood risk and vulnerability of rural communities in the Lower Shire Valley, Malawi

2015 ◽  
Vol 370 ◽  
pp. 139-145 ◽  
Author(s):  
A. J. Adeloye ◽  
F. D. Mwale ◽  
Z. Dulanya
Keyword(s):  
Rural Communities ◽  
Spatial Variability ◽  
Flood Risk ◽  
Flood Hazard ◽  
Sub Saharan Africa ◽  
Structured Interviews ◽  
Adaptation Measures ◽  
Flood Depth ◽  
Inundation Area ◽  
Sub Saharan

Abstract. In response to the increasing frequency and economic damages of natural disasters globally, disaster risk management has evolved to incorporate risk assessments that are multi-dimensional, integrated and metric-based. This is to support knowledge-based decision making and hence sustainable risk reduction. In Malawi and most of Sub-Saharan Africa (SSA), however, flood risk studies remain focussed on understanding causation, impacts, perceptions and coping and adaptation measures. Using the IPCC Framework, this study has quantified and profiled risk to flooding of rural, subsistent communities in the Lower Shire Valley, Malawi. Flood risk was obtained by integrating hazard and vulnerability. Flood hazard was characterised in terms of flood depth and inundation area obtained through hydraulic modelling in the valley with Lisflood-FP, while the vulnerability was indexed through analysis of exposure, susceptibility and capacity that were linked to social, economic, environmental and physical perspectives. Data on these were collected through structured interviews of the communities. The implementation of the entire analysis within GIS enabled the visualisation of spatial variability in flood risk in the valley. The results show predominantly medium levels in hazardousness, vulnerability and risk. The vulnerability is dominated by a high to very high susceptibility. Economic and physical capacities tend to be predominantly low but social capacity is significantly high, resulting in overall medium levels of capacity-induced vulnerability. Exposure manifests as medium. The vulnerability and risk showed marginal spatial variability. The paper concludes with recommendations on how these outcomes could inform policy interventions in the Valley.

scholarly journals Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 183
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

Selecting indicators of drought impacts: the importance of context

2021 ◽  
Author(s):  
Sarra Kchouk ◽  
Pieter van Oel ◽  
Lieke Melsen
Keyword(s):  
Early Stage ◽  
Contextual Information ◽  
Geographic Area ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Drought Impacts ◽  
Direct Linkage ◽  
Drought Indicators ◽  
Sub Saharan ◽  
Drought Early Warning

<p>Drought Early Warning Systems (DEWS) and Drought Monitoring Systems (DMS) are the principal tools used to tackle drought at an early stage and reduce the possibility of harm or loss. They are based on the use of drought indicators attributed to either : meteorological, agricultural and hydrological drought. This means that it is mostly hydro-climatic variables that are used to determine the onset, end and severity of a drought.  Drought impacts are rarely continuously monitored or even not included in DEWS and DMS. In this configuration, the likelihood of experiencing impacts is linearly linked to the severity of climatic features only. The aim of our study is to question the direct linkage between the delivery of hydro-climatic information and the detection of drought impacts and their severity. We reviewed scientific literature on drought drivers and impacts and analyzed how these two compare. We conducted a bibliometric analysis based on 4000+ scientific studies sorted by geographic area in which selected (i) drought indicators and (ii) impacts of drought were mentioned. Our review points toward an attachment to a conceptual view of drought by the main and broader use of meteorological (computed and remotely sensed) drought indicators. Studies reporting impacts related to food and water securities are more localized, respectively in Sub-Saharan Africa and Australasia. This mismatch suggests a tendency to translate hydroclimatic indicators of drought directly into impacts while neglecting relevant local contextual information. With the aim of sharpening the information provided by DEWS and DMS, we argue in favor of an additional consideration of drought indicators oriented towards the SDGs.</p>

scholarly journals Open source geoprocessing tools and meteorological satellite data for crop risk zones monitoring in Sub-Saharan Africa

2016 ◽  
Author(s):  
Tiziana De Filippis ◽  
Leandro Rocchi ◽  
Patrizio Vignaroli ◽  
Maurizio Bacci ◽  
Vieri Tarchiani ◽  
...  
Keyword(s):  
Food Security ◽  
Open Source ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Precipitation Forecast ◽  
Drought Risk ◽  
Data Set ◽  
Web Based ◽  
Risk Zones ◽  
Sub Saharan

In Sub-Saharan Africa analysis tools and models based on meteorological satellites data have been developed within different national and international cooperation initiatives, with the aim of allowing a better monitoring of the cropping season. In most cases, the software was a stand-alone application and the upgrading, in terms of analysis functions, database and hardware maintenance, was difficult for the National Meteorological Services (NMSs) in charge of agro-hydro-meteorological monitoring. The web-based solution proposed in this work intends to improve and ensure the sustainability of applications to support national Early Warning Systems (EWSs) for food security. The Crop Risk Zones (CRZ) model for Niger and Mali, integrated in a web-based open source framework, has been implemented using PL/pgSQL & PostGIS functions to process different meteorological data sets: a) the rainfall precipitation forecast images from Global Forecast System (GFS) b) the Climate Prediction Center (CPC) Rainfall Estimation (RFE) for Africa c) Multi-Sensor Precipitation Estimate (MPE) images from EUMETSAT Earth Observation Portal d) the MOD16 Global Terrestrial Evapotranspiration Data Set. Restful Web Services upload raster images into the PostgreSQL/PostGIS database. PL/pgSQL functions are used to run the CRZ model to identify installation and phenological phases of the main crops in the Region and to create crop risk zones images. This model is focused on the early identification of risks and the production of information for food security within the time prescribed for decision-making. The challenge and the objective of this work is to set up an open access monitoring system, based on meteorological open data providers, targeting NMSs and any other local decision makers for drought risk reduction and resilience improvement.

scholarly journals Flood Early Warning Systems using Machine Learning Techniques. Case the Tomebamba Catchment at the Southern Andes of Ecuador

2021 ◽  
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Flood Early Warning Systems (FEWSs) using Machine Learning (ML) has gained worldwide popularity. However, determining the most efficient ML technique is still a bottleneck. We assessed FEWSs with three river states, No-alert, Pre-alert, and Alert for flooding, for lead times between 1 to 12 hours using the most common ML techniques, such as Multi-Layer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1- and 12-hour cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of the society for floods.

Sign in / Sign up

Export Citation Format

Share Document