Power relations: a catalyst in the building of flood disaster resilience capacities in the orashi region of rivers state

Climate change-related disasters have in recent years become a global phenomenon with catastrophic consequences. Africa has had most of the consequences of climate change related disasters, resulting in monumental urban and rural flooding, widespread casualties, displacements, loss of property and sources of livelihood. Given the long-term implications of climate change, it is critically important to understand how vulnerable communities respond to the menace occasioned by flooding. The impact of the flooding is felt more in low-lying communities situated along the coastal fringes leaving inherently vulnerable communities to the vagaries of flooding. However, despite of their vulnerability to flooding disasters, some people displayed resilience capacities more than others because of their apparent access to resources and power within and outside their localities. The study investigated the issue, through the application of the qualitative approach that drew the Bourdieusian theory of practice, deploying the analytical concepts of fields, habitus and species of capital to gather useful information from relevant focus groups to understand how various forms of power was employed to capture resources that enhanced resilience capacities in the seasonal flood prone Orashi region of Rivers State of Nigeria. Following the outcome of the analysis of the information gathered from the focus group and a review of relevant literature, it was revealed that most of the vulnerable population displayed some ingrained disposition and the deployment of indigenous knowledge and social capital for adaptation to survive flood disasters. It is therefore concluded that dynamics of power is a key factor in the resilience capacities of the population of the study.

Assessment of flood discharge sensitivity to climate indexes in West Africa

Floods are natural disasters that widely affect people and goods. Its frequency and magnitude are projected to substantially increase due to the ongoing environmental change. At regional and national levels, some efforts have been made in predicting floods at a short-term range. However, the usefulness of flood prediction increases as the time lead increases. The objective of this work is therefore to investigate flood sensitivity to climate indexes in West Africa as a basis for seasonal flood forecasting. The methodology consists of optimizing the relationship between Annual Maximal Discharge (AMD), a proxy for flood discharge and various climate indexes using correlation coefficient, linear regression and statistical modeling based on 56 river gauging stations across West Africa. The climate indexes considered are the Sea Surface Temperature (SST) of the Tropical Northern Atlantic (TNA), SST of the Tropical Southern Atlantic (TSA), the Sea Level Pressure (SLP) of the Southern Oscillation Indexes (SOI) and the detrended El-Nino Southern Oscillation indexes. It was found that SOI/SLP indexes are the most strongly related to the AMD for the investigated stations with generally high, positive, and statistically significant correlation. The TSA/SST indexes indicated both positive and negative statistically significant correlations with river discharge in the region. The percentage change in AMD per unit change in SOI/SLP for most of the statistically significant stations is within 10 % and 50 % indicating a strong relationship between these two variables. This relationship could serve as a basis for seasonal flood forecasting in the study area.

Small Ruminants Grazing as a Rehabilitative Land Management Tool in the Negev Highland; Soil, Geomorphological and Topographical Perspectives

The ‘wadis’ (ephemeral incised channels in arid regions) concern badlands with low agriculture utilisation that expands to neighbouring cultivated areas. They are noticeable and unique landforms characterised by vegetation patches and seasonal flood flows with scenic beauty that must be conserved. The wadi characteristics have influenced the way of life of their indigenous residents from ancient times until now. The main one is grazing with small ruminants (SR). The authorities and public consider grazing in these areas as a destructive land management practice that should be reduced. To assess the viability of grazing in such regions, we hypothesised that fluvial and biological flows tightly correlate with the wadis’ landforms, channels and slopes. The site of study is located in the Yeroham mountains nearby the Rahma planned Bedouin village. Five different transects of channels and slopes were located over representative wadis, including those exposed to grazing. The finding indicates that a herbaceous vegetation expansion uphill was observed only in grazed transects, while the wadi slope patterns affect its patterns. It contains an increased soil water content (from a similar value of 5% until 13% change in the grazed transect), 1.5% higher soil organic matter, 0.08 mg Kg−1 higher Nitrite content and 1–2% higher clay content in the grazed transects, up to 4 m ahead from the channel. The novelty of this finding suggested that the SR influences the organic matter to reach the wadi channel and facilitate the adherence of aggregated clay and the formed colluvial layer that serves as a substrate to the expanded vegetation growth. Adequate implementation of these grazing patterns may rehabilitate degraded ‘wadis’ and increase their tourism eligibility.

Machine learning approaches to parameter calibration and uncertainty propagation for seasonal flood risk prediction

<div data-node-type="line"> <div data-node-type="line"><span>The increase in extreme weather associated with acute climate change is leading to more frequent and severe flood events. </span><span> In the window of months </span><span>and </span><span>years, climate change </span><span>adaption </span><span>is critical to </span><span>mitigate risk on socio-economic systems</span><span>. Mathematical and computational models have become widely used tool</span><span>s</span><span> to </span><span>quantify the impact of catastrophic flooding</span><span> and to predict future</span><span> flood</span> <span>risks</span><span>.</span><span> For decision makers to plan ahead and to select informed policies and interventions, it is </span><span>vital</span><span> that the uncertainties of these models are well estimated</span><span>.</span><span> Besides the inherent uncertainty of the mathematical model, uncertainties arise from parameter calibration and the driving observational climate data.</span></div> <div data-node-type="line"><span>Here we focus on the uncertainty of seasonal flood risk prediction for which we</span><span> treat u</span><span>ncertainty propagation</span><span> as a two step process. Firstly through calibration of model parameter distributions based on observational data. In order to propagate parameter uncertainties, the posed calibration framework is required to infer model parameter posterior distributions, as opposed to a single best-fit estimate. While secondly uncertainty is propagated by the </span><span>seasonal </span><span>weather </span><span>forecasts </span><span>driving the flood risk prediction models, such model drivers have their own inherent uncertainty as predictions. Through handling both sources of uncertainty and its propagation we investigate the impacts of combined</span><span> uncertainty</span><span> quantification methods</span><span> for flooding predictions. </span><span>The first step focussing on the flooding models own characterisation of uncertainty and the second characterising how uncertain model drivers impact our future predictions.</span></div> <div data-node-type="line"><span>In order to achieve the above features of a calibration framework for flood models we leverage concepts from machine learning. At the core we assume a minimisation of a loss function by the methods based on the supervised learning task in order to achieve calibration of the flood model. Uncertainty quantification is equally a growing field in machine learning or AI with regards the interpretability of parametric models. For this purpose we have adopted a Bayesian framework which contains natural descriptions of model expectation and variance. Through combining uncertainty quantification with the steps of supervised learning for parameter calibrations we propose a novel approach for seasonal flood risk prediction.</span></div> </div><div data-node-type="line"></div>

Effects of Seasonal Flooding in Benin City and the need for a Community-Based Adaptation Model in Disaster Management in Nigeria

The present study aimed to investigate the effects of hydrologically induced environmental problem in Benin City and how communities (considered as non-state actors) can be sustainably integrated/participate in monitoring of environmental change, disaster preparedness, post disaster management mechanisms and influence water resources development/management decisions. The study focused on the seasonal flood events of years 2016 and 2017. The study showed that the impacts of flooding in Benin City ranges from submergence of physical infrastructures, loss of agricultural lands/ farms. Using the Focused Group Discussion and Interview methods, 61.9% of flood affected persons agreed that their houses were submerged, 80.5% indicated that their farms, including fish farms, piggery, snail farms, crops and poultry were damaged by floods, 9.6%, indicated having experienced food stock losses due to floods. Most common diseases/sicknesses experienced were diarrhoea (27%), malaria (37%); cough (20%), while sickness due to snake bite was the least (4%). Fe and fecal coli form count values were high during seasonal flood event. Most of the hydraulic regulation projects have failed mainly due to poor feasibility study, inadequacy of hydrological data, non-involvement of relevant stakeholder and the complete absence of community based groups during engineering construction works. The study proposed a State-Non-state actors Integrated Model, which will be registered as a Corporate organization to plan and monitor environmental changes relating to climate change, flood and gully erosion disasters and with the active involvement of NEMA, SEMA, LEMA and other related agencies and NGO. Depending on the size of each Local Government Area in Benin, the proposed committee will comprise of 25-50 members. The study recommends capacity building of members in the form of training and re-training in the areas of early warning, preparedness, adaptation, emergency plan, data collection method/analysis, writing of research grants proposals to fund the activities of the committee and monitoring for environmental changes.

Spatial patterns and characteristics of flood seasonality in Europe

In Europe, floods are typically analysed within national boundaries and it is therefore not well understood how the characteristics of local floods fit into a continental perspective. To gain a better understanding at continental scale, this study analyses seasonal flood characteristics across Europe for the period 1960–2010. From a European flood database, the timing within the year of annual maximum discharges or water levels of 4105 stations is analysed. A cluster analysis is performed to identify large-scale regions with distinct flood seasons based on the monthly relative frequencies of the annual maxima. The clusters are further analysed to determine the temporal flood characteristics within each region and the Europe-wide patterns of bimodal and unimodal flood seasonality distributions. The mean annual timing of floods observed at individual stations across Europe is spatially well defined. Below 60∘ latitude, the mean timing transitions from winter floods in the west to spring floods in the east. Summer floods occurring in mountainous areas interrupt this west-to-east transition. Above 60∘ latitude, spring floods are dominant, except for coastal areas in which autumn and winter floods tend to occur. The temporal concentration of flood occurrences around the annual mean timing is highest in north-eastern Europe, with most of the floods being concentrated within 1–2 months. The cluster analysis results in six spatially consistent regions with distinct flood seasonality characteristics. The regions with winter floods in western, central, and southern Europe are assigned to Cluster 1 (∼ 36 % of the stations) and Cluster 4 (∼ 10 %) with the mean flood timing within the cluster in late January and early December respectively. In eastern Europe (Cluster 3, ∼ 24 %), the cluster average flood occurs around the end of March. The mean flood timing in northern (Cluster 5, ∼ 8 %) and north-eastern Europe (Cluster 6, ∼ 5 %) is approximately in mid-May and mid-April respectively. About 15 % of the stations (Cluster 2) are located in mountainous areas, with a mean flood timing around the end of June. Most of the stations (∼ 73 %) with more than 30 years of data exhibit a unimodal flood seasonality distribution (one or more consecutive months with high flood occurrence). Only a few stations (∼ 3 %), mainly located on the foothills of mountainous areas, have a clear bimodal flood seasonality distribution. This study suggests that, as a result of the consistent Europe-wide pattern of flood timing obtained, the geographical location of a station in Europe can give an indication of its seasonal flood characteristics and that geographical location seems to be more relevant than catchment area or catchment outlet elevation in shaping flood seasonality.