Early Flash Flood Warning: A Feasibility Study with a Distributed Hydrological Model and Threshold Exceedance

In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts. This report is focused on four case studies in Mediterranean part of Europe: i) The September 2002-flash flood event in the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash flood; ii) the August 2003-flash flood event in both Fella subcatchment of Tagliamento watershed and upstream part of Isonzo river basin, iii) the October 2006-flash flood event in Isonzo river basin and iv) the September 2007-flash flood event in Upper Sava river basin in Slovenia. The French case study is described in more detail with the principles and methodologies being explained that are then applied to the remaining three case studies. Also, there were more data available for the 1st case study. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with the threshold ¿ exceedance approach previously postulated for the European Flood Alert System (EFAS). The short-range weather forecasts, from the Local model of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.JRC.H.7-Land management and natural hazard

Implementation and evaluation of the Pitman model in seasonal hydrological forecasting mode using the Kraai River catchment in Eastern Cape South Africa as a case study

Seasonal hydrologic extremes such as drought and floods have devastating impacts on human and natural systems (e.g. 2015-2017 Western Cape drought). Sentence has been reworded to: Therefore, the need for a reliable seasonal hydrologic forecast is significant and becoming even more urgent under future climate, as the assimilation of seasonal forecast information in decision making. Hence, SHF becomes part of the short and long-term climate change adaptation strategies in a range of contexts such as energy supply, water supply and management, rural-urban, agriculture, infrastructure and disaster preparedness and relief. This work deals with implementation and evaluation of the Pitman/Water Resources Simulation Model 2012 model (WR2012) in seasonal hydrological forecasting mode. The aim of the study is to improve the understanding of seasonal hydrological forecasting by evaluating the performance of a hydrological model (Pitman Model) in the seasonal forecast mode in Kraai River tertiary catchment (D13) as a case study and the objectives are: To determine steps to be undertaken to implement integration of Pitman in WR2012 configuration with climate forecast to generate seasonal hydrological forecast and to evaluate the performance of the model forced by climate model data in the simulation and forecast mode. Pitman model in the WR2012 version works with a specific rainfall dataset spanning the period of 1920-2009. Operationalizing the seasonal hydrological forecast with Pitman model requires, therefore, updating of the WR2012 rainfall so that it extends to-date. To achieve that, two datasets were evaluated: Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), which is a satellite-based gridded rainfall dataset, and rain gauge-based dataset from South African Weather Service (SAWS). The analyses revealed that CHIRPS rainfall data had better correlation and lower bias with respect to the WR2012 data when compared with SAWS rainfall data for the overlap period 1981-2009. The CHIRPS data showed no significant difference from the WR2012 in all the three rainfall zones of the Kraai River catchment. Therefore, CHIRPS data were used to extend the WR2012 data and were used as input to set up Pitman model/WR2012 in the seasonal hydrological forecasting mode. The Pitman/WR2012 model was forced with 10 ensemble seasonal climate forecast from Climate Forecast Systems v.2 which is downscaled using the Principal Components Regression (PCR) approach. The generated seasonal hydrological forecast focused on the summer season, in particular on the Dec-Jan-Feb (DJF) period, which is the rainy season in the catchment. The hydrological forecast showed skills more especially in Dec and Feb (assessed through ROC and RPSS forecast verification methods) with Jan having a poor skill. Importantly, the skill of streamflow forecast was better than that of rainfall forecast, which likely results from the influence of initial conditions of the hydrological model. In conclusion Pitman/WR2012 model can perform realistically when implemented in seasonal hydrological forecasts mode, and it is important that in that model, the model is run with near real time rainfall data in order to achieve good initial conditions. However, the results in terms of forecast skill are specific to the studied catchment and analysed forecast, and skill of forecast in any other catchment has to be investigated separately

Contemporary disaster management framework quantification of flood risk in rural Lower Shire Valley, Malawi

Despite floods and droughts accounting for 80% and 70% disaster related deaths and economic loss respectively in Sub-Saharan Africa (SSA), there have been very few attempts in SSA to quantify flood-related vulnerability and risk, especially as they relate to the rural poor. This thesis quantifies and profiles the flood risk of rural communities in SSA focusing on the Lower Shire Valley, Malawi. Given the challenge of hydrometeorological data quality in SSA to support quantitative flood risk assessments, the work first reconstructs and extends hydro-meteorological data using Artificial Neural Networks (ANNs). These data then formed the input to a coupled IPCC-Sustainable Development Frameworks for quantifying flood vulnerability and risk. 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 of the catchment with Lisflood-FP, while the vulnerability was indexed through analysis of exposure, susceptibility and capacity and linked to social, economic, environmental and physical perspectives. Data on these were collected through structured interviews carried out with the communities and stakeholders in the valley and later analysed. The implementation of the entire analysis within a GIS environment 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 component largely because of the high to very high socio-economic and environmental vulnerability. 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. Both the vulnerability and risk showed marginal spatial variability. Given all this, the thesis argues for the need to mainstream disaster reduction in the rather plethoric conventional socio-economic developmental programmes in SSA. Additionally, the low spatial variability in both the risk and vulnerability in the valley suggests that any such interventions need to be valley-wide to be effective

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Flood Management in a Complex River Basin with a Real-Time Decision Support System Based on Hydrological Forecasts

During the last decades, the Upper Rhone River basin has been hit by several flood events causing significant damages in excess of 500 million Swiss Francs. From this situation, the 3rd Rhône river training project was planned in order to improve the flood protection in the Upper Rhone River basin in Vaud and Valais Cantons. In this framework, the MINERVE forecast system aims to contribute to a better flow control during flood events in this catchment area, taking advantage of the existing hydropower multi-reservoir network. This system also fits into the OWARNA national project of the Swiss Federal Office of Environment by establishing a national platform on natural hazards alarms. The Upper Rhone River basin has a catchment area with high mountains and large glaciers. The surface of the basin is 5521 km2 and its elevation varies between 400 and 4634 m a.s.l. Numerous hydropower schemes with large dams and reservoirs are located in the catchment area, influencing the hydrological regime. Their impact during floods can be significant as appropriate preventive operations can decrease the peak discharges in the Rhone River and its main tributaries, thus reducing the damages. The MINERVE forecast system exploits flow measurements, data from reservoirs and hydropower plants as well as probabilistic (COSMO-LEPS) and deterministic (COSMO-2 and COSMO-7) numerical weather predictions from MeteoSwiss. The MINERVE hydrological model of the catchment area follows a semi-distributed approach. The basin is split into 239 sub-catchments which are further sub-divided into 500 m elevation bands, for a total of 1050 bands. For each elevation band, precipitation, temperature and potential evapotranspiration are calculated. They are considered in order to describe the temperature-driven processes accurately, such as snow and glaciers melt. The hydrological model was implemented in the Routing System software. The object oriented programming environment allows a user-friendly modelling of the hydrological, hydraulic and operating processes. Numerical meteorological data (observed or predicted) are introduced as input in the model. Over the calibration and validation periods of the model, only observed data (precipitation, temperature and flows) was used. For operational flood forecast, the observed measurements are used to update the initial conditions of the hydrological model and the weather forecasts for the hydrological simulations. Routing System provides then hydrological predictions in the whole catchment area. Subsequently, a warning system was developed especially for the basin to provide a flood warning report. The warning system predicts the evolution of the hydrological situation at selected main check points in the catchment area. It displays three warning levels during a flood event depending on respective critical discharge thresholds. Furthermore, the multi-reservoir system is managed in an optimal way in order to limit or avoid damages during floods. A decision support tool called MINDS (MINERVE Interactive Decision Support System) has been developed for real-time decision making based on the hydrological forecasts. This tool defines preventive operation measures for the hydropower plants such as turbine and bottom outlet releases able to provide an optimal water storage during the flood peak. The overall goal of MINDS is then to retain the inflowing floods in reservoirs and to avoid spillway and turbine operations during the peak flow, taking into account all restrictions and current conditions of the network. Such a reservoir management system can therefore significantly decrease flood damages in the catchment area. The reservoir management optimisation during floods is achieved with deterministic and probabilistic forecasts. The definition of the objective function to optimise is realised with a multi-attribute decision making approach. Then, the optimisation is performed with an iterative Greedy algorithm or a SCE-UA (Shuffled Complex Evolution – University of Arizona) algorithm. The developed decision support system combines the high-quality optimisation system with its user-friendly interface. The purpose is to help decision makers by being directly involve in main steps of the decision making process as well as by understanding the measures undertaken and their consequences

Rule-based reservoir operation considering long range forecast

A model for long range and real time reservoir operations is developed, considering the medium and long range weather forecast provided by the meteorological agency. The reasoning employed by the reservoir operator to make the appropriate decision on the reservoir operations, in the presence of uncertainty and inevitable errors in the forecast, is modeled through a rule-based scheme. A fuzzy inference procedure is used to evaluate the rules and produce the control output. The forecast inputs are of medium and long range inflow rates and trends. The operations are conducted according to "control levels" that are related to control actions designed to keep the reservoir state as near as possible to the target one. The simulation of the operation of a single reservoir throughout the year is performed for water utilization, hydropower and river preservation purposes. The focus is on drought management, and the results show that the model behaviour is coherent with the model formulation

Vulnerability analysis in an Early Warning System for drought

Early Warning Systems (EWS) for drought are often based on risk models that do not, or marginally, take into account the vulnerability factor. The multifaceted nature of drought (hydrological, meteorological, and agricultural) is source of coexistence for different ways to measure this phenomenon and its effects. The mentioned issue, together with the complexity of impacts generated by this hazard, causes the current underdevelopment of drought EWS compared to other hazards. In Least Developed Countries, where drought events causes the highest numbers of affected people, the importance of correct monitoring and forecasting is considered essential. Existing early warning and monitoring systems for drought, produced at different geographic levels, provide only in a few cases an actual spatial model that tries to describe the cause-effect link between where the hazard is detected and where impacts occur. Integrate vulnerability information in such systems would permit to better estimate affected zones and livelihoods, improving the effectiveness of produced hazard-related datasets and maps. In fact, the need of simplification and, in general, of a direct applicability of scientific outputs is still a matter of concern for field experts and early warning products end-users. Even if the surplus of hazard related information produced on the occasion of catastrophic events has, in some cases, led to the creation of specific data-sharing platforms, the conveyed meaning and usefulness of each product has not yet been addressed. The present work is an attempt to fill this gap which is still an open issue for the scientific community as well as for the humanitarian aid world. The present study aims at conceiving a simplified vulnerability model to embed into an existing EWS for drought, which is based on the monitoring of vegetation phenological parameters, produced using free satellite derived datasets. The proposed vulnerability model includes (i) a pure agricultural vulnerability and (ii) a systemic vulnerability. The first considers the agricultural potential of terrains, the diversity of cultivated crops and the percentage of irrigated area as main driving factors. The second vulnerability aspect consists of geographic units that model the strategy and possibilities of people to access marketplaces; these units are shaped on the basis of the physical accessibility of market locations in one case, and according to a spatial gravity model of market catchments in other two proposed cases. Results of the model applied to two national case studies and evaluated with food insecurity data are presented

Technologies for climate change adaptation: agricultural sector

This Guidebook presents a selection of technologies for climate change adaptation in the agricultural sector. A set of twenty two adaptation technologies are showcased that are primarily based on the principals of agroecology, but also include scientific technologies of climate and biological sciences complemented with important sociological and institutional capacity building processes that are required to make adaptation function. The technologies cover monitoring and forecasting the climate, sustainable water use and management, soil management, sustainable crop management, seed conservation, sustainable forest management and sustainable livestock management. Technologies that tend to homogenize the natural environment and agricultural production have low possibilities of success in conditions of environmental stress that are likely to result from climate change. On the other hand, technologies that allow for, and indeed promote, diversity are more likely to provide a strategy which strengthens agricultural production in the face of uncertain future climate change scenarios. In this sense, the twenty two technologies showcased in this Guidebook have been selected because they facilitate the conservation and restoration of diversity while at the same time providing opportunities for increasing agricultural productivity. Many of these technologies are not new to agricultural production practices, but they are implemented based on assessment of current and possible future impacts of climate change in a particular location. Agro-ecology is an approach that encompasses concepts of sustainable production and biodiversity promotion and therefore provides a useful framework for identifying and selecting appropriate adaptation technologies for the agricultural sector. The Guidebook provides a systematic analysis of the most relevant information available on climate change adaptation technologies in the agriculture sector. It has been compiled based on a literature review of key publications, journal articles, and e-platforms, and by drawing on documented experiences sourced from a range of organizations working on projects and programmes concerned with climate change adaptation technologies in the agricultural sector. Its geographic scope is focused on developing countries where high levels of poverty, agricultural production, climate variability and biological diversity currently intersect. Key concepts around climate change adaptation are not universally agreed. It is therefore important to understand local contexts – especially social and cultural norms - when working with national and sub-national stakeholders to make informed decisions about appropriate technology options. Thus, decision-making processes should be participative, facilitated, and consensus-building oriented and should be based on the following key guiding principles: increasing awareness and knowledge, strengthening institutions, protecting natural resources, providing financial assistance and developing context-specific strategies. For decision-making the Community–Based Adaptation framework is proposed for creating inclusive governance that engages a range of stakeholders directly with local or district government and national coordinating bodies, and facilitates participatory planning, monitoring and implementation of adaptation activities. Seven criteria are suggested for the prioritization of adaptation technologies: (i) The extent to which the technology maintains or strengthens biological diversity and is environmentally sustainable; (ii) The extent to which the technology facilitates access to information systems and awareness of climate change information; (iii) Whether the technology support water, carbon and nutrient cycles and enables stable and/or increased productivity; (iv) Income-generating potential, cost-benefit analysis and contribution to improved equity; (v) Respect for cultural diversity and facilitation of inter-cultural exchange; (vi) Potential for integration into regional and national policies and can be scaled-up; (vii) The extent to which the technology builds formal and information institutions and social networks. Finally, recommendations are set out for practitioners and policy makers: • There is an urgent need for improved climate modelling and forecasting which can provide a basis for informed decision-making and the implementation of adaptation strategies. This should include traditional knowledge. • Information is also required to better understand the behaviour of plants, animals, pests and diseases as they react to climate change. • Potential changes in economic and social systems in the future under different climate scenarios should also be investigated so that the implications of adaptation strategy and planning choices are better understood. • It is important to secure effective flows of information through appropriate dissemination channels. This is vital for building adaptive capacity and decision-making processes. • Improved analysis of adaptation technologies is required to show how they can contribute to building adaptive capacity and resilience in the agricultural sector. This information needs to be compiled and disseminated for a range of stakeholders from local to national level. • Relationships between policy makers, researchers and communities should be built so that technologies and planning processes are developed in partnership, responding to producers’ needs and integrating their knowledge