Climate change and disaster impact reduction

Based on papers presented at the 'UK - South Asia Young Scientists and Practitioners Seminar on Climate Change and Disaster Impact Reduction' held at Kathmandu, Nepal on 5-6 June, 2008

The match between climate services demands and Earth System Models supplies

Earth System Models (ESM) are key ingredients of many of the climate services that are currently being developed and delivered. However, ESMs have more applications than the provision of climate services, and similarly many climate services use more sources of information than ESMs. This discussion paper elaborates on dilemmas that are evident at the interface between ESMs and climate services, in particular: (a) purposes of the models versus service development, (b) gap between the spatial and temporal scales of the models versus the scales needed in applications, and (c) Tailoring climate model results to real-world applications. A continued and broad-minded dialogue between the ESM developers and climate services providers’ communities is needed to improve both the optimal use and direction of ESM development and climate service development. We put forward considerations to improve this dialogue between the communities developing ESMs and climate services, in order to increase the mutual benefit that enhanced understanding of prospects and limitations of ESMs and climate services will bring.This work and its contributors (B. van den Hurk, C. Hewitt, J. Bessembinder, F. Doblas-Reyes, R. Döscher) were funded by the Horizon 2020 Framework Programme of the European Union: Project ref. 689029 (Climateurope project). The co-author and editor of the journal states that she was not involved in the review process of the paper.Peer ReviewedPostprint (published version

Modelling of a Watershed: A Distributed Parallel Application in a Grid Framework

This work proposes a joint implementation of spatially distributed runoff and soil erosion analysis in watersheds allowing subsequent modelization of nutrients transport processes originating from distributed sources. Implemented relying on the open source GRASS (Geographic Resources Analysis Support System) GIS (Geographical Information System), a new design for the raster operation routines is specially created to take advantage of the MPI possibilities and available GRID resources

Multi-step Ahead Inflow Forecasting for a Norwegian Hydro-Power Use-Case, Based on Spatial-Temporal Attention Mechanism

Hydrological forecasting has been an ongoing area of research due to its importance to improve decision making on water resource management, flood management, and climate change mitigation. With the increasing availability of hydrological data, Machine Learning (ML) techniques have started to play an important role, enabling us to better understand and predict complex hydrological events. However, some challenges remain. Hydrological processes have spatial and temporal dependencies that are not always easy to capture with traditional ML models, and a thorough understanding of these dependencies is essential when developing accurate predictive models. This thesis explores the use of ML techniques in hydrological forecasting and consists of an introduction, two papers, and an application developed alongside the case study. The motivation for this research is to enhance our understanding of the spatial and temporal dependencies in hydrological processes and to explore how ML techniques, particularly those incorporating attention mechanisms, can aid in hydrological forecasting. The first paper is a chronological literature review that explores the development of data-driven forecasting in hydrology, and highlighting the potential application of attention mechanisms in hydrological forecasting. These attention mechanisms have proven to be successful in various domains, allowing models to focus on the most relevant parts of the input for making predictions, which is particularly useful when dealing with spatial and temporal data. The second paper is a case study of a specific ML model incorporating these attention mechanisms. The focus is to illustrate the influence of spatial and temporal dependencies in a real-world hydrological forecasting scenario, thereby showcasing the practical application of these techniques. In parallel with the case study, an application has been developed, employing the principles and techniques discovered throughout the course of this research. The application aims to provide a practical demonstration of the concepts explored in the thesis, contributing to the field of hydrological forecasting by introducing a tool for hydropower suppliers.Masteroppgave i Programvareutvikling samarbeid med HVLPROG399MAMN-PRO

Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling

The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones(LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells

TWINLATIN: Twinning European and Latin-American river basins for research enabling sustainable water resources management. Combined Report D3.1 Hydrological modelling report and D3.2 Evaluation report

Water use has almost tripled over the past 50 years and in some regions the water demand already exceeds supply (Vorosmarty et al., 2000). The world is facing a “global water crisis”; in many countries, current levels of water use are unsustainable, with systems vulnerable to collapse from even small changes in water availability. The need for a scientifically-based assessment of the potential impacts on water resources of future changes, as a basis for society to adapt to such changes, is strong for most parts of the world. Although the focus of such assessments has tended to be climate change, socio-economic changes can have as significant an impact on water availability across the four main use sectors i.e. domestic, agricultural, industrial (including energy) and environmental. Withdrawal and consumption of water is expected to continue to grow substantially over the next 20-50 years (Cosgrove & Rijsberman, 2002), and consequent changes in availability may drastically affect society and economies. One of the most needed improvements in Latin American river basin management is a higher level of detail in hydrological modelling and erosion risk assessment, as a basis for identification and analysis of mitigation actions, as well as for analysis of global change scenarios. Flow measurements are too costly to be realised at more than a few locations, which means that modelled data are required for the rest of the basin. Hence, TWINLATIN Work Package 3 “Hydrological modelling and extremes” was formulated to provide methods and tools to be used by other WPs, in particular WP6 on “Pollution pressure and impact analysis” and WP8 on “Change effects and vulnerability assessment”. With an emphasis on high and low flows and their impacts, WP3 was originally called “Hydrological modelling, flooding, erosion, water scarcity and water abstraction”. However, at the TWINLATIN kick-off meeting it was agreed that some of these issues resided more appropriately in WP6 and WP8, and so WP3 was renamed to focus on hydrological modelling and hydrological extremes. The specific objectives of WP3 as set out in the Description of Work are

Workshop report: Integrated Food Security Modeling in Eastern and Southern Africa

CCAFS organized a workshop on Integrated Food Security Modeling in Eastern and Southern Africa on 10-13 February 2014 in Nairobi, Kenya. The workshop was attended by participants from global, regional, and national institutions, including: the World Food Programme (WFP); the Food and Agriculture Organization (FAO); the UN Office for Disaster Risk Reduction (UNISDR), USAID Famine Early Warning System Network (FEWS NET); the IGAD Climate Prediction and Applications Centre (ICPAC); the Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES); CGIAR Research Centers (CIMMYT, CIAT, ICRISAT, ICRAF, CIP, ILRI, AfricaRice, IRRI,); and the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS); Workshop presentations and discussions accomplished the following objectives: The concepts and components of Integrated Food Security Modeling were explained along with descriptions, methodologies, and progress of work for current modeling activities in Eastern Africa and globally, including climate models, bio-physical crop models, and econometric models. Data and knowledge gaps, technical challenges, and uncertainties which constrain the accuracy of model outputs were identified, including lack of access to data in formats suitable for model input, data quality issues, errors arising from the aggregation of data collected at points to represent heterogenous areas, and the challenge of quantifying uncertainty when different models are combined. Challenges specific to the region include improving the skill of seasonal climate forecasts for East Africa, adopting the crop models to smallholder farming systems. Institutions participating in in the workshop agreed to prepare a concept note for research on these topics and submit it to CCAFS for funding consideration under Flagship 2: Climate Information Services and Climate-informed Safety Nets

Flood risk mapping worldwide : a flexible methodology and toolbox

Flood risk assessments predict the potential consequences of flooding, leading to more effective risk management and strengthening resilience. However, adequate assessments rely on large quantities of high-quality input data. Developing regions lack reliable data or funds to acquire them. Therefore, this research has developed a flexible, low-cost methodology for mapping flood hazard, vulnerability and risk. A generic methodology was developed and customized for freely available data with global coverage, enabling risk assessment worldwide. The default workflow can be enriched with region-specific information when available. The practical application is assured by a modular toolbox developed on GDAL and PCRASTER. This toolbox was tested for the catchment of the river Moustiques, Haiti, for which several flood hazard maps were developed. Then, the toolbox was used to create social, economic and physical vulnerability maps. These were combined with the hazard maps to create the three corresponding flood risk maps. After creating these with the default data, more detailed information, gathered during field work, was added to verify the results of the basic workflow. These first tests of the developed toolbox show promising results. The toolbox allows policy makers in developing countries to perform reliable flood risk assessments and generate the necessary maps

Disaster management in smart cities

The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.info:eu-repo/semantics/publishedVersio