CLASSES DE AÇÕES PRIORITÁRIAS EM UMA NOVA ABORDAGEM À GESTÃO DO RISCO E DE DESASTRE DE SECA

Drought risk management has gradually emerged as an important discipline and the traditional negative drought management changes to active drought management. Drought risk assessment and control are the core of drought risk management. In this study, based on precipitation anomaly (Pa) and soil moisture content anomaly index, the stochastic drought index model was established to calculate the drought distribution under different probability. Considering risk of disaster (H), vulnerability of the environment (S), exposure of the disaster bearing body (V), and disaster prevention and mitigation capability (C), a water resource optimization allocation model based on drought disaster risk assessment model was established to minimize the regional drought disaster risk. The developed models were used in Heilongjiang Province, China, and the results showed that: (1) the drought indexes based on the stochastic method can reflect the regional drought under different probabilities, providing managers with comprehensive drought information to manage the disaster; (2) the optimal allocation of water resources can reduce the risk of drought disaster in drought-prone months and drought-prone areas; and (3) studying drought risk assessment and regulation considering grain yield can be used to effectively understand and alleviate drought effects in the study area, reduce farmers' economic losses and ensure local food security.

Drought disaster risk occurrence frequency is one of most high, and its affection is most severe in the worldwide, which is more serious in China and had threatened our food security. With the climate change, drought disaster had serious impacts on agricultural production and increasing risks of agricultural disasters. Therefore, the agricultural drought risk characteristics and management problem are particularly important under climate warming conditions. Drought disaster risk was effected by the spatial and temporal pattern of drought risk hazard factors such as the frequency and intensity of precipitation, environmental vulnerability, sensitivity and exposure. These three factors are both independent and connected each other. In view of this, based on the crops drought threshold and the risk mechanism, firstly, we analyzed the drought disaster risk research progress domestic and overseas in recent, which can provide information to the future studies. Secondly, the agriculture drought risk assessment must be established based on detailed agricultural drought dynamic monitoring and assessment methods, and the main technological process included building the assessed database using the meteorological observation data, drought disaster data, integration of data and remote sensing, a long sequence of yield and climate and drought disaster loss data (including agricultural drought of drought-induced areas, drought-occurred areas and no harvest areas), based on remote sensing inversion, field investigation, mathematical statistics and so on, comparing the adaptability of the drought monitoring index, identifying the drought disaster hazard index. Thirdly, based on the long term drought disaster and production data, selecting the occurring drought samples in the crop growth period and calculating reduction rate, building the relationship between production and drought disaster index, based on the reduction rate to quantify the hazard index critical value and grading, quantify the contribution degree of the drought in different growth period to production, confirming the different agriculture drought intensity threshold values and its classifications. And then, based on the theory of coupled risk simulation, combined the drought disaster hazard factor, environmental vulnerability, sensitivity and exposure theory to build the agriculture key growth period disaster risk assessment models. Based on the built models, the different grades drought threshold values, the drought risk development characteristics and drought regional difference can be identified. Lastly, revealed the contribution of key physical factor to risk and it physical mechanism to happen. These can provide some information on the agriculture key growth period drought risk physical mechanism and improving risk management levels.

In recent years, research on drought risk has expanded to include multiple types of drought hazards, various exposed elements and a multitude of factors that determine the vulnerability of a given system or sector. This has resulted in a call from the scientific community to adopt a systemic risk perspective on drought. However, a thorough understanding of how drought risks manifest, cascade and interact across different systems and sectors is still lacking, and methodological guidance on how to analyse and represent these interdependencies does not yet exist.  In order to explore these gaps, we have developed conceptual models of drought risks for key selected systems and sectors in the European Union. For each system and sector considered (rain fed and irrigated agricultural systems, forest ecosystems, freshwater ecosystems, public water supply, inland water transport and the energy sector), a conceptual model was constructed to depict how drivers and root causes interact to create drought risk. The models are based on the impact chains methodology and are informed by literature review and multiple expert consultations (including a series of validation workshops). Subsequently, the system-specific models were used to build an overarching conceptual model of the critical interdependencies that exist between all the systems and sectors considered. The analysis has revealed that, in each system, drought risks manifest through a complex web of interactions between drivers of risk, which are in part system-specific and in part shared across the systems considered. From this, multiple considerations for drought risk assessment and management can be derived. In particular, special attention should be placed in defining and representing what drought risk is in each system, as the underlying characteristics might greatly differ. Additionally, the use of conceptual models can constitute an important first step for risk assessment, as they contribute to addressing the complexity of drought risks. Finally, the existence of commonalities and interdependencies between systems implies that interventions can and must be designed so as to consider multiple systems at once, thus avoiding maladaptive solutions. In this sense, the conceptual models can serve as entry points for the identification of risk reduction and adaptation measures which go beyond the single-risk and single-sector perspective, thus contributing to a more systemic view on drought risk management and adaptation, as well as highlighting persisting knowledge gaps.

Carrying out risk assessments of agricultural drought disasters is helpful to understanding agricultural drought quantitatively and scientifically guiding drought prevention and drought relief work. In this paper, the risk assessment system and evaluation index of drought disasters are constructed, and they are composed of a drought risk subsystem, drought exposure subsystem, disaster damage sensitivity subsystem and drought resistance subsystem. Based on the grey matter-element analysis method, the agricultural drought risk evaluation model was established. Grey matter-element analysis method was used to evaluate the risk of agricultural drought in 18 regions of Henan Province, China in 2019. The results validation showed that high drought disaster risk area in Henan province is located in the western, north and the central area. This study provides a new method for the risk assessment of agricultural drought disasters. Understanding the risk in the study area can improve agricultural system resilience. This model could be used to provide support for increasing agricultural drought disaster resilience and risk management efficiency.

Drought has become a dominant climate risk both around the world and in Europe, adding to the already challenging task of farming and governing the agricultural sector under climate change. Drought risk management is extremely complex. Apart from irrigation, most drought risk management options have more than one goal and may potentially have negative trade-offs with other risk management objectives. Moreover, government regulations and market mechanisms influence farmers’ decision-making. However, previous studies, both in developed and in developing countries, have predominantly focused on attitudinal and structural influencing factors on farmers’ risk management behavior. In this paper, we comprehensively investigate farmers’ decision spaces with respect to drought risk management. We address two applied research questions: (1) What are farmers’ preferred drought risk management measures? (2) From a farmer’s perspective, what are the dominant factors influencing drought risk management decisions? We find that farmers primarily think of production-based rather than financial measures with respect to drought risk management. At the same time, natural and technical constraints and enabling factors dominate their mental decision space, followed by public and private institutional aspects. This research provides a basis for the design of integrated and holistic drought risk management policy and the drought risk governance needed for sustainable use of land and water resources such as needed to address systemic risks and achieve the Sustainable Development Goals. Moreover, we introduce a novel approach using mental models extracted from interviews to explore cognitive representations of farmers' decision spaces. This approach has the potential to complement mainstream research using standardized surveys and behavioral models to analyze drivers of risk management.

Drought management in rural areas with emphasis on resilience approach, studied in Kangavar city

The influence of international agreements on disaster risk reduction

<p>The last years have demonstrated the complex interplay and impacts that hazards can have on people’s lives, livelihoods and health, especially when multiple adverse events occur at the same time. The Sendai Framework for Disaster Risk Reduction 2015–2030 provides a solid foundation for disaster risk management (DRM) by specifically calling for multi-hazard and solution-driven research to address gaps, obstacles and interdependencies of disaster risks. However, most of the practices in DRM still adopt a single-hazard approach, which may not be sufficient for addressing the social, economic, educational, and environmental challenges of multi-hazard risk scenarios. Besides, questions remain about whether disaster risk is actually treated in a science-policy context, as demanded in the Sendai Framework, thus operating in the overlapping space of scientific research, political decision-making and public action. The large number of actors involved in, and affected by, multi-risk disasters make it harder to transfer knowledge into risk management decisions and set a two-way process for communicating such decisions and for collecting feedback from stakeholders. To face these challenges, the project ROADMAP (European observatory on disaster risk and crisis management best practices) aims to establish a European “Doctrine on disaster risk and crisis management”, funded on the cooperation among the scientific community and the DRM authorities. The project is developed by diverse specialized institutions from Italy (The Consortium Italian Centre for Risk Reduction “CI3R” and the Italian Civil Protection Department “ICPD”), Portugal (Association for the Development of Industrial Aerodynamics “ADAI”) and Norway (University of Stavanger). To achieve its goal, the project is identifying good practices in multi-hazard risk scenarios, by singling out the experiences in EU Member States and beyond the EU borders. Emphasis is given to the cumulative hazards that countries have had to face over the past two years, characterized by the spread of a global health emergency induced by the COVID-19 pandemic. Good practices are selected accounting for their capacity to produce results in the diverse DRM phases, as they stand out in terms of effectiveness, reach, feasibility, sustainability, and transferability. Such practices are not intended as static instruments, but rather as a guidance to be adapted if the needs of the users change and/or conditions in the application field evolve. This contribution will present the preliminary results of the research project and discuss how to create an efficient multi-hazard disaster management, focusing on a solution explorer platform collecting the good practices. When analysed closely it becomes apparent that there is a need for reinforcing actions dealing with multi-hazard disasters and for documenting successful stories and lessons learned within a bottom-up approach. By and large, it is envisaged that ROADMAP will contribute to increase access to information on DRM and disaster risk reduction (DRR) by systematically collecting, reviewing and analysing past and ongoing experiences and making them readily available and usable to communities and practitioners. The provision of good-practice guidance about a broad range of structural and non-structural risk management measures enables sharing information on how to overcome the obstacles and increasing the understanding of DRM solutions.</p>

In recent years, drought disaster has occurred frequently in China, causing significant agricultural losses. It is increasingly important to assess the risk of agricultural drought disaster (ADD) and to develop a targeted risk management approach. In this study, an ADD risk assessment model was established. First, an improved fuzzy analytic hierarchy process based on an accelerated genetic algorithm (AGA-FAHP) was used to build an evaluation indicator system. Then, based on the indicators, the ADD assessment connection numbers were established using the improved connection number method. Finally, the entropy information diffusion method was used to form an ADD risk assessment model. The model was applied to the Huaibei Plain in Anhui Province (China), with the assessment showing that, in the period from 2008 to 2017, the plain was threatened continuously by ADD, especially during 2011–2013. The risk assessment showed that southern cities of the study area were nearly twice as likely to be struck by ADD as northern cities. Meanwhile, the eastern region had a higher frequency of severe and above-grade ADD events (once every 21 years) than the western region (once every 25.3 years). Therefore, Huainan was identified as a high-risk city and Huaibei as a low-risk city, with Suzhou and Bengbu more vulnerable to ADD than Fuyang and Bozhou. Understanding the spatial dynamics of risk in the study area can improve agricultural system resilience by optimizing planting structures and by enhancing irrigation water efficiency. This model could be used to provide support for increasing agricultural drought disaster resilience and risk management efficiency.

Achieving long-term effectiveness in natural disaster risk management needs a multifaceted approach. This approach should integrate the disaster’s impact with the region's social, economic, and physical characteristics. A variety of models have been developed to measure the disaster’s impact and propose risk reduction solutions. However, finding the optimal local solution is challenging. To enhance the sustainability of these solutions, it is crucial to consider the local pressing issues, which may be social, economic, cultural, or physical in nature. These issues manifest in the decision criteria when determining the most appropriate risk mitigation or management strategies. Multi-Criteria Decision Analysis (MCDA) methods are instrumental in evaluating suitable solutions by integrating the outputs of risk assessment models with local priorities, which are represented as rankings of the decision criteria. Since the local experts and community representatives have the most practical information regarding regional issues, their input is essential in ranking the decision criteria. Various preference elicitation methods can be employed to capture experts’ perceptions on important issues.When it comes to disaster risk mitigation and management, the elicitation of stakeholders’ collective perception on important issues is challenging. Different experts with different backgrounds, concerns, and visions for the future can have different perceptions on important issues that should be addressed by the disaster risk mitigation solution. This difference of opinion can lead to conflict of priorities. Since the disaster risk mitigation and management solutions are usually led to policy making or implementation of those solutions, the existing conflicts can have a negative impact on the effectiveness of these solutions. As such, it is vital to address these conflicts and elicit the collective priorities of local stakeholders.In this research, a Simos-based silent negotiation process is developed for eliciting the stakeholders’ collective priorities for natural disaster risk mitigation and management. The developed process is designed to engage the representatives of local communities and other experts and decision-makers and systematically direct them to compromise on less important issues. The designed process benefits from different methods to increase robustness. By directing participants to compromise on their less important issues, this process provides the collective local priorities in mitigating disaster risk. Furthermore, it can gauge the level of conflicts among the stakeholders at the end of the silent negotiation. Additionally, it creates equal opportunity for all the participants to raise concerns and argue their point of view. This creates the opportunity to address issues and concerns from different communities.The process is developed and implemented in the Horizon Europe project MEDiate (Multi-hazard and risk-informed system for Enhanced local and regional Disaster risk management). The MEDiate project is dedicated to creating a decision-support system (DSS) for disaster risk management that considers the complexities of multiple interacting natural hazards and fits the final disaster risk management solution to the characteristics, priorities, and concerns of the local communities and decision-makers. The MEDiate framework is implemented on four different testbeds (Oslo (Norway), Nice (France), Essex (UK), and Múlaþing (Iceland)), each of which has a different multi-hazard pair and different socio-economic characteristics.

Quantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log‐Normal, Pearson‐III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quantile at 90‐year return period) quantile shows that the interaction of input data and hydrological parameter sets have a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10‐year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the primary factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk.Recommendations for Water Resource Managers The role of hydrological parameters and climate input data is significant in flood and low flow estimations and significantly impacts water resources and extremes management. Input data dominantly controlled flood magnitude and frequency, whilst the low flow magnitude and frequency were dominantly affected by both input data quality and hydrological parameters. It is crucial to consider the main features that cause considerable risk in water resource management and extreme risk management. Neglecting the primary factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk. Improved extreme water management through a complex modeling approach to better prepare for the impact of extreme high and low flow changes are the best long‐ and short‐term plans and strategies to combat and minimize the risk in water‐related sectors of the local economy.

Towards the harmonization of water-related policies for managing drought risks across the EU

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (GI4DM2020): PREFACE

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (Gi4DM2020): PREFACE

Evaluation of possible sources of uncertainty and their influence on water resource planning and extreme hydrological characteristics are very important for extreme risk reduction and management. The main objective is to identify and holistically address the uncertainty propagation from the input data to the frequency of hydrological extremes. This novel uncertainty estimation framework has four stages that comprise hydrological models, hydrological parameter sets, and frequency distribution types. The influence of uncertainty on the simulated flow is not uniform across all the selected eight catchments due to different flow regimes and runoff generation mechanisms. The result shows that uncertainty in peak flow frequency simulation mainly comes from the input data quality. Whereas, in the low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty in the estimation of QT90 (extreme peak flow quantile at 90-year return period) quantile shows the interaction of input data and extreme frequency models has significant influence. In contrast, the hydrological models and hydrological parameters have a substantial impact on the QT10 (extreme low flow quantile at 10-year return period) estimation. This implies that the four factors and their interactions may cause significant risk in water resource management and flood and drought risk management. Therefore, neglecting these factors in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to significant impact.