Flood hazard risk assessment model based on random forest

Previous studies have combined climate, crop and economic models to examine the impact of climate change on agricultural production and food security, but results have varied widely due to differences in models, scenarios and input data Recent work has examined (and narrowed) these differences through systematic model intercomparison using a high-emissions pathway to highlight the differences This paper extends that analysis to explore a range of plausible socioeconomic scenarios and emission pathways Results from multiple climate and economic models are combined to examine the global and regional impacts of climate change on agricultural yields, area, production, consumption, prices and trade for coarse grains, rice, wheat, oilseeds and sugar crops to 2050 We find that climate impacts on global average yields, area, production and consumption are similar across shared socioeconomic pathways (SSP 1, 2 and 3, as we implement them based on population, income and productivity drivers), except when changes in trade policies are included Impacts on trade and prices are higher for SSP 3 than SSP 2, and higher for SSP 2 than for SSP 1 Climate impacts for all variables are similar across low to moderate emissions pathways (RCP 45 and RCP 60), but increase for a higher emissions pathway (RCP 85) It is important to note that these global averages may hide regional variations Projected reductions in agricultural yields due to climate change by 2050 are larger for some crops than those estimated for the past half century, but smaller than projected increases to 2050 due to rising demand and intrinsic productivity growth Results illustrate the sensitivity of climate change impacts to differences in socioeconomic and emissions pathways Yield impacts increase at high emissions levels and vary with changes in population, income and technology, but are reduced in all cases by endogenous changes in prices and other variables

https://iopscience.iop.org/article/10.1088/1748-9326/10/8/085010/pdf

Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios

https://iopscience.iop.org/article/10.1088/1748-9326/aa518a/pdf

Comparing estimates of climate change impacts from process-based and statistical crop models

. This paper provides a review of multi-criteria decision-making  (MCDM) applications to flood risk management, seeking to highlight trends and identify research gaps. A total of 128 peer-reviewed papers published from 1995 to June 2015 were systematically analysed. Results showed that the number of flood MCDM publications has exponentially grown during this period, with over 82 % of all papers published since 2009. A wide range of applications were identified, with most papers focusing on ranking alternatives for flood mitigation, followed by risk, hazard, and vulnerability assessment. The analytical hierarchy process (AHP) was the most popular method, followed by Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW). Although there is greater interest in MCDM, uncertainty analysis remains an issue and was seldom applied in flood-related studies. In addition, participation of multiple stakeholders has been generally fragmented, focusing on particular stages of the decision-making process, especially on the definition of criteria weights. Therefore, addressing the uncertainties around stakeholders' judgments and endorsing an active participation in all steps of the decision-making process should be explored in future applications. This could help to increase the quality of decisions and the implementation of chosen measures.

/pdf/multi-criteria-decision-making-for-flood-risk-management-a-1873p52e44.pdf

Multi-criteria decision-making for flood risk management: a survey of the current state of the art

Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China

This study presents the methodology and procedure for risk assessment of flood disasters in central Liaoning Province, which was supported by geographical information systems (GIS) and technology of natural disaster risk assessment. On the basis of the standard formulation of natural disaster risk and flood disaster risk index, of which weights were developed using combined weights of entropy, the relative membership degree functions of variable fuzzy set (VFS) theory were calculated using improved set pair analysis, while level values were calculated using VFSs, including hazard levels, exposure levels, vulnerability levels and restorability levels, and the flood risk level for each assessment unit was obtained using the natural disaster index method. Consequently, integrated flood risk map was carried out by GIS spatial analysis technique. The results show that the southwestern and central parts of the study area possess higher risk, while the northwestern and southeastern parts possess lower risk. The results got by the assessment model fits the area of historical flood data; this study offer new insights and possibility to carry out an efficient way for flood disaster prevention and mitigation. The study also provides scientific reference in flood risk management for local and national governmental agencies.

Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China

This study presents a methodology for risk analysis, assessment, combination, and regionalization of integrated drought and waterlogging disasters in Anhui Province, which is supported by geographical information systems (GIS) and technique of natural disaster risk assessment from the viewpoints of climatology, geography, disaster science, environmental science, and so on. Along with the global warming, the occurrences of water-related disasters become more frequent and serious. It is necessary to determine the mode of spatial distribution of water-related disaster risk. Based on the principle of natural disaster risk, natural conditions, and socioeconomic situation, drought and waterlogging disaster risk index, which combined hazard, exposure, vulnerability, and restorability, was developed by using combined weights, entropy, and fuzzy comprehensive evaluation method. Drought and waterlogging disaster risk zoning map was made out by using GIS spatial analysis technique and gridding GIS technique. It was used for comparing the relative risk of economic and life losses in different grids of Anhui Province. It can also be used to compare the situation of different levels of drought and waterlogging disaster combination risk in a similar place. The result shows that the northwestern and central parts of Anhui Province possess higher risk, while the southwestern and northeastern parts possess lower risk. The information obtained from statistical offices and remote sensing data in relation to results compiled were statistically evaluated. The results obtained from this study are specifically intended to support local and national governmental agencies on water-related disaster management.

Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province, China

Impacts of climate change on crop yield can be divided into long-term and year-to-year impacts. The goal of this study is to evaluate whether the long-term and the year-to-year impacts are different using historical datasets. Data on corn yields for 1961–2005 in Jilin and Anhui, located in northeastern and Mideastern China, respectively, are combined with datasets on temperature to build the statistical regression models for evaluation of the impacts of long-term and year-to-year temperature change on corn yield. Over half of the yield trends result from non-climatic factors, such as management and technology. Corn yields could increase threefold (measured by method 1) or twofold (method 2) as long-term temperature increased per degree centigrade. And agriculture in northeastern China has benefitted from climate warming. But the year-to-year temperature change could only result in 12 % yield increase, almost 20 times less than the impacts of long-term temperature change. We conclude that the impacts of long-term and year-to-year temperature change on crop yield are quite different. So, the models derived from year-to-year variations cannot be applied to assess the impacts of long-term climate change.

The impacts of long-term and year-to-year temperature change on corn yield in China

Extreme weather events include unusual, severe or unseasonal weather, and weather at the extremes of the historical distribution. They have become more frequent and intense under global warming, especially in mid-latitude areas. They bring about great agricultural and economic losses. It is important to define the threshold of extreme weather event because it is the starting point of extreme weather event research, though it has been of seldom concern. Taking extreme precipitation events in Anhui, China as an example, the detrended fluctuation analysis (DFA) method is introduced to define the threshold of extreme weather events. Based on it, the spatial and temporal distributions of extreme precipitation events are analyzed. Compared to the traditional percentile method, DFA is based on the long-term correlation of time series. Thresholds calculated by DFA are much higher than the 99th percentile and the values are higher in the south and lower in the north. This spatial pattern is similar to the annual precipitation spatial pattern. There is an obvious increasing trend in the number of days with extreme precipitation, especially after the 1980s. This observation supports the point that more extreme events happen under global warming.

Extreme precipitation events identified using detrended fluctuation analysis (DFA) in Anhui, China

This study presents a discriminant analysis-based method for prediction of agriculture drought disaster risk. We selected the Chaoyang city in the Northeast China as the study area. We employed multi-scale standard precipitation index (SPI) to reflect drought hazard. We used the yield losses to indicate the drought disaster risk, which was divided into no, low, or high drought risk. We used the multi-scale SPI and drought disaster risk as the input factors for the discriminant analysis-based risk prediction model. The results showed that the model’s prediction accuracy varied between 40 and 82.4 %. The accuracy of high drought disaster risk category was higher than low and no drought disaster risk category. The prediction accuracy of the milky maturity stage was highest. We use leave-one-out cross-validation method to validate the model’s accuracy. And the results showed that the model validation accuracy of high drought group could reach 70.6 % in milky maturity stage. This study showed discriminant analysis is an effective and operable method for disaster risk prediction. This model can provide timely information for decision makers to make effective measures for drought disaster management and to reduce the drought effects to yields at the minimum level.

Qi Zhang

Papers

Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China

Integrated risk zoning of drought and waterlogging disasters based on fuzzy comprehensive evaluation in Anhui Province, China

The impacts of long-term and year-to-year temperature change on corn yield in China

Extreme precipitation events identified using detrended fluctuation analysis (DFA) in Anhui, China

Dynamic risk prediction based on discriminant analysis for maize drought disaster