Juliang Jin

Risk evaluation of China’s natural disaster systems: an approach based on triangular fuzzy numbers and stochastic simulation

To deal effectively with the evaluation problem of natural disaster risk system affected by many uncertain factors, a multivariate connection number expression is presented. This expression is based on the index samples and evaluation grade criterions of natural disaster risk system and is capable of describing the hierarchy property and fuzziness of membership relationship between index samples and evaluation grade criterions. In this proposed method, the fuzzy evaluation grade criterion problem is resolved by combining triangular fuzzy numbers with multivariate connection number theory, and triangular fuzzy numbers are used to express the discrepancy degree coefficients of connection number and evaluation index weights. Accordingly, a connection number-based evaluation method for the natural disaster system of China (named CN-TFN for short) is established using triangular fuzzy numbers and stochastic simulation. The application results show that the spatial distribution of natural disaster risk grades of China has the trend of aggrandizement from west to east of China. The economically developed and densely populated coastal areas are very likely to have a high level of natural disaster risk grade or above; thus, these areas are the key regions of the natural disaster risk management of China. The results also show that the CN-TFN is able to reflect practical conditions of the evaluation problem of natural disaster system and to provide more reliability information as compared to the existing evaluation methods. This is as a result of its comprehensive usage of various information of subjective and objective uncertainties in the evaluation process of natural disaster risk system and its expression by confidence intervals. Due to the simplicity and generalization, the CN-TFM is applicable to comprehensive risk grade evaluation of various natural disaster systems.

Forewarning of sustainable utilization of regional water resources: a model based on BP neural network and set pair analysis

Early warning for sustainable utilization of regional water resources is an important control measure for regional water security management. To establish operable and quantitative forewarning model, in this paper, a new forewarning model for sustainable utilization of water resources based on BP neural network and set pair analysis (named BPSPA-FM for short) was established. In the proposed approach, the accelerating genetic algorithm–based fuzzy analytic hierarchy process was suggested to determine the weights of evaluation indexes, back-propagation neural network updating model was used to predict the values of the evaluation indexes, and the set pair analysis was used to determine the function values of relative membership in variable fuzzy set of the samples. BPSPA-FM was applied to early warning for sustainable utilization of regional water resources of Yuanyang Hani terrace in Yunnan Province of China. The results show that the states of sustainable utilization in this system were near the critical value between nonalarm and slight alarm from 1990 to 2000, the states of the system fell into slight alarm and were rapidly close to intermediate alarm from 2001 to 2004, and the states of the system were predicted to be near the critical value between slight alarm and intermediate alarm from 2005 to 2010. The main alarm indexes of the system were utilization ratio of water in agriculture, control ratio of surface water, per capita water supply, per unit area irrigation water and per capita water consumption. BPSPA-FM can take full advantage of the changing information of the evaluation indexes in adjacent periods and the relationship between the samples and the criterion grades. The results of BPSPA-FM are reasonable with high accuracy. BPSPA-FM is general and can be applied to early warning problems of different natural hazards systems such as drought disaster.

Urban Household Water Demand in Beijing by 2020: An Agent-Based Model

Beijing is faced with severe water scarcity due to rapid socio-economic development and population expansion, and a guideline for water regulation has been released to control the volume of national water use. To cope with water shortage and meet regulation goal, it has great significance to study the variations of water demand. In this paper, an agent-based model named HWDP is developed for the prediction of urban household water demand in Beijing. The model involves stochastic behaviors and feedbacks caused by two agent roles which are government agent and household agent. The government agent adopts economic and propagandist means to make household agent optimize its water consumption. Additionally, the consumption is also affected by the basic water demand deduced from extended linear expenditure system. The results indicate that the total water demand of urban households in Beijing will increase to 317.5 million cubic meters by 2020, while the water price keeps growing at a low level. However, it would drop to 294.9 million cubic meters with high growth of water price and low increment in per capita disposable income. Finally, some policy recommendations on water regulation are made.

Risk analysis for drought hazard in China: a case study in Huaibei Plain

In recent decades, risk management is significant to mitigate the severe status caused by droughts. As one of the primary components in risk analysis, drought hazard analysis is basic but important. In this paper, the framework of drought risk analysis and the methodology for drought hazard analysis are presented, and Huaibei Plain of China is chosen as the study area. The whole study region is divided into three parts (northern, central, and southern) by geographical factors, and a developed index named drought comprehensive Z index (DCZI) containing hydrological and meteorological factors is employed for drought hazard analysis in each area. By comparison, it implicates that DCZI is applicable for Huaibei Plain and indicates drought extent more objectively. Moreover, the results of drought hazard analysis reflect that the northern area is affected by droughts more seriously. As for the whole region, there is a great probability of severe drought. Finally, some policy recommendations on drought management are also made.

China’s regional drought risk under climate change: a two-stage process assessment approach

China is predicted to have higher drought risk with global warming, and for better preparedness and mitigation, it is necessary to find out the risky areas and reveal the essential causes. This paper develops an integrated index of drought risk following the pressure–state–impact process in which drought hazard and vulnerability are reflected by two stages of pressure–state and state–impact, respectively. Accordingly, the network structure data envelopment analysis model is employed to calculate the degree of drought risk, hazard and vulnerability. Then, this study evaluates 31 provinces, municipalities and autonomous regions of China, and the tempospatial patterns of regional drought hazard, vulnerability and risk during 2006–2011 are presented. Moreover, the determinants of risk in different parts of China are also investigated. The results suggest that the northeast and southwest of China are more potential to be affected by drought due to the high degree of hazard and vulnerability. The dominant factor affecting drought risk in the northwest is vulnerability, because the hazard in this part is comparatively low. However, although the drought situation is severe in the middle and lower reaches of the Yangtze River and the southeastern coastal areas, the strong resilience to drought makes the risk remain low. It is concluded that the effective way to decrease drought risk is to promote the level of water efficiency and agricultural irrigation, so that low sensitivity and high adaptive capacity would contribute to vulnerability reduction.

Spatial fuzzy clustering approach to characterize flood risk in urban storm water drainage systems

Increasing environmental stress of drainage systems leads to frequent occurrence of urban flooding, which generates significant adverse economic, social, and environmental impacts. In this study, spatial fuzzy clustering approach (SFCA) is developed to estimate possible flood risks in storm water drainage systems and address complexity and uncertainties of flood risk assessment. The proposed approach is capable of gaining insights into system behavior by exploring spatial patterns of flood risk. The corresponding algorithm is utilized to divide a drainage system into various clusters to reflect flood risk levels along the network space. Application to a sample drainage system demonstrates that it provides an appropriate technique to determine spatial distribution of flood risks. Results reveal that areas with high, moderate, and low risk identified by SFCA correspond to a certain degree of environmental stress. The findings of this study can serve as a preliminary basis to guide managers in their evaluation of flood risks in various drainage management scenarios.

Flood routing model incorporating intensive streambed infiltration

Flood routing models are critical to flood forecasting and confluence calculations. In the streams that dry up and disconnect from groundwater, the streambed infiltration is intensive and has a significant effect on flood wave movement. Streambed infiltration should be considered in flood routing. A flood routing model incorporating intensive streambed infiltration is proposed. In the model a streambed infiltration simulation method based on soil infiltration theory is developed. In this method the Horton equation is used to calculate infiltration capacity. A trial-and-error method is developed to calculate infiltration rate and determine whether the flood wave can travel downstream. A formula is derived to calculate infiltration flow per unit length. The Muskingum-Cunge method with streambed infiltration flow as lateral outflow is used for flood routing. The proposed model is applied to the stream from the downstream of the Yuecheng Reservoir to the Caixiaozhuang Hydrometric Station in the Zhangwei River of the Haihe River Basin. Simulation results show that the accuracy of the model is high, and the infiltration simulation method can represent infiltration processes well. The proposed model is simple and practical for flood simulation and forecasting, and can be used in river confluence calculations in a rainfall-runoff model for arid and semiarid regions.

Inference of reference conditions for nutrient concentrations of Chaohu Lake based on model extrapolation

In the mid-eastern China, there are few or no lakes which are in the absence of anthropogenic disturbances, or their sediments remain undisturbed. As a result, the reference lakes distribution and paleolimnological reconstruction approaches usually are inappropriate to estimate lake reference conditions for nutrients. This yields the necessity of using the extrapolation methods to estimate the lake reference conditions for nutrients within those regions. The lake reference conditions for nutrients could be inferred inversely from the law of mass conservation, current lake nutrient concentration, and the loadings from watershed. Considering the scarcity of hydrological and water quality data associated with lakes and watersheds in China, as well as the low requirement of the watershed nutrient loadings models for these data, the soil conservation service (SCS) distributed hydrological model and the universal soil loss equation (USLE) were applied. The SCS model simulates the runoff process of the watershed, thereby calculating dissolved nutrients annually. The USLE estimates the soil erosion and particulate nutrients annually in a watershed. Then, with the loadings from atmospheric deposition and point source, the previous annual average nutrient concentrations could be acquired given the current nutrient concentrations in a lake. Therefore, the nutrient reference conditions minimally impacted by human activities could be estimated. Based on the proposed model, the reference conditions for total nitrogen and total phosphorus of Chaohu Lake, Anhui Province, China are 0.031 mg/L and 0.640 mg/L, respectively. The proposed reference conditions estimation model is of clear physical concept, and less data required. Thus, the proposed approach can be used in other lakes with similar circumstances.

Application of Interpolation Model Based on Genetic Algorithm to Comprehensive Evaluation of Flood Disaster Loss

Precise comprehensive evaluation of flood disaster loss can supply scientific decision-making basis for flood disaster loss management. Due to the evaluation result of each single index of the practical flood disaster sample is often incompatible, a new method, named project pursuit interpolation model (PPIM for short), was presented to evaluate the degree of flood disaster loss. In PPIM model multi-dimensional evaluation index data was condensed to one-dimensional data firstly, and then the one-dimensional index data and the corresponding evaluation grade were composed to form two dimensional coordinates sample points, finally proper control nodes were chosen to establish evaluation model with cubic natural spline and polynomial function. The evaluation results of flood disaster of Henan Province show that the average absolute grade error is below 0.1 grades, and that the method is simple, effective and general. So the method can be applied to many other grade evaluation systems.

Uncertainty analysis of designed flood on Bayesian MCMC algorithm: a case study of the Panjiakou Reservoir in China

Estimation of the magnitude of designed flood is a fundamental task crucial for the determination of scale of engineering construction and for the development of flood disaster risk management projects. Due to a high level of uncertainty in observed data, selection of frequency distribution model, and estimation of model parameters, the process of designed flood has uncertainties consequently. A Bayesian flood frequency analysis method is adopted for designed flood estimation with P-III probability distribution as its flood frequency model. In the Bayesian method, the adaptive metropolis Markov Chain Monte Carlo (AM-MCMC) sampling algorithm is employed to estimate posterior distributions of parameters, upon which estimation of expectations and credible intervals of designed floods is obtained. With analyzing the drawback of likelihood function expressed with the product of probability of occurrence of each sample individual, four likelihood functions expressed on residuals are presented, and then based on Bayesian AM-MCMC method, performance of presented likelihood functions is compared with that of the classical likelihood function, with taking peak flow uncertainty analysis of Panjiakou Reservoir as a case study. The results show that expectations of flood peak quantiles estimation with likelihood functions based on residuals between observed/censored and calculated values of flood peaks are almost the same, but there are obvious differences between likelihood function based on occurrence probability of flood sample and those based on residuals with respect to expectation of quantiles estimation and also show that expectation and credible interval of quantiles estimation with Bayesian AM-MCMC method based on the whole likelihood function are more reasonable than those acquired with maximum likelihood function. Finally, some relevant flood frequency analyses issues based on Bayesian AM-MCMC algorithm which need to be further studied are also presented.