Response of the flood peak to the spatial distribution of rainfall in the Yom River basin,Thailand

The response of the flood peak to the spatial distribution of rainfall has been reported in basins with nonuniform characteristics. However, prioritization of the influences of these characteristics is still poorly understood. This study evaluated the variability in the flood peak with the spatial distribution of rainfall at Sukhothai (city) in the Yom River basin, Thailand, and investigated the influence of the basin characteristics on the flood peak. For each of the 2-, 5- and 10-y rainfalls with durations of 24, 48 and 72 h, 1000 simulated rainfall events with various spatial distributions were generated according to the observed data by using a Monte Carlo analysis and Cholesky randomization. The floods from these rainfalls were then simulated, and the peak discharges were evaluated. The flood peaks from 24-h rainfalls were usually small but highly variable and could be extremely large when the rainfalls were concentrated over the mountainous region. The flood peaks from 48 to 72-h rainfalls were consistently large and correlated with the rainfalls over the joint area between the mountainous region and plain area. The basin characteristics that influenced the response of the flood peak to the spatial distribution of the rainfall appeared to depend on the rainfall duration and magnitude. For short-duration rainfalls, the response was mainly influenced by the surface storage when the rainfall was small and by the terrain steepness when the rainfall was large. For long-duration rainfalls, the response was mainly influenced by the soil percolation rate.     

A unique value function for an optimal control problem of irrigation water intake from a reservoir harvesting flash floods

Operation of reservoirs is a fundamental issue in water resource management. We herein investigate well-posedness of an optimal control problem for irrigation water intake from a reservoir in an irrigation scheme, the water dynamics of which is modeled with stochastic differential equations. A prototype irrigation scheme is being developed in an arid region to harvest flash floods as a source of water. The Hamilton–Jacobi–Bellman (HJB) equation governing the value function is analyzed in the framework of viscosity solutions. The uniqueness of the value function, which is a viscosity solution to the HJB equation, is demonstrated with a mathematical proof of a comparison theorem. It is also shown that there exists such a viscosity solution. Then, an approximate value function is obtained as a numerical solution to the HJB equation. The optimal control strategy derived from the approximate value function is summarized in terms of rule curves to be presented to the operator of the irrigation scheme.     

Risks of airborne pollution accidents in a major conurbation: case study of Zhangjiakou,a host city for the 2022 Winter Olympics

The number of airborne pollution accidents is second only to that of water-borne pollution accidents, in recorded environmental disasters. Acute casualties and public health costs have prompted many airborne pollution risk analyses. To date, few assessment methods have been carried out at regional-scale to quantify acute airborne pollution risk. Herein, a Hybrid Simulation and Risk Analysis approach, involving a systematic combination of simulation, risk ranking, and standardized analysis, is proposed at regional scale. Gaussian and heavy-gas models are utilized in the simulation process, and acute exposure limits preferentially adopted in the risk analysis. The case study shows that 34 of 243 townships in Zhangjiakou City of north China, one of the twin cities selected to host the 2022 Winter Olympics, are threatened by airborne risk sources. It is found that the accidental air pollution risk is comparatively higher in the Xuanhua and Wanquan conurbations. High-risk chemical enterprises (312–432 risk scores) are mostly located near urban areas with high population density where many people are vulnerable receptors to potential air pollution accidents. The resulting risk map indicates that acute airborne pollution from Zhangjiakou would not be a threat either to the proposed Olympic site at Chongli or to downwind Beijing.     

Changes in daily maximum temperature extremes across India over 1951–2014 and their relation with cereal crop productivity

This study used gridded daily maximum temperature data (1°?×?1°) for 1951–2014 period to analyze the trend in monthly extreme warm days (ExWD) and changes in its probability distribution in each grid. It also analyzed the trend in spatial spread of annual ExWD over the study period at four exceedance levels and further related the number of ExWDs with cereal crop productivity of India. Extreme warm days have increased throughout India but were statistically significant in 42% grids. The increase was consistent over all the months in north-eastern region, southern plateau and both the coastal plains. It also increased significantly over north-western and central India during April to June summer period. The probability distribution of ExWD also changed significantly in many grids, especially in southern plateau and both the coastal plains. The changes indicated increased frequency in the existing levels of extremes and new occurrences of higher frequency of extremes. The analysis of land area affected by different levels of extremes indicated significant increase, with the rate being highest for higher extremes. In terms of extreme warm day temperatures, the study identified southern plateau, east and west coast plains, and north-eastern India as highly vulnerable. Using copula probability model, study showed that increase in ExWD from 20 to 60% may increase the probability of 5% or more yield loss from 17 to 53% for Kharif cereals, 11 to 43% for Rabi cereals and 19 to 63% for wheat crop. The results may be used for devising zone specific adaptation strategies.     

Investigation of flow-rainfall co-variation for catchments selected based on the two main sources of River Nile

The co-variation of rainfall and flow was assessed in four selected catchments of the River Nile which has two main sources including the White Nile (in the Equatorial region) and the Blue Nile (from the Ethiopian highlands). The selected catchments included Kyoga and Kagera (from the Equatorial region), as well as Blue Nile and Atbara (in Sudan and Ethiopia). In each catchment, the flow-rainfall co-variation was investigated at both seasonal and annual time scales. To explain aggregated variation at larger temporal scale while investigating the possible change in catchment behavior, which may interfere with the flow-rainfall relationship, rainfall-runoff modeling was done at daily time scale using data (falling within the period 1949–2003) from Kagera and Blue Nile i.e. the major catchment of each region where the River Nile emanates. Correlation analysis was conducted to assess how well the variation of flow and that of catchment-wide rainfall resonate. The co-occurrence of the changes in observed and simulated overland flow was examined using the quantile perturbation method (QPM). Trends in the model residuals were detected using the Mann–Kendal (MK) and cumulative rank difference (CRD) tests. The null hypothesis H ₀ (no correlation between rainfall and flow) was rejected at the significance level α of 5% for all the selected catchments. The temporal changes in terms of the QPM anomalies for both the observed and simulated flow were in a close agreement. The evidence to reject the H ₀ (no trend in the model residuals) was generally statistically insufficient at α = 5% for all the models and selected catchments considering both the MK and CRD tests. These results indicate that change in catchment behavior due to anthropogenic influence in the Nile basin over the selected time period was minimal. Thus, the overall rainfall-runoff generation processes of the catchments did not change in a significant way over the selected data period. The temporal flow variation could be attributed mainly to the rainfall variation.     

Hydraulic diffusivity in a coastal aquifer: spectral analysis of groundwater level in responses to marine system

The hydraulic diffusivity gives a measure of diffusion speed of pressure disturbances in groundwater system; large values of hydraulic diffusivity lead to fast propagation of signals in aquifer. This research provides a novel design and derives spectral representation to determine hydraulic diffusivity using spectral analysis of groundwater levels coupled with time-dependent boundary adjacent to marine system and no flow boundary in aquifer system. To validate the proposed method, water levels of fluctuated boundary and groundwater well in a sandy confined aquifer were collected. The hydraulic diffusivity is then obtained by an inverse process in the non-linear complex form of spectral relationship. The method essentially is constructed on the conceptual design of natural forcing transmitted in large aquifer. It is unlike the conventional field pumping test which is only used to determine hydraulic properties of groundwater in small range around the well. Hydraulic diffusivity of the confined aquifer is determined using real observation and then checked by comparing to the published range. It suggests that without local aquifer test to estimate hydraulic diffusivity in a coastal aquifer using spectral representation with its relevant flow system and boundary has become feasible.     

Analysis of nonlinear associations between prenatal methylmercury exposure from fish consumption and neurodevelopmental outcomes in the Seychelles Main Cohort at 17 years

The Seychelles Child Development Study has been examining the relationship between prenatal methylmercury (MeHg) exposure from consuming fish during pregnancy and child development. This study re-analyzes seven outcomes in the 17 year Main Cohort data to determine if there are nonlinear or non-homogeneous (subgroup) associations that were not identified in the linear analysis. We adopted two statistical approaches. First, we carried out an additive nonlinear analysis assuming homogeneous prenatal MeHg-outcome relationships to explore overall associations. Second, we applied the regression tree to the Woodcock–Johnson Calculation subtest (it was significantly associated in earlier analyses) and identified 4 clusters based on covariates. Then we used additive models to assess the prenatal MeHg association in each of the four clusters for all seven outcomes. This approach assumes nonlinear associations in each cluster and non-homogeneous associations between clusters. The additive nonlinear analysis yielded prenatal MeHg curves similar to the linear analysis. For the regression tree analysis, the curves relating prenatal MeHg to outcomes between the 4 clusters differed and some crossed at higher prenatal MeHg levels, suggesting non-homogeneity in the upper range of exposure. Additionally, some of the curves suggested a possible non-linear relationship within the range of exposure we studied. This non-linear analysis supports the findings from the linear analysis. It shows little evidence to support an adverse association of prenatal MeHg exposure through maternal consumption of fish contaminated with natural background levels. However, the tree analysis suggests that the prenatal exposure/outcome relationship may not be homogeneous across all individuals and that some subpopulations may have an adverse association in the upper range of the exposures studied. More robust data in the higher levels of exposure in this cohort are needed to confirm this finding.     

Probability-credibility health risk assessment under uncertain environment

The ability to describe variables in a health risk model through probability theory enables us to estimate human health risk. These types of risk assessment are interpreted as probabilistic risk assessment (PRA). Generally, PRA requires specific estimate of the parameters of the probability density of the input variables. In all circumstances, such estimates of the parameters may not be available due to the lack of knowledge or information. Such types of variables are treated as uncertain variables. These types of information are often termed uncertainty which are interpreted through fuzzy theory. The ability to describe uncertainty through fuzzy set theory enables us to process both random variable and fuzzy variable in a single framework. The method of processing aleatory and epistemic uncertainties into a same framework is coined as hybrid method. In this paper, we are going to talk about such type of hybrid methodology for human health risk assessment. Risk assessment on human health through different pathways of exposure has been attempted many a times combining Monte Carlo analysis and extension principle of fuzzy set theory. The emergence of credibility theory enables transforming fuzzy variable into credibility distribution function which can be used in those hybrid analyses. Hence, an attempt, for the first time, has been made to combine probability theory and credibility theory to estimate risk in human health exposure. This method of risk assessment in the presence of credibility theory and probability theory is identified as probabilistic-credibility method (PCM). The results obtained are then interpreted through probability theory, unlike the other hybrid methodology where the results are interpreted in terms of possibility theory. The results obtained are then compared with probability-fuzzy risk assessment (PFRA) method. Generally, decision under hybrid methodology is made on the index of optimism. An optimistic decision maker estimates from the \(\alpha\)-cut at 1, whereas a pessimistic decision maker estimates from the \(\alpha\)-cut at 0. The PCM is an optimistic approach as the decision is always made at \(\alpha\)=1.     

Classification issues within ensemble-based simulation: application to surge floods forecasting

The paper proposes a high-level conceptual and technological approach to manage the ensemble-based simulation, taking into account changing states of both simulated system and system of models. The approach includes systematization of ensemble-based modeling and simulation techniques, analysis of simulation results, quality assessments, and detailed analysis of ensemble management procedures using classification operators. The technological basis for such an approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessments, etc.). Within this work, a set of case studies is addressed to examine the opportunities provided by the developed approach considering ensemble-based simulation of storm surges for flood prediction in St. Petersburg, Russia as an example.     

A non-linear and non-stationary perspective for downscaling mean monthly temperature: a wavelet coupled second order Volterra model

This study presents a multiscale framework for downscaling of the General Circulation Model (GCM) outputs to the mean monthly temperature at regional scale using a wavelet based Second order Voltera (SoV) model. The models are developed using the reanalysis climatic data from the National Centers for Environmental Prediction (NCEP) and are validated using the simulated climatic dataset from the Can CM4 GCM for five locations in the Krishna river basin, India. K-means clustering, based on the multiscale wavelet entropy of the predictors, is used for obtaining the clusters of the input climatic variables. Principal component analysis (PCA) is used to obtain the representative variables from each cluster. These input variables are then used to develop a wavelet based multiscale model using Second order Volterra approach to simulate observed mean monthly temperature for the selected locations in the basin. These models are called W-P-SoV models in this paper. For the purpose of comparison, linear multi-resolution models are developed using Multiple Linear regression (MLR) and are called W-P MLR models. The performance of the models is further compared with other Wavelet-PCA based models coupled with Multiple linear regression models (P-MLR) and Artificial Neural Networks (P-ANN), and, stand-alone MLR and ANN to establish the superiority of the proposed approach. The results indicate that the performance of the wavelet based models is superior in terms of downscaling accuracy when compared with the other models used.