Flood hazard mapping using the CCHE2D numerical model in the Hable-rud River-a reach located downstream of Bone-Kuh Village

In this research, given the condition of the Hable-rud River in a reach placed downstream of Bone-Kuh Village in Semnan Province, probability of flood occurrence and consequent damages, and also the exposure of various valuable elements of the floodplain to floods, using the CCHE2D numerical model, the flood hazard map was created for the 100-year return period as the base-flood. This research aims to map the base-flood zone and to identify the areas exposed to the flood in line with flood risk management.  For this purpose, the required data for modeling, such as a large-scale topography map were prepared and flow characteristics (velocity and depth) were measured in the study area. Consequently, the computational grid has been assigned. To achieve the best simulation, the model calibration was performed by choosing the superior network and by changing the roughness coefficient. To evaluate the simulation, the model estimations were compared with the measured values for velocity and flow depth in three sections along the reach. For flow velocity, the model error was estimated to be 0.084 and 7.41% considering the RMSE and the MAE criteria, respectively. Given the flood hazard map created for the return period of 100-year, it is predicted that the area which is covered by orchards, croplands, and rangelands will be inundated with the base flood. Also, the analysis shows that about 18% of the study area is located in the moderate to high flood risk classes.  Providing the findings of this research to the local communities in the form of the map illustrating the position of various land uses on the 100-year return period flood zone can be very effective in enhancing awareness and flood risk perception of them

Watershed road network analysis with an emphasis on fire fighting management

The aim of this study is fire hazard zoning the Chehel-Chay watershed and analysis of road network in order to fire-fighting management. Using effective factors on fire occurrence, the fire hazard map of the study area produced by support vector machine algorithm and then was divided into four hazard classes. The road length and type were investigated in the each fire hazard classes. The results showed that most of occurred fires are located in the close distances of roads and forest areas. The results showed that road types and land cover are important in fire occurrences and suppression. In high dangerous zone, the roads pass through forestlands, but in low dangerous zone, the roads are passing from farmlands. The roads do not cover the half of area and do not pass at two third of high hazard class zones. Therefore, appreciate road network planning is necessary according to fire-fighting management. 

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions : A Bayesian Belief Network Application

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development

Uncertainty Analysis in Predicting Ecological Impacts of Management Scenarios in the Chehl-Chai Watershed, Gorganrood River Basin

Implementing watershed management without considering all aspects may lead to instability, exacerbating unfavorable conditions. Adopting an integrated management approach is necessary for any watershed system. An important consideration in decision making and planning process is to quantify ecological impacts of management using landscape ecology framework. In this regard, uncertainty quantification is of great significance. This paper presents the concept of uncertainty and also the implication of uncertainty analysis for landscape ecology structure indices and also for weights assigned to the indices in a Multi-Criteria Decision Making (MCDM) technique in the Chehel-Chai Watershed. This watershed with an area of 256 km2 is located in the east of Golestan Province and in the upstream of the Gorganrood River Basin. The watershed is one of the most affected areas due to the land use change in the north of Iran. That is why it was chosen as the study the area. Based on the analysis, the highest and lowest uncertainty levels were identified for Edge Density (ED) and Riparian Proportion Index (RPI), respectively. In addition, the uncertainty analysis suggests that the weight assigned to Weighted Land Cover Area Index (WLCAI) has the highest uncertainty while the weight assigned to ED shows the lowest uncertainty. It is necessary to identify and quantify uncertainty so that more accurate and applicable inferences from research findings can be drawn

A Bayesian decision network approach for assessing the ecological impacts of salinity management

This paper outlines one component of a study being undertaken to provide a new tool for integrated management of dryland salinity, a major environmental problem in Australia. The Little River Catchment in the upper Macquarie River basin of New South Wales (NSW) is used as a case study. A Bayesian decision network (BDN) approach integrates the various system components - biophysical, social, ecological, and economic. The method of integration of the system components is demonstrated through an example application showing the impacts of various management scenarios on terrestrial and riparian ecology. The ecological impacts of management scenarios are assessed using a probabilistic approach to evaluate ecological criteria which are compared with those for the present situation. In considering different ecological indices, the direction and magnitude of change under different management scenarios varies because of the diverse influence of habitat fragmentation

Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995-2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management