Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin

ABSTRACT

The endorheic basin of Zayandehrud in Iran suffers from environmental problems, social tensions, and economic instability. Lack of understanding how the water system and the socio-economic system interact may explain these challenges. A system dynamics model, being a holistic simulation tool, was developed for the Zayandehrud basin and used to evaluate several policy scenarios. The indices of employment, gross regional product, the volume of groundwater and surface water stored, flow into the basin’s end lake, and the water flow in the river were used to evaluate the scenarios. The findings demonstrate that focusing on supply-based activities or water demand management cannot solely improve the condition of the Zayandehrud basin. It is required to reconsider the development policies of the region in a broader context. Reducing the irrigated area by 15% and developing new industries up to a certain limit may make the combined water and socio-economic system sustainable.     

HAZMAP: A REMOTE DIGITAL MEASUREMENT SYSTEM FOR WORK IN HAZARDOUS ENVIRONMENTS

In a continuing drive to reduce the radiation exposure of nuclear workers, many routine aspects of nuclear plant maintenance and refurbishment are increasingly undertaken by robotic or remote handling tools. Accurate “as built” documentation of plant records is vital to the successful planning and execution of such operations since an unexpected obstruction or undocumented site modification may result in a costly disruption or even failure of the intervention. In recognition of the importance of such documentation, a recent European Community ? ? Teleman-Robotics and remote sensing operations in hazardous nuclear environments
sponsored project has been directed towards the development of techniques for cost effective survey and modelling of such plant. This paper will detail the rationale behind the design of such a system and will describe typical results from a number of pilot projects.     

Synthetic light curves of two eclipsing binary systems,U Sge and AW UMa

Photometric curve fits have been investigated by means of numerical quadratures to develop theoretical light curves appropriate to stars built up in accordance with the Roche model. The method has been applied previously to β Per (Al-Naimiy and Budding, 1977) on the basis of available observations in red and infrared, while presently applied to two systems with contact components.

1. U Sge, spherical primary totally eclipsed by a contact component secondary. Improved photometric elements of the system have been found, and compared with those obtained by Kopal's method in the frequency domain. The outcome of the curve fitting corresponds well with the results of an analysis in the frequency domain.
2. AW UMa, exhibiting the shallowest minima known for totally eclipsing W UMa systems. The physical and geometrical elements of the system have been found, and the contact nature of the two components confirmed.

     

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m³/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m³/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m³/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m³/s, EC = 56.90%, R = .755) during the test period.     

Analyse multi-échelle par ondelettes des contacts géologiques : application à la carte gravimétrique du Maroc nord-oriental

The multiscale wavelet analysis has been applied to the gravity data from northeastern Morocco to map the major geological contacts, such us faults. Hence, the faults affecting the survey area were outlined with their importance level and dip direction. Seismic data confirm these results and testify the efficiency of this method, particularly in studying structure of plains. The structural map established is a very useful document in the planning of natural resources investigations (i.e. water, mines) to be undertaken in the area of study. To cite this article: D. Khattach et al., C. R. Geoscience 338 (2006).     

Soil organic carbon mapping using remote sensing techniques and multivariate regression model

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R² = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.     

Monitoring of dust storm and estimation of aerosol concentration in the Middle East using remotely sensed images

Arabian Journal of Geosciences - Dust storms are one of the major environmental disasters in the arid regions of Middle East, occurring in very high frequency. As a result, monitoring dust storms...     

Snow water equivalent prediction using Bayesian data assimilation methods

Using the U.S. National Weather Service’s SNOW-17 model, this study compares common sequential data assimilation methods, the ensemble Kalman filter (EnKF), the ensemble square root filter (EnSRF), and four variants of the particle filter (PF), to predict seasonal snow water equivalent (SWE) within a small watershed near Lake Tahoe, California. In addition to SWE estimation, the various data assimilation methods are used to estimate five of the most sensitive parameters of SNOW-17 by allowing them to evolve with the dynamical system. Unlike Kalman filters, particle filters do not require Gaussian assumptions for the posterior distribution of the state variables. However, the likelihood function used to scale particle weights is often assumed to be Gaussian. This study evaluates the use of an empirical cumulative distribution function (ECDF) based on the Kaplan–Meier survival probability method to compute particle weights. These weights are then used in different particle filter resampling schemes. Detailed analyses are conducted for synthetic and real data assimilation and an assessment of the procedures is made. The results suggest that the particle filter, especially the empirical likelihood variant, is superior to the ensemble Kalman filter based methods for predicting model states, as well as model parameters.