GIS技术在城市地下水重金属浓度识别与分级中的应用（英文）

Groundwater is the most appropriate and widely used source of drinking water,which is increasingly threatened by pollution from industrial and agricultural activities.To check the severity of the problem,156 groundwater samples were collected from various depths(60-110 ft) of 52 different localities in Faisalabad city,the third largest metropolis in Pakistan,and analyzed for the metals(Zn,Cu,Cd,Ni,Pb,Mn and Fe) concentration in 2009.Quantification was done by using Flame Atomic Absorption Spectrophotometer technique and the results were compared with WHO standards for drinking water quality.Results showed that the levels of Cu,Mn and Fe were below the WHO standards while the concentrations of Zn,Cd,Ni and Pb were above the recommended levels of safe drinking water.Correlation analysis among the occurrence of these heavy metals revealed a highly significant and positive correlation of Mn with Zn and Fe.A significant and positive correlation of Cd was also found with Cu and groundwater depth showing that there is strong association between Cu-Cd pair and that the Cd concentration varies with depth of groundwater in the study area.Regional patterns of heavy metals occurrence were mapped using Geographical Information System(GIS) for the identification and demarcation of risk areas.The concentration maps may be used by policymakers of the city to mitigate groundwater pollution.     

Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption

Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10-year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.     

Calculation of wave-induced turbulent flows in estuaries

The k-ε turbulence model which relates the eddy viscosity to turbulent kinetic energy, k, and to the rate of its dissipation, ε, and determines the distribution of these two quantities from modeled transport equations, is employed in calculating the vertical structure of wave-induced turbulent flows in two-dimensional estuaries. The empirical constants in this model are given the standard values cited in the literature and used successfully for calculating a large variety of steady flows; they are not tuned to the oscillating flows. The free surface elevation and the eddy-viscosity distribution are also calculated as a part of the solution. First, calculations performed for oscillatory laminar flows are compared with analytic solutions to ensure the proper performance of the numerical scheme. The turbulent flow in a laboratory-estuary model with one end closed, and in the Humber Estuary is simulated with the numerical model, and the results are compared with the corresponding flume and field measurements. The influence of frequency and roughness is thereby investigated. The results indicate that the time and space variation of the velocity field and the phase lag between the surface slope and the horizontal velocity can be predicted satisfactorily in wave-induced turbulent flows.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

OpenDA-NEMO framework for ocean data assimilation

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations.