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     Vol. 10. No. 1 Spring 2010

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• Model the effect of four artificial recharge dams on the quality of groundwater using geostatistical methods in GIS environment, Oman William Bajjali, Department of Biology and Earth Sciences, University of Wisconsin – Superior, Superior, WI 54880.
  
  Abstract: The geostatistical techniques of GPI, IDW and Kriging were applied in order to evaluate the use of these statistical approaches in GIS environment to examine artificial recharge dams and its effect on the quality of groundwater. Quantitative models were employed to investigate one aspect of the artificial dam’s role on improving the shallow groundwater quality.

  The TDS was taken as the chemical parameter to validate the applicability of the geostatistical models in the four dams. The decrease of salinity of groundwater along the subsurface flow path away from the dams toward the coast, in all the prepared interpolated maps is demonstrated. The generated interpolating maps revealed a trend in increasing the TDS away from the dams toward the coast. The infiltrated water below the dams is increasing the aquifer quantity and pushing the saline water toward the sea. The continuous fresh water seep into the shallow aquifer dilutes the groundwater salinity and progressively improves its quality. The interpolated fresh water areas downstream of the dams were estimated to be approximately 57%, 19%, and 31% in Ma’awil, Samail, and Sahalnawat watersheds respectively.

  The kriging and IDW methods generated similar results in the three watersheds. The kriging and IDW techniques were found to be the best when evaluating the performance of the artificial dams in coastal areas.

  Keywords: artificial recharge, TDS (Total Dissolved Solid), GPI (Global Polynomial Interpolation) IDW (Inverse Distance Weighting), TSA (Trend Surface Analysis), Kriging, geostatistical analysis.
   

• Evaluation of Land Development Impact on a tropical Watershed Hydrology Using Remote Sensing and GIS Y.M.Mustafa¹, M.S.M Amin², T.S.Lee³ and A.R.M Shariff^{3
  1}PhD student, ²Professor, ³Associated Professors, Department of Biological and Agricultural Engineering Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. Corresponding author’s e-mail: GS10494@mutiara.upm.edu.my
  
  Abstract: Understanding how the land use change influence the river basin hydrology will enable planners to formulate policies to minimize the undesirable effects of future land use changes. Land cover changes increase impervious ground surfaces, decrease infiltration rate and increase runoff rate, hence causing low base flow during the dry seasons. Efficient tools such as satellite remote sensing and Geographic Information System (GIS) are currently being used to manage the limited water resources. The need for spatial and temporal land-cover change detection at a larger scale makes satellite imagery the most cost effective, efficient and reliable source of data. The ability of GIS makes it an important and efficient tool for spatial hydrologic modeling. In this study, Satellite data and GIS were integrated with a spatial hydrological model to evaluate the impacts of land development in the Upper Bernam River Basin of Malaysia. HEC-1 (Hydrologic Engineering Center) model was calibrated and validated using actual flow data from the outlet of the watershed. The model performance was checked by means of four criteria viz., mean absolute error (MAE), root mean square error (RMSE), Theil’s coefficient (U) and coefficient of determination (R2) obtaining values of 0.14, 0.18, 0.097, and 0.86, respectively. From the hydrographs, it was found that the change in peak flow between the years 1989 and 1993 was 28% while it was 11% between the years 1993 -1995. The reduction of the time to peak was 7% for the same years. The model can be run for any future land development plans to investigate the hydrological impacts in order to avoid the shortage of irrigation water and mitigate the risk of floods occurrence.

  Keywords: Land Development, runoff, HEC, Water Resources, GIS, Remote Sensing.
   

• Support Soil Conservation Practices by Identifying Critical Erosion Areas within an American Watershed Using the GIS-AGNPS Model
  Xixi Wang and Peilian Cui, Respectively, Ph.D., P.E., Research Scientist, Energy & Environmental Research Center, University of North Dakota, Grand Forks, ND 58202; and Software Programmer, Department of Space Studies, University of North Dakota, Grand Forks, ND 58202 (E–Mail/Wang: xwang@undeerc.org).
  
  Abstract: Eroded soil from overland is one of the major nonpoint pollution sources in many watersheds. The subsequent sediment not only reduces conveyance capacity of streams and usable storage volume of reservoirs but it also adsorbs and transports pollutants into and impairs its receiving water bodies. These negative environmental impacts may be alleviated by reducing sediment loading, which is positively associated with soil erosion rate. Targeted to critical erosion areas, which have a soil erosion rate higher than the tolerable level (T value) of 4536 kg/ac-y (5 tons/ac-y), limited funds may be more efficiently used to control sediment. With this regard, it is necessary to identify these areas in a watershed using an efficient tool such as an ArcView GIS based AGNPS (AGriculture Non-Point Source) model. The objective of this study was to use the GISAGNPS model to identify erosion-source areas within the 7075-ha Lake Icaria watershed, located in the Adams County, Iowa. The simulation results indicated that under current conventional cultivation practices, approximately 20% of the watershed in size was incurring a soil erosion rate above the T value. However, iterative simulation results revealed that the erosion rates in more than 63% of these identified critical areas could be reduced to a magnitude less than the T value, provided that the cropping (C) factors corresponding to the conventional cultivation practices would be adjusted down by 25%.

  Keywords. AGNPS, C factor, erosion, GIS, Iowa, T value, water quality modeling,
   

• Spatial Distribution of Land Type in Regression Models of Pollutant Loading
  Evan J. Fedorko¹, Robert Gilmore Pontius Jr.¹, Stephen P. Aldrich², Luc Claessens³, Charles Hopkinson Jr., and Wilfred M. Wollheim⁵

  ¹ Graduate School of Geography and George Perkins Marsh Institute, Department of International Development, Community and Environment, Clark University, Worcester, Massachusetts, EMAIL  rpontius@clarku.edu, ² Michigan State University, East Lansing, Michigan, ³ San Diego State University, San Diego, California, ⁴Marine Biological Laboratory, Woods Hole, Massachusetts, ⁵University of New Hampshire, Durham, New Hampshire

  Abstract: This paper proposes a method to improve landscape-pollution interaction regression models through the inclusion of a variable that describes the spatial distribution of a land type with respect to the pattern of runoff within a drainage catchment. The proposed index is used as an independent variable to enhance the strength, as quantified by R² values, of regression relationships between empirical observations of in-stream pollutant concentrations and land type by considering the spatial distribution of key land-type categories within the sample point’s drainage area. We present an index that adds a new dimension of explanatory power when used in conjunction with a variable describing the proportion of the land type.

  We demonstrate the usefulness of this index by exploring the relationship between nitrate ( NO⁻³ ) and land type within 40 drainage sub-catchments in the Ipswich River watershed, Massachusetts. Nutrient loads associated with non-point source pollution paths are related to land type within the up-stream drainage catchments of sample sites. Past studies have focused on the quantity of particular land type within a sample point’s drainage catchment. Quantifying the spatial distribution of key land-type categories in terms of location on a runoff surface can improve our understanding of the relationship between sampled NO⁻³  concentrations and land type.

  Regressions that employ the proportion of residential and agricultural land type within catchments provide a fair fit (R² = 0.67). However, we find that a regression adding a variable that indicates the spatial distribution of residential land improves the overall relationship between instream NO⁻³ measurements and associated land types (R² = 0.712). We test the sensitivity of the results with respect to variations in the surface definition in order to determine the conditions under which the spatial index variable is useful.

  Keywords: GIS, Non-point source pollution, nutrient export, spatial distribution, regression modeling