Quantifying Spatiotemporal Change in Landuse and Land Cover and Accessing Water Quality: A Case Study of Missouri Watershed James Sub-Region, North Dakota

Spatial causal effects on water quality are essential in identification of vulnerable watersheds. Modelling landuse variables is an effective method of projecting localized impairment. This study presents an integrated index, designed to gauge the ability of an 8-digit Hydrologic Unit Code watershed in its ability to produce clean water. This index, I _APCW, can be successfully applied on a geospatial platform. In this study we utilized I _APCW to address forest cover dynamics of an impaired watershed, that is, Missouri Watershed James Sub-region in North Dakota. Specific parametric functions were analysed and combined within a customized GIS interface to provide a multi-faceted structured technique to derive I _APCW. These included ambient forest cover, housing density, agricultural land, soil erodibility and road density; it can be lucidly ascertained that where a prevailing forest cover undergoes conversion processes, the secondary effect may spur an exponential increase in water treatment costs. These parameters when projected statistically validated temporal and spatial relations of landuse/land cover dynamics to nutrient concentrations especially those that would be noted at the mouth of the watershed. In this study, we found that the levels of Total Dissolved Solids (TDS) were much higher for the years 2014 to 2016 with a discernible increased alkalizing effect within the watershed. When I _APCW was compared to Annualized Agricultural Nonpoint Source (AnnAGNPS), the spatial distribution generated by the AnnAGNPS study showed that fallow areas produced significant amounts of sediment loads from the sub-watershed. These same locations in this study generated a low I _APCW.

• Spatial Analyst
• Index of Ability to Produce Clean Water
• Landuse
• Water Quality

1. Introduction

Water and sediment supply, and their management, are critical to many hydraulic project operations. Trend analysis of water quality data is an essential environmental diagnosis of a stream allowing evaluation of how the water body has responded to change in landuse over a period of time. Change in landuse and land cover directly impact sustainable use of reservoirs, water quality, and riparian habitat [1] . However, we are limited by the tools and methodology available to understand the future impacts on a larger scale. Water and sediment supply has been measured only at limited locations and over a limited time period and hence the growing need for predictive models. Sekar and Randhir (2007) developed prioritization maps to characterize conjunctive water harvesting potential that is based on benefits and costs [2] . The results of their study demonstrate that a spatially variable harvesting strategy can be used to minimize runoff loss and to augment water supplies.

Changes in the composition of soil take place due to change in Landuse Land Cover (LULC). LULC is an integrated term that includes both categories of LULC and analysis of changes is of prime importance to understand many social, economic and environmental problems [3] . Landuse (LU) and Land Cover (LC) are the two fundamental components describing the terrestrial environment in relation to both natural and anthropogenic processes [4] [5] [6] . Environmental modifications worldwide are mostly caused due to LU and LC changes, thus it emerges as a key research question [7] . Quantifying landscape patterns enable us to identify and evaluate temporal changes and enable the study of the effects of pattern on ecological processes [8] . Jensen in his investigation of urban landscape perceived landuse as a way by which human beings utilize land while land cover exists as a natural environmental system [9] . Remote sensing and Geographic Information Science (GIS) techniques have been effectively utilized to identify and quantify periodic change in the landscape and its consequent environmental impacts [10] . Land cover is an important parameter for monitoring agricultural, hydrological and watershed modelling which constitute necessary tools for development, planning and management of natural resources in a particular region [10] .

Past research has shown that increase in agricultural landuse has direct consequence on sedimentation, nutrients, and pesticides in streams [11] [12] . Landuse change detection is therefore a critical requirement for the assessment of potential environmental impacts and developing effective land management and planning strategies [13] . Surface water bodies are the potential recipients of the contaminations contained in surface runoff from their catchments [14] . This makes surface water quality monitoring an important parameter. There are limited resources available for conservation that can be allocated to the erosion of susceptible areas. These areas can be highlighted through mapping, monitoring and prioritizing [15] . Erosion risk mapping of the area can enable decision makers to prioritize susceptible areas for conservation measures in accordance with the level of vulnerability [16] . According to USDA Forest Service, protecting and managing forests in source watersheds is an essential part of future strategies for providing clean safe drinking water. An Index of the Ability to Produce Clean water (APCW) can be generated through GIS overlay Analysis to prioritize impaired watersheds. Spatial Multi Criteria Decision Making (MCDM) has also become one of the most useful methods for landuse and environmental planning, as well as water and agricultural management [17] .

The request for GIS models and tools supporting collaborative decisions has increased over the last decade [18] . GIS-based MCDM involves a set of geographically defined basic units (e.g. polygons in vectors, or cells in rasters), and a set of evaluation criteria represented as map layers or attributes. Based on a particular ranking schema, it ultimately informs a spatially complex decision process by deriving a utility of these spatial entities through overlaying the criterion maps according to the attribute values and decision maker’s preferences using a set of weights. Therefore, besides criteria selection, criteria weight severely impacts the results of the MCDM [19] . Nutrients in a water body such as nitrogen and phosphorus are considered to be pollutants when these nutrient concentrations become excessive, causing some organisms to proliferate at the expense of others [20] . The situation is significantly multiplied by eutrophication, which is caused by excessive algae growth in a water body from surrounding agricultural watersheds due to the excessive presence of the necessary growth nutrients and ambient conditions that promote algal blooms. This enhanced plant growth reduces the dissolved oxygen levels when the plants decompose, potentially hindering the survival of fish and other aquatic life that depend on pristine conditions [21] . These physical and chemical changes may interfere with the recreational and aesthetic uses of the water body, while both taste and odour problems may make the water less desirable for water supply and human consumption [21] . Thus it is essential to estimate and qualify nutrient contaminations within the watershed. The objectives of this current study were to assess and analyze the LULC changes and to prepare a risk map through weighted overlay of influencing factors such as vegetation, rainfall, LULC, soil data and water quality data. In the process, we also identified the potential areas showing levels of vulnerability to change in soil and water quality.

2. Description of Study Area

The sub-basin of the Missouri River spreads over four counties of North Dakota namely Foster, Kidder, Stutsman, and Wells in the Missouri Region-James River Sub-Region [22] . James River, Maple River, Pipestem Creek, Beaver Creek and Spring Creek are located in this sub basin (Figure 1). Pipestem Creek starts from the Pipestem Dam downstream to its confluence with the James River which is about 5.6 miles. The mean annual precipitation is between 13 to 22 inches. Mean Annual air temperature ranges between 37˚F to 16˚F for mean elevation ranging from 1280 to 2560 feet. The type of soil found at this location is Williams-Bowbells loam which is a well-drained, non-saline clay loam with calcium carbonate of about 20%. Figure 2 shows a part of the watershed near Pingree, North Dakota which was one of the sampling locations. Figure 3 shows a view of the watershed. Riparian forests (Figure 4) are predominant along the rivers.

Figure 1

Location of study area―Pipestem Creek in North Dakota, USA showing sampling sites

Figure 2

Collecting water samples at the Pipestem Creek

Figure 3

Pipestem Creek near Pingree in North Dakota, USA

Figure 4

GIS weighted scoring showing percentage change in riparian forests

3. Materials and Methods

3.1. Data Processing and GIS Analysis

Historic data of Pipestem Creek was used to perform a spatial analysis and identify localised areas of impairment within the watershed. Forest Cover including riparian forests and agricultural landuse data was acquired from United States Department of Agriculture National Agricultural Statistics Service (NASS). Soil erodibility dataset was acquired from United States Department of Agriculture National Resources Conservation Service (NRCS). Road network data and year 2000 Housing Density data was acquired from the North Dakota GIS Hub. The landuse and land cover was classified using the Anderson classification system [23] . NLCD data, a raster dataset, was imported to ArcMap® 9.3, a GIS software, where only the study area was clipped. Each attribute dataset was processed individually to produce a raster grid. To summarize forest cover, the “Tabulate Areas” function was used in ArcMap® 9.3, to calculate the acreage of forested land for the watershed. The percent of the watershed that is forested was calculated by dividing the acreage of forested land by the total watershed land acreage [24] . The results were saved to the attribute field of this shapefile which was then converted to a 30 m raster dataset (Figure 5). The percent forest was reclassified into the four categories (Table 1(a)). Category break points were entered as half integers between the intervals. For example, a value of 24.5 was the break point for percent forest land scored as low or moderate. The results were saved in the corresponding attribute field. Agricultural land was summarized using grid values from the NLCD 2001 dataset of North Dakota. The same method was replicated to tabulate the areas under agricul- tural land. The percent agricultural land was reclassified into the four categories summarized in Table 1(b). Category break points were entered as half integers between the intervals. For example, 30 was the threshold for percent agricultural land scored as low. The results were saved in the attribute field of this shapefile which was then converted to a 30 m raster dataset (Figure 6). For riparian forest cover, the acreage of riparian forested land was divided by the total acreage of riparian buffer in the watershed. The percent riparian forest cover was reclassified into the four categories summarized in Table 1(c). Category break points were entered as half integers between the intervals. A value of 29 was the break point for percent riparian forest scored as low. The results were saved in the attribute field of this shapefile which was then converted to a 30 m raster dataset (Figure 4). The North Dakota national roads dataset was split into East

Figure 5

GIS weighted scoring showing percentage change in other forests

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