Application of integrated GIS and multimedia modeling on NPS pollution evaluation

In Taiwan, nonpoint source (NPS) pollution is one of the major causes of the impairment of surface waters. I-Liao Creek, located in southern Taiwan, flows approximately 90 km and drains toward the Kaoping River. Field investigation results indicate that NPS pollution from agricultural activities is one of the main water pollution sources in the I-Liao Creek Basin. Assessing the potential of NPS pollution to assist in the planning of best management practice (BMP) is significant for improving pollution prevention and control in the I-Liao Creek Basin. In this study, land use identification in the I-Liao Creek Basin was performed by properly integrating the skills of geographic information system (GIS) and global positioning system (GPS). In this analysis, 35 types of land use patterns in the watershed area of the basin are classified with the aid of Erdas Imagine process system and ArcView GIS system. Results indicate that betel palm farms, orchard farms, and tea gardens dominate the farmland areas in the basin, and are scattered around on both sides of the river corridor. An integrated watershed management model (IWMM) was applied for simulating the water quality and evaluating NPS pollutant loads to the I-Liao Creek. The model was calibrated and verified with collected water quality and soil data, and was used to investigate potential NPS pollution management plans. Simulated results indicate that NPS pollution has significant contributions to the nutrient loads to the I-Liao Creek during the wet season. Results also reveal that NPS pollution plays an important role in the deterioration of downstream water quality and caused significant increase in nutrient loads into the basin's water bodies. Simulated results show that source control, land use management, and grassy buffer strip are applicable and feasible BMPs for NPS nutrient loads reduction. GIS system is an important method for land use identification and waste load estimation in the basin. Linking the information of land utilization with the NPS pollution simulation model may further provide essential information of potential NPS pollution for all subregions in the river basin. Results and experience obtained from this study will be helpful in designing the watershed management and NPS pollution control strategies for other similar river basins.     

Relationship between landscape characteristics and surface water quality

The effects of landscape characteristics on surface water quality were evaluated in terms of land-use condition, soil type and slope. The case area, the Chichiawan stream in the Wulin catchment in Taiwan, is Formosan landlocked salmon's natural habitat. Due to the agriculture behavior and mankind's activities, the water and environmental quality has gradually worsened. This study applied WinVAST model to predict hydrological responses and non-point source pollution (NPSP) exports in the Wulin catchment. The land-use condition and the slope of land surface in a catchment are major effect factors for watershed responses, including flows and pollutant exports. This work discussed the possible variation of watershed responses induced by the change of land-use condition, soil type and slope, etc. The results show that hydrological responses are highly relative to the value of Curve Number (CN); Pollutant exports have large relation to the average slope of the land surface in the Wulin catchment.     

Critical Sampling Points Methodology: Case Studies of Geographically Diverse Watersheds

Only with a properly designed water quality monitoring network can data be collected that can lead to accurate information extraction. One of the main components of water quality monitoring network design is the allocation of sampling locations. For this purpose, a design methodology, called critical sampling points (CSP), has been developed for the determination of the critical sampling locations in small, rural watersheds with regard to total phosphorus (TP) load pollution. It considers hydrologic, topographic, soil, vegetative, and land use factors. The objective of the monitoring network design in this methodology is to identify the stream locations which receive the greatest TP loads from the upstream portions of a watershed. The CSP methodology has been translated into a model, called water quality monitoring station analysis (WQMSA), which integrates a geographic information system (GIS) for the handling of the spatial aspect of the data, a hydrologic/water quality simulation model for TP load estimation, and fuzzy logic for improved input data representation. In addition, the methodology was purposely designed to be useful in diverse rural watersheds, independent of geographic location. Three watershed case studies in Pennsylvania, Amazonian Ecuador, and central Chile were examined. Each case study offered a different degree of data availability. It was demonstrated that the developed methodology could be successfully used in all three case studies. The case studies suggest that the CSP methodology, in form of the WQMSA model, has potential in applications world-wide.     

Modeling of non-point source pollution in a Mediterranean drainage basin

SWAT ver. 2000 was used to predict hydrographs, and sediment, nitrate and total phosphorus loadings from a 1349 km² mountainous/agricultural watershed in Northern Greece. The model was calibrated and verified using continuous meteorological data from eight stations within the drainage area, and runoff, sediment and nutrient concentrations measured at nine stations located within the main tributaries of the watershed, for the time period from May 1st, 1998 to January 31st, 2000. Model validation methodology and resulting input parameters appropriate for Mediterranean drainage basins are presented. Predicted by the model hydrographs, sedimentographs and pollutographs are plotted against observed values and show good agreement. Model performance is evaluated using the root mean square error computation and scattergrams of predicted versus observed data. The validated model is also used to test the effectiveness of three alternative cropping scenarios in reducing nutrient loadings from the agricultural part of the watershed. The study showed that this model, if properly validated, can be used effectively in testing management scenarios in Mediterranean drainage basins.     

Derivation of unit and flood hydrographs using a gis

A geographic information system (GIS) supporting a flood hydrograph prediction software package is described. The hydrograph prediction method is based on the convolution of excess rainfall with a synthetic unit hydrograph, derived by the Soil Conservation Service runoff curve number and a regional dimensionless unit hydrograph method, respectively. The GIS uses a raster method to store the following data: land use and land cover, soil type, rainfall intensity-frequency-duration statistics, runoff curve numbers (CN), regional dimensionless unit hydrograph, and regional lag-time relationship. The GIS has also the capability of computing a number of watershed and hydrologic parameters required for predictions, such as a watershed average rainfall and CN value, area, centroid, stream length etc. Most of the data for such computations are input from a digitizer. Substantial time and cost savings are possible once the data base has been created. Application of the system is illustrated by an example predicting flood frequency curves for selected watersheds in Alberta's Rocky Mountain foothills, Canada.     

A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid’s Exporting and Delivery model, was introduced to estimate annual in-stream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid’s features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model’s validity and good performance in estimating the annual NPS nutrients’ fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.     

A water quality monitoring network design methodology for the selection of critical sampling points: Part II

In order to resolve the spatial component of the design of a water quality monitoring network, a methodology has been developed to identify the critical sampling locations within a watershed. This methodology, called Critical Sampling Points (CSP), focuses on the contaminant total phosphorus (TP), and is applicable to small, predominantly agricultural-forested watersheds. The CSP methodology was translated into a model, called Water Quality Monitoring Station Analysis (WQMSA). It incorporates a geographic information system (GIS) for spatial analysis and data manipulation purposes, a hydrologic/water quality simulation model for estimating TP loads, and an artificial intelligence technology for improved input data representation. The model input data include a number of hydrologic, topographic, soils, vegetative, and land use factors. The model also includes an economic and logistics component. The validity of the CSP methodology was tested on a small experimental Pennsylvanian watershed, for which TP data from a number of single storm events were available for various sampling points within the watershed. A comparison of the ratios of observed to predicted TP loads between sampling points revealed that the model's results were promising.     

Spatial distribution and transport of heavy metals in soil, ponded-surface water and grass in a pb-contaminated watershed as related to land-use practices

The aim of this study was to investigate the spatial distribution of heavy metal in soil and evaluate the dissolution of metal from soil to ponded-surface water, leaching through soil profiles and metal uptake in grass as related to different land-use practices. The data provided a scientific basis for best-management practices for land use in Khli Ti watershed. The watershed has a Pb-contamination problem from the previous operation of a Pb-ore concentrator and abandoned Zn–Pb mine. Sampling sites were selected from a land-use map, with land-use types falling into the following four categories: forest, agricultural land, residential area and road. Soil, ponded-surface water, grass samples and soil profiles were collected. The study related soil characteristics from different land-use practices and locations with observed metal concentrations in ponded-surface water and soil. High enrichment factors of Pb and As in soil were found. Partitioning coefficient, K_d values were in the order: Cr > Pb > Ni > Cu > Cd > Zn. Soil disturbance from land-use activities including tillage and traffic increased leaching of trace metal from soils. Pb in soil was significantly taken up by grass even though the Transfer Factor, TF values were rather low. Agricultural activities in the watershed must be limited. Moreover, land encroachments in the upper and middle part of the watershed which have high potential of Pb must be strictly controlled in order to reduce the Pb contamination from non-point sources.     

Soil disturbance/restoration effects on stream sediment loading in the Tahoe Basin—detection monitoring

Quantifying the relative impacts of soil restoration or disturbance on watershed daily sediment and nutrients loads is essential towards assessing the actual costs/benefits of the land management. Such quantification requires stream monitoring programs capable of detecting changes in land-use or soil functional and erosive area “connectivity” conditions across the watershed. Previously, use of a local-scale, field-data based runoff and erosion model for three Lake Tahoe west-shore watersheds as a detection monitoring “proof of concept” suggested that analyses of midrange average daily flows can reveal sediment load reductions of relatively small watershed fractional areas (～5 %) of restored soil function within a few years of treatment. Developing such an effective stream monitoring program is considered for tributaries on the west shore of the Lake Tahoe Basin using continuous (15-min) stream monitoring information from Ward (2,521 ha), Blackwood (2,886 ha), and the Homewood (260 ha, HMR) Creek watersheds. The continuous total suspended sediment (TSS) and discharge monitoring confirmed the hysteretic TSS concentration—flowrate relationship associated with the daily and seasonal spring snowmelt hydrographs at all three creeks. Using the complete dataset, daily loads estimated from 1-h sampling periods during the day indicated that the optimal sampling hours were in the afternoon during the rising limb of the spring snowmelt hydrograph, an observation likely to apply across the Sierra Nevada and other snowmelt driven watersheds. Measured rising limb sediment loads were used to determine if soils restoration efforts (e.g., dirt road removal, ski run rehabilitation) at the HMR creek watershed reduced sediment loads between 2010 and 2011. A nearly 1.5-fold decrease in sediment yields (kg/ha per m³/s flow) was found suggesting that this focused monitoring approach may be useful towards development of TMDL “crediting” tools. Further monitoring is needed to verify these observations and confirm the value of this approach.     

AgInput: An Agricultural Nutrient and Pesticide Source Model

Agriculture can be a major nonpoint source (NPS) of nutrient and pesticide contamination in the environment. Available databases do not provide accurate and dynamic data on fertilizer and pesticide application, which limits the ability of complex watershed models to simulate contaminant loads into impaired water bodies. A model for estimating agricultural nutrient and pesticide input for watershed modeling has been developed. Climate, soils, and major agricultural operations are considered within the model, so that it can be adapted to any watershed or subregion within a watershed. The timing of the agricultural operations is a function of the weather data, providing realistic results at daily, monthly, or annual application rates. The model also predicts irrigation demand and biomass production, which can be used to calibrate the model. Model output can be used in any watershed model that considers agricultural land uses. Two case studies were evaluated, using grape vineyards in the Napa River and strawberry production in Newport Bay as examples. The predicted time to maturity corresponded well with actual data. Irrigation and fertilizer needs were very sensitive to weather input. Although the model can generate weather from long-term averages, the simulated results are best when at least observed precipitation and temperature are provided, to capture extreme events. The model has data for 98 crops and 126 pesticides, based on the California Department of Pesticide Regulation database. The databases are easily modifiable by the user to adapt them to local conditions. The output from AgInput is much needed for watershed modeling and for development of total maximum daily loads (TMDLs), based on realistic targets of irrigation, nutrient, and pesticide inputs. The model is available for free download at .     

An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models

This paper presents a study on the effect of topographic variability on grid-based empirical estimation of soil erosion and sediment transport with raster geographic information systems (GIS). An original digital elevation model (DEM) of 10 m resolution for a case watershed is resampled to six realizations of greater grid sizes for a comparative examination. The Universal Soil Loss Equation (USLE) and a distance-based sediment delivery equation are applied to the watershed to calculate soil loss from each cell and total sediment transport to streams, respectively. The results suggest that the selection of the DEM gird size has considerable influence on the soil loss estimation with the empirical models. The estimate of total soil loss from the watershed decreases significantly with the increasing DEM cell size as the spatial variability is reduced by the cell aggregation. The empirical modeling approach is a useful tool for qualitative assessment of soil erosion, provided that spatial variability can be adequately represented by applied DEMs. However, discretion is suggested for its applications to quantitative estimation of soil loss concerning the sensitivity to the grid size selection.     

Adapting the RUSLE and GIS to model soil erosion risk in a mountains karst watershed, Guizhou Province, China

Soil erosion is a serious environmental problem in Guizhou Province, which is located in the centre of the karst areas of southwestern China. Unfortunately, Guizhou Province suffers from a lack of financial resources to research, monitor and model soil erosion at large watershed. In order to assess the soil erosion risk, soil erosion modeling at the watershed scale are urgently needed to be undertaken. This study integrated the Revised Universal Soil Loss Equation (RUSLE) with a Geographic Information System (GIS) to estimate soil loss and identify the risk erosion areas in the Maotiao River watershed, which is a typical rural watershed in Guizhou Province. All factors used in the RUSLE were calculated for the watershed using local data. It was classified into five categories ranging from minimal risk to extreme erosion risk depending on the calculated soil erosion amount. The soil erosion map was linked to land use, elevation and slope maps to explore the relationship between soil erosion and environmental factors and identify the areas of soil erosion risk. The results can be used to advice the local government in prioritizing the areas of immediate erosion mitigation. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil erosion. It thus indicates that RUSLE-GIS model is a useful and efficient tool for evaluating and mapping soil erosion risk at a large watershed scale in Guizhou Province.     

A Data-Based Conservation Planning Tool for Florida Panthers

Habitat loss and fragmentation are the greatest threats to the endangered Florida panther (Puma concolor coryi). We developed a data-based habitat model and user-friendly interface so that land managers can objectively evaluate Florida panther habitat. We used a geographic information system (GIS) and the Mahalanobis distance statistic (D ²) to develop a model based on broad-scale landscape characteristics associated with panther home ranges. Variables in our model were Euclidean distance to natural land cover, road density, distance to major roads, human density, amount of natural land cover, amount of semi-natural land cover, amount of permanent or semi-permanent flooded area–open water, and a cost–distance variable. We then developed a Florida Panther Habitat Estimator tool, which automates and replicates the GIS processes used to apply the statistical habitat model. The estimator can be used by persons with moderate GIS skills to quantify effects of land-use changes on panther habitat at local and landscape scales. Example applications of the tool are presented.     

Soil Organic Carbon (SOC) Equilibrium and Model Initialisation Methods: an Application to the Rothamsted Carbon (RothC) Model

Carbon (C) emissions from anthropogenic land use have accelerated climate change. To reduce C emissions, dynamic models can be used to assess the impact of human drivers on terrestrial C sequestration. Model accuracy requires correct initialisation, since incorrect initialisation can influence the results obtained. Therefore, we sought to improve the initialisation of a process-based SOC model, RothC, which can estimate the effect of climate and land-use change on SOC. The most common initialisation involves running the model until equilibrium (‘spin-up run’), when the SOC pools stabilise (method 1). However, this method does not always produce realistic results. At our experimental sites, the observed SOC was not at equilibrium after 10 years, suggesting that the commonly used spin-up initialisation method assuming equilibrium might be improved. In addition to method 1, we tested two alternative initialisations for RothC that involved adjusting the total or individual SOC pool equilibrium values by regulating the C input during the entire spin-up initialisation period (method 2) and initialising each SOC pool with recently measured SOC values obtained by SOC fractionation (method 3). Analysis of the simulation accuracy for each model initialisation, quantified using the root mean square error (RMSE), indicated that a variant of method 2 that involved adjusting the equilibrium total SOC to observed values (method 2-T) generally showed less variation in the individual SOC pools and total SOC. Furthermore, as total SOC is the sum of all SOC pools, and because total SOC data are more readily available than the individual SOC pool data, we conclude that method 2-T is best for initialising RothC.     

Identification of effective best management practices in sediment yield diminution using GeoWEPP: the Kasilian watershed case study

Identifying areas that are susceptible to soil erosion is crucial for water resource planning and management efforts. Furthermore, modeling has proven helpful in recognizing and monitoring high-risk areas at the watershed scale. The Water Erosion Prediction Project (WEPP) geospatial interface (GeoWEPP) software integrates GIS with the WEPP to analyze the spatial variation in soil loss, and it has been used as a modeling tool to determine the areas that are most prone to soil erosion and to evaluate best management practices for the Kasilian watershed in Iran. As much as 62.4 % of the agronomic land in the Kasilian watershed is affected by a high magnitude of erosion (>5 t/ha). On the basis of this study, by using soybeans, high fertilization levels, and the drill-no-tillage system, reductions of erosion by almost 32.68–34.02 % are perceivable in three critical subwatersheds that are located in the cultivated lands. Also, it is projected that reductions in the production of sediment in the range of about 36.7–47.1 % are achievable by structural management within two critical, upland subwatersheds. So, by utilizing the best management strategies, sediment yield can be lowered and the conservation of soil and water is feasible at the watershed scale. These results objectively indicate that GeoWEPP can be efficaciously used for evaluating effective management practices for developing watershed conservation.     

The Radial Increment and Stemwood Element Concentrations of Scots Pine in the Area Influenced by the Narva Power Plants in Northeast Estonia

Soil is an important component of a watershed. Understanding soils and their interactions with the other components are, thus, considered to be critical and essential for conservation of resources and management of the watershed. Development of soil sampling and analysis programs are crucial for these purposes. Site-specific soil data are needed to identify current soil characteristics, as well as to validate datasets gathered for watershed-scale modelling of non-point sources (NPS) of pollutants arising from various land-use activities, hydrodynamics and water quality. The Koycegiz Lake–Dalyan Lagoon watershed, located in the southwest of Turkey along the Mediterranean Sea Coast, was selected as the study area for watershed modelling purposes. Development of soil sampling plans, their practical optimization, soil analyses and interpretation are presented in this article. The soil analyses conducted include physical, chemical and specific soil characteristics. Within the framework of this study, soil fertility parameters are presented and evaluated. Such an approach used is recommended for especially developing countries where up-to-date data sets are not fully available and/or centrally publicized.     

Modeling the Effects of Air Quality Policy Changes on Water Quality in Urban Areas

This paper describes the development and application of an integrated modeling framework composed of an urban air chemistry model, an urban runoff model, and a water-quality model. The models were linked to simulate the fate and transport of air emissions of nitrogen compounds in the air, urban watershed, surface water runoff, and in a coastal receiving-water body. The model linkage is demonstrated by evaluating the potential water quality implications of reducing NO _x emissions by 32%, volatile organic compound emissions by 51%, and ammonia emissions by 30%, representing changes from 1987 levels to proposed 2000 target levels in Los Angeles, California, USA. Simulations of the Los Angeles dry season during the summer of 1987 (June 1 to August 31) indicated that by reducing emissions from 1987 to proposed year 2000 levels, the dry deposition nitrogen loads to Santa Monica Bay and the Ballona Creek watershed were reduced 21.4% and 15.0%, respectively. Water quality modeling results indicated that dry season atmospheric load reductions to the Ballona Creek Estuary did not reduce chlorophyll-a levels or significantly raise nighttime dissolved oxygen levels because the magnitude of the reductions was negligible compared to non-atmospheric inputs of nitrogen compounds. Simulations of the time period from November 18, 1987 to December 4, 1987 during the Los Angeles wet season indicated that air emissions reductions produced an 18.6% reduction in the dry deposition nitrogen load to Santa Monica Bay, a 15.5% reduction in the dry deposition nitrogen load to the Ballona Creek watershed, a 16.8% reduction in the wet deposition nitrogen load to the Ballona Creek watershed, and a 16.1% reduction in the stormwater discharge load from the Ballona Creek watershed. Although the wet season load reductions are significant, modeling results of the ultimate effect on the Ballona Creek Estuary water quality were inconclusive.     

Use of USLE/GIS Methodology for Predicting Soil Loss in a Semiarid Agricultural Watershed

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.     

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.     

Uncertainty in watershed response predictions induced by spatial variability of precipitation

Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the “true” rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km² with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.