A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay Watershed

The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.     

A Water Quality Monitoring Network Design Methodology for the Selection of Critical Sampling Points: Part I

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.     

Characterizing Small Subbasins: A Case Study from Coastal Oregon

A fine-grained statisticaly robust probability sample of stream segments is used to compare two small (20,000 hectare) subbasins of the Tillamook watershed, north coastal Oregon. The two subbasins are matched with respect to several variables [size coastal climates], but vary in terms of geology and consequently land use. A total of 67 wadeable + non-wadeable sizes were identified for sampling in the two subbasins (combined) over two field seasons from a sampling universe consisting of the River Reach File 3 (blue lines on 1:100,000 maps). Target variables include an extensive array of physical habitat endpoints, selected water chemistry endpoints, species composition, and relative abundance of both benthic macroinvertebrates and fish. Field protocols generally followed those of the U.S. EPA's Environmental Monitoring and Assessment Program (EMAP).Eleven fish species were encountered, a typically low number for coastal Oregon streams. Exploratory analysis using nonmetric multidimensional scaling revealed that 92.4% of the variation in the fish assemblages could be explained with two ordination axes. Environmental factors related to stream size and substate were the most correlated to these axes. Further, stream segments for the two subbasins tended to map in different areas of species space. Therefore, we also give unweighted probability distributions for several of the factors that heavily on these two axes by subbasins, as well as probability distributions for chemical endpoints. Results from the subset of sites sampled during the first year (21 wadeable sites) reveal: 1) differences between samples from the two subbasins relates to dream size and substrate composition that are consistent with known differences in geology and land use, 2) unexpectedly minor differences between samples from the two subbasins for stream temperature, canopy cover, and dissolved oxygen, 3) differences between samples from the two subbasins for total P, and total N, possibly related to land use, and 4) unexpected differences in samples from the two subbasins for conductivity, probably related to geological factors. Sample size for each subbasin is low and therefore our samples cannot be taken to necessarily characterize either subbasin. However, our findings are consistent with a comprehensive assessment that had been previously produced for one of the two subbasins.All field work was completed in 8 weeks 3-person field crew. We conclude that rapid assessment protocols, based on probability samples at this level of resolution, can be a cost-effective approach to watershed analysis. This approach should be seen as a complement to, rather than a replacement for, systematic surveys that produced finer scale, reach specific information on factors such as channel complexity and cover relevant to in-stream restoration planning.     

Assessing the ecological condition of streams in a southeastern Brazilian basin using a probabilistic monitoring design

Prompt assessment and management actions are required if we are to reduce the current rapid loss of habitat and biodiversity worldwide. Statistically valid quantification of the biota and habitat condition in water bodies are prerequisites for rigorous assessment of aquatic biodiversity and habitat. We assessed the ecological condition of streams in a southeastern Brazilian basin. We quantified the percentage of stream length in good, fair, and poor ecological condition according to benthic macroinvertebrate assemblage. We assessed the risk of finding degraded ecological condition associated with degraded aquatic riparian physical habitat condition, watershed condition, and water quality. We describe field sampling and implementation issues encountered in our survey and discuss design options to remedy them. Survey sample sites were selected using a spatially balanced, stratified random design, which enabled us to put confidence bounds on the ecological condition estimates derived from the stream survey. The benthic condition index indicated that 62 % of stream length in the basin was in poor ecological condition, and 13 % of stream length was in fair condition. The risk of finding degraded biological condition when the riparian vegetation and forests in upstream catchments were degraded was 2.5 and 4 times higher, compared to streams rated as good for the same stressors. We demonstrated that the GRTS statistical sampling method can be used routinely in Brazilian rain forests and other South American regions with similar conditions. This survey establishes an initial baseline for monitoring the condition and trends of streams in the region.     

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.     

Watershed-Based Survey Designs

Watershed-based sampling design and assessment tools help serve the multiple goals for water quality monitoring required under the Clean Water Act, including assessment of regional conditions to meet Section 305(b), identification of impaired water bodies or watersheds to meet Section 303(d), and development of empirical relationships between causes or sources of impairment and biological responses. Creation of GIS databases for hydrography, hydrologically corrected digital elevation models, and hydrologic derivatives such as watershed boundaries and upstream–downstream topology of subcatchments would provide a consistent seamless nationwide framework for these designs. The elements of a watershed-based sample framework can be represented either as a continuous infinite set defined by points along a linear stream network, or as a discrete set of watershed polygons. Watershed-based designs can be developed with existing probabilistic survey methods, including the use of unequal probability weighting, stratification, and two-stage frames for sampling. Case studies for monitoring of Atlantic Coastal Plain streams, West Virginia wadeable streams, and coastal Oregon streams illustrate three different approaches for selecting sites for watershed-based survey designs.     

Spatial Prediction of Stream Temperatures Using Top-Kriging with an External Drift

Top-kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-kriging weights are estimated by a family of variogram models (regularisations) for different catchment areas (kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data situation: top-kriging gives most weight to close-by sites at the same river system, but when the next hydrologically connected site is far away, more weight is given to a close-by site at an adjacent river system. The distribution of weights is in contrast to ordinary kriging and stream distance-based kriging which does not account for both spatial proximity and network connectivity. We extend the top-kriging method by incorporating an external drift function to account for the deterministic patterns of the spatial variable. We test the method for a comprehensive Austrian stream temperature dataset. The drift is modelled by exponential regression with catchment altitude. Top-kriging is then applied to the regression residuals. The variogram used in top-kriging is fitted by a semiautomatic optimisation procedure. A leave-one-out cross-validation analysis shows that the model performs well for the study domain. The residual mean squared error (cross-validation) decreases by 20 % when using top-kriging in addition to the regression model. For regions where the observed stream temperatures deviate from the expected value of the drift model, top-kriging corrects these regional biases. By exploiting the topological information of the stream network, top-kriging is able to improve the local adjustment of the drift model for the main streams and the tributaries.     

The impact of watershed delineation on hydrology and water quality simulation

Flood routing is a significant calculation for predicting watershed responses, involving discharge and pollutant exports. The computation of flow routing is highly relative to the relationship between downstream and upstream subbasins. A watershed could always be divided into several subbasins based on its topography and stream distribution. How detailed of the delineation of the stream distribution in a watershed would influence the modeling accuracy of flow routing and the prediction of watershed responses. The objective of this work was to discuss the effect of watershed delineation on runoff and pollutant transport predictions. When the number of divided subbasins increases, the stream distribution could be delineated more clearly. The case area was usually regarded as two subbasins only. In the present study, the case area was divided into 43, 25, 15 and 9 subbasins respectively. If the modeling result under 43 subdivisions is assumed to be the actual situation, the relative error of runoff simulation due to the simplified delineation of stream distribution is only around 25% in two subdivisions. However, the relative error of suspended solids (SS), total nitrogen (TN) and orthophosphate (Orth-P) simulation can reach 85%, 71 and 70% in two subdivisions respectively. The uncertainties or errors induced by too much simplification of watershed delineation could be carried over and amplified to the pollutant transport process and the modeling results of pollutant exports.     

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.     

Role of Geographic Information System (GIS) in Watershed Simulation By Winvast Model

The uncertainty of modeling input will increase the simulation error, and this situation always happens in a model without user-friendly interface. WinVAST model, developed by the University of Virginia in 2003, treats an entire multi-catchment by a tree-view structure. Its extra computer programs can connect geographic information system (GIS). Model users can prepare all the necessary information in ArcGIS. Extracting information from GIS interface can not only decrease the inconvenience of data input, but also lower the uncertainty due to data preparation. The Daiyuku Creek and Qupoliao Creek in the Fei-tsui reservoir watershed in Northern Taiwan provided the setting for the case study reported herein. The required information, including slope, stream length, subbasin area, soil type and land-use condition, for WinVAST model should be prepared in a Microsoft Access database, which is the project file of WinVAST with extension mdb. In ArcGIS interface, when the soil layer, land-use layer, and Digital Elevation Model (DEM) map are prepared, all the watershed information can be created as well. This study compared the simulation results from automatically generated input and manual input. The results show that the relative simulation error resulting from the rough process of data input can be around 30% in runoff simulation, and even reach 70% in non-point source pollution (NPSP) simulation. It could conclude that GIS technology is significant for predicting watershed responses by WinVAST model, because it can efficiently reduce the uncertainty induced by input errors.     

Modeling Runoff Response to Land Cover and Rainfall Spatial Variability in Semi-Arid Watersheds

Hydrologic response is an integrated indicator of watershed condition, and significant changes in land cover may affect the overall health and function of a watershed. This paper describes a procedure for evaluating the effects of land cover change and rainfall spatial variability on watershed response. Two hydrologic models were applied on a small semi-arid watershed; one model is event-based with a one-minute time step (KINEROS), and the second is a continuous model with a daily time step (SWAT). The inputs to the models were derived from Geographic Information System (GIS) theme layers of USGS digital elevation models, the State Soil Geographic Database (STATSGO) and the Landsat-based North American Landscape Characterization classification (NALC) in conjunction with available literature and look up tables. Rainfall data from a network of 10 raingauges and historical stream flow data were used to calibrate runoff depth using the continuous hydrologic model from 1966 to 1974. No calibration was carried out for the event-based model, in which six storms from the same period were used in the calculation of runoff depth and peak runoff. The assumption on which much of this study is based is that land cover change and rainfall spatial variability affect the rainfall-runoff relationships on the watershed. To validate this assumption, simulations were carried out wherein the entire watershed was transformed from the 1972 NALC land cover, which consisted of a mixture of desertscrub and grassland, to a single uniform land cover type such as riparian, forest, oak woodland, mesquite woodland, desertscrub, grassland, urban, agriculture, and barren. This study demonstrates the feasibility of using widely available data sets for parameterizing hydrologic simulation models. The simulation results show that both models were able to characterize the runoff response of the watershed due to changes of land cover.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

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.     

A Fish-Based Index of Biotic Integrity to Assess Intermittent Headwater Streams in Wisconsin, USA

I developed a fish-based index of biotic integrity (IBI) to assess environmental quality in intermittent headwater streams in Wisconsin, USA. Backpack electrofishing and habitat surveys were conducted four times on 102 small (watershed area 1.7–41.5 km²), cool or warmwater (maximum daily mean water temperature ≥22 C), headwater streams in spring and late summer/fall 2000 and 2001. Despite seasonal and annual changes in stream flow and habitat volume, there were few significant temporal trends in fish attributes. Analysis of 36 least-impacted streams indicated that fish were too scarce to calculate an IBI at stations with watershed areas less than 4 km² or at stations with watershed areas from 4–10 km² if stream gradient exceeded 10 m/km (1% slope). For streams with sufficient fish, potential fish attributes (metrics) were not related to watershed size or gradient. Seven metrics distinguished among streams with low, agricultural, and urban human impacts: numbers of native, minnow (Cyprinidae), headwater-specialist, and intolerant (to environmental degradation) species; catches of all fish excluding species tolerant of environmental degradation and of brook stickleback (Culaea inconstans) per 100 m stream length; and percentage of total individuals with deformities, eroded fins, lesions, or tumors. These metrics were used in the final IBI, which ranged from 0 (worst) to 100 (best). The IBI accurately assessed the environmental quality of 16 randomly chosen streams not used in index development. Temporal variation in IBI scores in the absence of changes in environmental quality was not related to season, year, or type of human impact and was similar in magnitude to variation reported for other IBI's.     

Nutrient concentrations in Maryland non-tidal streams

Using a spatially extensive database from the Maryland Biological Stream Survey (MBSS), we describe nutrient relationships of small-order, non-tidal streams to Maryland watershed basins, Maryland Tributary Strategy basins, and stream order. In addition, we estimate the number of stream km affected by nutrient loading, using derived nutrient criteria. Based on the MBSS spring water quality sampling, we determined several important factors relating to nutrient levels in non-tidal streams. There are strong linear relationships of nutrients to the percentage of agriculture and forested land present within MBSS sampling strata. Both mean total nitrogen (TN) and mean total phosphorus (TP) levels for watershed basins by stream order show exceedances of derived nutrient reference criteria for Maryland. Four Maryland basins have over 85% of their stream kilometers exceeding the TN criterion, with three basins over 90% of the TP criterion. To protect small stream integrity in Maryland, we recommend an upper stream TN criterion between 1.34 and 1.68 mg/L and an upper stream TP criterion between 0.025 and 0.037 mg/L, based on quantile analyses. Elevated levels of both TN and TP are present in non-tidal streams, with subsequent nutrient inputs into the upper freshwater tidal reaches of the Chesapeake Bay.     

基于SWAT模型的湘江永州流域非点源氮磷污染特征分析

为精准治理流域非点源氮磷污染,基于SWAT模型,运用本地区第二次全国污染源普查数据和2000—2019年流域水文、水质数据,开展湘江永州流域非点源氮磷污染模拟。结果表明：湘江永州流域建立的SWAT模型具有较好的模拟效果,流域2005—2019年的总氮月均污染负荷为383.40～17 998.70 t/m;总磷月均污染负荷为64.62～567.86 t/m,总氮和总磷各月污染负荷均与各月降雨量呈显著相关关系;农田和林地是本流域总氮、总磷污染负荷总量最大的2种用地类型,但两者之间单位面积输出的污染负荷强度却相反,林地对流域水污染防控具有正面效应,农田种植面源污染是非点源氮磷污染治理的重点。     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

Watershed Nitrogen and Mercury Geochemical Fluxes Integrate Landscape Factors in Long-term Research Watersheds at Acadia National Park, Maine, USA

This paper is an overview of this special issue devoted to watershed research in Acadia National Park (Acadia NP). The papers address components of an integrated research program on two upland watersheds at Acadia NP, USA (44° 20′ N latitude; 68° 15′ E longitude). These watersheds were instrumented in 1998 to provide a long-term foundation for regional ecological and watershed research. The research was initiated as part of EPA/NPS PRIMENet (Park Research and Intensive Monitoring of Ecosystems Network), a system of UV-monitoring stations and long-term watershed research sites located in US national parks. The initial goals at Acadia NP were to address research questions about mercury, acid rain, and nitrogen saturation developed from prior research. The project design was based on natural differences in forests and soils induced by an intense wildfire in one watershed in 1947. There is no evidence of fire in the reference watershed for several hundred years. We are testing hypotheses about controls on surface water chemistry, and bioavailability of contaminants in the contrasting watersheds. The unburned 47-ha Hadlock Brook watershed is 70% spruce-fir mature conifer forest. In contrast, burned 32-ha Cadillac Brook watershed, 4 km northeast of the Hadlock watershed, is 20% regenerating mixed northern hardwoods and 60% shrub/rocky balds. Differences in atmospheric deposition are controlled primarily by forest stand composition and age. The watersheds are gauged and have water chemistry stations at 122 m (Cadillac) and 137 m (Hadlock); watershed maximum elevations are 468 and 380 m, respectively. The stream water chemistry patterns reflect, in part, the legacy of the intense fire, which, in turn, controls differences in forest vegetation and soil characteristics. These factors result in higher nitrogen and mercury flux from the unburned watershed, reflecting differences in atmospheric deposition, contrasting ecosystem pools of nitrogen and mercury, and inferred differences in internal cycling and bioavailabilty.     

Dynamic Hydrologic Simulation of the Bear Brook Watershed in Maine (BBWM)

Bear Brook Watershed in Maine (BBWM) consists of a pair of research watersheds, East Bear Brook (EBB) and West Bear Brook (WBB). Years of research and observations have shown both watersheds have high similarity in geographic and hydrologic characteristics; a simple comparison of hydrographs from these two watersheds further substantiates this similarity. The Object Watershed Link Simulation (OWLS) model was developed and used to simulate the hydrological processes within the BBWM. The OWLS model is a 3-dimensional, vector-based, visualized, physically-based, distributed watershed hydrologic model. Simulation results not only provide a close examination of hydrologic processes within a watershed, but also dynamically visualize the processes of flow separations and Variable Source Areas (VSA). Results from flow separations suggest that surface flow from riparian area is the predominate component for the flood rising limb and that macropore flow from riparian area dominates during the falling limb. Soil matrix flow has little effect flood period but is a persistent contributor to base flow. Results from VSA visualization demonstrate 3-D dynamic changes in surface flow distribution and suggest that downstream riparian areas are the major contributing area for peak flow. As water chemistry is highly relevant to the flow paths within a watershed, simulations have provided valuable information about source of stream flow and the water migration dynamics to support the study of watershed chemistry in the BBWM. More specific linkages between the chemistry behavior and the dynamic hydrologic processes should become the next simulation effort in the watershed study. There are many questions that are critical to watershed chemistry studies like: which flow component (surface flow, macropore flow, soil matrix flow) predominates during peak flows? How do the flow components distribute during a flood event? How do flow contributions differ between these two watersheds? Which portion of the watershed contributes the most to the peak flows? These questions remain unknown from previous observations and only can be addressed with a physically-based distributed model.     

EMAP Design and River Reach File 3 (RF3) as a Sample Frame in the Central Valley, California

The Central Valley, California, R-EMAP project assessed the effects of highly modified, agriculturally dominated landuse on the aquatic resources of the lower portion of the Central Valley watersheds. The focus of this paper is to assess the utility of the EMAP design and the River Reach File version 3 (RF3) 1:100,000 scale Digital Line Graph (DLG) as a sampling frame. The study area is 34,099 mi²(88,316 km²) and comprises the lower reaches of the Sacramento River and San Joaquin River watersheds to the 1000 ft. (305 m) elevation. Sampling sites are selected using a tessellation stratified design to represent the two main populations of interest: natural streams and man-made waterways. Sites are selected to represent 13,226 miles of streams and sloughs, and 14,648 miles of irrigation canals, ditches and drains. To achieve an approximately equal sample size across stream orders and basins, the sample design was weighted by Strahler order categories to ensure sampling occurred in the higher order streams. Based on office and field reconnaissance, the study provided information on the quality of RF3 as a sampling frame. Site selection using RF3 had a success rate of approximately 44%. The RF3 database has an error rate of approximately 7%. When human influence factors were included, the error rate increased to 16%. There was an 11% error rate when selecting sites for natural streams, and approximately a 14% error rate for man-made waterways. The reconnaissance information indicated that presence or absence of irrigation ditches and return drains depends on changing agricultural uses. Some of the error in the RF3 for natural streams and man-made waterways can be attributed to rapid urban expansion, especially in the San Joaquin basin.