Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.     

Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States1

Preston, Stephen D., Richard B. Alexander, Gregory E. Schwarz, and Charles G. Crawford, 2011. Factors Affecting Stream Nutrient Loads: A Synthesis of Regional SPARROW Model Results for the Continental United States. Journal of the American Water Resources Association (JAWRA) 47(5):891‐915. DOI: 10.1111/j.1752‐1688.2011.00577.x Abstract: We compared the results of 12 recently calibrated regional SPARROW (SPAtially Referenced Regressions On Watershed attributes) models covering most of the continental United States to evaluate the consistency and regional differences in factors affecting stream nutrient loads. The models – 6 for total nitrogen and 6 for total phosphorus – all provide similar levels of prediction accuracy, but those for major river basins in the eastern half of the country were somewhat more accurate. The models simulate long‐term mean annual stream nutrient loads as a function of a wide range of known sources and climatic (precipitation, temperature), landscape (e.g., soils, geology), and aquatic factors affecting nutrient fate and transport. The results confirm the dominant effects of urban and agricultural sources on stream nutrient loads nationally and regionally, but reveal considerable spatial variability in the specific types of sources that control water quality. These include regional differences in the relative importance of different types of urban (municipal and industrial point vs. diffuse urban runoff) and agriculture (crop cultivation vs. animal waste) sources, as well as the effects of atmospheric deposition, mining, and background (e.g., soil phosphorus) sources on stream nutrients. Overall, we found that the SPARROW model results provide a consistent set of information for identifying the major sources and environmental factors affecting nutrient fate and transport in United States watersheds at regional and subregional scales.     

Modeling Streams and Hydrogeomorphic Attributes in Oregon From Digital and Field Data1

Abstract: Managers, regulators, and researchers of aquatic ecosystems are increasingly pressed to consider large areas. However, accurate stream maps with geo‐referenced attributes are uncommon over relevant spatial extents. Field inventories provide high‐quality data, particularly for habitat characteristics at fine spatial resolutions (e.g., large wood), but are costly and so cover relatively small areas. Recent availability of regional digital data and Geographic Information Systems software has advanced capabilities to delineate stream networks and estimate coarse‐resolution hydrogeomorphic attributes (e.g., gradient). A spatially comprehensive coverage results, but types of modeled outputs may be limited and their accuracy is typically unknown. Capitalizing on strengths in both field and regional digital data, we modeled a synthetic stream network and a variety of hydrogeomorphic attributes for the Oregon Coastal Province. The synthetic network, encompassing 96,000 km of stream, was derived from digital elevation data. We used high‐resolution but spatially restricted data from field inventories and streamflow gauges to evaluate, calibrate, and interpret hydrogeomorphic attributes modeled from digital elevation and precipitation data. The attributes we chose to model (drainage area, mean annual precipitation, mean annual flow, probability of perennial flow, channel gradient, active‐channel width and depth, valley‐floor width, valley‐width index, and valley constraint) have demonstrated value for stream research and management. For most of these attributes, field‐measured, and modeled values were highly correlated, yielding confidence in the modeled outputs. The modeled stream network and attributes have been used for a variety of purposes, including mapping riparian areas, identifying headwater streams likely to transport debris flows, and characterizing the potential of streams to provide high‐quality habitat for salmonids. Our framework and models can be adapted and applied to areas where the necessary field and digital data exist or can be obtained.     

HYDROLOGIC PARAMETERIZATION OF WATERSHEDS FOR RUNOFF PREDICTION USING SWAT1

ABSTRACT: The use of continuous time, distributed parameter hydrologic models like SWAT (Soil and Water Assessment Tool) has opened several opportunities to improve watershed modeling accuracy. However, it has also placed a heavy burden on users with respect to the amount of work involved in parameterizing the watershed in general and in adequately representing the spatial variability of the watershed in particular. Recent developments in Geographical Information Systems (GIS) have alleviated some of the difficulties associated with managing spatial data. However, the user must still choose among various parameterization approaches that are available within the model. This paper describes the important parameterization issues involved when modeling watershed hydrology for runoff prediction using SWAT with emphasis on how to improve model performance without resorting to tedious and arbitrary parameter by parameter calibration. Synthetic and actual watersheds in Indiana and Mississippi were used to illustrate the sensitivity of runoff prediction to spatial variability, watershed decomposition, and spatial and temporal adjustment of curve numbers and return flow contribution. SWAT was also used to predict stream runoff from actual watersheds in Indiana that have extensive subsurface drainage. The results of this study provide useful information for improving SWAT performance in terms of stream runoff prediction in a manner that is particularly useful for modeling ungaged watersheds wherein observed data for calibration is not available.     

Sensitivity of Thermal Habitat of a Trout Stream to Potential Climate Change,Wisconsin, United States1

Abstract: Airborne thermal remote sensing from four flights on a single day from a single‐engine airplane was used to collect thermal infrared data of a 10.47‐km reach of the upper East Branch Pecatonica River in southwest Wisconsin. The study uses a one‐dimensional stream temperature model calibrated with the longitudinal profiles of stream temperature created from the four thermal imaging flights and validated with three days of continuous stream temperature data from instream data loggers on the days surrounding the thermal remote‐sensing campaign. Model simulations were used to quantify the sensitivity of stream thermal habitat to increases in air and groundwater temperature and changes in base flow. The simulations indicate that stream temperatures may reach critical maximum thresholds for brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) mortality, particularly if both air temperature increases and base flow declines. The approach demonstrates that thermal infrared data can greatly assist stream temperature model validation due to its high spatial resolution, and that this spatially continuous stream temperature data can be used to pinpoint spatial heterogeneity in groundwater inflow to streams. With this spatially distributed data on thermal heterogeneity and base‐flow accretion, stream temperature models considering various climate change scenarios are able to identify thermal refugia that will be critical for fisheries management under a changing climate.     

The Road to NHDPlus — Advancements in Digital Stream Networks and Associated Catchments

A progression of advancements in Geographic Information Systems techniques for hydrologic network and associated catchment delineation has led to the production of the National Hydrography Dataset Plus (NHDPlus). NHDPlus is a digital stream network for hydrologic modeling with catchments and a suite of related geospatial data. Digital stream networks with associated catchments provide a geospatial framework for linking and integrating water‐related data. Advancements in the development of NHDPlus are expected to continue to improve the capabilities of this national geospatial hydrologic framework. NHDPlus is built upon the medium‐resolution NHD and, like NHD, was developed by the U.S. Environmental Protection Agency and U.S. Geological Survey to support the estimation of streamflow and stream velocity used in fate‐and‐transport modeling. Catchments included with NHDPlus were created by integrating vector information from the NHD and from the Watershed Boundary Dataset with the gridded land surface elevation as represented by the National Elevation Dataset. NHDPlus is an actively used and continually improved dataset. Users recognize the importance of a reliable stream network and associated catchments. The NHDPlus spatial features and associated data tables will continue to be improved to support regional water quality and streamflow models and other user‐defined applications.     

A Multi‐Scale Analysis of Single‐Family Residential Water Use in the Phoenix Metropolitan Area

Studies that evaluate determinants of residential water demand typically use data from a single spatial scale. Although household‐scale data are preferred, especially when econometric models are used, researchers may be limited to aggregate data. There is little, if any, empirical analysis to assess whether spatial scale may lead to ecological fallacy problems in residential water use research. Using linear mixed‐effects models, we compare the results for the relationship of single‐family water use with its determinants using data from the household and census tract scales in the city of Phoenix. Model results between the household and census tract scale are similar suggesting the ecological fallacy may not be significant. Common significant determinants on these two spatial scales include household size, household income, house age, pool size, irrigable lot size, precipitation, and temperature. We also use city/town scale data from the Phoenix metropolitan area to parameterize the linear mixed‐effects model. The difference in the parameter estimates of those common variables compared to the first two scales indicates there is spatial heterogeneity in the relationship between single‐family water use and its determinants among cities and towns. The negative relationship between single‐family house density and residential water use suggests that residential water consumption could be reduced through coordination of land use planning and water demand management.     

Delineation and Validation of River Network Spatial Scales for Water Resources and Fisheries Management

Identifying appropriate spatial scales is critically important for assessing health, attributing data, and guiding management actions for rivers. We describe a process for identifying a three-level hierarchy of spatial scales for Michigan rivers. Additionally, we conduct a variance decomposition of fish occurrence, abundance, and assemblage metric data to evaluate how much observed variability can be explained by the three spatial scales as a gage of their utility for water resources and fisheries management. The process involved the development of geographic information system programs, statistical models, modification by experienced biologists, and simplification to meet the needs of policy makers. Altogether, 28,889 reaches, 6,198 multiple-reach segments, and 11 segment classes were identified from Michigan river networks. The segment scale explained the greatest amount of variation in fish abundance and occurrence, followed by segment class, and reach. Segment scale also explained the greatest amount of variation in 13 of the 19 analyzed fish assemblage metrics, with segment class explaining the greatest amount of variation in the other six fish metrics. Segments appear to be a useful spatial scale/unit for measuring and synthesizing information for managing rivers and streams. Additionally, segment classes provide a useful typology for summarizing the numerous segments into a few categories. Reaches are the foundation for the identification of segments and segment classes and thus are integral elements of the overall spatial scale hierarchy despite reaches not explaining significant variation in fish assemblage data.     

ASSESSING SATELLITE‐BASED AND AIRCRAFT‐BASED THERMAL INFRARED REMOTE SENSING FOR MONITORING PACIFIC NORTHWEST RIVER TEMPERATURE1

One central issue affecting the health of native fish species in the Pacific Northwest is water temperature. In situ observation methods monitor point temperatures, while thermal infrared (TIR) remote sensing captures spatial variations. Satellite‐based TIR sensors have the ability to view large regions in an instant. Four Pacific Northwest river reaches were selected to test the ability of both satellite‐based and moderate resolution aircraft‐based TIR remote sensing products to measure river temperatures. Images with resolutions of 5, 15, and 90 meters were compared with instream temperature observations to assess how along stream radiant temperatures are affected by resolution, reach width, and sensor platform. Where the stream reach can be resolved by the sensor, all sensors obtain water temperatures within ±2°C of instream observations. Along stream temperature variations of up to ±5°C were also observed. Trends were similar between two sets of TIR images taken several hours apart, indicating that the sensors are observing actual temperature patterns from the river surface. If sensor resolution is sufficient to obtain fully resolved water pixels in the river reach, accurate temperatures and spatial patterns can be observed. The current generation of satellite‐based TIR sensors is, however, only able to resolve about 6 percent of all Washington reaches listed as thermally impaired.     

Modeling the relationship between land use and surface water quality

It is widely known that watershed hydrology is dependent on many factors, including land use, climate, and soil conditions. But the relative impacts of different types of land use on the surface water are yet to be ascertained and quantified. This research attempted to use a comprehensive approach to examine the hydrologic effects of land use at both a regional and a local scale. Statistical and spatial analyses were employed to examine the statistical and spatial relationships of land use and the flow and water quality in receiving waters on a regional scale in the State of Ohio. Besides, a widely accepted watershed-based water quality assessment tool, the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS), was adopted to model the plausible effects of land use on water quality in a local watershed in the East Fork Little Miami River Basin. The results from the statistical analyses revealed that there was a significant relationship between land use and in-stream water quality, especially for nitrogen, phosphorus and Fecal coliform. The geographic information systems (GIS) spatial analyses identified the watersheds that have high levels of contaminants and percentages of agricultural and urban lands. Furthermore, the hydrologic and water quality modeling showed that agricultural and impervious urban lands produced a much higher level of nitrogen and phosphorus than other land surfaces. From this research, it seems that the approach adopted in this study is comprehensive, covering both the regional and local scales. It also reveals that BASINS is a very useful and reliable tool, capable of characterizing the flow and water quality conditions for the study area under different watershed scales. With little modification, these models should be able to adapt to other watersheds or to simulate other contaminants. They also can be used to study the plausible impacts of global environmental change. In addition, the information on the hydrologic effects of land use is very useful. It can provide guidelines not only for resource managers in restoring our aquatic ecosystems, but also for local planners in devising viable and ecologically-sound watershed development plans, as well as for policy makers in evaluating alternate land management decisions.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Reduced complexity models for regional aquatic habitat suitability assessment

Generalizable methods that identify suitable aquatic habitat across large river basins and regions are needed to inform resource management. Habitat suitability models intersect environmental variables to predict species occurrence, but are often data intensive and thus are typically developed at small spatial scales. This study estimated mean monthly aquatic habitat suitability throughout Utah (USA) for Bonneville Cutthroat Trout (Oncorhynchus clarkii utah) and Bluehead Sucker (Catostomus discobolus) with publicly available, geospatial datasets. We evaluated 15 habitat suitability models using unique combinations of percent of mean annual discharge, velocity, gradient, and stream temperature. Environmental variables were validated with observed conditions and species presence observations to verify habitat suitability estimates. Stream temperature, gradient, and discharge best predicted Bonneville Cutthroat Trout presence, and gradient and discharge best predicted Bluehead Sucker presence. Simple aquatic habitat suitability models outperformed models that used only streamflow to estimate habitat for both species, and are useful for conservation planning and water resources decision-making. This modeling approach could enable resource managers to prioritize stream restoration across vast regions within their management domain, and is potentially compatible with water management modeling to improve ecological objectives in management models.     

Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale

Regionalization frameworks cluster geographic data to create contiguous regions of similar climate, geology and hydrology by delineating land into discrete regions, such as ecoregions or watersheds, often at several spatial scales. Although most regionalization schemes were not originally designed for aquatic ecosystem classification or management, they are often used for such purposes, with surprisingly few explicit tests of the relative ability of different regionalization frameworks to group lakes for water quality monitoring and assessment. We examined which of 11 different lake grouping schemes at two spatial scales best captures the maximum amount of variation in water quality among regions for total nutrients, water clarity, chlorophyll, overall trophic state, and alkalinity in 479 lakes in Michigan (USA). We conducted analyses on two data sets: one that included all lakes and one that included only minimally disturbed lakes. Using hierarchical linear models that partitioned total variance into within-region and among-region components, we found that ecological drainage units and 8-digit hydrologic units most consistently captured among-region heterogeneity at their respective spatial scales using all lakes (variation among lake groups = 3% to 50% and 12% to 52%, respectively). However, regionalization schemes capture less among-region variance for minimally disturbed lakes. Diagnostics of spatial autocorrelation provided insight into the relative performance of regionalization frameworks but also demonstrated that region size is only partly responsible for capturing variation among lakes. These results suggest that regionalization schemes can provide useful frameworks for lake water quality assessment and monitoring but that we must identify the appropriate spatial scale for the questions being asked, the type of management applied, and the metrics being assessed.     

MACROSCALE HYDROLOGIC MODELING FOR REGIONAL CLIMATE ASSESSMENT STUDIES IN THE SOUTHEASTERN UMTED STATES1

ABSTRACT: A macroscale hydrologic model is developed for regional climate assessment studies under way in the southeastern United States. The hydrologic modeling strategy is developed to optimize spatial representation of basin characteristics while maximizing computational efficiency. The model employs the “grouped response unit” methodology, which follows the natural drainage pattern of the area. First order streams are delineated and their surface characteristics are tested so that areas with statistically similar characteristics can be combined into larger computational zones for modeling purposes. Hydrologic response units (HRU) are identified within the modeling units and a simple three‐layer water balance model, Soil and Water Assessment Tool (SWAT), is executed for each HRU. The runoff values are then convoluted using a triangular unit hydrograph and routed by Muskingum‐Cunge method. The methodology is shown to produce accurate results relative to other studies, when compared to observations. The model is used to evaluate the potential error in hydrologic assessments when using GCM predictions as climatic input in a rainfall‐runoff dominated environment. In such areas, the results from this study, although limited in temporal and spatial scope, appear to imply that use of GCM climate predictions in short term quantitative analyses studies in rainfall‐runoff dominated environments should proceed with caution.     

A GIS Model of Subsurface Water Potential for Aquatic Resource Inventory,Assessment, and Environmental Management

Biological, chemical, and physical attributes of aquatic ecosystems are often strongly influenced by groundwater sources. Nonetheless, widespread access to predictions of subsurface contributions to rivers, lakes, and wetlands at a scale useful to environmental managers is generally lacking. In this paper, we describe a neighborhood analysis approach for estimating topographic constraints on spatial patterns of recharge and discharge and discuss how this index has proven useful in research, management, and conservation contexts. The Michigan Rivers Inventory subsurface flux model (MRI-DARCY) used digital elevation and hydraulic conductivity inferred from mapped surficial geology to estimate spatial patterns of hydraulic potential. Model predictions were calculated in units of specific discharge (meters per day) for a 30-m²-cell raster map and interpreted as an index of potential subsurface water flux (shallow groundwater and event through-flow). The model was evaluated by comparison with measurements of groundwater-related attributes at watershed, stream segment, and local spatial scales throughout Lower Michigan (USA). Map-based predictions using MRI-DARCY accounted for 85% of the observed variation in base flow from 128 USGS gauges, 69% of the observed variation in discharge accrual from 48 river segments, and 29% of the residual variation in local groundwater flux from 33 locations as measured by hyporheic temperature profiles after factoring out the effects of climate. Although it does not incorporate any information about the actual water table surface, by quantifying spatial variation of key constraints on groundwater-related attributes, the model provides strata for more intensive study, as well as a useful spatial tool for regional and local conservation planning, fisheries management, wetland characterization, and stream assessment.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

A Regional‐Scale Habitat Suitability Model to Assess the Effects of Flow Reduction on Fish Assemblages in Michigan Streams1

Zorn, Troy G., Paul W. Seelbach, and Edward S. Rutherford, 2012. A Regional‐Scale Habitat Suitability Model to Assess the Effects of Flow Reduction on Fish Assemblages in Michigan Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 871‐895. DOI: 10.1111/j.1752‐1688.2012.00656.x Abstract: In response to concerns over increased use and potential diversion of Michigan’s freshwater resources, and the resulting state legislative mandate, an advisory council created an integrated assessment model to determine the potential for water withdrawals to cause an adverse resource impact to fish assemblages in Michigan’s streams. As part of this effort, we developed a model to predict how fish assemblages characteristic of different stream types would change in response to decreased stream base flows. We describe model development and use in this case study. The model uses habitat suitability information (i.e., catchment size, base‐flow yield, and July mean water temperature) for over 40 fish species to predict assemblage structure in an individual river segment under a range of base‐flow reductions. By synthesizing model runs for individual fish species at representative segments for each of Michigan’s 11 ecological stream types, we developed curves describing how typical fish assemblages in each type respond to flow reduction. Each stream type‐specific, fish response curve was used to identify streamflow reduction levels resulting in adverse resource impacts to characteristic fish populations, the regulatory standard. Used together with a statewide map of stream types, our model provided a spatially comprehensive framework for evaluating impacts of flow withdrawals on biotic communities across a diverse regional landscape.     

PREDICTION OF STREAM TEMPERATURE IN FORESTED WATERSHEDS1

ABSTRACT: Removal of streamside vegetation changes the energy balance of a stream, and hence its temperature. A common approach to mitigating the effects of logging on stream temperature is to require establishment of buffer zones along stream corridors. A simple energy balance model is described for prediction of stream temperature in forested headwater watersheds that allows evaluation of the performance of such measures. The model is designed for application to “worst case” or maximum annual stream temperature, under low flow conditions with maximum annual solar radiation and air temperature. Low flows are estimated via a regional regression equation with independent variables readily accessible from GIS databases. Testing of the energy balance model was performed using field data for mostly forested basins on both the west and east slopes of the Cascade Mountains, and was then evaluated using the regional equations for low flow and observed maximum reach temperatures in three different east slope Cascades catchments. A series of sensitivity analyses showed that increasing the buffer width beyond 30 meters did not significantly decrease stream temperatures, and that other vegetation parameters such as leaf area index, average tree height, and to a lesser extent streamside vegetation buffer width, more strongly affected maximum stream temperatures.     

INTERFACING GIS WITH WATER RESOURCE MODELS: A STATE‐OF‐THE‐ART REVIEW1

Two distinctive, independently developed technologies, geographic information systems (GIS) and predictive water resource models, are being interfaced with varying degrees of sophistication in efforts to simultaneously examine spatial and temporal phenomena. Neither technology was initially developed to interact with the other, and as a result, multiple approaches to interface GIS with water resource models exist. Additionally, continued model enhancements and the development of graphical user interfaces (GUIs) have encouraged the development of application “suites” for evaluation and visualization of engineering problems. Currently, disparities in spatial scales, data accessibility, modeling software preferences, and computer resources availability prevent application of a universal interfacing approach. This paper provides a state‐of‐the‐art critical review of current trends in interfacing GIS with predictive water resource models. Emphasis is placed on discussing limitations to efficient interfacing and potential future directions, including recommendations for overcoming many current challenges.