Land-use forecasting and hydrologic model integration for improved land-use decision support

This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within central Washington State. The land-use forecasting model, What If? is implemented to forecast future spatial distributions of low-density residential land-uses under low and high population growth estimates. Forecasted land-use distribution patterns for the years 2015, 2025, and 2050 are then used as land-use data input for the calibrated hydrologic model, keeping all other parameters constant. Impacts to the stream discharge hydrograph are predicted as the study area becomes increasingly developed as forecasted by What If?. The initial results of this integration process demonstrate the synergy that can be generated through the linkage of the selected models. The ability to quantifiably forecast the potential hydrologic implications of proposed land-use policies before their implementation offers land-use decision-makers a valuable tool for discerning which proposed land-use alternatives will be effective at minimizing storm water runoff.     

EVALUATING THE INFLUENCE OF SOURCE BASINS ON DOWNSTREAM WATER QUALITY IN THE MISSISSIPPI RIVER1

ABSTRACT: Chemical variability in the Mississippi River during water years 1989 to 1998 was evaluated using stream discharge and water‐quality data in conjunction with the DAFLOW/BLTM hydraulic model. Model simulations were used to identify subbasin contributions of water and chemical constituents to the Mississippi River upstream from its confluence with the Ohio and the Mississippi River and at the Atchafalaya Diversion in Louisiana. Concentrations of dissolved solids, sodium, and sulfate at the Thebes site showed a general decreasing trend, and concentrations of silica and nitrate showed a general increasing trend as the percentage of discharge from the Mississippi River upstream from Grafton increased. Concentrations of most chemical constituents in the Mississippi River at the Atchafalaya Diversion exhibited a decreasing trend as the percentage of water from the Ohio River increased. Regression models were used to evaluate the importance of the source of water to the water chemistry in the Mississippi River at Thebes and the Atchafalaya Diversion. The addition of terms in regression equations to account for the percent of water from sub‐basins improved coefficients of determination for predicting chemical concentrations by as much as nine percent at the Thebes site and by as much as 48 percent at the Atchafalaya Diversion site. The addition of source‐water terms to regression equations increased the estimated annual loads of nitrate and silica delivered from the Mississippi River Basin to the Gulf of Mexico by as much as 14 and 13 percent, respectively.     

Models of total and presumed wildlife sources of fecal coliform bacteria in coastal ponds

Models that accurately predict fecal coliform bacteria (FCB) concentrations, one of the most widely used measures of estuarine water quality, are needed to improve land use decision-making. Rapidly occurring changes in coastal land uses and the influence on water quality increases the urgency of having improved decision tools. For this study, samples were collected monthly from six coastal ponds, two tidal creeks and four shallow water wells for up to 212 years. These data were used along with other measures of environmental conditions and land classes within each watershed to construct quantitative relationships between combinations of variables and both total and presumed wildlife sources of FCB. Linear regression, bootstrapping and generalized additive modeling that incorporates both linear and nonlinear terms were used. Results of repeated simultaneous sampling on the same tide stage of ponds and downstream estuarine creeks suggest that most FCB come from wildlife and that the ponds effectively remove these bacteria except immediately following heavy rainfall. Predictive models for concentrations of total and presumed wildlife bacteria are provided along with simple measures to estimate watershed boundaries. It is proposed that these tools can be used to minimize impacts on receiving water body quality. The models can be used to test alternative development approaches within coastal watersheds similar to that found in the southeastern USA coastal zone as well as to evaluate specific proposed landscape alterations.     

COMPUTING THE RISKS ASSOCIATED WITH WASTELOAD) ALLOCATION MODELING1

ABSTRACT: The risks associated with a traditional wasteload allocation (WLA) analysis were quantified with data from a recent study of the Upper Trinity River (Texas). Risk is define here as the probability of failing to meet an established in-stream water quality standard. The QUAL-TX dissolved oxygen (DO) water quality model was modified to a Monte Carlo framework. Flow augmentation coding was also modified to allow an exact match to be computed between the predicted and an established DO concentration standard, thereby providing an avenue for linking input parameter uncertainty to the assignment of a wasteload permit (allowable mass loading rate). Monte Carlo simulation techniques were employed to propagate input parameter uncertainties, typically encountered during WLA analysis, to the computed effluent five-day carbonaceous biochemical oxygen demand requirements for a single major wastewater treatment plant (WWTP). The risk of failing to meet an established in-stream DO criterion may be as high as 96 percent. The uncertainty associated with estimation of the future total Kjeldahl nitrogen concentration for a single tributary was found to have the greatest impact on the determination of allowable WWTP loadings.     

MODELING THE IMPACT OF SUBSURFACE NUTRIENT FLUX ON WATER QUALITY IN THE LOWER TRUCKEE RWER,NEVADA1

ABSTRACT: The impacts of regional groundwater quality and local agricultural activities on in-stream water quality in the Lower Truckee River, Nevada, were assessed through a detailed program of monitoring and computer simulation. An agricultural diversion and return-flow were monitored in great detail to determine mass loading rates of nutrients from agriculture in the area. Once characterized, the cumulative impacts of agricultural diversions and return-flows were evaluated using the Water Quality Assessment Program (WASP) to model nitrogen, phosphorus, periphyton, and dissolved oxygen. Monitoring showed that a significant proportion of the water diverted for agricultural purposes returned to the river as surface point return-flow (estimated at 13.9 percent $ 0.1 percent), and as groundwater diffuse return-flow (estimated at 27 percent $ 6 percent). Modeling efforts demonstrated the significant effect of assumed regional groundwater quality (nitrate) upon predicted periphyton growth and associated diel fluctuations of dissolved oxygen.     

SPATIAL MODELLING WITH GEOGRAPHIC INFORMATION SYSTEMS FOR DETERMINATION OF WATER RESOURCES VULNERABILITY APPLICATION TO AN AREA IN MASSIF CENTRAL (FRANCE)1

ABSTRACT: A method for water resources protection based on spatial variability of vulnerability is proposed. The vulnerability of a water resource is defined as the risk that the resource will become contaminated if a pollutant is placed on the surface at one point as compared to another. A spatial modelling method is defined in this paper to estimate a travel time between any point of a catchment and a resource (river or well). This method is based on spatial analysis tools integrated in Geographical Information Systems (GIS). The method is illustrated by an application to an area of Massif Central (France) where three different types of flow appear: surface flow, shallow subsurface flow, and permanent ground water flow (baseflow). The proposed method gives results similar to classical methods of estimation of travel time. The contribution of GIS is to improve the mapping of vulnerability by taking the spatial variability of physical phenomena into account.     

USE OF FIRST-ORDER UNCERTAINTY ANALYSIS TO OPTIMIZE SUCCESSFUL STREAM WATER QUALITY SIMULATION1

ABSTRACT: A first-order uncertainty technique is developed to quantify the relationship between field data collection and a modeling exercise involving both calibration and subsequent verification. A simple statistic (LTOTAL) is used to quantify the total likelihood (probability) of successfully calibrating and verifying the model. Results from the first-order technique are compared with those from a traditional Monte Carlo simulation approach using a simple Streeter-Phelps dissolved oxygen model. The largest single difference is caused by the filtering or removal of unrealistic outcomes within the Monte Carlo framework. The amount of bias inherent in the first-order approach is also a function of the magnitude of input variability and sampling location. The minimum bias of the first-order technique is approximately 20 percent for a case involving relatively large uncertainties. However the bias is well behaved (consistent) so as to allow for correct decision making regarding the relative efficacy of various sampling strategies. The utility of the first-order technique is demonstrated by linking data collection costs with modeling performance. For a simple and inexpensive project, a wise and informed selection resulted in an LTOTAL value of 86 percent, while an uninformed selection could result in an LTOTAL value of only 55 percent.     

DEVELOPMENT OF LARGE SCALE GRIDDED RIVER NETWORKS FROM VECTOR STREAM DATA1

ABSTRACT: The Network Tracing Method (NTM) has been developed to determine gridded coarse river networks for modeling large hydrologic systems. For a coarse resolution grid, the NTM determines the downstream cell of each cell and the distance along the actual meandering flow paths between them. Unlike previously developed methods, the NTM uses fine resolution vector river networks as the source of information of the flow patterns rather than digital elevation models. The main advantage of using vector river networks as input is that they capture the hydrologic terrain features better than topographic data do, particularly in areas of low topographic relief. The NTM was applied to South America with a grid resolution of 1 degree by 1 degree and to the globe with a resolution of 2.815 degrees by 2.8125 degrees. Overall, the method captured the flow patterns well. Generated digital river networks and drainage divides showed minor disagreement with those obtained from existing maps, and most of them were consistent with the resolution of the coarse river network. The majority of estimated basin areas were also close to documented values. River lengths calculated with the NTM, however, were consistently underpredicted.     

Assessing hydrological impact of potential land use change through hydrological and land use change modeling for the Kishwaukee River basin (USA)

We connected a cellular, dynamic, spatial urban growth model and a semi-distributed continuous hydrology model to quantitatively predict streamflow in response to possible future urban growth at a basin scale. The main goal was to demonstrate the utility of the approach for informing public planning policy and investment choices. The Hydrological Simulation Program-Fortran (HSPF) was set up and calibrated for the Kishwaukee River basin in the Midwestern USA and was repeatedly run with various land use scenarios generated either by the urban growth model (LEAMluc) or hypothetically. The results indicate that (1) the land use scenarios generated by LEAMluc result in little changes in total runoff but some noticeable changes in surface flow; (2) the argument that low flows tend to decrease with more urbanized areas in a basin was confirmed in this study but the selection of indicators for low flows can result in misleading conclusions; (3) dynamic simulation modeling by connecting a distributed land use change model and a semi-distributed hydrological model can be a good decision support tool demanding reasonable amount of efforts and capable of long-term scenario-based assessments.     

Analysis and application of forest fuels data

Recent advances in fire modeling permit quantitative estimations of fire behavior from quantitative inputs that describe the fuel array and conditions, such as weather and site data, under which it will burn. This paper describes the collection, analysis, and stratification of flammable forest fuels data for coniferous forest ecosystems in Montana and then illustrates the resource management application of these data in three areas: the development of the fire behavior model, a determination of the model's sensitivity to input errors as reflected by fire behavior prediction errors, and the development of a fire hazard simulator (TAROT). A new integrated stand simulator, GANDALF, is highlighted.Conclusions center on the need to integrate fire management into the land management planning decision-making process.This work was supported by a USDI National Park Service contract to Gradient Modeling, Inc., a nonprofit research foundation devoted to ecologic research and resource management applications, and by cooperative aid agreements between Gradient Modeling, Inc. and the USDA Forest Service, Intermountain Forest and Range Experiment Station, Northern Forest Fire Laboratory (Fire in Multiple Use Management, R, D, and A Program).     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

The Effect of Rain and Runoff When Assessing Timing of Manure Application and Dissolved Phosphorus Loss in Runoff1

Vadas, Peter A., William E. Jokela, Dory H. Franklin, and Dinku M. Endale, 2011. The Effect of Rain and Runoff When Assessing Timing of Manure Application and Dissolved Phosphorus Loss in Runoff. Journal of the American Water Resources Association (JAWRA) 47(4):877‐886. DOI: 10.1111/j.1752‐1688.2011.00561.x Abstract: A significant pathway of nonpoint source, agricultural phosphorus (P) transport is surface runoff, to which surface‐applied manure can contribute. Increasing the time between manure application and the first rain‐runoff event is proposed as a practice to reduce runoff P loss. Few studies have investigated this aspect of manure P loss in runoff, with mixed results. Studies observing a decrease in runoff P as the time between application and the first rain‐runoff attribute the decrease to adsorption of manure P by soil and manure drying effects, but do not consider the effect of storm hydrology on runoff P. We ran the manure P runoff model SurPhos with data from nine published studies that investigated the effect of time between application and the first rain event on runoff P. SurPhos successfully simulated the experimental conditions in the studies and predicted runoff P loss. Simulation results suggest soil adsorption of manure P is not the dominant mechanism that will significantly decrease manure P availability to runoff. Rather, regardless of when the first rain‐runoff event occurs, storm hydrology will significantly affect manure P loss in runoff. Although model scenarios indicate that increasing the time between manure application and the first rain‐runoff event will typically decrease P loss in runoff, runoff P could be equal to or greater 30 days after application than the day after application if a more intense rain and runoff event occurs at the latter date.     

Gradient modeling: A new approach to fire modeling and wilderness resource management

Managers of wilderness resources must maintain, preserve, and sometimes restore pristine ecosystems while providing for public use and enjoyment of these areas. These managers require a resource information system that can store, retrieve and integrate basic data, synthesize components to solve particular problems, and provide simulations and predictions of natural processes and management actions. Traditional information systems based on land classification and type-mapping do not provide these capabilities.Gradient modeling, a new approach to resource management and forest fire simulation, has been developed to meet these needs in Glacier National Park. The method links four major components: (1) a terrestrial site inventory coded from aerial photographs that offers 10-m resolution; (2) gradient models of vegetation and fuel that derive quantitative stand compositional data from the parameters stored in the coded inventory; (3) a fuel moisture and microclimate model that extrapolates basestation weather data to remote sites using the parameters stored in the inventory; and (4) fire behavior and fire ecology models that integrate the data from the inventory and models to calculate real-time fire behavior and ecological succession following a fire.     

A review on the integration of artificial intelligence into coastal modeling

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.     

DISSOLVED OXYGEN MODEL OF A SHORT DETENTION TIME RESERVOIR WITH ANAEROBIC HYPOLIMNION1

ABSTRACT: A vertical dissolved oxygen model was calibrated and verified using independent field data sets from 1972 and 1973 in Lake Lyndon B. Johnson (LBJ), a short detention time reservoir with anaerobic hypolimnion. Ammonia, carbonaceous BOD, conductivity, and temperature were also simulated as state variables. The conductivity results provided a check on the mass balance and the method of entering the inflows to the reservoir model. Emphasis was placed on calculating the sinks of dissolved oxygen in the hypolimnion which could be useful in management decisions.     

AN OBJECTWE TEST FOR HYDROLOGIC SCALE1

ABSTRACT: Improving the reliability of parametric hydrologic models (sometimes called cenceptual rainfall-runoff models) in the continuous simulation of runoff from ungaged catchments has been frustrated by difficulties in estimating model parameters from catchment characteristics. An underlying problem is that these models use parameters to represent catchments as a whole, whereas data on catchment characteristics are collected at multiple field locations and are difficult to transform into one measure of collective impact. Subdividing the catchment and calibrating a stochastic parametric model to estimate distributions for the parameters that covered the range of observed streamflow values was found to improve the simulations. This paper presents an optimization of the amount of subdivision to use in simulation with a version of the Stanford Watershed Model using available climatological data. The calibration process assumes that catchment heterogeneity introduces errors that can be reduced by calibrating parameters as spatial distributions rather than single values. Calibrations for three diverse small gaged catchments located in California and in Virginia found the optimal number of subdivisions to range from 4 to 25 and the optimal scale to range from 0.3 to 2.1 mi².     

Component modeling for the spatial representation of wildlife movements

This paper has two main objectives: (1) to discuss the basics of component modeling and its conceptual implications in environmental modeling; and (2) based on this discussion, to present a component-based design to support the simulation of wildlife movements. Components are independently developed, ready-to-use software units that can be assembled together to construct a modeling system. The conceptual implications of component modeling for the representation of environmental phenomena are discussed. A comparison is made between these conceptual implications and those of the more familiar object-oriented approach. A component-based design for supporting the simulation of wildlife movements is presented in this paper in order to illustrate how component modeling can be used to support the spatial representation of ecological phenomena and processes.     

ASSESSING CHANGES IN WATERSHED FLOW REGIMES WITH SPATIALLY EXPLICIT HYDRAULIC MODELS1

ABSTRACT: Urbanization, farming, and other watershed activities can significantly alter storm hydrographs and sediment erosion rates within a watershed. These changes routinely cause severe economic and ecological problems manifested in the form of increased flooding and significant changes in channel morphology. As the activities within a watershed influence the hydrologic, hydraulic, and ecological conditions within a river, interdisciplinary approaches to predict and assess the impacts that different land uses have on streams need to be developed. An important component of this process is ascertaining how hydrologic changes induced by specific watershed activities will affect hydraulic conditions and the accompanying flood levels, sediment transport rates, and habitat conditions within a stream. A conceptual model for using spatially explicit (two‐dimensional) hydraulic models to help evaluate the impacts that changes in flow regime might have on a river is presented. This framework proposes that reproducing and quantifying flow complexity allows one to compare the hydraulic conditions within urban, urbanizing, and non‐urban streams in a more biologically and economically meaningful way. The justification, advantage, and need for such a method is argued through the results of one‐ and two‐dimensional hydraulic model studies. The implementation of this methodology in watershed urbanization studies is described.     

SWMHMS - SMALL WATERSHED MONTHLY HYDROLOGIC MODELING SYSTEM1

ABSTRACT: SWMHMS is a conceptual computer modeling program developed to simulate monthly runoff from a small nonurban watershed. The input needed to run model simulations include daily precipitation, monthly data for evapotranspiration determination (average temperature, crop consumptive coefficients, and percent daylight hours), and six watershed parameter values. Evapotranspiration was calculated with the Blaney-Criddle equation while surface runoff was determined using the Soil Conservation Service curve number procedure. For watershed parameter evaluation, SWMHMS provides options for both optimization and sensitivity analysis. Observed runoff data are required along with the model input previously mentioned in order to conduct parameter optimization. SWMEIMS was tested with data from six watersheds located in different regions of the United States. Model accuracy was generally found to be very good except on watersheds having substantial snowfall accumulation. In having only six watershed parameters, SWMHMS is less complex to use than many other computer programs that calculate monthly runoff. Consequently, SWMHMS may find its greatest application as an educational tool for students learning principles of hydrologic modeling, such as parameter evaluation procedures and the impacts of input data uncertainty on model results.     

MODELING THE ENVIRONMENTAL EFFECTS OF A NAVIGATION CHANNEL IN A TIDAL BAY1

ABSTRACT: A circulation and salinity model was used to predict the effects of wind, fresh water inflow, and the construction of a navigation channel on Vermilion Bay, Louisiana. The model numerically solved continuity and motion equations and provided a time history and spatial distribution of tidal depths, flows, velocities, and salinity in two lateral dimensions. The model predicted that high south winds or high fresh water inflow would reduce average bay salinities, as would the construction of a channel through Vermilion Bay. The results suggested the main reason for this behavior is the presence of two bay outlets to the Gulf of Mexico.