MODELING INFILTRATION WITH VARYING HYDRAULIC CONDUCTWITY UNDER SIMULATED RAINFALL CONDITIONS1

ABSTRACT: Physically-based models are extensively used to simulate the infiltration process under varied field conditions. Most models are based on the deterministic nature of input parameters related to the flow process (such as hydraulic conductivity). These models yield poor predictions of infiltration rates because they do not include the field-scale variations of flow parameters. The paper presents an approach for integrating the field-scale variability of hydraulic conductivity with an infiltration model to simulate infiltration under the rainfall conditions. A model describing the spatial structure of hydraulic conductivity has been developed using stochastic techniques. The stochastic structure of hydraulic conductivity was then incorporated in the Green-Ampt and Mein-Larson infiltration model. The model outputs on the instantaneous infiltration rates and cumulative infiltration were evaluated using the field infiltration data measured under simulated rainfall conditions. The results show that the combined model is capable of rep. resenting the instantaneous infiltration rates and cumulative infiltration of the study soils. The model may, therefore, be used to simulate the rainfall infiltration process for spatially-variable soils under the field conditions.     

SOLUTE TRANSPORT IN THREE‐DIMENSIONAL SATURATED POROUS MEDIA HAVING SELF‐SIMILAR HYDRAULIC CONDUCTIVITY DISTRIBUTION1

ABSTRACT: Customarily, it has been assumed that hydraulic conductivity is a stationary, homogeneous stochastic process with a finite variance for stochastic analysis of solute transport in the subsurface. That the distribution of hydraulic conductivity may have a fractal behavior with long range correlations was suggested from field data analyses. This motivates us to further investigate how the fractal behavior of permeability distribution impacts solute transport in porous media. This study provides longitudinal and transverse macrodispersivity coefficients and the variance of the solute concentration. Longitudinal and transverse macrodispersivity coefficients are found to depend strongly on the fractal dimension (D) of logarithmic hydraulic conductivity (logK). The longitudinal and transverse macrodispersivity coefficients are the highest when D = 1, and the values decrease monotonically to zero at D = 2. Both coefficients correspond to the characteristic length scale of the logK distribution, thus are scale dependent parameters. The ratio of the transverse to the longitudinal macrodispersivity coefficient is on the order of 10^‐1 to 10^‐4. Concentration variance also decreases with the fractal dimension of logK. There is no spatial spreading of solute for D = 2, and the concentration variance reaches zero for this case.     

Applying Zonation Methods and Tabu Search to Improve the Ground‐Water Modeling1

Abstract: An optimization procedure combining zonation methods with the Tabu Search method is proposed to identify the spatial distribution of hydraulic conductivity field. Three zonation methods, Voronoi diagram (VD), multiplicatively weighted Voronoi diagram (MWVD), and pattern zonation are adopted for the parameterization purposes. With the consideration of the homogeneity and the heterogeneity, there are four spatial distributions of hydraulic conductivity designed to test whether the parameter structure can be successfully identified. The fitting residual error is first considered to determine an adequate number of zones without over parameterization. Then, the parameter uncertainty is evaluated the decision of the number of zones. The results indicate that the MWVD performs better than other two methods because the MWVD has better flexibility in describing the zonal boundaries with small number of decision variables.     

VERIFICATION OF THE RHEA-OROGRAPHIC-PRECIPITATION MODEL1

ABSTRACT: Observed April 1 snowpack accumulations within and near the Gunnison River basin in southwestern Colorado are compared with simulations from the Rhea-orographic-precipitation model to determine if the model simulates reliable magnitudes and temporal and spatial variability in winter precipitation for the basin. Twenty simulations of the Rhea model were performed using‘optimal’parameter sets determined for 10-kilometer (km) grids (10-km by 10-km grid cells) through stochastic calibration. Comparisons of Rhea-model simulations of winter precipitation with April 1 snowpack accumulations at 32 snowcourse stations were performed for the years 1972–1990. For most stations and most years the Rhea model reliably simulates the temporal and spatial variability in April 1 snowpack accumulations. However, in general, the Rhea-model underestimates April 1 snowpack accumulations in the Gunnison River basin area, and the underestimation is greatest for locations that receive the largest amount of snow. A significant portion of the error in Rhea-model simulations is due to the calibration of the Rhea model using gauge-catch precipitation measurements which can be as much as 50 percent below actual snowfall accumulations. Additional error in the Rhea-model simulations is a result of the comparison of gridded precipitation values to observed values measured at points.     

USING SOIL TEXTURE TO ESTIMATE SATURATED HYDRAULIC CONDUCTIVITY AND THE IMPACT ON RAINFALL-RUNOFF SIMULATIONS1

ABSTRACT: In this paper a new set of soil texture data is used to estimate the spatial distribution of saturated hydraulic conductivity values for a small rangeland catchment. The estimates of conductivity are used to re-excite and re-evaluate a quasi-physically based rainfall-runoff model. The performance of the model is significantly reduced with conductivity estimates gleaned from soil texture data rather than the infiltration data used in our previous efforts.     

A STRIP MODEL APPROACH TO PARAMETERIZE A COUPLED GREEN‐AMPT KINEMATIC WAVE MODEL1

ABSTRACT: Infiltration processes at the plot scale are often described and modeled using a single effective hydraulic conductivity (Kg) value. This can lead to errors in runoff and erosion prediction. An integrated field measurement and modeling study was conducted to evaluate: (1) the relationship among rainfall intensity, spatially variable soil and vegetation characteristics, and infiltration processes; and (2) how this relationship could be modeled using Green and Ampt and a spatially distributed hydrologic model. Experiments were conducted using a newly developed variable intensity rainfall simulator on 2 m by 6 m plots in a rangeland watershed in southeastern Arizona. Rainfall application rates varied between 50 and 200 mm/hr. Results of the rainfall simulator experiments showed that the observed hydrologic response changed with changes in rainfall intensity and that the response varied with antecedent moisture condition. A distributed process based hydrologic simulation model was used to model the plots at different levels of hydrologic complexity. The measurement and simulation model results show that the rainfall runoff relationship cannot be accurately described or modeled using a single Kg value at the plot scale. Multi‐plane model configurations with infiltration parameters based on soil and plot characteristics resulted in a significant improvement over single‐plane configurations.     

AN EVALUATION OF TWO HYDROLOGIC MODELS FOR CLIMATE CHANGE SCENARIOS1

ABSTRACT: Understanding the effects of climate change on water resources requires coupling atmospheric and hydrologic models. With the wide array of hydrologic models, from simple empirical to complex physically based, it is not clear which is preferable to simulate hydrologic variations over long time scales. To address this issue, a black-box artificial neural network (ANN) model was compared to a distributed parameter conceptual Geographic Information System based Hydrologic Modeling System (GIS-HMS). Both models computed daily direct surface runoff in four sub-basins of the West Branch of the Susquehanna River Basin, Pennsylvania and were evaluated with five objective functions. Overall, results were comparable between models. However, the ANN was favored in the larger sub-basins, while GIS-HMS was more accurate in the smaller catchments. Both models were impaired by the poor spatial and temporal resolution of precipitation data and the simplified representation of antecedent soil-moisture conditions. In the context of climate change, where simulations are limited by computing power, results suggest that both models are appropriate. When detailed simulations are essential, GIS-HMS is a preferable model to use. On the other hand, the ANN model is more suitable when multiple scenarios require immediate analysis and the distributed qualities of runoff are not required.     

KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: I. REVIEW,ALGORITHM, AND SIMULATION EXPERIMENTS1

An important class of models, frequently used in hydrology for the forecasting of hydrologic variables one or more time periods ahead, or for the generation of synthetic data sequences, is the class of autoregressive(AR) models. As the AR models belong to the family of linear stochastic difference equations, they have both a deterministic and a stochastic component. The stochastic component is often assumed to have a Gaussian distribution. It is well known that hydrologic observations (e.g., stream flows) are heavily affected by noise. To account explicitly for the observation noise, the linear stochastic difference equation is expressed in state variable form and an observation model is introduced. The discrete Kalman filter algorithm can then be used to obtain estimates of the state variable vector. Typically, in hydrologic systems, model parameters, system noise statistics and measurement noise statistics are unknown, and have to be estimated. In this study an adaptive algorithm is discussed which estimates these quantities simultaneously with the state variables. The performance of the algorithm is evaluated by using simulated data.     

A MULTICOMPONENT MODEL OF PHOSPHORUS DYNAMICS IN RESERVOIRS1

The models available for simulating phosphorus dynamics and trophic state in impoundments vary widely. The simpler empirically derived phosphorus models tend to be appropriate for long-term, steady or near steady state analyses. The more complex ecosystem models, because of computational expense and the importance of input parameter uncertainty, are impractical for very long-term simulation and most applicable for time-variable water quality simulations generally of short to intermediate time frames. An improved model for time variable, long-term simulation of trophic state in reservoirs with fluctuating inflow and outflow rates and volume is needed. Such a model is developed in this paper representing the phosphorus cycle in two-layer (i.e., epilimnion and hypolimnion) reservoirs. The model is designed to simulate seasonally varying reservoir water quality and eutrophication potential by using the phosphorus state variable as the water quality indicator. Long-term simulations with fluctuating volumes and variable influent and effluent flow rates are feasible and practical. The model utility is demonstrated through application to a pumped storage reservoir characteristic of these conditions.     

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.     

MODELING DEVELOPED COASTAL WATERSHEDS WITH THE AGRICULTURAL NON-POINT SOURCE MODEL1

ABSTRACT: Many coastal states are facing increasing urban growth along their coast lines. The growth has caused urban non-point source nitrogen runoff to be a major contributor to coastal and estuarine enrichment. Water resource managers are responsible for evaluating the impacts from point and non-point sources in developed watersheds and developing strategies to manage future growth. Non-point source models provide an effective approach to these management challenges. The Agricultural Non-Point Source Model (AGNPS) permits the incorporation of important spatial information (soils, landuse, topography, hydrology) in simulating surface hydrology and nitrogen non-point source runoff. The AGNPS model was adapted for developed coastal watersheds by deriving urban coefficients that reflect urban landuse classes and the amount of impervious surface area. Popperdam Creek watershed was used for model parameter development and model calibration. Four additional watersheds were simulated to validate the model. The model predictions of the peak flow and total nitrogen concentrations were close to the field measurements for the five sub-basins simulated. Measured peak flow varied by 30 fold among the sub-basins. The average simulated peak flow was within 14 percent of the average measured peak flow. Measured total nitrogen loads varied over an order of magnitude among the sub-basins yet error between the measured and simulated loads for a given sub-basin averaged 5 percent. The AGNPS model provided better estimates of nitrogen loads than widely used regression methods. The spatial distribution of important watershed characteristics influenced the impacts of urban landuse and projecting future residential expansion on runoff, sediment and nitrogen yields. The AGNPS model provides a useful tool to incorporate these characteristics, evaluate their importance, and evaluate fieldscale to watershed-scale urban impacts.     

Using a geographical information system for monitoring space shuttle launches: Determining cumulative distribution of deposition and an empirical test of a spatial model

Space shuttle launches produce localized hydrochloric acid deposition. The interaction of solid rocket motor exhaust and deluge water released on the pad at the time of launch results in the formation of an exhaust cloud. The spatial pattern and extent of deposition from the launch cloud are predicted by the rocket exhaust effluent diffusion (REED) model. The actual pattern of deposition has been mapped by field surveys for each shuttle launch since 1981. In this paper we use a geographical information system (GIS) to compare model predictions with ground patterns for 49 shuttle launches. We also compile cumulative maps of deposition patterns needed to consider long-term impacts. The direction of launch cloud movement did not differ significantly from model predictions. The REED model overpredicted both the area that received deposition and the maximum distance from the launch pad that deposition occurred. Severe vegetation damage was restricted to near-field deposition areas within 1980 m north of each launch pad. Total area impacted from launches has been 87.0 ha around pad 39A and 52.9 ha around pad 39B. Far-field deposition has caused leaf spotting from acid droplets or aluminum oxide over a wider and more variable area than near-field. A total of 19,397 ha has received deposition, but 63.6% of this area has received deposition only one time and 92.2% not more than three times. GIS techniques provide means to test spatial models and compile information useful for assessing cumulative impacts.     

ROLE OF WATERSHED SUBDIVISION ON MODELING THE EFFECTIVENESS OF BEST MANAGEMENT PRACTICES WITH SWAT1

Distributed parameter watershed models are often used for evaluating the effectiveness of various best management practices (BMPs). Streamflow, sediment, and nutrient yield predictions of a watershed model can be affected by spatial resolution as dictated by watershed subdivision. The objectives of this paper are to show that evaluation of BMPs using a model is strongly linked to the level of watershed subdivision; to suggest a methodology for identifying an appropriate subdivision level; and to examine the efficacy of different BMPs at field and watershed scales. In this study, the Soil and Water Assessment Tool (SWAT) model was calibrated and validated for streamflow, sediment, and nutrient yields at the outlet of the Dreisbach (623 ha) and Smith Fry (730 ha) watersheds in Maumee River Basin, Indiana. Grassed waterways, grade stabilization structures, field borders, and parallel terraces are the BMPs that were installed in the study area in the 1970s. Sediment and nutrient outputs from the calibrated model were compared at various watershed subdivision levels, both with and without implementation of these BMPs. Results for the study watersheds indicated that evaluation of the impacts of these BMPs on sediment and nutrient yields was very sensitive to the level of subdivision that was implemented in SWAT. An optimal watershed subdivision level for representation of the BMPs was identified through numerical simulations. For the study watersheds, it would appear that the average subwatershed area corresponding to approximately 4 percent of total watershed area is needed to represent the influence of these BMPs when using the SWAT model.     

MODELING GROUND WATER FLOW USING FLUX BOUNDARY CONDITIONS1

ABSTRACT: Determination of the boundary conditions for modeling ground water flow is a critical point especially in regional models. Normally the regional models require model areas that are greater than the given area of interest. This work focuses on the prediction of hydraulic heads in regional models using flux boundary conditions. The model uses flux boundary conditions that were estimated using a radial flow analog and Darcy's law. The regional model that is presented uses no parameter identification (inverse estimation) procedures. In the present work, the Houston area was used. The simulation of the hydrological conditions of the Chicot and Evangeline Aquifers that underlie the Houston area were made using the available information about the geological profile in the Houston region and the current information about the existing production wells. The regional model works as a forward problem. The system parameters such as hydraulic conductivity, specific storage, and hydrological stresses were specified, and the model predicts the hydraulic head. Actual data from piezometers operated by the U.S. Geological Survey (USGS) in many places throughout Houston were used as initial conditions. Some piezometric head data were generated using the regional variable theory called kriging to supply head estimates in areas where data were unavailable. The Modular Three Dimensional Finite Difference Groundwater Flow Model developed by the USGS was used to predict the hydraulic heads. The predicted ground water heads are compared to the actual data. The results show that the model performs well for locations where data were available.     

NATIONWIDE REGRESSION MODELS FOR PREDICTING URBAN RUNOFF WATER QUALITY AT UNMONITORED SITES1

ABSTRACT: Regression models are presented that can be used to estimate mean loads for chemical oxygen demand, suspended solids, dissolved solids, total nitrogen, total ammonia plus nitrogen, total phosphorous, dissolved phosphorous, total copper, total lead, and total zinc at unmonitored sites in urban areas. Explanatory variables include drainage area, imperviousness of drainage basin to infiltration, mean annual rainfall, a land-use indicator variable, and mean minimum January temperature. Model parameters are estimated by a generalized-least-squares regression method that accounts for cross correlation and differences in reliability of sample estimates between sites. The regression models account for 20 to 65 percent of the total variation in observed loads.     

EQUIVALENCE BETWEEN TOPMODEL AND THE NRCS CURVE NUMBER METHOD IN PREDICTING VARIABLE RUNOFF SOURCE AREAS1

An equivalence is proposed between two rainfall‐runoff methods with a long history of use in the United States and Europe. In watersheds where variable source areas dominate runoff, the two methods can have comparable probability distribution functions of moisture deficit, and therefore predict similar saturated runoff source areas. A novel approach is introduced to determine the S parameter in the Natural Resources Conservation Service (NRCS) method. This approach constrains S by the physical soil and topography characteristics of the catchment and depth to water table. The NRCS curve number method is at the core of many rainfall‐runoff models in hydrology. As a simple lumped parameter method, it is often scrutinized because it is not obvious how to derive S from catchment hydromorphological characteristics. The novel approach provides a clear physical meaning for S, allowing better estimation of this parameter in humid shallow water table environments where the variable source area can be the dominant runoff mechanism.     

Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds1

Maurer, Edwin P., Levi D. Brekke, and Tom Pruitt, 2010. Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds. Journal of the American Water Resources Association (JAWRA) 46(5):1024–1035. DOI: 10.1111/j.1752-1688.2010.00473.x Abstract: We compare the projected changes to streamflows for three Sierra Nevada rivers using statistically downscaled output from 22 global climate projections. The downscaled meteorological data are used to drive two hydrology models: the Sacramento Soil Moisture Accounting model and the variable infiltration capacity model. These two models differ in their spatial resolution, computational time step, and degree and objective of calibration, thus producing significantly different simulations of current and future streamflow. However, the projected percentage changes in monthly streamflows through mid-21st Century generally did not differ, with the exceptions of streamflow during low flow months, and extreme low flows. These findings suggest that for physically based hydrology models applied to snow-dominated basins in Mediterranean climate regimes like the Sierra Nevada, California, model formulation, resolution, and calibration are secondary factors for estimating projected changes in extreme flows (seasonal or daily). For low flows, hydrology model selection and calibration can be significant factors in assessing impacts of projected climate change.     

REDUCING THE RISK OF FISHERY RESOURCE DISASTERS: A BIOECONOMIC APPROACH TO SUSTAINABLE RESOURCE MANAGEMENT1

Abstract: Recognition is growing that fisheries must be both ecologically and commercially sustainable. The bioeconomic models proposed herein constitute an analytic framework capable of integrating the ethics and Societal values associated with fisheries preservation. Specifically, we focus on the normalized optimal (equilibrium) fish population, z*, a dimensionless variable representing biomass as a proportion of environmental capacity. We model z* as a function of (a) the dimensionless "bionomic growth ratio", γ, which is the ratio of the discount rate to the intrinsic population growth rate, and (b) the preservation coefficient, Ω, which is the ratio of the preservation value (a measure of Society's value for the stock) to price, assuming that the population growth rate and intrinsic growth rate are fixed. It is shown that increasing Ω significantly impacts z*, particularly for moderate values of γ (2 γ 4). Finally, stochastic population models are used to analyze the risk of a fish stock collapse due to harvesting pressures. The bioeconomic models and simulations herein described improve the accuracy and reliability of maximum sustainable yield management.     

Model Development and Calibration of a Saltwater Intrusion Model in Southern California1

Abstract: A nine‐layered confined‐unconfined flow and transport model is developed for the Alamitos saltwater intrusion barrier in Southern California. The conceptual model is based on the geological structure of the coastal aquifer system. The key parameters in the flow and transport models are calibrated using a two‐phase procedure which matches the types of data available for calibration. Because of the abundance of point measurements of hydraulic conductivity, the heterogeneous and random hydraulic conductivity field for each of the five aquifers is estimated by the geostatiscal method of natural‐neighbor‐kriging in Phase 1. In Phase 2, the longitudinal and transverse dispersivities in the transport model are estimated by a traditional inverse procedure that minimizes the least‐squares error for concentration (LSE‐CON). The minimum LSE‐CON is achieved near 15.2 and 1.52 m for the longitudinal and transverse dispersivities, respectively. Additional simulations with increasing transport parameter complexity did not yield significant improvements in LSE‐CON. Also, tracking least‐squares error for head while parametrically varying the transport parameters revealed there is a negligible interaction between predicted head and transport parameters.     

基于高分数据的村级地块面向对象分类方法研究

针对农作物对地抽样调查工作中村级抽样单元野外调查工作量大的问题,研究了一种基于高分数据的村级农作物种植面积提取方法。以辽宁省北镇市孟家村SPOT5遥感影像为实验数据(主要包括农田、裸地、道路、大棚、水体、房屋等类型),基于eCognition平台,根据局部方差法筛选地物最优分割尺度,按照最优分割尺度从大到小组织实验区地物分割次序,结合影像对象的光谱、形状等特征,建立分类规则,完成了村级农作物种植面积的提取。该方法有效避免了在影像分割时参数反复试错带来的复杂性和随机性,提高了农作物种植面积面向对象分类精度和效率。通过野外调查样地进行精度验证表明,精度达到90.4%,为提高农作物种植面积对地抽样调查工作效率,减少野外调查的工作量提供技术支撑。