RUNOFF MODELING OF A MOUNTAINOUS CATCHMENT USING TOPMODEL: A CASE STUDY1

ABSTRACT: The rainfall‐runoff response of the Tygarts Creek Catchment in eastern Kentucky is studied using TOPMODEL, a hydrologic model that simulates runoff at the catchment outlet based on the concepts of saturation excess overland flow and subsurface flow. Unlike the traditional application of this model to continuous rainfall‐runoff data, the use of TOPMOEL in single event runoff modeling, specifically floods, is explored here. TOPMODEL utilizes a topographic index as an indicator of the likely spatial distribution of rainfall excess generation in the catchment. The topographic index values within the catchment are determined using the digital terrain analysis procedures in conjunction with digital elevation model (DEM) data. Select parameters in TOPMODEL are calibrated using an iterative procedure to obtain the best‐fit runoff hydrograph. The calibrated parameters are the surface transmissivity, T_O, the transmissivity decay parameter, m, and the initial moisture deficit in the root zone, S_r0. These parameters are calibrated using three storm events and verified using three additional storm events. Overall, the calibration results obtained in this study are in general agreement with the results documented from previous studies using TOPMODEL. However, the parameter values did not perform well during the verification phase of this study.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

CO-KRIGING TO INCORPORATE SCREENING DATA: HUDSON RIVER SEDIMENT PCBs1

ABSTRACT: Kriging methods of geostatistical analysis provide valuable techniques for analysis of sediment contamination problems, including interpolation of concentration maps from point data and estimation of global mean concentrations. Sample collection efforts frequently include preliminary screening data of considerably more extensive coverage than the laboratory analyses on which estimation is usually based. How should these be incorporated in kriging? Screening and laboratory analysis constitute two separate estimates of the same spatial field but of very different characteristics. A modified version of co-kriging is developed to include the imprecise screening information in the analysis of contaminant distribution. Use of the method is demonstrated on a data set of sediment PCB samples from the Upper Hudson River, for which preliminary categorical mass spectrometry screening was used to select a smaller set of samples for gas chromatograph analysis. The method is widely applicable to many situations of contaminant and natural resource estimation.     

GEOLOGIC INFERENCE FROM “FLOW NET” TRANSMISSIVITY DETERMINATION: THREE CASE STUDIES1

A graphical inverse method for determining the regional transmissivity distribution was applied to three field problems. The study areas were the Hanford Site, Washington; the Rocky Mountain Arsenal, Colorado; and the Nevada Test Site, Nevada. This method can aid in flow system conceptualization by revealing the location of bedrock controls for groundwater flow. It is a valuable tool for aiding the hydrogeologist in asking questions about the nature of trends in the pattern of transmissivity values. Quantitative estimates of regional transmissivities can be used as starting points for further parameter refinement. Sensitivity analysis using Monte Carlo simulation shows that quantitative estimates of transmissivity can be obtained when measurement error in the hydraulic head does not cause a large error in the hydraulic gradient.     

Statistical approach for evaluating the significance of generating new data to the management of the brackish groundwater fields in Kuwait

The brackish groundwater production fields in Kuwait have been drilled intensively during the past three decades. However, poor spatial distribution of some of the measured parameters and limited measurements of others are forcing extended extrapolation during numerical simulations of management schemes. On the other hand, operational, time and budget constraints allow only limited additional field-generated data, raising the question of the significance of their impact on numerical modeling results. This study evaluates this significance by using nearest neighbor statistics and a synthesized data set to assess the impacts of a proposed field investigation on the spatial distribution and kriged values of transmissivity and storage coefficient arrays of the brackish groundwater production fields. A scale for the significance of the impacts was prepared based on the sensitivity of the aquifer system towards the studied properties. The transmissivity data array indicated a 25% reduction in the maximum distance and an 8% increase in the interquartile of the nearest neighbor analysis. The storage coefficient data arrays indicated the extension of the covered area to reach the southern boundary of the study area, which was reflected in more than doubling the maximum distance between the nearest neighbors. For the kriged values of the transmissivity, about 35% of the study area was significantly changed due to the addition of the new data points, while only 10% of the study area was significantly changed for the storage coefficient values. Overall, while the spatial distribution indicators were not conclusive, the significance of the new data was clear in the changes in the kriged values of the aquifer properties. Additionally, this approach highlights the aquifer test locations where the generated data had no impact on the kriged values, which may be utilized for optimizing the allocation and number of the aquifer tests.     

TEMPORAL AND SPATIAL CHARACTERIZATION OF RAINFALL OVER CENTRAL AND SOUTH FLORIDA1

requency evaluation and spatial characterization of rainfall in Central and South Florida are presented. Point frequency analysis performed at all available sites has shown that the 2‐parameter Gamma probability density function is the best model for monthly rainfall frequency over Central and South Florida. The model's parameters estimated at 145 stations were used to provide monthly rainfall estimates for 10‐ and 100‐year dry and wet return periods. Experimental and theoretical variograms computed for these estimates, as well as the Kriging estimation variance maps, show that the existing rain gage network is less capable of resolving monthly rainfall variation in the wet season than the dry season. May is the dry‐to‐wet transition month, while October is the wet‐to‐dry transition month with average rainfall of 4.5 inches. Monthly average rainfall is above 7 inches during the wet season and below 3 inches during the dry season. Two‐thirds of the annual rainfall is accumulated in the wet season. Annual average rainfall is maximum (above 60 inches) in many areas along the east coast, and is minimum (below 45 inches) in many areas over Lake Okee‐chobee and Central Florida. Rainfall maps show a changing pattern between the wet and the dry seasons. Frontal rainfall occurs in the dry season, while convective rainfall, tropical depression, and hurricanes occur in the wet season. Average rainfall is higher along the east coast area in the dry season and it is higher along the west coast area in the wet season.     

Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping

The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of ≥6.25 m3/d/m (Case 1) and T values of ≥3.79 m2/d (Case 3) corresponding to a yield (Y) of ≥500 m3/d, and SPC values of ≥3.75 m3/d/m (Case 2) and T values of ≥2.61 m2/d (Case 4) corresponding to a Y of ≥300 m3/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W+ and W- of each factor's rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 71.20% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration.     

Evaluating The National Land Cover Database Tree Canopy and Impervious Cover Estimates Across the Conterminous United States: A Comparison with Photo-Interpreted Estimates

The 2001 National Land Cover Database (NLCD) provides 30-m resolution estimates of percentage tree canopy and percentage impervious cover for the conterminous United States. Previous estimates that compared NLCD tree canopy and impervious cover estimates with photo-interpreted cover estimates within selected counties and places revealed that NLCD underestimates tree and impervious cover. Based on these previous results, a wall-to-wall comprehensive national analysis was conducted to determine if and how NLCD derived estimates of tree and impervious cover varies from photo-interpreted values across the conterminous United States. Results of this analysis reveal that NLCD significantly underestimates tree cover in 64 of the 65 zones used to create the NCLD cover maps, with a national average underestimation of 9.7% (standard error (SE) = 1.0%) and a maximum underestimation of 28.4% in mapping zone 3. Impervious cover was also underestimated in 44 zones with an average underestimation of 1.4% (SE = 0.4%) and a maximum underestimation of 5.7% in mapping zone 56. Understanding the degree of underestimation by mapping zone can lead to better estimates of tree and impervious cover and a better understanding of the potential limitations associated with NLCD cover estimates.     

CLIMATE FACTOR FOR SMALL-BASIN FLOOD FREQUENCY1

ABSTRACT: A climate factor, C_T, (T = 2–, 25-, and 100-year recurrence intervals) that delineates regional trends in small-basin flood frequency was derived using data from 71 long-term rainfall record sites. Values of C_T at these sites were developed by a regression analysis that related rainfall-runoff model estimates of T-year floods to a sample set of 50 model calibrations. C_T was regionalized via kriging to develop maps depicting its geographic variation for a large part of the United States east of the 105th meridian. Kriged estimates of C_T and basin-runoff characteristics were used to compute regionalized T-year floods for 200 small drainage basins. Observed T-year flood estimates also were developed for these sites. Regionalized floods are shown to account for a large percentage of the variability in observed flood estimates with coefficients of determination ranging from 0.89 for 2-year floods to 0.82 for 100-year floods. The relative importance of the factors comprising regionalized flood estimates is evaluated in terms of scale (size of drainage area), basin-runoff characteristics (rainfall. runoff model parameters), and climate (C_T).     

A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION1

One of the problems which often arises in engineering hydrology is to estimate data at a given site because either the data are missing or the site is ungaged. Such estimates can be made by spatial interpolation of data available at other sites. A number of spatial interpolation techniques are available today with varying degrees of complexity. It is the intent of this paper to compare the applicability of various proposed interpolation techniques for estimating annual precipitation at selected sites. The interpolation techniques analyzed include the commonly used Thiessen polygon, the classical polynomial interpolation by least-squares or Lagrange approach, the inverse distance technique, the multiquadric interpolation, the optimal interpolation and the Kriging technique. Thirty years of annual precipitation data at 29 stations located in the Region II of the North Central continental United States have been used for this study. The comparison is based on the error of estimates obtained at five selected sites. Results indicate that the Kriging and optimal interpolation techniques are superior to the other techniques. However, the multiquadric technique is almost as good as those two. The inverse distance interpolation and the Thiessen polygon gave fairly satisfactory results while the polynomial interpolation did not produce good results.     

宁夏近51年气候变化特征分析

利用宁夏10个气象站1960-2010年日气温和降水资料,对宁夏气温和降水的时空变化特征进行了分析.结果显示:①宁夏整体呈现暖干化趋势,但存在区域间差异.②近51年来宁夏年平均气温呈上升趋势,冬季升温幅度大,秋季升温幅度最小.③年平均降水量总体呈现减小趋势,春季微弱增加,冬季降水量显著增加,夏季降水微弱减少,秋季降水减少明显.④通过Kriging空间差值法分析表明,年平均气温总体呈现由西南向西北递增的趋势,中、北部增温较明显；平均年总降水量总体呈现由西南向西北递减的规律,中、北部降水量减小最为明显.     

SPATIAL CLUSTERS OF SUBSURFACE DRAINAGE WATER NO3‐N LEACHING LOSSES1

ABSTRACT: Grouping of nitrate‐nitrogen (NO₃‐N) leaching losses from agricultural fields into spatial clusters can help determine the cause/effect relationships for their occurrence. This study was designed to investigate the spatial relationships of low, medium, and high NO₃‐N leaching losses clusters with soil and landscape attributes using cluster and discriminant analysis and the map overlay capability of a geographical information system (GIS). Field measured data of a six‐year (1993 through 1998) study on NO₃‐N leaching losses from 36 experimental fields at the Iowa State University's northeastern research center near Nashua, Iowa, were normalized on an annual basis to compare over the years. The cluster analysis resulted in the formation of three clusters based on the satisfactory evaluation criteria of pseudo‐F statistic, cubic clustering criterion, and R² values. The discriminant analysis, carried out on the basis of clusters, identified elevation and subsurface drainage as the factors that contributed significantly (p > 0.01) in discriminating among these clusters. The verification of discriminant functions developed on these factors predicted the cluster membership for all the groups with an overall accuracy of 86 percent. The map overlay analyses of GIS showed that spatial occurrence of the clusters transporting high NO₃‐N leaching losses was affected by the interaction of soil type and elevation levels.     

Integrating Satellite Imagery with Simulation Modeling to Improve Burn Severity Mapping

Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the Relative differenced Normalized Burn Ratio, a satellite image-based change detection procedure commonly used to map burn severity, with output from the Fire Hazard and Risk Model, a simulation model that estimates fire effects at a landscape scale. Using 285 Composite Burn Index (CBI) plots in Washington and Montana as ground reference, we found that an integrated model explained more variability in CBI (R ² = 0.47) and had lower mean squared error (MSE = 0.28) than image (R ² = 0.42 and MSE = 0.30) or simulation-based models (R ² = 0.07 and MSE = 0.49) alone. Overall map accuracy was also highest for maps created with the Integrated Model (63 %). We suspect that Simulation Model performance would greatly improve with higher quality and more accurate spatial input data. Results of this study indicate the potential benefit of combining satellite image-based methods with a fire effects simulation model to create improved burn severity maps.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

Accuracy Assessment of NOAA Gridded Daily Reference Evapotranspiration for the Texas High Plains

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ET_ref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large‐scale spatial representation of ET_ref, which is essential for regional scale water resources management. Data used in the development of NOAA daily ET_ref maps are derived from observations over surfaces that are different from short (grass — ET_os) or tall (alfalfa — ET_rs) reference crops, often in nonagricultural settings, which carries an unknown discrepancy between assumed and actual conditions. In this study, NOAA daily ET_os and ET_rs maps were evaluated for accuracy, using observed data from the Texas High Plains Evapotranspiration (TXHPET) network. Daily ET_os, ET_rs and the climatic data (air temperature, wind speed, and solar radiation) used for calculating ET_ref were extracted from the NOAA maps for TXHPET locations and compared against ground measurements on reference grass surfaces. NOAA ET_ref maps generally overestimated the TXHPET observations (1.4 and 2.2 mm/day ET_os and ET_rs, respectively), which may be attributed to errors in the NLDAS modeled air temperature and wind speed, to which reference ET_ref is most sensitive. Therefore, a bias correction to NLDAS modeled air temperature and wind speed data, or adjustment to the resulting NOAA ET_ref, may be needed to improve the accuracy of NOAA ET_ref maps.     

Spatial Variability of Groundwater Depth and Quality Parameters in the National Capital Territory of Delhi

The groundwater quantity and quality scenario is of much concern in the National Capital Territory of Delhi, India, which necessitates an investigation to envisage the extent of spatial variability of groundwater depth and pollutant concentration levels in this region. Therefore, in this study, an effort was made to generate the spatial variability map of groundwater depth and quality parameters (viz. chloride, electrical conductivity, fluoride, magnesium, and nitrate). Ordinary kriging was used to analyze the spatial variability of groundwater depth and quality parameters, whereas indicator kriging was used to analyze groundwater quality parameters equal to or greater than the pollution threshold values. It was observed that the semivariogram parameters fitted well in the exponential model for water depth and in the spherical model for water quality parameters. The generated spatial variability maps indicated that in 43% of the study area, groundwater depth was within 20 m. The salinity level was higher than 2.5 dS m⁻¹ in 69% of the study area and the nitrate concentration exceeded 45 mg l⁻¹ in 36% of the area. The probability maps showed that about 24% of the area had the highest probability (0.8–1.0) of exceedence of the threshold electrical conductivity value and an area of 2% exhibited the highest probability of exceedence of the threshold value of nitrate concentration in the groundwater. The generated spatial variability and probability maps will assist water resource managers and policymakers in development of guidelines in judicious management of groundwater resources for agricultural and drinking purposes in the study area.     

DEFINING REGIONAL POPULATIONS OF LAKES FOR THE ASSESSMENT OF SURFACE WATER QUALITY1

ABSTRACT: Topographic maps are commonly used to define populations of lakes in regional surveys of surface water quality. To illustrate the effect of different maps on that process, we compared the lakes represented on the 1:250,000-scale maps used for the Northeast Region of the Eastern Lake Survey—Phase I (ELS-I) to the lakes on a sample of large-scale maps (1:24,000 or 1:62,500). Lake areas at or near the lower limit of representation delimited “smallest-lake” values for the compared 1:250,000-scale maps. The regional median for these values was 4.5 hectares (ha) and ranged from 0.6 to 24.8 ha. Lake representation is influenced by cartographic limitations such as map scale, age, and complexity as well as the inherent variability of waterbodies (e.g., water level fluctuations or the creation of reservoirs, beaver impoundments, and oxbows). The total number of lakes on large-scale maps increased markedly as lake area decreased. Approximately 15,700 of the estimated 29,000 lakes in the EPA's Northeast Region were 1 to 4 ha in area. Because maps affect the size distribution of lakes included in a regional survey and because lake areas are thought to modify lake chemistry, maps ultimately affect the estimates of regional surface water quality.     

Multi-objective optimization of a novel crude lipase-catalyzed fatty acid methyl ester (FAME) production using low-order polynomial and Kriging models

In this paper, conventional response surface methodology (RSM) based on low-order polynomials and an alternative Kriging-based method are used for the model-based single and multi-objective optimization of fatty-acid methyl ester (FAME) production catalyzed by a novel crude lipase from the yeast Cryptococcus diffluens (D44). The coefficient of determination for the two modeling approaches was calculated as 0.97 for the Kriging method, and 0.86 for RSM; showing a more reliable representation of experimental data by Kriging. Both models were used to perform single (maximizing FAME titer and temporal productivity separately) and multi-objective (maximizing FAME titer and temporal productivity simultaneously) optimizations of four important operating conditions (reaction time and temperature; amount of crude enzyme; and volume of methanol used). In all cases, the highest temperature considered (60°C) gave the best results. A reduction of reaction time in half was seen to be necessary to achieve optimum productivity compared to titer, when the two objectives were considered separately. The observed trade-off between the two objectives was quantified via multi-objective optimization using Pareto-front analysis.     

Weighting Nitrogen and Phosphorus Pixel Pollutant Loads to Represent Runoff and Buffering Likelihoods

Watershed models often estimate annual nitrogen (N) or phosphorus (P) pollutant loads in rural areas with export coefficient (EC) (kg/ha/yr) values based on land cover, and in urban areas as the product of spatially uniform event mean concentration (EMC) (mg/L) values and runoff volume. Actual N and P nonpoint source (NPS) pollutant loading has more spatial complexity due to watershed variation in runoff likelihood and buffering likelihood along surface and subsurface pathways, which can be represented in a contributing area dispersal area (CADA) NPS model. This research develops a CADA NPS model to simulate how watershed properties of elevation, land cover, and soils upslope and downslope of each watershed pixel influence nutrient loading. The model uses both surface and subsurface runoff indices (RI), and surface and subsurface buffer indices (BI), to quantify the runoff and buffering likelihood for each watershed pixel, and generate maps of weighted EC and EMC values that identify NPS pollutant loading hotspots. The research illustrates how CADA NPS model maps and pixel loading values are sensitive to the spatial resolution and accuracy of elevation and land cover data, and model predictions can represent the lower and upper bounds of NPS loading. The model provides managers with a tool to rapidly visualize, rank, and investigate likely areas of high nutrient export.     

An Object-Based Image Analysis of Pinyon and Juniper Woodlands Treated to Reduce Fuels

Mechanical and prescribed fire treatments are commonly used to reduce fuel loads and maintain or restore sagebrush steppe rangelands across the Great Basin where pinyon (Pinus) and juniper (Juniperus) trees are encroaching and infilling. Geospatial technologies, particularly remote sensing, could potentially be used in these ecosystems to (1) evaluate the longevity of fuel reduction treatments, (2) provide data for planning and designing future fuel-reduction treatments, and (3) assess the spatial distribution of horizontal fuel structure following fuel-reduction treatments. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for pinyon and juniper woodland plots where fuels were reduced by either prescribed fire, tree cutting, or mastication at five sites in Oregon, California, Nevada, and Utah. Imagery was taken with a Vexcel UltraCam X digital camera in June 2009. Within each treatment plot, ground cover was measured as part of the Sagebrush Steppe Treatment Evaluation Project. Trimble eCognition Developer was used to classify land cover classes using object-based image analysis (OBIA) techniques. Differences between cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within ±5 % of each other. The overall accuracy and the K _hat statistic for classified thematic maps for each treatment were: prescribed burn 85 % and 0.81; cut and fell 82 % and 0.77, and mastication 84 % and 0.80. Although cover assessments from OBIA differed somewhat from ground measurements, they are sufficiently accurate to evaluate treatment success and for supporting a broad range of management concerns.