ESTIMATING AVERAGE MONTHLY LAKE EVAPORATION IN THE NORTHEAST UNITED STATES1

ABSTRACT: A methodology to estimate the average monthly lake evaporation, E(τ), (month τ=1,12) for fresh water bodies located in the northeast United States is presented. The approach combines analysis of at‐site, lake‐specific vertical water temperature profile data and a previously developed regional air temperature based model approximation of the widely accepted modified Penman energy budget estimate of mean monthly potential evaporation, E_p(τ) (mm/day). The paper presents procedures to develop site‐specific estimates of E_p(τ) and to convert water temperature data to average monthly conductive heat flux, G(τ). With monthly estimates of G(τ), the average monthly potential evaporation, E_p(τ), is then convertible to estimates of the average monthly lake evaporation, E(τ). This new method permits a good estimate of site‐specific lake evaporation rates without the data and computational requirements of the Penman energy budget procedure nor the comparatively expensive, time consuming field eddy correlation approach.     

Estimating turf pesticide volatilization from simple evapotranspiration models

A previously developed model by Haith et al. (2002) related pesticide volatilization from turf to evapotranspiration (ET) by scaling factors determined from vapor pressures and heats of vaporization. Although the model provided volatilization estimates that compared well with field measurements, it relied on the Penman ET equation, requiring hourly temperature, wind speed, and solar radiation data, none of which are routinely available at field sites. The current study determined that the volatilization model works equally well with a simpler ET equation requiring only daily temperatures. Three daily temperature-based ET models were evaluated as vehicles for estimating pesticide volatilization from turf: Hamon, Hargreaves-Samani, and a modified Priestley-Taylor. When compared with field volatilization measurements for eight pesticides, volatilization estimates produced from the Hargreaves-Samani model most closely approximated both the field observations and the previous estimates based on the more data-intensive Penman model. Mean estimated volatilization exceeded mean observations by 15% and the coefficient of variation (R2) between estimates and observations was 0.65. The comparable values based on Penman ET were 17% and 0.63, respectively.     

EVAPOTRANSPIRATION MEASUREMENT AND ESTIMATION OF THREE WETLAND ENVIRONMENTS IN THE UPPER ST. JOHNS RIVER BASIN,FLORIDA1

ABSTRACT: Accurate estimates of evapotranspiration from areas dominated by wetland vegetation are needed in the water budget of the Upper St. Johns River Basin. However, local data on evapotranspiration rates, especially in wetland environments, were lacking in the project area. In response to this need, the St. Johns River Water Management District collected evapotranspiration field data in Fort Drum Marsh Conservation Area over the period 1996 through 1999. Three large lysimeters were installed to measure the evapotranspiration from different wetland environments: sawgrass (Cladium jamaicense), cattail (Typha domingensis), and open water. In addition, pan evaporation was measured with a standard class “A” pan. Concurrently, meteorological data including rainfall, solar radiation, wind speed, relative humidity, air temperature, and atmospheric pressure were collected. By comparing computed evapotranspiration rates with those measured in the lysimeters, parameters in the Penman‐Monteith, the Priestley‐Taylor, and Reference‐ET methods, and evaporation pan coefficients were estimated for monthly and seasonal cycles. The results from the data collected in this study show that mean monthly evapotranspiration rates, computed by the different methods, are relatively close. From a practical point of view, results indicate that the evaporation pan can be used equally well as the more complex and data‐intensive methods. This paper presents the measured evapotranspiration rates, evaporation pan coefficients, and the estimated parameter values for three different methods to compute evapotranspiration in the project area. Since local data on evaporation are often scarce or lacking, this information may be useful to watershed hydrologists for practical application in other project regions.     

Error Autocorrelation and Linear Regression for Temperature‐Based Evapotranspiration Estimates Improvement1

Medeiros, Patrick Valverde, Francisco Fernando Noronha Marcuzzo, Cristián Youlton, and Edson Wendland, 2012. Error Autocorrelation and Linear Regression for Temperature‐Based Evapotranspiration Estimates Improvement. Journal of the American Water Resources Association (JAWRA) 48(2): 297‐305. DOI: 10.1111/j.1752‐1688.2011.00614.x Abstract: Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)‐Penman‐Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO‐Penman‐Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature‐based estimates by Camargo and Jensen‐Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.     

ESTIMATING REFERENCE EVAPORATION IN THE CENTRAL VALLEY OF CALIFORNIA USING THE LINACRE MODEL1

ABSTRACT: The Linacre (1988) model for calculating evaporation from open water or well-watered surfaces only requires inputs of air temperature, latitude and elevation, and windspeed if it is available. The model was developed using data collected at a large number of sites in different climatic regions of the world, while independent tests of the model have shown it to be suitable for estimating evaporation in a variety of locations. This study was intended to contribute to the broad goal of evaluating temperature-based evaporation models for use in California by testing the Linacre model in the agriculturally intensive Central Valley. Observed monthly mean reference evaporation (E_o) and meteorological data for periods ranging up to 72 months were obtained from 25 California Irrigation and Management Information System (CIMIS) stations distributed throughout the Central Valley. Uncalibrated and calibrated Linacre models were used to estimate monthly mean reference evaporation, and the performance of each model was evaluated using indices that quantified the random and systematic errors and overall model performance. The accuracy of the radiation and ventilation components of the model were evaluated separately. The uncalibrated model was found to systematically overestimate E_o with most of the model error being attributed to the ventilation component. Calibration of the radiation and ventilation components removed most of the systematic model errors, and the root mean square error for monthly mean E_o was 0.676 mm day^?1 (16.8 percent of the mean observed value). (KEY TERMS: reference evaporation; Linacre model; irrigation scheduling.)     

UNCERTAINTIES IN ESTIMATING THE WATER BALANCE OF LAKES1

ABSTRACT: Evaluation of hydrologic methodology used in a number of water balance studies of lakes in the United States shows that most of these studies calculate one or more terms of the budget as the residual. A literature review was made of studies in which the primary purpose was error analysis of hydrologic measurement and interpretation. Estimates of precipitation can have a wide range of error, depending on the gage placement, gage spacing, and areal averaging technique. Errors in measurement of individual storms can be as high as 75 percent. Errors in short term averages are commonly in the 15-30 percent range, but decrease to about 5 percent or less for annual estimates. Errors in estimates of evaporation can also vary widely depending on instrumentation and methodology. The energy budget is the most accurate method of calculating evaporation; errors are in the 10–15 percent range. If pans are used that are located a distance from the lake of interest, errors can be considerable. Annual pan-to-lake coefficients should not be used for monthly estimates of evaporation because they differ from the commonly used coefficient of 0.7 by more than 100 percent. Errors in estimates of stream discharge are often considered to be within 5 percent. If the measuring section, type of flow profile, and other considerations, such as stage discharge relationship, are less than ideal errors in estimates of stream discharge can be considerably greater than 5 percent. Errors in estimating overland (nonchannelized) flow have not been evaluated, and in most lake studies this component is not mentioned. Comparison of several lake water balances in which the risdual consists solely of errors in measurement, shows that such a residual, if interpreted as ground water, can differ from an independent estimate of ground water by more than 100 percent.     

DEFINING WATERSHED-SCALE EVAPORATION USING A NORMALIZED DIFFERENCE VEGETATION INDEX1

Monthly composites of the Normalized Difference Vegetation Indices (NDVI), derived from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVILRR), were transformed linearly into monthly evaporation rates and compared with detailed hydrologic-model simulation results for five watersheds across the United States. Model-simulated monthly evaporation values showed high correlations (mean R²= .77) with NDVI-derived evaporation estimates. These latter estimates, used in a classical water balance model, resulted in equally accurate simulations of monthly runoff than when the model was run to estimate monthly evaporation via soil moisture accounting. Comparison of NDVI-derived evaporation estimates with pan data showed promise for transforming NDVI values into evaporation estimates under both wet and water-limiting conditions without resorting to the application of any kind of calibrated hydrologic models.     

Mechanistic Simulation of Tree Effects in an Urban Water Balance Model1

Abstract: A semidistributed, physical‐based Urban Forest Effects – Hydrology (UFORE‐Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre‐processed by UFORE using Penman‐Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green‐Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL‐based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model. Once calibrated, the model can examine how alternative tree management schemes impact urban runoff. UFORE‐Hydro model testing in the urban Dead Run catchment of Baltimore, Maryland, illustrated how trees significantly reduce runoff for low intensity and short duration precipitation events.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

Loss Rates from Lake Powell and Their Impact on Management of the Colorado River

As demand for water in the southwestern United States increases and climate change potentially decreases the natural flows in the Colorado River system, there will be increased need to optimize the water supply. Lake Powell is a large reservoir with potentially high loss rates to bank storage and evaporation. Bank storage is estimated as a residual in the reservoir water balance. Estimates of local inflow contribute uncertainty to estimates of bank storage. Regression analyses of local inflow with gaged tributaries have improved the estimate of local inflow. Using a stochastic estimate of local inflow based on the standard error of the regression estimator and of gross evaporation based on observed variability at Lake Mead, a reservoir water balance was used to estimate that more than 14.8 billion cubic meters (Gm³) has been stored in the banks, with a 90% probability that the value is actually between 11.8 and 18.5 Gm³. Groundwater models developed by others, observed groundwater levels, and simple transmissivity calculations confirm these bank storage estimates. Assuming a constant bank storage fraction for simulations of the future may cause managers to underestimate the actual losses from the reservoir. Updated management regimes which account more accurately for bank storage and evaporation could save water that will otherwise be lost to the banks or evaporation.     

Assessing Uncertainty in Estimates of Nitrogen Loading to Estuaries for Research, Planning, and Risk Assessment

Parametric (propagation for normal error estimates) and nonparametric methods (bootstrap and enumeration of combinations) to assess the uncertainty in calculated rates of nitrogen loading were compared, based on the propagation of uncertainty observed in the variables used in the calculation. In addition, since such calculations are often based on literature surveys rather than random replicate measurements for the site in question, error propagation was also compared using the uncertainty of the sampled population (e.g., standard deviation) as well as the uncertainty of the mean (e.g., standard error of the mean). Calculations for the predicted nitrogen loading to a shallow estuary (Waquoit Bay, MA) were used as an example. The previously estimated mean loading from the watershed (5,400 ha) to Waquoit Bay (600 ha) was 23,000 kg N yr⁻¹. The mode of a nonparametric estimate of the probability distribution differed dramatically, equaling only 70% of this mean. Repeated observations were available for only 8 of the 16 variables used in our calculation. We estimated uncertainty in model predictions by treating these as sample replicates. Parametric and nonparametric estimates of the standard error of the mean loading rate were 12–14%. However, since the available data include site-to-site variability, as is often the case, standard error may be an inappropriate measure of confidence. The standard deviations were around 38% of the loading rate. Further, 95% confidence intervals differed between the nonparametric and parametric methods, with those of the nonparametric method arranged asymmetrically around the predicted loading rate. The disparity in magnitude and symmetry of calculated confidence limits argue for careful consideration of the nature of the uncertainty of variables used in chained calculations. This analysis also suggests that a nonparametric method of calculating loading rates using most frequently observed values for variables used in loading calculations may be more appropriate than using mean values. These findings reinforce the importance of including assessment of uncertainty when evaluating nutrient loading rates in research and planning. Risk assessment, which may need to consider relative probability of extreme events in worst-case scenarios, will be in serious error using normal estimates, or even the nonparametric bootstrap. A method such as our enumeration of combinations produces a more reliable distribution of risk.     

Modeling pesticide volatilization from turf

Pesticide volatilization models are typically based on equilibrium partitioning of the chemical into solid, liquid, and gaseous phases in the soil environment. In turf systems direct vaporization from vegetation surfaces is a more likely source, and it is difficult to apply equilibrium methods to plant material due to the uncertainties of solid-liquid-gas partitioning. An alternative approach is to assume that pesticide volatilization is governed by the same processes that affect water evaporation. A model was developed in which evapotranspiration values, as determined by the Penman equation, were adjusted to chemical vaporization using ratios of water and chemical saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was tested by comparisons of predictions with measurements of volatilization for eight pesticides measured during 3 to 7 d in 11 field experiments. Measured volatilization fluxes ranged from 0.1 to 22% of applied chemical. Pesticides were divided into two groups based on saturated vapor pressures and organic C partition coefficients. One pesticide was selected from each group to calibrate the model's volatilization constant for the group, and the remaining pesticides were used for model testing. Testing results indicated that the model provides relatively conservative estimates of pesticide volatilization. Predicted mean losses exceeded observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses.     

Estimating Crop Water Use via Remote Sensing Techniques vs. Conventional Methods in the South Platte River Basin,Colorado

We compared two methods of estimating crop water consumption to assess whether remote sensing techniques provide consumptive use (CU) estimates commensurate with conventional methods. Using available historical satellite and meteorological data, we applied Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC) to 317,455 ha in the South Platte basin, in northeastern Colorado, for the 2001 irrigation season. We then compared these derived CU estimates with values calculated by using the Colorado Water Conservation Board's South Platte Decision Support System StateCU model. Evaluating the data by irrigation ditch service area, we disaggregated the output to allow for comparison by service area size, crop type, irrigation method, water supply source, and water availability. We concluded that METRIC is a suitable alternative to StateCU in the South Platte basin and could help to identify areas with inhibited crop growth or deficit irrigation practices. In addition, METRIC could be used as a complement to StateCU to refine StateCU model parameters, allowing for more accurate estimates of crop water shortages and groundwater recharge associated with irrigation delivery and application.     

CLIMATOLOGICAL WATERSHED CALIBRATION1

ABSTRACT: This study tests the hypothesis that climatic data can be used to develop a watershed model so that stream flow changes following forest harvest can be determined. Measured independent variables were precipitation, daily maximum and minimum temperature, and concurrent relative humidity. Computed variables were humidity deficit, saturated vapor pressure, and ambient vapor pressure. These climatic variables were combined to compute a monthly evaporation index. Finally, the evaporation index and monthly precipitation were regressed with measured monthly stream flow and the monthly estimates of stream flow were combined for the hydrologic year. A regression of predicted versus measured annual stream flow had a standard error of 1.5 inches (within 6.1 percent of the measured value). When 10, 15, and 20 years of data were used to develop the regression equations, predicted minus measured stream flow for the last 7 years of record (1972–1978) were within 16.8, 11.5, and 9.7 percent of the measured mean, respectively. Although single watershed calibration can be used in special conditions, the paired watershed approach is expected to remain the preferred method for determining the effects of forest management on the water resource.     

INTERPLAY BETWEEN PARAMETER UNCERTAINTY AND MODEL AGGREGATION ERROR1

ABSTRACTS: Modeling error can be divided into two basic components: use of an incorrect model and input parameter uncertainty. Incorrect model usage can be further subdivided into inappropriate model selection and inherent modeling error due to process aggregation. Total modeling error is a culmination of these various modeling error components, with overall optimization requiring reductions in all. A technique, utilizing Monte Carlo analysis, is employed to investigate the relative importance of input parameter uncertainty versus process aggregation error. An expanded form of the Streeter-Phelps dissolved oxygen equation is used to demonstrate the application of this technique. A variety of scenarios are analyzed to illustrate the relative obfuscation of each modeling error component. Under certain circumstances an aggregated model performs better than a more complex model, which perfectly simulates the real system. Alternately, process aggregation error dominates total modeling error for other situations. The ability to differentiate modeling error impact is a function of the desired or imposed model performance level (accuracy tolerance).     

LAKE EVAPORATION STUDIES USING SATELLITE THERMAL INFRARED DATA1

Thermal infrared radiation data were acquired by the Heat Capacity Mapping Mission (HCMM) satellite over the surface area (385 km²) of Utah Lake during periodic overpasses in 1978 and 1979. The thermal infrared data were converted to lake surface temperatures which were subsequently used in correlations with lake evaporation. Correlations between HCMM surface temperature and pan-derived evaporation exceeded r = 0.90 when HCMM night and day/night average temperatures and two-day average evaporation values were tested. Similar regression studies were done using monthly data from a conceptual evaporation model and the evaporation pan versus monthly HCMM temperature data. In this test both the HCMM day and night monthly temperature versus the monthly model or pan evaporation had correlations exceeding r = 0.95. Empirical estimates of both short and long term lake evaporation using satellite thermal infrared data seem feasible. Attempts to use the HCMM thermal information as direct input to a theoretical approach to calculating evaporation were inconclusive; however, a definite potential seems to exist.     

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).     

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.     

HYDROLOGIC BUDGET FOR A FRESHWATER MARSH IN FLORIDA1

ABSTRACT: Accurate water balance calculations are essential for water resource and environmental management decisions, but many of the terms used in the equation are difficult to measure. In this study, a method for measuring rates of evapotranspiration and net seepage from a freshwater marsh in southwest Florida is described. The results are compared to evaporation pan estimates as well as to calculations that balanced all the terms in the hydrologic budget. The measured rates of evapotranspiration showed a. distinct seasonal trend ranging from an average high of 0.24 in/d during July 1992 to a low of 0.06 in/d in January 1993. Evapotranspiration rates were higher than Class A evaporation pan measurements during July and August, indicating transpiration by plants exceeded evaporation by pans. Net ground water seepage flowed out of the marsh except during periods of high water table conditions. When all terms in the hydrologic budget were evaluated, the equation balanced on a yearly basis with an error of 2 percent, on a seasonal basis with errors less than 7 percent, but on a monthly basis errors were as great as 30 percent. Total annual rainfall on the marsh was 45 percent of the total marsh hydrologic input and was approximately equal to the loss by evapotranspiration of 41 percent.     

EVAPOTRANSPIRATION MEASUREMENTS AND MODELING FOR THREE WETLAND SYSTEMS IN SOUTH FLORIDA1

ABSTRACT: At the Everglades Nutrient Removal project in south Florida, three lysimeters were installed to measure daily evapotranspiration (ET) rates from cattails (Typha domingensis), mixed marsh vegetation, and an open water/algae system. The cattail lysimeter began operation in February 1993. The mixed marsh vegetation lysimeter began operation in January 1994, and the open water lysimeter with occasional algae cover began operation in December 1993. The mean measured ET rate was 3.6 mm, 3.5 mm, and 3.7 mm per day for the cattail, mixed marsh vegetation, and open water/algae system, respectively. High resolution weather data were continuously measured at the site. Six models were applied to estimate daily ET rates of the three systems. The Penman-Monteith equation best estimated ET of cattail and mixed marsh vegetation, and the Penman Combination equation was most suitable for the open water/algae system. Empirical equations based on solar radiation and maximum temperature produced estimates of daily ET from the three systems that are comparable to models that require many more parameters. In cases where limited data is available, the calibrated simple models can be used to estimate ET from wetlands in south Florida.