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.     

Evaluation of Reference Evapotranspiration Methods in Arid,Semiarid, and Humid Regions

It is often necessary to find a simpler method in different climatic regions to calculate reference crop evapotranspiration (ET_o) since the application of the FAO‐56 Penman‐Monteith method is often restricted due to the unavailability of a comprehensive weather dataset. Seven ET_o methods, namely the standard FAO‐56 Penman‐Monteith, the FAO‐24 Radiation, FAO‐24 Blaney Criddle, 1985 Hargreaves, Priestley‐Taylor, 1957 Makkink, and 1961 Turc, were applied to calculate monthly averages of daily ET_o, total annual ET_o, and daily ET_o in an arid region at Aksu, China, in a semiarid region at Tongchuan, China, and in a humid region at Starkville, Mississippi, United States. Comparisons were made between the FAO‐56 method and the other six simple alternative methods, using the index of agreement D, modeling efficiency (EF), and root mean square error (RMSE). For the monthly averages of daily ET_o, the values of D, EF, and RMSE ranged from 0.82 to 0.98, 0.55 to 0.98, and 0.23 to 1.00 mm/day, respectively. For the total annual ET_o, the values of D, EF, and RMSE ranged from 0.21 to 0.91, ?43.08 to 0.82, and 24.80 to 234.08 mm/year, respectively. For the daily ET_o, the values of D, EF, and RMSE ranged from 0.58 to 0.97, 0.57 to 0.97, and 0.30 to 1.06 mm/day, respectively. The results showed that the Priestly‐Taylor and 1985 Hargreaves methods worked best in the arid and semiarid regions, while the 1957 Makkink worked best in the humid region.     

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.     

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.     

OPTIMIZATION MODELING OF A NEW FACILITY FOR THE CALIFORNIA STATE WATER PROJECT1

ABSTRACT: A non-linear optimization model is applied to the California State Water Project (SWP) and portions of the Central Valley Project (CVP). The model accounts for the major hydrologic, regulatory, and operational features of both projects. The model maximizes long-term SWP yields over a 70-year period, using a quarterly time step. The potential for increased yield associated with a proposed facility improvement is evaluated with the model. The proposed facility is an extension of the Folsom-South Canal, which would allow water to be conveyed from the American River below Folsom Reservoir into New Melones Reservoir on the Stanislaus River or into the California Aqueduct. Model results indicate that extension of the Folsom-South Canal has the potential to increase SWP yields by 13 percent.     

THE POTENTIAL EFFECTS OF GLOBAL WARMING ON THE PRIMARY PRODUCTIVITY OF A SUBALPINE LAKE1

ABSTRACT Atmospheric scientists have predicted that large-scale climatic changes will result from increasing levels of tropospheric CO₂ We have investigated the potential effects of climate change on the primary productivity of Castle Lake, a mountain lake in Northern California. Annual algal productivity was modeled empirically using 25 years of limnological data in order to establish predictive relationships between productivity and the climatic variables of accumulated snow depth and precipitation. The outputs of monthly temperature and precipitation from three general circulation models (GCMs) of doubled atmospheric CO₂ were then used in the regression model to predict annual algal productivity. In all cases, the GCM scenarios predicted increased algal productivity for Castle Lake under cenditions of doubled atmospheric CO₂The primary cause of enhanced productivity was the increased length of the growing season resulting from earlier spring ice-out.     

Causes of decreased reference evapotranspiration and pan evaporation in the Jinghe River catchment, northern China

This study focused on the changes of reference evapotranspiration (ET₀) and pan evaporation (ET_pan) to study the impacts of climate change on the hydrological cycle in the Jinghe River catchment. Based on the Penman–Monteith equation, the ET₀ was calculated. The temporal trend and spatial distribution of ET₀ and E_pan measured with a 20-cm pan were examined at the 14 stations during 1957–2005. The effects of meteorological factors on the variation of ET₀ were determined by analyzing the trends in themselves with comparison between original climate and detrended climate scenarios and then their sensitivity to ET₀. Both the ET₀ and E_pan showed remarkable decreasing trends from 1957 to 2005 and their decreasing rate was 40.9 and 17.7 mm per 10 years, respectively. Trend analysis of meteorological factors exhibited that the reduction in ET₀ and ET_pan was principally caused by both significant decreases in wind speed and sunshine hours. Furthermore, the decreasing trend of ET₀ was mainly dominated by the significant decrease in wind speed with high sensitivity, to a less extent, by the decrease in net radiation. Although relative humidity is one of the most sensitive variables, its effect on ET₀ was negligible because of its temporal constancy. The contribution of wind speed reduction to decreased ET₀ has increased from 50 to 76.1%, but net radiation, by contrast, decreased from 50 to 23.9%.     

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.     

CONTROLLING GROUND WATER DEGRADATION IN THE TULARE LAKE BASIN1

ABSTRACT This study examined the feasibility of extending the Accelerated Salt Transport (ASTRAN) method of groud water quality control to a complex, closed basin which is experiencing ground water quality degradation from irrigated agriculture (e.g., the Tulare Lake Basin in the Southern portion of the California Central Valley). A linear programming model was constructed and parametric analysis conducted which produced results with a “general appraisal” (or “level B”) degree of accuracy. The study concluded that a drainage water export drain is required in order to implement a long-term solution but that ground water degradation can be mitigated by a combination of the ASTRAN method and other measures even with existing entitlements and legal constraints.     

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.     

Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley1

Saleh, Dina K., David L. Lorenz, and Joseph L. Domagalski, 2010. Comparison of Two Parametric Methods to Estimate Pesticide Mass Loads in California’s Central Valley. Journal of the American Water Resources Association (JAWRA) 00(0):1‐11. DOI: 10.1111/j.1752‐1688.2010.00506.x Abstract: Mass loadings were calculated for four pesticides in two watersheds with different land uses in the Central Valley, California, by using two parametric models: (1) the Seasonal Wave model (SeaWave), in which a pulse signal is used to describe the annual cycle of pesticide occurrence in a stream, and (2) the Sine Wave model, in which first‐order Fourier series sine and cosine terms are used to simulate seasonal mass loading patterns. The models were applied to data collected during water years 1997 through 2005. The pesticides modeled were carbaryl, diazinon, metolachlor, and molinate. Results from the two models show that the ability to capture seasonal variations in pesticide concentrations was affected by pesticide use patterns and the methods by which pesticides are transported to streams. Estimated seasonal loads compared well with results from previous studies for both models. Loads estimated by the two models did not differ significantly from each other, with the exceptions of carbaryl and molinate during the precipitation season, where loads were affected by application patterns and rainfall. However, in watersheds with variable and intermittent pesticide applications, the SeaWave model is more suitable for use on the basis of its robust capability of describing seasonal variation of pesticide concentrations.     

GIS‐BASED WATER QUALITY MODELING IN THE SANDUSKY WATERSHED,OHIO, USA1

ABSTRACT: This study focused on the Sandusky Watershed (SW) in Ohio, located within the Great Lakes Basin, with emphasis on two of its subwatersheds, namely Honey Creek (HC) and Rock Creek (RC). The goal was to assess the capabilities of the Soil and Water Assessment Tool (SWAT) to simulate suspended sediment (SS), phosphorus (P) and nitrogen (N) yield in the SW that contribute major sediment and nutrient loads into Lake Erie. The model was calibrated using water flow and water quality parameters for water years 1998 to 1999 and validated model simulations covering the period of water years 2000 to 2001 for monthly conditions. The validation of SS showed correlation coefficients of 0.29 (SW), 0.75 (HC) and 0.69 (RC). Correlation coefficients for P were 0.68 (SW), 0.78 (HC) and 0.37 (RC); for N0₂‐N 0.84 (HC) and 0.38 (RC); for N0₃‐N 0.27 (HC) and 0.76 (RC); for NH₃‐N 0.57 (SW), 0.49 (HC), and 0.13 (RC). In addition, mean errors, root mean square errors, Nash‐Sutcliffe coefficients, and graphs were used to compare simulated to measured data. Simulation success was variable with poor and good simulations, but in most cases, simulated water quality values followed the trend of measured data except for extreme (or intense) rainfall/runoff events. Reviews of 17 applications indicated that the SWAT is suitable for long term continuous simulations but not for storm events. A spatially distributed modeling approach generated maps showing the spatial distribution of SS, P, and N for each simulation element across the Sandusky Watershed.     

MODELED REGIONAL CLIMATE CHANGE IN THE HYDROLOGIC REGIONS OF CALIFORNIA: A CO2 SENSITIVITY STUDY1

ABSTRACT: Using a regional climate model (RegCM2.5), the potential impacts on the climate of California of increasing atmospheric CO₂ concentrations were explored from the perspective of the state's 10 hydrologic regions. Relative to preindustrial CO₂ conditions (280 ppm), doubled preindustrial CO₂ conditions (560 ppm) produced increased temperatures of up to 4°C on an annual average basis and of up to 5°C on a monthly basis. Temperature increases were greatest in the central and northern regions. On a monthly basis, the temperature response was greatest in February, March, and May for nearly all regions. Snow accumulation was significantly decreased in all months and regions, with the greatest reduction occurring in the Sacramento River region. Precipitation results indicate drier winters for all regions, with a large reduction in precipitation from December to April and a smaller decrease from May to November. The result is a wet season that is slightly reduced in length. Findings suggest that the total amount of water in the state will decrease, water needs will increase, and the timing of water availability will be greatly perturbed.     

SPARROW Modeling of Nitrogen Sources and Transport in Rivers and Streams of California and Adjacent States,U.S.

The SPARROW (SPAtially Referenced Regressions On Watershed attributes) model was used to evaluate the spatial distribution of total nitrogen (TN) sources, loads, watershed yields, and factors affecting transport and decay in the stream network of California and portions of adjacent states for the year 2002. The two major TN sources to local catchments on a mass basis were fertilizers and manure (51.7%) and wastewater discharge (15.9%). Other sources contributed < 12%. Fertilizer use is widespread in the Central Valley region of California, and also important in several other regions because of the diversity of California agriculture. Precipitation, sand content of surficial soils, wetlands, and tile drains were important for TN movement to stream reaches. Median streamflow in the study area is about 0.04 m³/s. Aquatic losses of nitrogen were found to be most important in intermittent and small to medium sized streams (0.2‐14 m³/s), while larger streams showed less loss, and therefore are important for TN transport. Nitrogen loss in reservoirs was found to be insignificant, possibly because most of the larger ones are located upstream of nitrogen sources. The model was used to show loadings, sources, and tributary inputs to several major rivers. The information provided by the SPARROW model is useful for determining both the major sources contributing nitrogen to streams and the specific tributaries that transport the load.     

Climatic Controls on Watershed Reference Evapotranspiration Varied during 1961–2012 in Southern China

Reference evapotranspiration (ET_o) is an important hydrometeorological term widely used in understanding and projecting the hydrological effects of future climate and land use change. We conducted a case study in the Qinhuai River Basin that is dominated by a humid subtropical climate and mixed land uses in southern China. Long‐term (1961–2012) meteorological data were used to estimate ET_o by the FAO‐56 Penman–Monteith model. The individual contribution from each meteorological variable to the trend of ET_o was quantified. We found basin‐wide annual ET_o decreased significantly (p < 0.05) by 3.82 mm/yr during 1961–1987, due to decreased wind speed, solar radiation, vapor pressure deficit (VPD), and increased relative humidity (RH). However, due to the increased VPD and decreased RH, the ET_o increased significantly (p < 0.05) in spring, autumn, and annually at a rate of 2.55, 0.56, and 3.16 mm/yr during 1988–2012, respectively. The aerodynamic term was a dominant factor controlling ET_o variation in both two periods. We concluded the key climatic controls on ET_o have shifted as a result of global climate change during 1961–2012. The atmospheric demand, instead of air temperature alone, was a major control on ET_o. Models for accurately predicting ET_o and hydrological change under a changing climate must include VPD in the study region. The shifts of climatic control on the hydrological cycles should be considered in future water resource management in humid regions.     

Calibration and Validation of ADAPT and SWAT for Field‐Scale Runoff Prediction1

Abstract: The pollutant reduction possible with a given agricultural best‐management practice (BMP) is complex and site‐specific. Water‐quality models can evaluate BMPs, but model results are often limited by the lack of calibrated parameters for a given BMP. This study calibrated runoff prediction of two models (ADAPT and SWAT) for individual field plots having one till and two no‐till management practices. The factors used for runoff calibration were curve number II (CN_II) and saturated hydraulic conductivity (Ksat) for ADAPT, and CN_II, Ksat, and available water capacity for SWAT. Results were evaluated using coefficient of determination (R²), Nash‐Sutcliffe efficiency (E_f), root‐mean square error, median‐based E_f, and sign tests. Results indicated that for ADAPT, the best‐fit CN_II was 66 for the NT/SB (no‐till plot with surface‐broadcast fertilizer) treatment, 68 for the NT/DB (no‐till with deep‐banded fertilizer) treatment, and 70 for the tilled plot, whereas for SWAT the best‐fit CN_II was much higher, 86, for all treatments. Neither agreed with the textbook CN_II, 78, for sorghum in silty clay loam soil. The best‐fit model parameters for both runoff calibration phases had excellent correlation to monthly totals and moderate correlation to individual events.     

The global solar radiation estimation and analysis of solar energy: Case study for Osmaniye,Turkey

This paper investigates the prediction of solar radiation model and actual solar energy in Osmaniye, Turkey. Four models were used to estimate using the parameters of sunshine duration and average temperature. In order to obtain the statistical performance analysis of models, the coefficient of determination (R²), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and root mean square error (RMSE) were used. Results obtained from the linear regression using the parameters of sunshine duration and average temperature showed a good prediction of the monthly average daily global solar radiation on a horizontal surface. In order to obtain solar energy, daily and monthly average solar radiation values were calculated from the five minute average recorded values by using meteorological measuring device. As a result of this measurement, the highest monthly and yearly mean solar radiation values were 698 (April in 2013) and 549 (2014 year) W/m² respectively. On an annual scale the maximum global solar radiation changes from 26.38 MJ/m²/day by June to 19.19 MJ/m²/day by September in 2013. Minimum global solar radiation changes from 14.05 MJ/m²/day by October to 7.20 MJ/m²/day by January in 2013. Yearly average energy potential during the measurement period was 16.53 MJ/m²/day (in 2013). The results show that Osmaniye has a considerable solar energy potential to produce electricity.     

Spatio-Temporal Variability of Soil Respiration of Forest Ecosystems in China: Influencing Factors and Evaluation Model

Understanding the influencing factors of the spatio-temporal variability of soil respiration (R _s) across different ecosystems as well as the evaluation model of R _s is critical to the accurate prediction of future changes in carbon exchange between ecosystems and the atmosphere. R _s data from 50 different forest ecosystems in China were summarized and the influences of environmental variables on the spatio-temporal variability of R _s were analyzed. The results showed that both the mean annual air temperature and precipitation were weakly correlated with annual R _s, but strongly with soil carbon turnover rate. R _s at a reference temperature of 0°C was only significantly and positively correlated with soil organic carbon (SOC) density at a depth of 20 cm. We tested a global-scale R _s model which predicted monthly mean R _s (R _s,monthly) from air temperature and precipitation. Both the original model and the reparameterized model poorly explained the monthly variability of R _s and failed to capture the inter-site variability of R _s. However, the residual of R _s,monthly was strongly correlated with SOC density. Thus, a modified empirical model (TPS model) was proposed, which included SOC density as an additional predictor of R _s. The TPS model explained monthly and inter-site variability of R _s for 56% and 25%, respectively. Moreover, the simulated annual R _s of TPS model was significantly correlated with the measured value. The TPS model driven by three variables easy to be obtained provides a new tool for R _s prediction, although a site-specific calibration is needed for using at a different region.