ESTIMATION OF REGIONAL EVAPOTRANSPIRATION FROM ARID AND SEMI-ARID SURFACES1

ABSTRACT: Evapotranspiration (ET) from oasis and Gobi surfaces in the Heihe River region of China was estimated by Kotoda (1986) and Advection-Aridity (Brutsaert and Stricker, 1979) models. The ET estimates were compared with eddy-correlation ET estimates. The ET estimated by both models are in good agreement with ET obtained by means of eddy-correlation method for the oasis surface while underestimating ET as compared to the eddy-correlation method. For the Gobi surface, the models yielded obviously overestimates of evapotranspiration. In order to estimate evapotranspiration from arid surfaces, the Kotoda model was modified by introducing the surface moisture availability parameter a from Barton (1979). The modified Kotoda model yielded ET estimates that were very close to that from the eddy-correlation method for the Gobi surface. The modified Kotoda model was used to estimate evapotranspiration from the Heihe River watershed, an area with complicated topography and land use, and the results compared with those from a water balance method. A sensitivity analysis of the modified model was performed. The results show that the modified Kotoda model can reflect the relationship between the actual evapotranspiration and the main controlling factors on it for both wet and arid surfaces reasonably. From this study, it can be concluded that the modified Kotoda model is applicable fro the estimation of regional evapotranspiration from areas with complicated topography and land use.     

Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

SPATIAL DISTRIBUTION OF POINT SOIL MOISTURE ESTIMATES USING LANDSAT TM DATA AND FUZZY-C CLASSIFICATION1

ABSTRACT: Many hydrologic models have input data requirements that are difficult to satisfy for all but a few well-instrumented, experimental watersheds. In this study, point soil moisture in a mountain watershed with various types of vegetative cover was modeled using a generalized regression model. Information on sur-ficial characteristics of the watershed was obtained by applying fuzzy set theory to a database consisting of only satellite and a digital elevation model (DEM). The fuzzy-c algorithm separated the watershed into distinguishable classes and provided regression coefficients for each ground pixel. The regression model used the coefficients to estimate distributed soil moisture over the entire watershed. A soil moisture accounting model was used to resolve temporal differences between measurements at prototypical measurement sites and validation sites. The results were reasonably accurate for all classes in the watershed. The spatial distribution of soil moisture estimates corresponded accurately with soil moisture measurements at validation sites on the watershed. It was concluded that use of the regression model to distribute soil moisture from a specified number of points can be combined with satellite and DEM information to provide a reasonable estimation of the spatial distribution of soil moisture for a watershed.     

A REEVALUATION OF THE GROUND WATER BUDGET FOR LAS VEGAS VALLEY,NEVADA, WITH EMPHASIS ON GROUND WATER DISCHARGE1

ABSTRACT: An essential component to the ground water budget for the Las Vegas Valley (LVV) in southern Nevada is discharge from the ground water system. Discharge for the LW has been based on estimates made more than 50 years ago of 35,524,224 m3 per year as evapotranspiration (ET) and 0 m³ per year as subsurface outflow. Newly published values for recharge based on a more robust data set (70,308,360 m³) indicate a large imbalance associated with the earlier discharge estimates, providing the basis for the reevaluation conducted in this study. ET estimates in this study, as opposed to previous studies, were assigned a range in values that included an approach that assigned higher weight to the unique soil, plant, water, and climatic conditions that existed in predevelopment (1905) LW. The earlier discharge estimates also assumed that the basin was hydrologically closed; however, based on our evaluation, a range in yearly discharge by subsurface outflow from 1,480,176 m³ to 19,735,680 m³ could be assigned. Likewise, a range in yearly ET from 20,475,768 m³ to 78,819,372 m³ could be assigned. Based on newly published recharge values, closure can only occur if higher values are assigned to both the subsurface outflow and/or ET components of ground water discharge. We cannot provide a complete water balance closure with our ground water discharge estimate of 64,140,960 m³. However our reevaluation gives support to the higher recharge estimates and provides the rationale for future studies to be conducted based on a more rigorous scientific assessment.     

EVAPOTRANSPIRATION AND SOIL MOISTURE DYNAMICS ON A SEMIARID PONDEROSA PINE HILLSLOPE1

ABSTRACT: Detailed measurements of soil moisture and ET in semiarid forest environments have not been widely reported in the literature. In this study, soil moisture and water balance components were measured over a four‐year period on a semiarid ponderosa pine hillslope, with evapotranspiration (ET) determined as the residual of measured precipitation, runoff, and change in soil moisture storage. ET accounts for approximately 95 percent of the water budget and has a distinctly bimodal annual pattern, with peaks occurring after spring snowmelt and during the late summer monsoon season, periods that coincide with high soil moisture. Weekly growing season ET rates determined by the hillslope water balance are found to be invariably below calculated potential rates. Normalized ET rates are linearly correlated (r²= 0.62) with soil moisture; therefore, a simple linear relation is proposed. Growing season soil moisture dynamics were modeled based on this relation. Results are in fair agreement (r²= 0.63) with the observed soil moisture data over the four growing seasons; however, for two dry summers with little surface runoff, much better results (r² > 0.90) were obtained.     

PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

Global Daily Reference Evapotranspiration Modeling and Evaluation1

Abstract: Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro‐climatic locations. Since 2001, the National Oceanic and Atmospheric Administration’s Global Data Assimilation System (GDAS) has been producing six‐hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1‐degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station‐based reference ET estimates, we evaluated the GDAS‐based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS‐based reference ET at different spatial and temporal scales using five‐year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ～100 km grid cell) between the two datasets, the correlations between station‐based ET and GDAS‐ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter‐based reference ET for regional water and energy balance studies in many parts of the world. While the study revealed the potential of GDAS ETo for large‐scale hydrological applications, site‐specific use of GDAS ETo in complex hydro‐climatic regions such as coastal areas and rugged terrain may require the application of bias correction and/or disaggregation of the GDAS ETo using downscaling techniques.     

APPLICATION OF THE RHESSys MODEL TO A CALIFORNIA SEMIARID SHRUBLAND WATERSHED1

ABSTRACT: Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro‐Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKESHE, for a 34 km² watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes.     

EVAPOTRANSPIRATION CONCEPTUALIZATION IN THE HSPF‐MODFLOW INTEGRATED MODELS1

In 1988, the Florida Institute of Phosphate Research (FIPR) funded project to develop an advanced hydrologic model for shallow water table systems. The FIPR hydrologic model (FHM) was developed to provide an improved predictive capability of the interactions of surface water and ground water using its component models, HSPF and MODFLOW. The Integrated Surface and Ground Water (ISGW) model was developed from an early version of FHM and the two models were developed relatively independently in the late 1990s. Hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, and base flow are explicitly accounted for in both models. Considerable review of FHM and ISGW and their applications occurred through a series of projects. One model evolved, known as the Integrated Hydrological Model IHM. This model more appropriately describes hydrologic processes, including evapotranspiration fluxes within small distributed land‐based discretization. There is a significant departure of many IHM algorithms from FHM and ISGW, especially for soil water and evapotranspiration (ET). In this paper, the ET concepts in FHM, ISGW, and IHM will be presented. The paper also identifies the advantages and data costs of the improved methods. In FHM and IHM, ground water ET algorithms of the MODFLOW ET package replace those of HSPF (ISGW used a different model for ground water ET). However, IHM builds on an improved understanding and characterization of ET partitioning between surface storages, vadose zone storage, and saturated ground water storage. The IHM considers evaporative flux from surface sources, proximity of the water table to land surface, relative moisture condition of the unsaturated zone, thickness of the capillary zone, thickness of the root zone, and relative plant cover density. The improvements provide a smooth transition to satisfy ET demand between the vadose zone and deeper saturated ground water. While the IHM approach provides a more sound representation of the actual soil profile than FHM, and has shown promise at reproducing soil moisture and water table fluctuations as well as field measured ET rates, more rigorous testing is necessary to understand the robustness and/or limitations of this methodology.     

APPLICATION OF A HYDROLOGIC MODEL FOR LAND USE PLANNING IN FLORIDA1

ABSTRACT: An environmental simulation model of the Upper St. Johns River Basin, Florida, has been developed in order to predict hydrologic responses under proposed management plans. Land use projections for each of 19 hydrologic planning units are provided by a linear programming analysis of agricultural activities. Inputs to the model include rainfall, runoff, evapotranspiration (ET), aquifer properties, topography, soil types, and vegetative patterns. A water balance is developed in the uplands based on infiltration, ET, surface runoff, and groundwater flow. Valley continuity is based on stage-volume relationship for inflows and outflows and a variable roughness coefficient dependent on vegetative patterns. Land use changes form the basis for predicting hydroperiod variation under alternative management schemes. Plans are ranked according to two criteria, deviation from a natural hydroperiod and flood or drought control provided. Results indicate that (1) a single reservoir without irrigation and (2) floodplain preservation plans are superior to (3) multiple reservoir with irrigation and (4) uncontrolled floodplain plans with regard to both criteria.     

Annual Estimates of Recharge,Quick‐Flow Runoff,and Evapotranspiration for the Contiguous U.S. Using Empirical Regression Equations

This study presents new data‐driven, annual estimates of the division of precipitation into the recharge, quick‐flow runoff, and evapotranspiration (ET) water budget components for 2000‐2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick‐flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick‐flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick‐flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first‐order estimates of these quantities across the CONUS, even where field measurements are sparse.     

A REVISED VERSION OF PnET‐II TO SIMULATE THE HYDROLOGIC CYCLE IN SOUTHEASTERN FORESTED AREAS1

ABSTRACT: The PnET‐II model uses hydroclimatic data on maximum and minimum temperatures, precipitation, and solar radiation, together with vegetation and soil parameters, to produce estimates of net primary productivity, evapotranspiration (ET), and runoff on a monthly time step for forested areas. In this study, the PnET‐II model was employed to simulate the hydrologic cycle for 17 Southeastern eight‐digit hydrologic unit code (HUC) watersheds dominated by evergreen or deciduous tree species. Based on these control experiments, model biases were quantified and tentative revision schemes were introduced. Revisions included: (1) replacing the original single soil layer with three soil layers in the water balance routine; (2) introducing calibrating factors to rectify the phenomenon of overestimation of ET in spring and early summer months; (3) parameterizing proper values of growing degree days for trees located in different climate zones; and (4) adjusting the parameter of fast‐flow (overland flow) fraction based on antecedent moisture condition and precipitation intensity. The revised PnET‐II model, called PnET‐II3SL in this work, substantially improved runoff simulations for the 17 selected experimental sites, and therefore may offer a more powerful tool to address issues in water resources management.     

Spatial scaling between leaf area index maps of different resolutions

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.     

REGIONALIZATION OF LOW-FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y1

ABSTRACT: Low-flow estimates, as determined by probabilistic modeling of observed data sequences, are commonly used to describe certain streamflow characteristics. Unfortunately, however, reliable low-flow estimates can be difficult to come by, particularly for gaging sites with short record lengths. The shortness of records leads to uncertainties not only in the selection of a distribution for modeling purposes but also in the estimates of the parameters of a chosen model. In flood frequency analysis, the common approach to mitigation of some of these problems is through the regionalization of frequency behavior. The same general approach is applied here to the case of low-flow estimation, with the general intent of not only improving low-flow estimates but also illustrating the gains that might be attained in so doing. Data used for this study is that which has been systematically observed at 128 streamflow gaging sites across the State of Alabama. Our conclusions are that the log Pearson Type 3 distribution is a suitable candidate for modeling of Alabama low-flows, and that the shape parameter of that distribution can be estimated on a regional basis. Low-flow estimates based on the regional estimator are compared with estimates based on the use of only at-site estimation techniques.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

ROLE OF BASEFLOW IN RAINFALL-RUNOFF RELATIONS1

ABSTRACT. The role of initial baseflow, or the baseflow at the beginning of storm precipitation, in modifying mathematical rainfall-runoff relations is analyzed by using data from 95 storms over a drainage basin in Illinois. A regression model is set up with total runoff, surface runoff, baseflow runoff, and peak flow as dependent variables, and storm precipitation, initial baseflow, effective and total storm durations, and highest and lowest temperatures during the storm as independent variables. Stepwise regression analyses show that storm precipitation and initial baseflow are the most important variables for making dependent variable estimates. The standard error estimates using only storm precipitation and initial baseflow as predictors show a seasonal trend with a peak in July, August, or September. An understanding of the role of baseflow as an indicator of average soil moisture condition over the basin can be of great help in short-term reservoir regulation and flood warning.     

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.     

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.     

A Satellite Model of Forest Flammability

We describe a model of forest flammability, based on daily satellite observations, for national to regional applications. The model defines forest flammability as the percent moisture content of fuel, in the form of litter of varying sizes on the forest floor. The model uses formulas from the US Forest Service that describe moisture exchange between fuel and the surrounding air and precipitation. The model is driven by estimates of temperature, humidity, and precipitation from the moderate resolution imaging spectrometer and tropical rainfall measuring mission multi-satellite precipitation analysis. We provide model results for the southern Amazon and northern Chaco regions. We evaluate the model in a tropical forest-to-woodland gradient in lowland Bolivia. Results from the model are significantly correlated with those from the same model driven by field climate measurements. This model can be run in a near real-time mode, can be applied to other regions, and can be a cost-effective input to national fire management programs.