Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape.<p> Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori.<p> The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series.

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape.<p> Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori.<p> The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series.

<p>A large portion of the anthropogenic emissions of greenhouse gases (<italic>GHG</italic>s) are cycled through the terrestrial biosphere. Quantifying the exchange of these gases between the terrestrial biosphere and the atmosphere is critical to constraining their atmospheric budgets now and in the future. These fluxes are governed by biophysical processes like photosynthesis, transpiration, and microbial respiratory processes which are driven by factors like meteorology, disturbance regimes, and long term climate and land cover change. These complex processes occur over a broad range of temporal (seconds to decades) and spatial (millimeters to kilometers) scales, necessitating the application of simplifying models to forecast fluxes at the scales required by climate mitigation and adaptation policymakers. </p><p>Over the long history of biophysical research, much progress has been made towards developing appropriate models for the biosphere-atmosphere exchange of <italic>GHG</italic>s. Many processes are well represented in model frameworks, particularly at the leaf scale. However, some processes remain poorly understood, and models do not perform robustly over coarse spatial scales and long time frames. Indeed, model uncertainty is a major contributor to difficulties in constraining the atmospheric budgets of greenhouse gases. </p><p>The central objective of this dissertation is to reduce uncertainty in the quantification and forecasting of the biosphere-atmosphere exchange of greenhouse gases by addressing a diverse array of research questions through a combination of five unique field experiments and modeling exercises. In this first chapter, nocturnal evapotranspiration -- a physiological process which had been largely ignored until recent years -- is quantified and modeled in three unique ecosystems co-located in central North Carolina, U.S.A. In the second chapter, more long-term drivers of evapotranspiration are explored by developing and testing theoretical relationships between plant water use and hydraulic architecture that may be readily incorporated into terrestrial ecosystem models. The third chapter builds on this work by linking key parameters of carbon assimilation models to structural and climatic indices that are well-specified over much of the land surface in an effort to improve model parameterization schemes. The fourth chapter directly addresses questions about the interaction between physiological carbon cycling and disturbance regimes in current and future climates, which are generally poorly represented in terrestrial ecosystem models. And the last chapter explores effluxes of methane and nitrous oxide (which are historically understudied) in addition to CO₂ exchange in a large temperate wetland ecosystem (which is an historically understudied biome). While these five case studies are somewhat distinct investigations, they all: a) are all grounded in the principles of biophysics, b) rely on similar measurement and mathematical modeling techniques, and c) are conducted under the governing objective of reducing measurement and model uncertainty in the biosphere-atmosphere exchange of greenhouse gases.</p> / Dissertation

Saltcedar is an invasive shrub that has moved into western United States riparian areas and is continuing to spread. Saltcedar is a phreatophyte that can utilize a saturated water table for moisture once established and is also highly tolerant of saline soil and water conditions. Literature has indicated that depth to the water table and salinity have a significant effect on growth and water use by saltcedar. Several studies were initiated to help develop a simulation model of saltcedar growth and water use based on the EPIC9200 simulation model. A study was initiated at the USDA-ARS Blackland Research Center Temple, Texas in the summer of 2002 to better understand the effects of water table depth and salinity on (1) saltcedar above and below ground biomass, root distribution, leaf area and (2) water use. Five different salinity levels (ranging from 0 ppm to 7500 ppm) and three different water table depths (0.5m, 1.0m, and 1.75m) were studied. Results indicated that increasing depth to the water table decreased saltcedar water use and growth. For the 0.5m water table depth, saltcedar water use during the 2002 growing season averaged 92.7 ml d-1 while the 1.75m depth averaged 56.6 ml d-1. Both root and shoot growth were depressed by increasing water table depth. Salinity had no effect on saltcedar growth or water use except at the 1250 ppm level, which used 110 ml of H2O d-1. This salinity had the highest water use indicating that this may be near the ecological optimum level of salinity for saltcedar. A predictive equation was developed for saltcedar water use using climatic data for that day, the previous day's climatic data, water table depth and salinity that included: previous day total amount of solar radiation, water table depth, previous day average wind speed, salinity, previous day total precipitation, previous day average vapor pressure, minimum relative humidity, previous day average wind direction, and maximum air temperature. Data from the field study and a potential growth study were integrated into the model. The model was parameterized for the Pecos River near Mentone, Texas. Predicted saltcedar water use was slightly lower than results reported by White et al. 2003.

Ground-water conductivity data were obtained from shallow wells in a 12 km2 stream-basin along a 400 m transect, extending from the divide to the stream. The stream, Pringle Branch, is a second-order perennial stream in Hillsborough County, Florida. The shallow stratigraphy consists of 2-3 m of fine sand over a layer of clayey silt and silty clay. Vegetation cover includes grasses on the upper and middle slope, and riparian woodlands on the foot slope and floodplain. Precipitation is about 1.3 m per year. Shallow ground-water conductivity is about 50 uS/cm at the divide. It increases moderately along the mid slope, then increases markedly within the riparian woodlands, reaching a maximum of about 500 uS/cm at 30m from the stream and then decreases to about 150 uS/cm at the stream. The spatial variation of terrain electrical conductivity data collected using electromagnetic methods (EM 31) is similar to the spatial variation of ground-water conductivity.Dry season through wet season monitoring shows that ground-water conductivity in each well varies about 40%, generally following variations in potential evapotranspiration (ETpan). The more than five-fold increase in ground-water conductivity from divide to riparian woodlands is maintained during both dry and wet seasons. The ground-water conductivity in this basin appears to be determined principally by spatial variations in ET and not by temporal variations in ET or interaction with soil minerals. The data suggest that patterns of ground-water conductivity can be used to infer patterns of ET variation within a small basin. A mass transport model constructed to test the hypothesis that evapotranspiration has the dominant effect on ground-water conductivity closely duplicates the observed variation in ground-water conductivity from divide to stream. The model uses two evaporation rates, 0.73 m/y for the grasses and 1.46 m/y for the riparian woodlands, and no contribution from solution of matrix materials.

This dissertation focuses on a variety of vadose zone processes that impact water table fluctuations. The development of vadose zone process conceptualization has been limited due to both the lack of recognition of the importance of the vadose zone and the absence of suitable field data. Recent studies have, however, shown that vadose zone soil moisture dynamics, especially in shallow water table environments, can have a significant effect on processes such as infiltration, recharge to the water table, and evapotranspiration. This dissertation, hence, attempts to elucidate approaches for modeling vadose zone soil moisture dynamics. The ultimate objective is to predict different vertical and horizontal hydrological fluxes. The first part of the dissertation demonstrates a new methodology using soil moisture and water table data collected along a flow transect. The methodology was found to be successful in the estimation of hydrological fluxes such as evapotranspiration, infiltration, runoff, etc. The observed dataset was also used to verify an exponential model developed to quantify the ground water component of total evapotranspiration. This analysis was followed by a study which analyzed the impact of soil moisture variability in the vadose zone on water table fluctuations. It was found that antecedent soil moisture conditions in the vadose zone greatly affected the specific yield values, causing a broad range of water table fluctuations for similar boundary fluxes. Hence, use of a constant specific yield value can produce inaccurate results. Having gained insight into the process of evapotranspiration and specific yield, a threshold based model to determine evapotranspiration and subsequent water table fluctuation was conceptualized and validated. A discussion of plant root water uptake and its impact on vadose zone soil moisture dynamics is presented in the latter half of this dissertation. A methodology utilizing soil moisture and water table data to determine the root water uptake from different sections of roots is also described. It was found that, unlike traditional empirical root water uptake models, the uptake was not only proportional to the root fraction, but was also dependent on the ambient soil moisture conditions. A modeling framework based on root hydraulic characteristics is provided as well. Lastly, a preliminary analysis of observed data indicated that, under certain field conditions, air entrapment and air pressurization can significantly affect the observed water table values. A modeling technique must be developed to correct such observations.

Western riparian zones have undergone significant landscape changes over the past several decades, with introduced saltcedar (Tamarix spp.) as a crucial component of this transformation. Saltcedar, now a dominating presence along many western rivers, due to its high tolerance to drought, salinity and stress, is considered to be a high-water-use plant that can desiccate disturbed river systems. Where native and saltcedar plant communities occur together, it is important to understand water use patterns and the physiological responses of each species to environmental stress factors, as a way to project an eventual course of succession processes and management options at a given site. Stress and disturbance in the form of reduced stream flows and land use changes may influence these interactions. Understanding the conditions that allow for saltcedar dominance is critical in determining riparian water budgets, and developing effective management strategies. Sap flux sensors were used to measure the physiological response of co-occurring communities of saltcedar and native trees to these environmental stress factors during the pre-monsoon period in early summer, a time of maximum stress for riparian vegetation. The results suggest that native trees are still competitive with salt cedar so that a mixed plant community is likely to continue on the San Pedro River on the condition that current groundwater levels and river flows are maintained. If base flows and depth to groundwater continue to decline, this competitive balance between saltcedar and native trees likely could change.

Three experiments were conducted to examine restoration of disturbed land in Arizona. The first experiment attempted to revegetate abandoned farmland by direct seeding native seeds and using various soil preparation techniques, amendments, and weeding of Salsola iberica. Only irrigation and weeding had a significant effect on seed germination and canopy cover. Irrigation increased plant cover on plots, but weeds dominated the cover. A seedbank study conducted near the end of the second growing season found the soil was dominated by weeds and contained few viable native seeds. The results illustrate the difficulty of establishing native plants on abandoned desert farmland due to the dominance of weedy species, the presence of salts in the soil, and the lack of adequate soil moisture.The second experiment, a lysimeter study, tested the efficacy of different evapotranspiration (ET) soil cover designs for stabilization of acidic copper mine tailing piles. The study evaluated the effectiveness of capillary barriers (CB) to contain the waste found in tailings and different plants to revegetate the piles. The ET covers reduced infiltration of water into tailings. Copper concentrations increased significantly in plant tissue grown on the ET covers compared to plants grown in the greenhouse. Plants did not exhibit signs of phytotoxicity and concentrations were below levels toxic to all domestic animals except sheep. The CB did not reduce water infiltration into the tailings or upward migration of copper into the soil cover. Vegetation is vital to an effective ET cover. A mix of transplanted shrubs and seeded grasses and forbs establish long-term, sustainable vegetation.The third experiment examined the influence of biosolids on the bacterial communities within mine tailings by bacterial counts and bacterial diversity. The diversity of neutral copper mine tailings two weeks after biosolid application was compared with that of desert soil via cloning and sequencing of PCR amplified community 16S rRNA. Culturable heterotrophic plate counts (HPC) increased following biosolid addition. Total direct counts exceeded HPC by approximately two orders of magnitude. Overall, biosolid-amended tailings contained large numbers of bacteria diverse in nature and with many of the traits of normal desert soil bacterial communities.

The high population growth rate and rapid urbanization that the world is experiencing today has aggravated the competition for the already scarce resource ¡V water ¡V between the agricultural sector and the other economic sectors. Moreover, within the agricultural sector, water is increasingly being used for commercial plantations as opposed to growing food crops, threatening food security. Therefore, it is very important that this scarce resource is managed in an efficient and sustainable manner, for now and future use. This requires understanding the process of evaporation for accurate determination of water-use from agricultural lands. In the past, direct measurements of evaporation have proven difficult because of the cost and complexity of the available equipments, and level of expertise involved. This justifies a quest for relatively simple, accurate and inexpensive methods of determining evaporation for routine field applications. Estimation of sensible heat flux (H) from high frequency air temperature measurements and then calculating latent energy flux (ƒÜE) and hence evaporation as a residual of the shortened surface energy balance equation, assuming that closure is met, is appealing in this sense. Concurrent net irradiance (Rn) and soil heat flux (G) measurements can be conducted with relative ease for use in the energy balance equation. Alternately, evaporation can also be mathematically modelled, using single- or multi-layer models depending on vegetation cover, from less expensive routine meteorological observations. Therefore, the ultimate objective of this study is to estimate and model H and ƒÜE, and thereby evaporation, accurately over sparsely vegetated agricultural lands at low cost and effort. Temperature-variance (TV) and surface renewal (SR) methods, which use high-frequency (typically 2 to 10 Hz) air temperature measurements, are employed for estimation of H. The TV method is based on the Monin and Obukhov Similarity Theory (MOST) and uses statistical measures of the high frequency air temperature to estimate H, including adjustments for stability. The SR method is based on the principle that an air parcel near the surface is renewed by an air parcel from above and, to determine H, it uses higher order air temperature differences between two consecutive sample measurements lagged by a certain time interval. Single- and double-layer models that are based on energy and resistance combination theory were also used to estimate evaporation and H from sparse vegetation. Single- and double-layer models that were extended to include inputs of radiometric temperature in order to estimate H were also used. The transmission of solar irradiance to the soil beneath in sparse canopies is variable and depends on the vegetation density, cover and apparent position of the sun. A three-dimensional radiation interception model was developed to estimate this transmission of solar irradiance and was used as a sub-module in the double-layer models. Estimations of H from the TV (HTV), SR (HSR) and double-layer models were compared against H obtained from eddy covariance (HEC), and the modelled ƒÜE (single- and double-layer) were compared with that obtained from the shortened energy balance involving HEC. Besides, long-term ƒÜE calculated from the shortened energy balance using HTV and HSR were compared with those calculated using HEC. Unshielded and naturally-ventilated fine-wire chromel-constantan thermocouples (TCs), 75 ƒÝm in diameter, at different heights above the ground over sparse Jatropha curcas trees, mixed grassland community and bare fallow land were used to measure air temperature. A three-dimensional sonic anemometer mounted at a certain height above the ground surface was also used to measure virtual temperature and wind speed at all three sites. All measurements were done differentially at 10-Hz frequency. Additional measurements of Rn, G and soil water content (upper 60 mm) were also made. The Jatropha trees were planted in a 3-m plant and inter-row spacing in a 50 m ¡Ñ 60 m plot with the surrounding plots planted to a mixture of Jatropha trees and Kikuyu grass. Average tree height and leaf area index measurements were taken on monthly and bimonthly basis respectively. An automatic weather station about 10 m away from the edge of the Jatropha plot was also used to obtain solar irradiance, air temperature and relative humidity, wind speed and direction and precipitation data. Soil water content was measured to a depth of 1000 mm from the surface at 200 mm intervals. Soil and foliage surface temperatures were measured using two nadir-looking infrared thermometers with one mounted directly above bare soil and the other above the trees. The three-dimensional solar irradiance interception model was validated using measurements conducted on different trees and planting patterns. Solar irradiance above and below tree canopies was measured using LI-200 pyranometer and tube solarimeters respectively. Leaf area density (LAD) was estimated from LAI, canopy shape and volume measurements. It was also determined by scanning leaves using either destructive sampling or tracing method. The performance of the TV method over sparse vegetation of J. curcas, mixed grassland community and fallow land was evaluated against HEC. Atmospheric stability conditions were identified using (i) sensor height (z) and Obukhov length (L) obtained from EC and (ii) air temperature difference between two thermocouple measurement heights. The HTV estimations, adjusted and not adjusted for skewness (actual and estimated) of air temperature (sk), for unstable conditions only and for all stability conditions were used. An improved agreement in terms of slope, coefficient of determination (r2) and root mean square error (RMSE), almost over all surfaces, was obtained when the temperature difference rather than the z/L means of identifying stability conditions was used. The agreement between the HTV and HEC was improved for estimations adjusted for actual sk than not adjusted for sk. Improved agreement was also noted when HTV was adjusted using estimated sk compared to not adjusting for sk over J. curcas. The TV method could be used to estimate H for surfaces with varying homogeneity with reasonable accuracy. Long-term water-use of a fetch-limited sparse vegetation of J. curcas was determined as a residual of the shortened surface energy balance involving HTV and HSR and compared with those estimated using HEC. Concurrent measurements of Rn and G were also performed. The long-term water-use of J. curcas trees calculated from the shortened surface energy balance involving HTV and HSR agreed very well when compared with those obtained from HEC. The seasonal HTV and HSR also agreed very well when compared with HEC. Changes in structure of the canopy and environmental conditions appeared to influence partitioning of the available energy into H and ƒÜE. The seasonal total evaporation for the EC, TV and SR methods amounted to 626, 640 and 674 mm respectively with a total rainfall of 690 mm. Footprint analysis also revealed that greater than 80% of the measured flux during the day originates from within the surface of interest. The TV and SR methods, therefore, offer a relatively low-cost means for long-term estimation of H, and ƒÜE, hence the total evaporation, using the shortened surface energy balance along with measurements of Rn and G. Evaporation and biomass production estimations from tree crops requires accurate representation of solar irradiance transmission through the canopy. A relatively simple three-dimensional, hourly time-step tree-canopy radiation interception model was developed and validated using measurements conducted on isolated trees, hedgerows and tree canopies arranged in tramline mode. Measurements were obtained using tube solarimeters placed 0.5 m from each other starting from the base of a tree trunk in four directions, along and perpendicular to the row up to mid-way between trees and rows. Model-simulations of hourly radiant transmittance were in good agreement with measurements with an overall r2 of 0.91; Willmott.s index of agreement of 0.96; and general absolute standard deviation of 17.66%. Agreement between model-estimations and measurements, however, was influenced by distance and direction of the node from the tree trunk, sky conditions, symmetry of the canopy, and uniformity of the stand and leaf distribution of the canopy. The model could be useful in planning and management applications for a wide range of tree crops. Penman-Monteith (PM) equation and the Shuttleworth and Wallace (SW) model, representing single- and dual-source models respectively, were used to determine the total evaporation over a sparse vegetation of J. curcas from routine automatic weather station observations, resistance parameters and vegetation indices. The three-dimensional solar irradiance interception model was used as a sub-module in the SW model. The total evaporation from the sparse vegetation was also determined as a residual of the shortened surface energy balance using measurements of Rn, G and HEC. The PM equation failed to reproduce the .measured. daily total evaporation during periods of low LAI, with improved agreement with increased LAI. The SW model, however, produced total evaporation estimates that agreed very well with the .measured. with a slope of 0.96, r2 of 0.91 and RMSE of 0.45 mm for a LAI ranging from 0 (no leaves) to 1.83 m2 m-2. The SW model also estimated soil evaporation and plant transpiration separately, and about 66 % of the cumulative evaporation was attributed to soil evaporation. These findings suggest that the PM equation should be replaced by the SW model for surfaces that assume a range of LAI values during the growing season. The H was estimated using (i) SW model that was further developed to include surface radiometric temperature measurements; (ii) one-layer model, but linked with a two-layer model for estimation of excess resistance, that uses surface radiometric temperature; and (iii) the SW model (unmodified). The agreement between modelled and measured H, using 10-min data, was in general reasonably good with RMSE (W m-2) of 45.11, 43.77 and 39.86 for the three models respectively. The comparative results that were achieved from (iii) were not translated into the daily data as all models appeared to have a tendency to underestimate H. The resulting RMSEs for the daily H data for the three models were (MJ m-2) 1.16, 1.17 and 1.18 respectively. It appears that similar or better agreement between measured and estimated H can be forged without the need for surface radiometric temperature measurements. The study showed, in general, that high frequency air temperature measurements can be used to estimate H with reasonable accuracy using the simple and relatively low-cost TV and SR methods. Moreover, these methods can be used to calculate ƒÜE, hence ET, as a residual of the shortened surface energy balance equation along with measurements of Rn and G assuming that energy balance closure is met. The simple and low-cost nature of these methods makes replication of measurements easier and their robust nature allows long-term measurements of energy fluxes. The study also showed that H and ƒÜE can be modeled using energy and resistance combination equations with reasonable accuracy. It also reiterated that the SW-type models, which treat the plant canopy and soil components separately, are more appropriate for estimation of H and ƒÜE over sparse vegetation as opposed to the PM-type models. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

Increased pressure on the available limited water resources for agricultural production has a significant impact on sugarcane production. Routine monitoring of evaporation with reliable accuracy is essential for irrigation scheduling, for more efficient use of the available water resources and for management purposes. An indirect method for estimating evaporation involves measuring the sensible heat flux (H) from which latent energy flux and hence total evaporation can be calculated, as a residual using the shortened energy balance from measurements of net irradiance and soil heat flux. Various methods for measuring H may include Bowen ratio energy balance, eddy covariance (EC), flux variance (FV), optical scintillation, surface renewal (SR) and temperature variance (TV). Each method has its own advantages and disadvantages, in terms of method theoretical assumptions, accuracy, complexity, cost, fetch requirements and power consumption. The TV and SR methods are inexpensive and reasonably simple with a reduced power requirement compared to other methods since they require high frequency air temperature data which is obtained by using an unshielded naturally-ventilated type-E fine-wire thermocouple at a single point above the canopy surface. The TV method is based on the Monin-Obukhov similarity theory (MOST) and uses the mean and standard deviation of the air temperature for each averaging period. Currently, there are two TV methods used for estimating sensible heat flux (HTV) at sub-hourly time intervals, one includes adjustment for stability, and a second that includes adjustment for air temperature skewness. Another method used to estimate sensible heat flux from the mean and standard deviation of air temperature is based on MOST and uses spatial second-order air temperature structure function. For the TV method adjusted for stability and the method based on MOST that uses a spatial second-order air temperature structure function, the Monin-Obukhov atmospheric stability parameter () is needed. The parameter  can be estimated from EC measurements or alternatively estimated independently using an iteration process using horizontal wind speed measurements. The TV method including adjustment for air temperature skewness requires the mean and standard deviation of the air temperature and air temperature skewness for each averaging time period as the only input. The SR method is based on the coherent structure concept. Currently, there are various SR models method for estimating sensible heat flux. These include an ideal SR analysis model method based on an air temperature structure function analysis, the SR analysis model with a finite micro-front period, combined SR with K-theory and combined SR model method based on MOST. The ideal SR analysis model based on an air temperature structure function analysis should be calibrated to determine the SR weighting factor (). The other SR approaches require additional measurements such as crop height and horizontal wind speed measurements. In all of the SR approaches, air temperature time lags are used when calculating the air temperature structure functions. In this study, the performance of TV and SR methods were evaluated for estimation of sensible heat and latent energy fluxes at different heights for air temperature time lags of 0.4 and 0.8 s for daytime unstable conditions against EC above a sugarcane canopy at the Baynesfield Estate in KwaZulu-Natal, South Africa. For all methods, latent energy flux (LE) and hence evaporation was estimated as a residual from the shortened energy balance equation using H estimates and net irradiance and soil heat flux density measurements. The ideal SR analysis model method based on an air temperature structure function analysis approach was calibrated and validated against the EC method above the sugarcane canopy using non-overlapping data sets for daytime unstable conditions during 2008. During the calibration period, the SR weighting factor was determined for each height and air temperature time lag. The magnitude of ranged from 0.66 to 0.55 for all measurement heights and an air temperature time lag of 0.8 s. The value increased with a decrease in measurement height and an increase in air temperature time lag. For the validation data set, the SR sensible heat flux (HSR) estimates corresponded well with EC sensible heat flux (HEC) for all heights and both air temperature time lags. The agreement between HSR and HEC improved with a decrease in measurement height for the air temperature time lag of 0.8 s. The best HSR vs HEC comparisons were obtained at a height of 0.20 m above the crop canopy using = 0.66 for an air temperature time lag of 0.8 s. The residual estimates of latent energy flux by SR and EC methods were in good agreement. The LESR at a height of 0.20 m above the canopy yielded the best comparisons with LEEC estimated as a residual. The performance of the TV method, including adjustment for stability, and / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.