This document reports the results of 4 studies of subalpine ecosystem ecology, describing ways that spatial heterogeneity in soils and plant communities mediate ecosystem responses to environmental change. Ecosystem responses to environmental change are also mediated by regional climate patterns and interannual variability in weather. In the first chapter we report the results of an experiment to test for the mediating effects of associational resistance in a forest community that experienced wide-spread beetle kill. We found that Engelmann spruce were more likely to survive a beetle outbreak when growing in low densities (host dilution) and not through other types of associational resistance that relate to higher tree-species richness or greater phylogenetic diversity of the forest community. In the second chapter we report the effects of early snowmelt on soil moisture in subalpine meadow and aspen communities. We found that soil organic matter, soil texture, and forest cover mediated the effects of early snowmelt and were more important drivers of growing-season soil moisture than was snow-free date. In the third chapter we report the effect of early snowmelt on growth and seed production of early-season and midsummer herbaceous species. We found that the primary effect that snowmelt timing had on plant growth was through its effect on species distribution. Changes in the timing of snowmelt had limited effect on the growth, flowering, and seed count of species after they were established. In the final chapter, we report the effect of early snowmelt on soil respiration, microbial biomass, dissolved organic carbon and soil organic carbon. We found that early snowmelt resulted in warmer soil temperatures compared to neighboring snow-cover plots, and that microbial biomass and soil respiration showed no signs of a snowmelt legacy effect during the growing season. Soil organic carbon in rapid and slow-turnover pools was affected more by plant community than by snowmelt timing, and the primary drivers of soil respiration during the snow-free period were first soil organic matter and second soil temperature. Taken together, this dissertation reports our findings that subalpine ecosystems are resilient to environmental change in part because organisms in these systems are adapted to environmental conditions that are highly variable between sites, seasons, and years.

<p>Economics is about the management of scare resources. In agricultural production, water stress and excess heat are the main constraints. The three essays of this dissertation try to improve our understandings of how climate and water resources interact with agricultural markets, and how global changes in agricultural markets may affect water resources. I construct empirical and simulation models to explain the interplay between agriculture and water. These models integrate economic theories with environmental sciences to analyze the hydroclimatic and economic information at different geospatial scales in a changing climate. </p> <p>In the first essay, I illustrate how irrigation, as a potential adaptation channel, can reduce the volatility of crop yields and year-on-year variations caused by the projected heat stress. This work includes estimation of yield response to climate variation for irrigated and rainfed crops; and global projections of change in the mean and the variation of crop yields. I use my estimated response function to project future yield variations using NASA NEX-GDDP climate data. I show that the impact of heat stress on rainfed corn is around twice as big as irrigated practices. </p> <p>In the second essay, I establish a framework for estimating the value of soil moisture for rainfed production. This framework is an extension of Schlenker and Roberts (2009) model enabled by the detailed soil moisture information available from the Water Balance Model (WBM). An important contribution is the introduction of a cumulative yield production function considering the daily interaction of heat and soil moisture. I use this framework to investigate the impacts of soil moisture on corn yields in the United States. However, this framework can be used for the valuation of other ecosystem services at daily basis.</p> <p>In the third essay, I have constructed a model that explains how the global market economy interacts with local land and water resources. This helps us to broaden the scope of global to local analysis of systems sustainability. I have employed SIMPLE-G-W (a Simplified International Model of agricultural Prices, Land use, and the Environment- Gridded Water version) to explain the reallocation across regions. The model is based on a cost minimization behavior for irrigation technology choice for around 75,000 grid cells in the United States constrained by water rights, water availability, and quasi-irreversibility of groundwater supply. This model is used to examine the vulnerability of US land and water resources from global changes.</p>

Landslides cause significant damage to property and human lives throughout the world. Rainfall is the most common triggering factor for the occurrence of landslides. This dissertation presents two novel methodologies for assessment of rainfall-triggered shallow landslide hazard. The first method focuses on using remotely sensed soil moisture and soil surface properties in developing a framework for real-time regional scale landslide hazard assessment while the second method is a deterministic approach to landslide hazard assessment of the specific sites identified during first assessment. In the latter approach, landslide inducing transient seepage in soil during rainfall and its effect on slope stability are modeled using numerical analysis. Traditionally, the prediction of rainfall-triggered landslides has been performed using pre-determined rainfall intensity-duration thresholds. However, it is the infiltration of rainwater into soil slopes which leads to an increase of porewater pressure and destruction of matric suction that causes a reduction in soil shear strength and slope instability. Hence, soil moisture, pore pressure and infiltration properties of soil must be direct inputs to reliable landslide hazard assessment methods. In-situ measurement of pore pressure for real-time landslide hazard assessment is an expensive endeavor and thus, the use of more practical remote sensing of soil moisture is constantly sought. In past studies, a statistical framework for regional scale landslide hazard assessment using remotely sensed soil moisture has not been developed. Thus, the first major objective of this study is to develop a framework for using downscaled remotely sensed soil moisture available on a daily basis to monitor locations that are highly susceptible to rainfall- triggered shallow landslides, using a well-structured assessment procedure. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a proven superior prediction accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. Furthermore, this model provides satisfactory classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil sliding. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can determine the threshold probability for identifying locations with a high soil slide hazard is proposed. On the other hand, many deterministic methods based on analytical and numerical methodologies have been developed in the past to model the effects of infiltration and subsequent transient seepage during rainfall on the stability of natural and manmade slopes. However, the effects of continuous interplay between surface and subsurface water flows on slope stability is seldom considered in the above-mentioned numerical and analytical models. Furthermore, the existing seepage models are based on the Richards equation, which is derived using Darcy’s law, under a pseudo-steady state assumption. Thus, the inertial components of flow have not been incorporated typically in modeling the flow of water through the subsurface. Hence, the second objective of this study is to develop a numerical model which has the capability to model surface, subsurface and infiltration water flows based on a unified approach, employing fundamental fluid dynamics, to assess slope stability during rainfall-induced transient seepage conditions. The developed model is based on the Navier-Stokes equations, which possess the capability to model surface, subsurface and infiltration water flows in a unified manner. The extended Mohr-Coulomb criterion is used in evaluating the shear strength reduction due to infiltration. Finally, the effect of soil hydraulic conductivity on slope stability is examined. The interplay between surface and subsurface water flows is observed to have a significant impact on slope stability, especially at low hydraulic conductivity values. The developed numerical model facilitates site-specific calibration with respect to saturated hydraulic conductivity, remotely sensed soil moisture content and rainfall intensity to predict landslide inducing subsurface pore pressure variations in real time.

"July 2004." / Bibliography: leaves 120-136. / vii, 136 leaves : ill. (some col.), maps ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Investigates the effects of soil moisture dynamics on the growth and interactions between invasive annual grasses and native perennial grasses in the mid-north of South Australia. At most sites annual grass abundance was positively correlated with rainfall, soil moisture after rainfall and higher soil productivity. Perennial grass abundance was negatively correlated with annual grass abundance and soil moisture after rainfall, and was weakly positively correlated with percentage summer rainfall, elevation, radiation, gravel and slope. Overall perennial grasses responded little to the environmental variables investigated, but strongly to annual grass abundance, while for annual grasses soil moisture was the driving variable. / Thesis (Ph.D.)--University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, 2004

The forest product industry is keenly interested in extending the normal planting season, as well as in the comparative field performance of standard nursery bare-root seedlings and containerized rooted cuttings. The effect of seasonal planting dates on survival, above and belowground biomass allocation, water relations, gas exchange attributes and foliar carbon isotope composition (δ13C) of two stock types of slash pine (Pinus elliottii Engelm.) were examined. Slash pine bare-root seedlings (BRS) and containerized rooted cuttings (CRC) were hand planted in September, November, January, March and April in three consecutive planting seasons (2000-2001, 2001-2002 and 2002-2003) on three sites with silt loam topsoils in southwestern Louisiana. First-year mean survival of CRC across all planting dates and sites was consistently high at 96 to 98%, whereas BRS survival was significantly (P < 0.0001) lower at 59 to 81% and highly variable among study sites and dates through three planting seasons. Generally, there was a negative relationship between soil moisture at the time of planting and first-year survival of BRS planted September through March in 2001-2002 and 2002-2003 planting seasons, whereas the opposite was observed only for BRS planted in April 2002 and 2003. Survival of CRC was affected very little by the variation in soil moisture. Containerized rooted cuttings had higher early above and belowground biomass, and height and diameter than did BRS. However, three years after planting the size differences between stock types disappeared or became negligible. Early size differences among trees planted September through March also decreased after three years, although September trees were tallest. Growth of the April-planted trees was poor compared to trees planted in other months. Late-planted April trees had higher δ13C values, and higher water-use efficiency in the first growing season compared to earlier planted trees. Differences in δ13C values among the planting dates disappeared in the second growing season. Net photosynthesis rates did not differ considerably between stock types or among planting dates in the second and third growing seasons. This study indicates that it is possible to extend the planting season to as early as September and as late as March by using CRC.

The overall purpose of this study was to define the water balance of feedlot pens in a Saskatchewan cattle feeding operation for a one year period. Although the initial intention of the study was focused upon an active feedlot, cattle were removed from the pens in July 2003. Therefore, the year of analysis was conducted on the manured surface of an inactive feedlot. The water balance was also performed on a scraped soil surface, since manure is removed from the pens and spread on agricultural land, leaving the pen surfaces bare for a short period of time each year. <p>During the monitoring period (Sept. 2003 to Aug. 2004), 313 mm of precipitation was received at the feedlot, but only 84 mm of that total was received before June 2004. Winter precipitation was very low (33 mm) and there was no observed runoff from it. Runoff collection weirs in operation for only part of the summer recorded no runoff. The Green-Ampt and USDA SCS runoff models, as well as a snowmelt runoff equation, were used to predict runoff from both the manure pack, as well as the scraped soil surface. Using manure and soil hydraulic parameters determined in the laboratory (from falling head permeameter measurements) and the field (from rainfall simulations), as well as incorporating the greatest 24 hour rainfall amounts and 30 minute intensities experienced at the feedlot, the USDA model found that 29 mm of runoff would occur from the scraped soil surface. Additionally, snowmelt runoff was estimated to be 19 mm for the winter precipitation received. Drainage beneath the 0.6 m soil depth was negligible and the top 0.6 m of soil experienced an increase in moisture of 54 mm. Finally, 211 mm was lost as evaporation. For the manure pack, no runoff was predicted using the Green-Ampt and USDA SCS models and snowmelt runoff equation, which corresponded well to the lack of runoff measured both from the weir and rainfall simulations. Drainage beneath 0.6 m soil depth was negligible. Of the 313 mm of precipitation that fell during the study year, 42 mm was stored within the manure pack and the rest was lost as evaporation (271 mm).

The use of remote sensing technology has made it possible for the non-contact measurement of soil moisture content (SMC). Many remote sensing techniques can be used such as microwave sensors, electromagnetic waves sensors, capacitance, and thermal infrared sensors. Some of those techniques are constrained by their high fabrication cost, operation cost, size, or complexity. In this study, a thermal infrared technique was used to predict soil moisture content with the aid of using weather meteorological variables. <p>The measured variables in the experiment were soil moisture content (%SMC), soil surface temperature (Ts) measured using thermocouples, air temperature (Ta), relative humidity (RH), solar radiation (SR), and wind speed (WS). The experiment was carried out for a total of 12 soil samples of two soil types (clay/sand) and two compaction levels (compacted/non-compacted). After data analysis, calibration models relating soil moisture content (SMC) to differential temperature (Td), relative humidity (RH), solar radiation (SR), and wind speed (WS) were generated using stepwise multiple linear regression of the calibration data set. The performance of the models was evaluated using validation data. Four mathematical models of predicting soil moisture content were generated for each soil type and configuration using the calibration data set. Among the four models, the best model for each soil type and configuration was determined by comparing root mean of squared errors of calibration (RMSEC) and root mean of squared errors of validation (RMSEV) values. Furthermore, a calibration model for the thermal infrared sensor was developed to determine the corrected soil surface temperature as measured by the sensor (Tir) instead of using the thermocouples. The performance of the thermal infrared sensor to predict soil moisture content was then tested for sand compacted and sand non-compacted soils and compared to the predictive performance of the thermocouples. This was achieved by using the measured soil surface temperature by the sensor (Tir), instead of the measured soil surface temperature using the thermocouples to determine the soil-minus-air temperature (Td). The sensor showed comparable prediction performance, relative to thermocouples. <p>Overall, the models developed in this study showed high prediction performance when tested with the validation data set. The best models to predict SMC for compacted clay soil, non-compacted clay soil, and compacted sandy soil were three-variable models containing three predictive variables; Td, RH, and SR. On the other hand, the best model to predict SMC for compacted sandy soil was a two-variable model containing Td, and RH. The results showed that the prediction performance of models for predicting SMC for the sandy soils was superior to those of clay soils.

This thesis details the development and calibration of a model created by coupling a land surface simulation model named CLASS with a hydrologic model named WATFLOOD. The resulting model, known as WatCLASS, is able to serve as a lower boundary for an atmospheric model. In addition, WatCLASS can act independently of an atmospheric model to simulate fluxes of energy and moisture from the land surface including streamflow. These flux outputs are generated based on conservation equations for both heat and moisture ensuring result continuity. WatCLASS has been tested over both the data rich BOREAS domains at fine scales and the large but data poor domain of the Mackenzie River at coarse scale. The results, while encouraging, point to errors in the model physics related primarily to soil moisture transport in partially frozen soils and permafrost. Now that a fully coupled model has been developed, there is a need for continued research by refining model processes and test WatCLASS's robustness using new datasets that are beginning to emerge. Hydrologic models provide a mechanism for the improvement of atmospheric simulation though two important mechanisms. First, atmospheric inputs to the land surface, such as rainfall and temperature, are transformed by vegetation and soil systems into outputs of energy and mass. One of these mass outputs, which have been routinely measured with a high degree of accuracy, is streamflow. Through the use of hydrologic simulations, inputs from atmospheric models may be transformed to streamflow to assess reliability of precipitation and temperature. In this situation, hydrologic models act in an analogous way to a large rain gauge whose surface area is that of a watershed. WatCLASS has been shown to be able to fulfill this task by simulating streamflow from atmospheric forcing data over multi-year simulation periods and the large domains necessary to allow integration with limited area atmospheric models. A second, more important, role exists for hydrologic models within atmospheric simulations. The earth's surface acts as a boundary condition for the atmosphere. Besides the output of streamflow, which is not often considered in atmospheric modeling, the earth's surface also outputs fluxes of energy in the form of evaporation, known as latent heat and near surface heating, known as sensible heat. By simulating streamflow and hence soil moisture over the land surface, hydrologic models, when properly enabled with both energy and water balance capabilities, can influence the apportioning of the relative quantities of latent and sensible heat flux that are required by atmospheric models. WatCLASS has shown that by improving streamflow simulations, evaporation amounts are reduced by approximately 70% (1271mm to 740mm) during a three year simulation period in the BOREAS northern old black spruce site (NSA-OBS) as compared to the use of CLASS alone. To create a model that can act both as a lower boundary for the atmosphere and a hydrologic model, two choices are available. This model can be constructed from scratch with all the caveats and problems associated with proving a new model and having it accepted by the atmospheric community. An alternate mechanism, more likely to be successfully implemented, was chosen for the development of WatCLASS. Here, two proven and well tested models, WATFLOOD and CLASS, were coupled in a phased integration strategy that allowed development to proceed on model components independently. The ultimate goal of this implementation strategy, a fully coupled atmospheric - land surface - hydrologic model, was developed for MC2-CLASS-WATFLOOD. Initial testing of this model, over the Saguenay region of Quebec, has yet to show that adding WATFLOOD to CLASS produces significant impacts on atmospheric simulation. It is suspected, that this is due to the short term nature of the weather simulation that is dominated by initial conditions imposed on the atmospheric model during the data assimilation cycle. To model the hydrologic system, using the domain of an atmospheric model, requires that methods be developed to characterize land surface forms that influence hydrologic response. Methods, such as GRU (Grouped Response Unit) developed for WATFLOOD, need to be extended to taken advantage of alternate data forms, such as soil and topography, in a way that allows parameters to be selected <I>a priori</I>. Use of GIS (Geographical Information System) and large data bases to assist in development of these relationships has been started here. Some success in creating DEMs, (Digital Elevation Model) which are able to reproduce watershed areas, was achieved. These methods build on existing software implementations to include lake boundaries information as a topographic data source. Other data needs of hydrologic models will build on relationships between land cover, soil, and topography to assist in establishing grouping of these variables required to determine hydrologic similarity. This final aspect of the research is currently in its infancy but provides a platform from which to explore for future initiatives. Original contributions of this thesis are centered on the addition of a lateral flow generation mechanism within a land surface scheme. This addition has shown a positive impact on flux returns to the atmosphere when compared to measured values and also provide increased realism to the model since measured streamflow is reproduced. These contributions have been encapsulated into a computer model known as WatCLASS, which together with the implementation plan, as presented, should lead to future atmospheric simulation improvements.

This thesis details the development and calibration of a model created by coupling a land surface simulation model named CLASS with a hydrologic model named WATFLOOD. The resulting model, known as WatCLASS, is able to serve as a lower boundary for an atmospheric model. In addition, WatCLASS can act independently of an atmospheric model to simulate fluxes of energy and moisture from the land surface including streamflow. These flux outputs are generated based on conservation equations for both heat and moisture ensuring result continuity. WatCLASS has been tested over both the data rich BOREAS domains at fine scales and the large but data poor domain of the Mackenzie River at coarse scale. The results, while encouraging, point to errors in the model physics related primarily to soil moisture transport in partially frozen soils and permafrost. Now that a fully coupled model has been developed, there is a need for continued research by refining model processes and test WatCLASS's robustness using new datasets that are beginning to emerge. Hydrologic models provide a mechanism for the improvement of atmospheric simulation though two important mechanisms. First, atmospheric inputs to the land surface, such as rainfall and temperature, are transformed by vegetation and soil systems into outputs of energy and mass. One of these mass outputs, which have been routinely measured with a high degree of accuracy, is streamflow. Through the use of hydrologic simulations, inputs from atmospheric models may be transformed to streamflow to assess reliability of precipitation and temperature. In this situation, hydrologic models act in an analogous way to a large rain gauge whose surface area is that of a watershed. WatCLASS has been shown to be able to fulfill this task by simulating streamflow from atmospheric forcing data over multi-year simulation periods and the large domains necessary to allow integration with limited area atmospheric models. A second, more important, role exists for hydrologic models within atmospheric simulations. The earth's surface acts as a boundary condition for the atmosphere. Besides the output of streamflow, which is not often considered in atmospheric modeling, the earth's surface also outputs fluxes of energy in the form of evaporation, known as latent heat and near surface heating, known as sensible heat. By simulating streamflow and hence soil moisture over the land surface, hydrologic models, when properly enabled with both energy and water balance capabilities, can influence the apportioning of the relative quantities of latent and sensible heat flux that are required by atmospheric models. WatCLASS has shown that by improving streamflow simulations, evaporation amounts are reduced by approximately 70% (1271mm to 740mm) during a three year simulation period in the BOREAS northern old black spruce site (NSA-OBS) as compared to the use of CLASS alone. To create a model that can act both as a lower boundary for the atmosphere and a hydrologic model, two choices are available. This model can be constructed from scratch with all the caveats and problems associated with proving a new model and having it accepted by the atmospheric community. An alternate mechanism, more likely to be successfully implemented, was chosen for the development of WatCLASS. Here, two proven and well tested models, WATFLOOD and CLASS, were coupled in a phased integration strategy that allowed development to proceed on model components independently. The ultimate goal of this implementation strategy, a fully coupled atmospheric - land surface - hydrologic model, was developed for MC2-CLASS-WATFLOOD. Initial testing of this model, over the Saguenay region of Quebec, has yet to show that adding WATFLOOD to CLASS produces significant impacts on atmospheric simulation. It is suspected, that this is due to the short term nature of the weather simulation that is dominated by initial conditions imposed on the atmospheric model during the data assimilation cycle. To model the hydrologic system, using the domain of an atmospheric model, requires that methods be developed to characterize land surface forms that influence hydrologic response. Methods, such as GRU (Grouped Response Unit) developed for WATFLOOD, need to be extended to taken advantage of alternate data forms, such as soil and topography, in a way that allows parameters to be selected <I>a priori</I>. Use of GIS (Geographical Information System) and large data bases to assist in development of these relationships has been started here. Some success in creating DEMs, (Digital Elevation Model) which are able to reproduce watershed areas, was achieved. These methods build on existing software implementations to include lake boundaries information as a topographic data source. Other data needs of hydrologic models will build on relationships between land cover, soil, and topography to assist in establishing grouping of these variables required to determine hydrologic similarity. This final aspect of the research is currently in its infancy but provides a platform from which to explore for future initiatives. Original contributions of this thesis are centered on the addition of a lateral flow generation mechanism within a land surface scheme. This addition has shown a positive impact on flux returns to the atmosphere when compared to measured values and also provide increased realism to the model since measured streamflow is reproduced. These contributions have been encapsulated into a computer model known as WatCLASS, which together with the implementation plan, as presented, should lead to future atmospheric simulation improvements.

The overall purpose of this study was to define the water balance of feedlot pens in a Saskatchewan cattle feeding operation for a one year period. Although the initial intention of the study was focused upon an active feedlot, cattle were removed from the pens in July 2003. Therefore, the year of analysis was conducted on the manured surface of an inactive feedlot. The water balance was also performed on a scraped soil surface, since manure is removed from the pens and spread on agricultural land, leaving the pen surfaces bare for a short period of time each year. <p>During the monitoring period (Sept. 2003 to Aug. 2004), 313 mm of precipitation was received at the feedlot, but only 84 mm of that total was received before June 2004. Winter precipitation was very low (33 mm) and there was no observed runoff from it. Runoff collection weirs in operation for only part of the summer recorded no runoff. The Green-Ampt and USDA SCS runoff models, as well as a snowmelt runoff equation, were used to predict runoff from both the manure pack, as well as the scraped soil surface. Using manure and soil hydraulic parameters determined in the laboratory (from falling head permeameter measurements) and the field (from rainfall simulations), as well as incorporating the greatest 24 hour rainfall amounts and 30 minute intensities experienced at the feedlot, the USDA model found that 29 mm of runoff would occur from the scraped soil surface. Additionally, snowmelt runoff was estimated to be 19 mm for the winter precipitation received. Drainage beneath the 0.6 m soil depth was negligible and the top 0.6 m of soil experienced an increase in moisture of 54 mm. Finally, 211 mm was lost as evaporation. For the manure pack, no runoff was predicted using the Green-Ampt and USDA SCS models and snowmelt runoff equation, which corresponded well to the lack of runoff measured both from the weir and rainfall simulations. Drainage beneath 0.6 m soil depth was negligible. Of the 313 mm of precipitation that fell during the study year, 42 mm was stored within the manure pack and the rest was lost as evaporation (271 mm).