The interaction between climate and land-surface hydrology is extremely important in relation to long term water resource planning. This is especially so in the presence of global warming and massive land use change, issues which seem likely to have a disproportionate impact on developing countries. This thesis develops tools aimed at the study and prediction of climate effects on land-surface hydrology (in particular streamflow), which require a minimum amount of site specific data. This minimum data requirement allows studies to be performed in areas that are data sparse, such as the developing world. ¶ A simple lumped dynamics-encapsulating conceptual rainfall-runoff model, which explicitly calculates the evaporative feedback to the atmosphere, was developed. It uses the linear streamflow routing module of the rainfall-runoff model IHACRES, with a new non-linear loss module based on the Catchment Moisture Deficit accounting scheme, and is referred to as CMD-IHACRES. In this model, evaporation can be calculated using a number of techniques depending on the data available, as a minimum, one to two years of precipitation, temperature and streamflow data are required. The model was tested on catchments covering a large range of hydroclimatologies and shown to estimate streamflow well. When tested against evaporation data the simplest technique was found to capture the medium to long term average well but had difficulty reproducing the short-term variations. ¶ A comparison of the performance of three limited area climate models (MM5/BATS, MM5/SHEELS and RegCM2) was conducted in order to quantify their ability to reproduce near surface variables. Components of the energy and water balance over the land surface display considerable variation among the models, with no model performing consistently better than the other two. However, several conclusions can be made. The MM5 longwave radiation scheme performed worse than the scheme implemented in RegCM2. Estimates of runoff displayed the largest variations and differed from observations by as much as 100%. The climate models exhibited greater variance than the observations for almost all the energy and water related fluxes investigated. ¶ An investigation into improving these streamflow predictions by utilizing CMD-IHACRES was conducted. Using CMD-IHACRES in an 'offline' mode greatly improved the streamflow estimates while the simplest evaporation technique reproduced the evaporative time series to an accuracy comparable to that obtained from the limited area models alone. The ability to conduct a climate change impact study using CMD-IHACRES and a stochastic weather generator is also demonstrated. These results warrant further investigation into incorporating the rainfall-runoff model CMD-IHACRES in a fully coupled 'online' approach.

The water and heat exchange in thesoil-snow-vegetation-atmosphere system was studied in order toimprove the quantitative knowledge of land surface processes.In this study, numerical simulation models and availabledatasets representing arable land, sub-alpine snowpack, andboreal forest were evaluated at both diurnal and seasonaltimescales. Surface heat fluxes, snow depth, soil temperatures andmeteorological conditions were measured at an agriculturalfield in central Sweden during three winters and two summersfrom 1997 to 2000 within the WINTEX project. A one-dimensionalsimulation model (COUP) was used to simulate the water and heatbalance of the field. Comparison of simulated and measured heatfluxes in winter showed that parameter values governing theupper boundary condition were more important for explainingmeasured fluxes than the formulation of the internal mass andheat balance of the snow cover. The assumption of steady stateheat exchange between the surface and the reference height wasinadequate during stable atmospheric conditions. Independentestimates of the soil heat and water balance together with thecomparison of simulated and measured surface heat fluxes showedthat the eddy-correlation estimates of latent heat fluxes fromthe arable field were on average 40 % too low. The ability of a multi-layered snowpack model (SNTHERM) tosimulate the layered nature of a sub-alpine snowpack wasevaluated based on a dataset from Switzerland. The modelsimulated the seasonal development of snow depth and densitywith high accuracy. However, the models ability to reproducethe strong observed snowpack layering was limited by theneglection of the effect of snow microstructure on snowsettling, and a poor representation of water redistributionwithin the snowpack. The representation of boreal forest in the land surfacescheme used within a weather forecast (ECMWF) model was testedwith a three-year dataset from the NOPEX forest site in centralSweden. The new formulation with separate energy balances forvegetation and the soil/snow beneath the tree cover improvedthe simulation of seasonal and diurnal variations in latent andsensible heat flux. Further improvements of simulated latentheat fluxes were obtained when seasonal variation in vegetationproperties was introduced. Application of the COUP model withthe same dataset showed that simulation of evaporation fromintercepted snow contributed to a better agreement with themeasured sensible heat flux above forests, but also indicatedthat the measurements might have underestimated latent heatflux. The winter sensible heat flux above the forest wasfurther improved if an upper limit of the aerodynamicresistance of 500 s m-1 was applied for stable conditions. A comparison of the water and heat balance of arable landand forest confirmed the general knowledge of the differencesbetween these two surface types. The forest contributed withconsiderably more sensible heat flux to the atmosphere than thearable land in spring and summer due to the lower albedo andrelatively less latent heat flux. Latent heat flux from theforest was higher in winter due to the evaporation ofintercepted snow and rain. The net radiation absorbed by theforest was 60 % higher than that absorbed by the arable land,due to the lower surface albedo in winter. Key words:soil; snow; land surface heat exchange;forest; arable land; eddy-correlation. / QC 20100614

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw.

This thesis research consists of two separate studies. The first study presents the assessment and representation of the effects of soil macropores on the soil hydraulic properties in land surface models for more accurate simulations of soil moisture and surface hydrology. Hydraulic properties determine the soil water content and its transport in the soil. They are provided in most current climate models as empirical formulas by functions of the soil texture. Such is not realistic if the soil contains a substantial amount of macropores. A two-mode soil pore size distribution is incorporated into a land surface model and tested using an observational dataset at a tropical forest site with aggregated soils. The result showed that the existence of macropores greatly affects the estimation of hydraulic properties. Their influence can be included in land models by adding a second function to the pore-size distribution. A practical hydraulic scheme with macropore considerations was proposed given that the existing schemes are not applicable for large-scale simulations. The developed scheme was based on the physical attributes of the water in soil capillary pores and the statistics of several global soil databases. The preliminary test showed that it captures part of soil macropore hydraulic features without sacrificing the estimation accuracy of hydraulic properties of water in soil matrix. The second study presents the development of an integrated land/ecosystem model by combining the advanced features of a biophysically based land model, the Community Land Model, and an ecosystem biochemical model. The results from tests of the integrated model at four forest sites showed that the model reasonably captures the seasonal and interannual dynamics of leaf area index and leaf nitrogen control on carbon assimilation across different environments. With being coupled to an atmospheric general circulation model (AGCM), the integrated model showed a strong ability to simulate terrestrial ecosystem carbon fluxes together with heat and water fluxes. Its simulated land surface physical variables are reasonable in both geographic distribution and temporal variation with considering the interactive vegetation parameters.

In this thesis, land surface emissivity variations are examined with respect to the land surface type, wavelength, and time (season and month) for the seven selected regions in Turkey using MODIS emissivity database and precipitation amount. Investigating land surface emissivity variations are important in many applications and it is known that studies about these variations are done for many regions except Turkey. This study is prior knowledge for Turkey to be used in infrared (IR) background models, surface radiation budget calculations, and land cover type classifications specific for Turkey. The results indicate that precipitation has a great influence on monthly/seasonal emissivity values depending on the land cover type and causes spectral emissivity variations. As a result, we determined appropriate IR wavelengths for the investigation of the seasonal emissivity variations and seasonal factors causing emissivity variations according to the land cover types.

Reliable prediction of climate change and its impact on and feedbacks from terrestrial carbon cycles requires realistic representation of physiological and ecological processes in coupled climate-carbon models. This is hampered by various deficiencies in model structures and parameters. The goal of my study is to improve model realism by incorporating latest advances of fundamental eco-physiological processes and further to use such improved models to investigate climate-carbon interactions at regional to global scales. I focus on the CO₂ diffusion within leaves (a key plant physiological process) and large-scale disturbances (a fundamental ecological process) as extremely important but not yet in current models. The CO₂ diffusion within plant leaves is characterized by mesophyll conductance (g[subscript m]), which strongly influences photosynthesis. I developed a g[subscript m] model by synthesizing new advances in plant-physiological studies and incorporated this model into the Community Land Model (CLM), a state-of-art climate-carbon model. I updated associated photosynthetic parameters based on a large dataset of leaf gas exchange measurements. Major findings are: (1) omission of g[subscript m] underestimates the maximum carboxylation rate and distorts its relationships with other parameters, leading to an incomplete understanding of leaf-level photosynthesis machinery; (2) proper representation of g[subscript m] is necessary for climate-carbon models to realistically predict carbon fluxes and their responsiveness to CO₂ fertilization; (3) fine tuning of parameters may compensate for model structural errors in contemporary simulations but introduce large biases in future predictions. Further, I have corrected a numerical deficiency of CLM in its calculation of carbon/water fluxes, which otherwise can bias model simulations. Large-scale disturbances of terrestrial ecosystems strongly affect their carbon sink strength. To provide insights for modeling these processes, I used satellite products to examine the temporal-spatial patterns of greenness after a massive ice storm. I found that the greenness of impacted vegetation recovered rapidly, especially in lightly and severely impacted regions. The slowest rebound occurred over moderately impacted areas. This nonlinear pattern was caused by an integrated effect of natural regrowth and human interventions. My results demonstrate mechanisms by which terrestrial carbon sinks could be significantly affected and help determine how these sinks will behave and so affect future climate. / text

The work presented in this dissertation informs on river network modeling at large scales using geographic information systems, parallel computing and the latest advancements of atmospheric and land surface modeling. This work is motivated by the availability of a vector-based Geographic Information System dataset that describes the networks of streams and rivers in the United States, and how they are connected. A land surface model called Noah-distributed is used to provide lateral inflow to an NHDPlus river network in the Guadalupe River Basin in Texas. Challenges related to the projection of gridded hydrographic data from a coordinate system to another are investigated. The different representations of the shape of the Earth used in atmospheric science (spherical) and hydrology (spheroidal) can lead to a significant North-South shift on the order of 20 km at mid latitudes. A river network model called RAPID is developed and applied in a four-year study of the Guadalupe and San Antonio River Basins in Texas using the river network of NHDPlus. Gage measurements are used to estimate flow wave celerities in a river network and to assess the quality of RAPID flow computations. The performance of RAPID in a massively-parallel computing environment is tested and further investigation of its scalability is needed before using RAPID at the state or federal level. The replacement by RAPID of the river routing scheme used in SIM-France -- a hydro-meteorological model -- is investigated in a ten-year study of river flow in France. While the formulation of RAPID improves the functionality of SIM-France, the flow simulations are comparable in accuracy to those previously obtained by SIM-France. Sub-basin parameterization was found to improve model results. A single criterion for quantifying the quality of river flow simulations using several river gages globally in a river network is developed that normalizes the square error of modeled flow to allow equal treatment of all gaging stations regardless of the magnitude of flow. The use of this criterion as the cost function for parameter estimation in RAPID allows better results than by increasing the degree of spatial variability in model parameters. / text

Water is essential to life on Earth. Since water exists in all three phases (solid, liquid, and gas) on Earth, it exists in various reservoirs throughout the planet that compose the hydrologic cycle, and its movement through these reservoirs requires energy. Thus, water is a key component of the energy balance of the Earth. Despite its importance, its representation in modeling and dataset generation is problematic. Here, the depiction of three phenomena, ocean surface turbulent fluxes, humidity inversions, and groundwater, are assessed, and suggestions for improvements of their representations are made. First, ocean surface turbulent fluxes, including those of moisture (latent heat flux), heat (sensible heat flux), and momentum (wind stress), from reanalysis, satellite-derived, and combined products which are commonly used to produce climatologies and to evaluate global climate models are compared to in situ observations from ship cruises to ascertain which products are the least problematic. The National Aeronautics and Space Administration’s reanalysis, the Modern Era Retrospective Analysis for Research and Applications, is the least problematic for all three fluxes, while a couple of others are the least problematic for only one of the three fluxes. Also, the product biases are disaggregated into uncertainties from the grid cell mean quantities, or bulk variables, used plus the residual uncertainties which includes the algorithm uncertainties due to the parameterization used to relate the small-scale turbulent processes to the large-scale bulk variables. The latter contribute the most to the majority of product latent heat fluxes, while both uncertainties can contribute the most to product sensible heat fluxes and wind stress. Thus, both algorithms and bulk variables need to be improved in ocean surface flux datasets. Second, humidity inversion climatologies in five reanalyses are evaluated. Humidity inversions, similar to its thermal counterpart, are layers in which specific humidity increases with height rather than the usual decrease with height. These are especially persistent in the polar regions in autumn and winter. However, Arctic inversions are the strongest in summer corresponding to the time of year that low cloud cover is the highest. Comparing the reanalysis inversions to radiosonde observations reveals some problems with the realization of humidity inversions in reanalyses including the misrepresentation of the diurnal cycle and of the overproduction of inversions in areas outside the polar regions. Finally, the simulation of groundwater in the Community Land Model (CLM) as used in the Community Earth System Model is made more realistic by including variable soil thickness. Because the bottom of the model soil column is placed at effectively bedrock, the unconfined aquifer model currently used in CLM is removed and a zero bottom water flux is put in place. The removal of the unconfined aquifer allows the simulation of groundwater to not be treated separately from soil moisture. The model is most affected where the number of soil layers is reduced from the original constant 10 layers and largely unaffected where the number of soil layers is increased except for baseflow where the mean annual range in rainfall is large.

Droughts are a recurrent part of our climate, and are still considered to be one of the most complex and least understood of all natural hazards in terms of their impact on the environment. In recent years drought has become more common and more severe across the world. For more than a decade, the US southwest has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources. The focus of this work is achieving a better understanding of the impact of drought on the lands of the Hopi Tribe and Navajo Nation, situated in the Northeastern corner of Arizona. This research explores the application of remote sensing data and geospatial tools in two studies to monitor drought impacts on vegetation productivity. In both studies we used land surface phenometrics as the data tool. In a third related study, I have compared satellite-derived land surface phenology (LSP) to field observations of crop stages at the Maricopa Agricultural Center to achieve a better understanding of the temporal sensitivity of satellite derived phenology of vegetation and understand their accuracy as a tool for monitoring change. The first study explores long-term vegetation productivity responses to drought. The paper develops a framework for drought monitoring and assessment by integrating land cover, climate, and topographical data with LSP. The objective of the framework is to detect long-term vegetation changes and trends in the Normalized Difference Vegetation Index (NDVI) related productivity. The second study examines the major driving forces of vegetation dynamics in order to provide valuable spatial information related to inter-annual variability in vegetation productivity for mitigating drought impacts. The third study tests the accuracy of remote sensing-derived LSP by comparing them to the actual seasonal phases of crop growth. This provides a way to compare and validate the various LSP algorithms, and more crucially, helps to characterize the remote sensing-based metrics that contrast with the actual biological phenophases of the crops. These studies demonstrate how remote sensing data and simple statistical tools can be used to assess drought effects on vegetation productivity and to inform about land conditions, as well as to better understand the accuracy of satellite derived LSP.

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw.