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Soils are a critical resource for supporting ecosystems, agricultural systems, and human wellbeing. However, these same soils have been degraded by human activities throughout human history. Despite the rapid development of global models that include dynamic changes in land use and land cover (LULC) and biogeochemical processes to assess climate and hydrological impacts, soil properties are often assumed to be spatially or temporally constant. These assumptions can affect the results of model projections, impact assessments and underestimate the human impact on Earth systems. This study reveals the physical impacts of human-altered soil conditions on the global water balance through a meta-analysis study and soil degradation modeling. We link major global LULCs to four hydrologic soil groups: sandy (sand, sandy loam, and loamy sand), loamy (loam, silty loam, and silt)), clayey soils (clay, sandy clay, clay loam, silty clay, and silty clay loam), and sandy clay loam) from 850 to 2015 AD, and identified loamy and clayey soils as the preferred soils for most human land uses. Humans selectively use those soils for intensive agriculture and pasture activities, while grazing occurs on sandier soils. To simulate the impact of human activities on soils, several soil change models were built for soil organic carbon (SOC) content, soil texture (sand, silt, and clay), and soil bulk density from meta-analyses of site observations. The models were applied globally based on the LULC and soil relations, global environmental and soil conditions, and LULC distributions. Pedotransfer functions were applied to estimate soil water-holding capacity using those soil properties, then a Thornthwaite-type water balance model was used to assess the impacts of soil degradation on the global water balance. Results show that under a high-intensity LULC scenario (conventional tillage on croplands and heavy grazing), SOC decreases by 363 Pg and water deficit increases 78 km3 globally. The impacts on SOC and deficit are reduced to 213 Pg and 51 km3, respectively, when reducing land-use intensity by substituting animal ploughing/no-till and light grazing for conventional tillage and heavy grazing. Impacts from other LULC types are identical for these two LULC scenarios. Development of this history between LULC and soil properties allows for improved simulation of human impacts on global water, energy, and biogeochemical cycles. The results of the water balance simulations demonstrate how different soils representations in models can significantly alter the estimates of global evapotranspiration, water deficit, and surplus. This study contributes to developing a better understanding of the processes by which human-induced soil degradation impacts climate/hydrological models and providing a mechanism to better assess the impacts of humans on the Earth system. The outcome will also complement numerous ongoing global studies that evaluate the impacts of climate change on water resources and society. / Graduate / 2023-08-09

Dissolved organic matter (DOM) is a major component of natural waters and provides essential nutrients for aquatic organisms. However, excess DOM in the water results in water quality issues and affects the aquatic life negatively. The present research evaluated the source, composition, reactivity, dynamics, and the spatial distribution of DOM in diverse water bodies using spectrofluorometric methods in tandem with multivariate statistics. The study was conducted in the inland and coastal water bodies of Mississippi, Louisiana, and Alabama over a period of three years (2016 to 2018). Surface water samples were collected from spatially separated waterbodies with diverse land use and land cover classes. In addition, reactivity of DOM was assessed by conducting a series of laboratory experiments at varying magnitudes of sunlight and bacterial activity. Spatial distribution and mobility of DOM, nutrients and trace elements with respect to land cover classes and hydrology was evaluated using watershed delineation and multivariate statistics. Results suggest that microbial humic-like or protein-like DOM compositions derived from microbial/anthropogenic sources were less reactive than the terrestrial humic-like compositions originated from forests and woody wetlands. Furthermore, the sunlight was the major factor causing the degradation of DOM in the water bodies, while temperature had a minor effect. Additionally, the results also suggest that livestock fields in the pastoral and rangelands release a high amount of microbial humic-like DOM along with nutrients such as phosphates and nitrates into the water bodies. Present research identified the presence of four types of DOM in the study areas and were terrestrial humic-like, microbial humic-like, soil-derived humic-like and protein-like compositions. Additionally, trace element availability and mobility of coastal areas is influenced by local hydrology and precipitation. Research also identified forested areas as the major source of DOM to the water bodies of Mississippi. In conclusion, present research found that watershed land use and land cover, hydrology, and climate control the dynamics of DOM, other nutrients, and trace element delivery to the water bodies, while combined effects of light and bacteria are more efficient in reprocessing DOM chemistry within the waterbody.

The present dissertation research has three purposes: the first one is to predict anthropogenic deforestation caused by small farmers firstly using only pathways of past land cover change and secondly using demographic, socioeconomic and land cover data at the farm level. The second purpose is to compare the explanatory and predictive capacity of both approaches at identifying areas at high risk of deforestation among small farms in Rondõnia, Brazil. The third purpose is to test the assumptions of stationary probabilities and homogeneous subjects, both commonly used assumptions in predictive stochastic models applied to small farmers' deforestation decisions. This study uses the following data: household surveys, maps, satellite images and their land cover classification at the pixel level, and pathways of past land cover change for each farm. These data are available for a panel sample of farms in three municipios in Rondõnia, Brazil (Alto Paraiso, Nova União, and Rolim de Moura) and cover a ten-year period of study (1992-2002). Pathways of past land cover change are graphic representations in the form of flow charts that depict Land Cover Change (LCC) in each farm during the ten-year period of study. Pathways were constructed using satellite images, survey data and maps, and a set of interviews performed on a sub-sample of 70 farms. A panel data analysis of the estimated empirical probabilities was conducted to test for subject and time effects using a Fixed Group Effects Model (FGEM), specifically the Least Square Dummy Variable (LSDV1) fixed effects technique. Finally, the two predictive modeling approaches are compared. The first modeling approach predicts future LCC using only past land cover change data in the form of empirical transitional probabilities of LCC obtained from pathways of past LCC. These empirical probabilities are used in a LSDV1 for fixed–group effects, a LSDV1 for fixed-time effects, and an Ordinary Least Square model (OLS) for the pooled sample. Results from these models are entered in a modified Markov chain model's matrix multiplication. The second modeling approach predicts future LCC using socio-demographic and economic survey variables at the household level. The survey data is used to perform a multinomial logit regression model to predict the LC class of each pixel. In order to compare the explanatory and predictive capacity of both modeling approaches, LCC predictions at the pixel level are summarized in terms of percentage of cells in which future LC was predicted correctly. Percentage of correct predicted land cover class is compared against actual pixel classification from satellite images. The presence of differences among farmers in the LSDV1-fixed group effect by farmer suggests that small farmers are not a homogeneous group in term of their probabilities of LCC and that further classification of farmers into homogeneous subgroups will depict better their LCC decisions. Changes in the total area of landholdings proved a stronger influence in farmer's LCC decisions in their main property (primary lot) when compared to changes in the area of the primary lot. Panel data analysis of the LCC empirical transition probabilities (LSDV1 fixed time effects model) does not find enough evidence to prefer the fixed time effects model when compared to a Ordinary Least Square (OLS) pooled version of the probabilities. When applying the results of the panel data analysis to a modified markov chain model the LSDV1-farmer model provided a slightly better accuracy (59.25% accuracy) than the LSDV1-time and the OLS-pooled models (57.54% and 57.18%, respectively). The main finding for policy and planning purposes is that owners type 1—with stable total landholdings over time—tend to preserve forest with a much higher probability (0.9033) than owner with subdividing or expanding properties (probs. of 0.0013 and 0.0030). The main implication for policy making and planning is to encourage primary forest preservation, given that the Markov chain analysis shows that primary forest changes into another land cover, it will never go back to this original land cover class. Policy and planning recommendations are provided to encourage owner type 1 to continue their pattern of high forest conservation rates. Some recommendations include: securing land titling, providing health care and alternative sources of income for the OT1's family members and elderly owners to remain in the lot. Future research is encouraged to explore spatial autocorrelation in the pixel's probabilities of land cover change, effects of local policies and macro-economic variables in the farmer's LCC decisions. / Ph. D.

Numerous studies indicate that the hydrology of a river basin is influenced by Land Use Land Cover (LULC) and climate. LULC affects the quality and quantity of water resources through its influence on Evapotranspiration (ET) and initiation of surface runoff while climate affects the intensity and spatial distribution of rainfall and temperature which are major drivers of the hydrologic cycle. Literature reports several works on either the effect of changing LULC or climate on the hydrology. However, changes in LULC and climate occur simultaneously in reality. Thus, there is a need to perform an integrated impact assessment of such changes on the hydrological regime at a basin scale. In order to carry out the impact assessment, physically-based hydrologic models are often employed. The present study focuses on assessment of the effect of changing LULC and climate on the hydrology of the Upper Ganga basin (UGB), India, using the Variable Infiltration Capacity (VIC) hydrologic model. In order to obtain the changes that have occurred in the LULC of the basin over a time period, initially LULC analysis is carried out. For this purpose, high resolution multispectral satellite imageries from Landsat are procured for the years 1973, 1980, 2000 and 2011. The images are pre-processed to project them to a common projection system and are then co-registered. The processed images are used for classification into different land cover classes. This step requires training sites which are collected during the field visit as part of this work. The classified images, thus obtained are used to analyse temporal changes in LULC of the region. The results indicate an increase in crop land and urban area of the region by 47% and 122% respectively from 1973 to 2011. After initial decline in dense forest for the first three decades, an increase in the dense forest is observed between 2000- 2011 (from 11.44% to 14.8%). Scrub forest area and barren land are observed to decline in the study region by 62% and 96% respectively since 1973. The land cover information along with meteorological data and soil data are used to drive the VIC model to investigate the impact of LULC changes on streamflow and evapotranspiration (ET) components of hydrology in the UGB. For the simulation purpose, the entire basin is divided into three regions (1) upstream (with Bhimgodha as the outlet), (2) midstream (with Ankinghat as the outlet) and (3) downstream (with Allahabad as the outlet). The VIC model is calibrated and validated for all the three regions independently at monthly scale. Model performance is assessed based on the criterion of normalized root mean square error (NRMSE), coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE). It is observed that the model performed well with reasonable accuracy for upstream and midstream regions. In case of the downstream region, due to lack of observed discharge data, model performance could not be assessed. Hence, the simulations for the downstream region are performed using the calibrated model of the midstream region. The model outputs from the three regions are aggregated appropriately to generate the total hydrologic response of the UGB. Using the calibrated models for different region of the UGB, sensitivity analysis is performed by generating hydrologic scenarios corresponding to different land use (LU) and climate conditions. In order to investigate the impact of changing LU on hydrological variables, a scenario is generated in which climate is kept constant and LU is varied. Under this scenario, only the land cover related variables are altered in the model keeping the meteorological variables constant. Thus, the effect of LU change is segregated from the effect of climate. The results obtained from these simulations indicated that the change in LU significantly affects peak streamflow depth which is observed to be 77.58% more in August 2011 in comparison with the peak streamflow of August, 1973. Furthermore, ET is found to increase by 46.44% since 1973 across the entire basin. In order to assess the impact of changing climate on hydrological variables, a scenario is generated in which LU is kept constant and climate is varied from 1971-2005. Under this scenario, land cover related variables are kept constant in the model and meteorological variables are varied for different time periods. The results indicate decline in the simulated discharge for the years 1971, 1980, 1990, 2000 and 2005, which is supported by decline in observed annual rainfall for the respective years. Amongst 1971 and 2005, year 2005 received 26% less rainfall resulting in 35% less discharge. Furthermore, ET is observed to be negligibly affected. To understand the integrated impact of changing LU and climate on hydrological variables, a scenario is generated in which both climate and LU are altered. Based on the data available, three years (1973, 1980 and 2000) are considered for the simulations. Under this scenario, both land cover and meteorological variables are varied in the model. The results obtained showed that the discharge hydrograph for the year 1980 has significantly higher peak compared to the hydrographs of years 1973 and 2000. This could be due to the fact that the year 1980 received maximum rainfall amongst the three years considered for simulations. Although the basin received higher rainfall in the year 1980 compared to that in 2000, ET from the basin in the year 1980 is found to be 21% less than that of the year 2000. This could be attributed to the change in LU that occurred between the years 1980 and 2000. Amongst the years 1973 and 2000, there is not much difference in the observed rainfall but ET for the year 2000 is observed to be significantly higher than that of year 1973. It is concluded from the present study that in the UGB, changing LULC contributes significantly to the changes in peak discharge and ET while rainfall pattern considerably influences the runoff pattern of the region. Future work proposed includes assessment of hydrologic response of basin under future LULC and climate scenarios. Also the model efficiency can be assessed by performing hydrologic simulations at different grid sizes.

This study used a comparison of one hundred and forty-eight historical (1888-1913) and current (2008-2014) oblique photographs from thirty-two stations to identify land cover changes that have occurred in portions of the Bow and Elbow valleys as well as surrounding Kananaskis Country region. Implications of these changes for flooding and flood management were explored. Forest cover was found to have drastically increased over the past century, particularly in the Bow valley, as did areas of direct human development. In the same time period, grasslands increased in the Elbow valley but decreased in the Bow, while regenerating areas decreased uniformly throughout both valleys. An analysis of pre (2008)-and-post (2014) flood conditions demonstrated no change in coniferous forest cover in both valleys over the 6-year period, but uncovered a decline of 20% in the Elbow and 3% in the Bow in the broadleaf/mixedwood category. The Elbow’s channel zone was larger in 2014 compared to 2008, whereas the extent of the Bow’s channel zone remained constant. However, both the Bow and Elbow’s bare exposed bars increased substantially, most likely as a result of the 2013 flood. The major source of water flows that contributed to the 2013 flood event originated in high elevation rock and scree areas, which, unlike floodplains, are elements of the watershed that cannot be manipulated over time. It is now recognized that forest cover should act as a buffer to floods. Nevertheless, the 2013 flood event occurred despite the massive buffering effect of a huge increase in older forest stands across the study area. The final discussion includes recommendations for improving flood management in the area. / Graduate / 0329, 0768, 0478 / tanya.taggarthodge@gmail.com

Résumé : La texture dispose d’un bon potentiel discriminant qui complète celui des paramètres radiométriques dans le processus de classification d’image. L’indice Compact Texture Unit (CTU) multibande, récemment mis au point par Safia et He (2014), permet d’extraire la texture sur plusieurs bandes à la fois, donc de tirer parti d’un surcroît d’informations ignorées jusqu’ici dans les analyses texturales traditionnelles : l’interdépendance entre les bandes. Toutefois, ce nouvel outil n’a pas encore été testé sur des images multisources, usage qui peut se révéler d’un grand intérêt quand on considère par exemple toute la richesse texturale que le radar peut apporter en supplément à l’optique, par combinaison de données. Cette étude permet donc de compléter la validation initiée par Safia (2014) en appliquant le CTU sur un couple d’images optique-radar. L’analyse texturale de ce jeu de données a permis de générer une image en « texture couleur ». Ces bandes texturales créées sont à nouveau combinées avec les bandes initiales de l’optique, avant d’être intégrées dans un processus de classification de l’occupation du sol sous eCognition. Le même procédé de classification (mais sans CTU) est appliqué respectivement sur : la donnée Optique, puis le Radar, et enfin la combinaison Optique-Radar. Par ailleurs le CTU généré sur l’Optique uniquement (monosource) est comparé à celui dérivant du couple Optique-Radar (multisources). L’analyse du pouvoir séparateur de ces différentes bandes à partir d’histogrammes, ainsi que l’outil matrice de confusion, permet de confronter la performance de ces différents cas de figure et paramètres utilisés. Ces éléments de comparaison présentent le CTU, et notamment le CTU multisources, comme le critère le plus discriminant ; sa présence rajoute de la variabilité dans l’image permettant ainsi une segmentation plus nette, une classification à la fois plus détaillée et plus performante. En effet, la précision passe de 0.5 avec l’image Optique à 0.74 pour l’image CTU, alors que la confusion diminue en passant de 0.30 (dans l’Optique) à 0.02 (dans le CTU). / Abstract : Texture has a good discriminating power which complements the radiometric parameters in the image classification process. The index Compact Texture Unit multiband, recently developed by Safia and He (2014), allows to extract texture from several bands at a time, so taking advantage of extra information not previously considered in the traditional textural analysis: the interdependence between bands. However, this new tool has not yet been tested on multi-source images, use that could be an interesting added-value considering, for example, all the textural richness the radar can provide in addition to optics, by combining data. This study allows to complete validation initiated by Safia (2014), by applying the CTU on an optics-radar dataset. The textural analysis of this multisource data allowed to produce a "color texture" image. These newly created textural bands are again combined with the initial optical bands before their use in a classification process of land cover in eCognition. The same classification process (but without CTU) was applied respectively to: Optics data, then Radar, finally on the Optics-Radar combination. Otherwise, the CTU generated on the optics separately (monosource) was compared to CTU arising from Optical-Radar couple (multisource). The analysis of the separating power of these different bands (radiometric and textural) with histograms, and the confusion matrix tool allows to compare the performance of these different scenarios and classification parameters. These comparators show the CTU, including the CTU multisource, as the most discriminating criterion; his presence adds variability in the image thus allowing a clearer segmentation (homogeneous and non-redundant), a classification both more detailed and more efficient. Indeed, the accuracy changes from 0.5 with the Optics image to 0.74 for the CTU image while confusion decreases from 0.30 (in Optics) to 0.02 (in the CTU).