Temporary streams have received undeservedly little scientific attention and as a result their role in hydrological, biogeochemical and ecological processes is not yet fully understood. The ultimate goal of the research was to gain a better understanding of the temporary stream network and the processes that control it and determine how the active and connected stream length change with catchment wetness conditions to find simple methods to map seasonal and event-based changes in temporary flowing stream networks. Streams, springs and wetlands of four relatively small headwater catchments (11.7 – 25.3 km2) and one wetland in the steep and remote Zwäckentobel catchment in Alptal, canton Schwyz (Switzerland), were mapped and stream segments were classified by flow type during different weather conditions using direct observations. The mapping was performed by an elite orienteer with mapping experience. The variation in streamflow was analysed and related to the catchment wetness and topography using the TWI-values and the upslope accumulated area of the stream segments. As the catchments wetted up in response to fall rainfall events after a dry summer the flowing stream density increased up to five times and the connected stream density increased up to six times with a 150-fold increase in discharge. Also the number of flowing stream heads increased up to ten times. The best description of the pattern of stream expansion is a combination of the variable source area and the element threshold concepts, where surface topography, particularly TWI (Topographic Wetness Index) and upslope accumulated area (A), and local storage areas controls where streamflow is initiated and how flow in different stream segments connects. Streams in the Alptal show a seasonally bottom up or disjointed connection pattern. Mapping the temporary streams in steep and remote watersheds as a function of hydrological conditions is not an easy task. It is however necessary in order to fully understand where water is flowing or not. A combination of field observations with monitoring equipment can facilitate this extensive work by providing a more detailed temporal resolution.

Sweden have faced decreasing groundwater storage with critical low groundwater levels for several years. Gotland is one example with issues of providing freshwater due to the low groundwater levels. These circumstances can be related to impacts caused by early agriculture development, an increased demand of freshwater and climate change. There is a need in this region to increase the groundwater storage to ensure enough freshwater. The aim of the study is to increase freshwater storage. Digital geographical information system (GIS) was chosen as a tool in this study in order to cover large geographical areas.  The study was divided into two parts, with focus to determine hydrological and hydrogeological conditions and to identify suitable areas where groundwater storage could be increased. The first part studied: specific capacity, groundwater storage, groundwater balance and topographic wetness index. The second part locked at four methods to increase freshwater storage: Lakes, controlled drainage, wetland and subsurface dam. The result tells us that lakes have the potential to provide freshwater for the municipal distribution network. The controlled drainage method has the ability reduce the outflow of surface water and to increase the groundwater infiltration. Earlier drained wetland areas was identified which could serve as freshwater storage. Suitable areas for subsurface dams were identified. They could work as a large groundwater storage as a decentralized system with the ability to provide groundwater for wells that are spread out. However the identified areas for each methods needs further investigations in more detail to determine the accuracy of the results.

A method was developed for extending a fine-scaled forest gap model to a watershed-scaled landscape, using the Eastern Cross Timbers ecoregion as a case study for the method. A topographic wetness index calculated from digital elevation data was used as a measure of hydrologic across the modeled landscape, and the gap model modified to have with a topographically-based hydrologic input parameter. The model was parameterized by terrain type units that were defined using combinations of USDA soil series and classes of the topographic wetness index. A number of issues regarding the sources, grid resolutions, and processing methods of the digital elevation data are addressed in this application of the topographic wetness index. Three different grid sizes, 5, 10, and 29 meter, from both LiDAR-derived and contour-derived elevation grids were used, and the grids were processed using both single-directional flow algorithm and bi-directional flow algorithm. The result of these different grids were compared and analyzed in context of their application in defining terrain types for the forest gap model. Refinements were made in the timescale of gap model’s weather model, converting it into a daily weather generator, in order to incorporate the effects of the new topographic/hydrologic input parameter. The precipitation model was converted to use a Markov model to initiate a sequence of wet and dry days for each month, and then daily precipitation amounts were determined using a gamma distribution. The output of the new precipitation model was analyzed and compared with a 100-year history of daily weather records at daily, monthly, and annual timescales. Model assumptions and requirements for biological parameters were thoroughly investigated and questioned. Often these biological parameters are based on little more than assumptions and intuition. An effort to base as many of the model’s biological parameters on measured data was made, including a new technique for estimating optimal volumetric growth rate by measuring tree rings. The gap model was set up to simulate various terrain types within the landscape.

Thesis (M.Sc. (Geography)) --University of Limpopo, 2018 / Soil erosion, which is a critical component of land degradation, is one of the serious global environmental problems often threatening food security, water resources, and biodiversity. A comprehensive assessment and analysis of remote sensing applications in the spatial soil erosion mapping and monitoring over time and space is therefore, important for providing effective management and rehabilitation approaches at local, national and regional scales. The overall aim of the study was to assess the use of multispectral remote sensing sensors in mapping and monitoring the spatio-temporal variations in levels of soil erosion in the former homelands of Sekhukhune district, South Africa. Firstly, the effectiveness of the new and freely available moderate-resolution multispectral remote sensing data (Landsat 8 Operation Land Imager: OLI and Sentinel-2 Multi-Spectral Instrument: MSI) derived spectral bands, vegetation indices, and a combination of spectral bands and vegetation indices in mapping the spatio-temporal variation of soil erosion in the former homelands of Sekhukhune District, South Africa is compared. The study further determines the most optimal individual sensor variables that can accurately map soil erosion. The results showed that the integration of spectral bands and spectral vegetation indices yielded high soil erosion overall classification accuracies for both sensors. Sentinel-2 data produced an OA of 83, 81% whereas Landsat 8 has an OA of 82.86%. The study further established that Sentinel-2 MSI bands located in the NIR (0.785-0.900 μm), red edge (0.698-0.785μm) and SWIR (1.565-2.280 μm) regions were the most optimal for discriminating degraded soils from other land cover types. For Landsat 8 OLI, only the SWIR (1.560-2.300 μm), NIR (0.845-0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) and Global Environmental Monitoring Index (GEMI) were selected as the most suitable for detecting and mapping soil erosion. Secondly, the study assessed soil erosion in the former homelands of Sekhukhune, South Africa by applying a time-series analysis (2002 and 2017), to track changes of areas affected by varying degrees of erosion. Specifically, the study assessed and mapped changes of eroded areas (wet and dry season), using multi-date Landsat products 8 OLI and 7 Enhanced Thematic Mapper (ETM+)). Additionally, the study used extracted eroded areas and overlay analysis was performed together with geology, slope and the Topographic Wetness Index (TWI) of the area under study to assess whether and to what extent the observed erosional trends can be explained. ii Time series analysis indicated that the dry season of 2002, experienced 16.61 % (224733 ha) of erosion whereas in 2017 19.71% was observed. A similar trend was also observed in the wet season. This work also indicates that the dominant geology type Lebowa granite: and Rustenburg layered its lithology strata experienced more erosional disturbances than other geological types. Slopes between 2-5% (Nearly level) experienced more erosion and vice-versa. On the hand, the relationship between TWI and eroded areas showed that much erosion occurred between 3 and 6 TWI values in all the seasons for the two different years, however, the dry season of 2002 had a slightly higher relationship and vice-versa. We, therefore, recommend use and integration of freely and readily available new and free generation broadband sensors, such as Landsat data and environmental variables if soil erosion has to be well documented for purposes of effective soil rehabilitation and conservation. Keywords: Food security Global changes, Land degradation, Land-based ecosystems, Land management practices, Satellite data, Soil conservation, Sustainable Development; Topographic Wetness Index; Time series analysis.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.