Balancing water for food and environment : hydrological determinants across scales in the Thukela River Basin.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2008.In this study, geophysical measurements (Electrical Resistivity Tomography-ERT) and remote sensing techniques were applied in the Thukela river basin at various scales to complement the classical hydrometeorological networks. Detailed process hydrological studies were carried out at the Potshini catchment in the Thukela river basin to provide an in-depth understanding of the influence of different land use management practices, notably the impact of conservation tiJlage practices, on runoff generation and soil moisture retention characteristics at field scale. The general trend that was observed in the field studies is that conservation tillage systems influenced the partitioning of rainfall, by significantly reducing surface runoff over agricultural lands under conservation tillage practices, with a reduction ranging from 46 to 67%. The field soil-water balance studies also indicated that more soil moisture was retained in plots under conservation tillage practices compared to plots under conventional tillage and hence the wider adoption of such a practice could influence the partitioning of rainfall across scales. The field based study was integrated into catchment process studies where a classical hydrometrical network was complemented with geophysical measurements (ERT) along catchment transects to determine the interaction of the surface and sub-surface water and the relative contribution of the subsurface water to catchment response. The study revealed that the shallow ground water contributes significantly, close to 75%, of the stream flows in the Potshini catchment, especially during the dry seasons, with the response of the shallow ground water being a function of both the rainfall intensity and daily total amount. The potential of integrating the catchment process studies with the larger river basin scale was explored through the evaporative term of the water balance by applying the Surface Energy Balance Algorithm for Land (SEBAL), a remote sensing methodology, to estimate total evaporation (ET) from the Moderate Imaging Spectroradiometer (MODIS) satellite images. This was validated with ground measurements from a Large Aperture Scintilometer (LAS) installed in the Potshini catchment. Good comparison was established between the remotely sensed estimates and LAS measurements with a deviation range of between -14 to 26% on discrete days, where the deviation was defined as the departure of the remotely sensed estimates of ET from the respective LAS measurements. The results from this study compare well with results from similar studies in other countries with different climatic conditions. Subsequently, the evaporative water use of various land uses in the upper Thukela river basin was assessed using MODIS images. Commercial forestry was identified to be the land use with a consistent and relatively high evaporative water use In the study area. High evaporation rates over water bodies were observed during the wet summer season when both the natural and man made water bodies were at full capacity. Nevertheless, it is recognized that the inherent low resolution ofthe MODIS images could have impacted on the SEBAL results. Finally, a conceptual framework, drawing the strengths of classical hydrometeorological networks, geophysical measurements, isotope tracers and remote sensing is suggested with the potential of enhancing our understanding and conceptualization of hydrological determinants across scales. The relevance of the framework to water resources management is highlighted through its application to the Potshini catchment and the Thukela river basin using results and findings from this study

A Report by the Climate Change Science Program and the Subcommittee on Global Change Research

This strategic plan has been prepared by the 13 federal agencies participating in the CCSP, with coordination by the CCSP staff under the leadership of Dr. Richard H. Moss. This strategic plan responds to the President's direction that climate change research activities be accelerated to provide the best possible scientific information to support public discussion and decision-making on climate-related issues.The plan also responds to Section 104 of the Global Change Research Act of 1990, which mandates the development and periodic updating of a long-term national global change research plan coordinated through the National Science and Technology Council.This is the first comprehensive update of a strategic plan for U.S. global change and climate change research since the original plan for the U.S. Global Change Research Program was adopted at the inception of the program in 1989

Geo-physical parameter forecasting on imagery{based data sets using machine learning techniques

>Magister Scientiae - MScThis research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater