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The aim of the study was to establish the effects of human-induced watershed changes on strearnflows. The research hypothesised that land use change influences base flows. Enjoro river in Kenya was used as the case study. In the 1940s, the watershed was characterised with a sparse population, forestry and large scale conservative agriculture. The river regime was naturally perennial. Between 1960 and 1990 land subdivision, intensive cultivation, urbanisation, and deforestation changed such stable ecosystem Several approaches were used to evaluate the perceived cause-effect relations in the watershed. The time series of the flows, rainfall, and other climatic records were used to infer effects of changed physical characteristics in the watershed. Quantitative evaluation of the changes was accomplished by simple graphs, homogeneity tests, satellite imagery and model simulations of hydrologic variables. Analysis of the data series before and after the presumed changes provided an understanding of the variability masked in the hydrologic system. These comparisons allowed for the determination of the period in which the watershed changes influenced the river regime. The combinede ffect of humana nd natural factors decreasedth e river basef lows. A 30% increase in deforestation, 20% in agriculture and 10.4% in urbanisation was observed- Water availability decreased from a runoff coefficient of 22% in the 1960s, 10% in the 1970s and 8% in the 1980s. This progressive decline in runoff developed into hydrologic drought regime in the 1980s. Normalized difference vegetation index (NDVI) predicted well the flow changes in the watershed. Simulations of rainfall and flow supported the changes observed in the hydrologic variables. The optimised parameters with HYRROM showed 'store' parameters (SS, RDEL, GDEL) to be sensitive to changes in vegetation cover especially during the dry years of 1965,1973 and 1984. The model simulated some parameters in the watershed which could be used to infer changes in strearnflows due changes in land use. It was however, difficult to estimate and to validate long-term model parameters because of limited data and the contrasting geography of the region which induced hydrologic variability. The model did not isolate effects of specific land uses, although it predicted the observed flows. There is evidently, a need for future research on the problem. The investigation demonstrated the difficulty in identifying differences in strearnflows from watersheds undergoing simultaneous physical changes and human interventions. Since a specific effect of a particular land use change could not be isolated independently, continued research on the development of an integrating watershed coefficient is recommended. Remote sensing techniques should be incorporated in the development of integrating watershed coefficients.

In the stochastic hydrology literature, suitable time series modelling approaches have been developed for modelling daily streamflow. However, problems arise with this approach if changes are occurring to the precipitation regime generating the historic streamflow data, or if land-use changes are occurring within the catchment which may alter the water balance and the streamflow regime. Traditional time series modelling approaches employ historic streamflow data only and will generate synthetic data which are representative only of the historic conditions. It is not possible to predict how the model parameters should be changed to reflect changes in the climate (precipitation) and catchment response regimes. Developing a methodology to deal with the stochastic generation of daily streamflow that reflects changes to the catchment system and climatic inputs (rainfall and potential evapotranspiration) and then applying the corresponding methodology to a study catchment (upper Thames) in England is the focus of this study. To study the water resources impacts of land-use change on the daily streamflow regime of a catchment, a daily rainfall-runoff model is needed which can accommodate various land cover characteristics and provide separate estimates of potential and actual evapotranspiration in its evapotranspiration component for each land cover type. Given a model with this capability, the impacts of various land-use scenarios on daily streamflow can be investigated. In the case of climate change, since GCMs do not provide useable results on a short time scale such as a day and on a spatial scale such as a catchment of about 1000 km2, a methodology is required to predict the changes which may occur in the climate inputs of a catchment, and the resulting impacts on water resources. The approach developed here for water resources impact studies of land-use change and climate change has three main elements: (I) Two stochastic models, one for rainfall (Neyman-Scott Rectangular Pulses, NSRP, model) and the other for potential evapotranspiration (PET), are employed to generate daily rainfall and daily PET sequencesr,e spectively. Thesem odels have been validated using historic records for the study catchment. ABSTRACT ii (II) The ARNO model has been calibrated and validated using daily streamflow data for the study catchment. The evapotranspiration component of the model has been modified to obtain a satisfactory water balance. The model is then extended to include the explicit calculation of interception for different land cover types within the catchment. The runoff from these areas is then routed to the catchment outlet. The rainfall and PET models are used to generate synthetic daily input series to the modified ARNO model for present catchment land-use conditions, and overall procedure is validated using the historic streamflow record. This is then worked out using the extended model and referred to as the constructed` control' scenariow hich is used as a benchmarkf or assessingla nd-usec hange impacts on water resources for two different land-use scenarios. (III) The transient GCM climate scenarios are used as the starting point for assessing climate change impacts. Regression relationships are derived between atmospheric circulation variables and rainfall statistics used in fitting the NSRP model for present climate conditions and then used to predict the rainfall statistics for future conditions using GCM outputs. That is, the scenarios of a climate model are downscaled by a regression technique to a resolution sufficient to represent daily rainfall at the catchment scale. To generate potential evapotranspiration (PET) scenarios, an empirical equation is used to estimate PET daily values as a function of temperature, thus enabling future scenarios to be generated as a function of GCM temperature predictions. Generated rainfall and PET scenarios are used as inputs to the adapted ARNO catchment response model to generate daily streamflow data. Impact assessments using both land-use change and climate change scenarios are then carried out using a range of water resources assessment measures such as flow duration curves, cumulative run sums and storage/yield relationships, and the practical implications discussed.

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