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No description available.

No description available.

No description available.

Development of the shallow alluvial groundwater of the Hadejia-Jama'are-Yobe River valley flood plain (northern Nigeria) has been taking place with increasing intensity over the past decade. However little has previously been known about the nature of the Yobe River-aquifer interaction, including the recharge mechanisms. This thesis reports on a study of the river-aquifer interaction in the middle section of the basin, centred on a field site near Gashua, Yobe State. Detailed field studies were undertaken over a period of 14-months, which involved geoelectical sounding, drilling and water level monitoring. The results of the field study show that the Yobe basin is underlain by a sand and gravel aquifer, which is covered by an average of 1-3m of clay. The Yobe River is in hydraulic continuity with the adjacent alluvial aquifer and variations in aquifer storativity have been recognised as an important factor in understanding the hydraulic behaviour of the Yobe River-alluvial groundwater system. A confinedunconfined groundwater regime exists within the present site and is an inherent characteristic of the alluvial groundwater system. The recognition of this state enables a conceptual flow model of the system to be developed. A multi-layer, spatially distributed model is proposed, in which transitions between confined and unconfined conditions can be realistically represented. A numerical model needs to be designed with these concepts in mind in order to simulate the system. The research techniques employed in the study are appropriate for the assessment of the Yobe basin system where detailed data is currently not available. The combination of detailed geophysical survey, water level monitoring and conceptual modelling has led to a good understanding of the Yobe River-alluvial aquifer interaction. It is for this reason that the techniques employed in this study can be adapted for investigating the remainder of the basin downstream of the present site.

This research applies, evaluates and compares approaches to hydrological modelling and stream flow forecasting within a GIS environment. Three different approaches to modelling stream flow were investigated, namely; TOPMODEL, a regression approach and a GIS-based model, HydroGrid. TOPMODEL is a parametrically simple, physically-distributed model that allows the topological modelling of catchment processes. Regression modelling is a statistical technique that derives an empirical equation based on the assumption that the values of a dependent variable will depend upon the values of the independent variables. HydroGrid is a purpose-built GIS-based model for catchment modelling using the functionality that GIS offers for modelling the spatial variations of catchment characteristics. All three approaches were evaluated using readily available data for a lowland catchment, in Staffordshire, U.K. Model validation used six years of data covering the period 1991-92 through to 1996-97 - with years running from March-February. Five performance indicators were used to assess the models enabling both for detailed evaluation of the models and comparisons to be drawn with other research. The performance of the three models tested showed great similarities, with all approaches tending to over-predict stream flow. Model performance was also evaluated using three different evapotranspiration models - the Penman formula, the Crowe-irrigation method and the sine curve method. All three models performed best during wet years or wetter seasons indicating a common weakness in the accurate modelling of low stream flows. Despite similarities in performance, clear benefits of hydrological modelling within a GIS framework are identified. Overall, the results show that although the methods used here can help in daily flow modelling, there is a major need to improve methods for catchment modelling with routine data sources. An important development could be to loose-couple hydrological models with a GIS to improve their ability to use available information but also, as shown in this work, to model catchment processes directly within a GIS.

This research evaluates the feasibility of combining Global Positioning System (GPS) measurements with high precision gravity observations over time as a tool to calculate the mass flux within an aquifer system. This study has three major components: long-period tidal gravity measurements, repeated gravity surveys, and GPS measurements. Long-period gravity measurements were used to determine the coefficients for the gravity data reduction algorithm. Lunar and Solar components of the tidal potential were determined from long-period records to be 1.12 and 1.16 respectively. The barometric correction was calculated to be 0.40 μGals/mbar at Eloy, Arizona, and 0.23 μGals/mbar at station WR52 in Tucson, Arizona. The gravimeter was found to be sensitive to temperature and to rate of change of temperature during long-period observations. In an ongoing project (since September, 1986) in cooperation with the Water Resources Division, U.S. Geological Survey, monthly gravity observations were made at two wells and a bedrock base station and near Eloy. By regressing gravity with static water level, a specific storage of 0.12 was determined. Gravity readings showed an annual periodicity with a 20 μGal amplitude and 25 μGals/year linear drift. Gravity observations were taken at 27 stations throughout the Tucson basin at 3 month intervals for 2 years. These data were tried to bedrock reference stations and gravity variations over time were integrated to determine change in subsurface mass with time (mass flux). Temporal gravity changes ranged from 0 to 25 μGals/month (equivalent to 0-60 cm of water) in various parts of the basin. Accuracy on the order of ±30 μGals was obtained and seasonal gravity changes observed. Regional gravity change resulting from regional recharge/discharge could not be observed using my survey design and equipment. The survey precision was not sufficient to yield reliable estimates of aquifer properties. Good GPS positioning vectors were obtained for 27 of the 100 surveys. Accuracy on the order of 1-5 ppm was obtained for most vectors. Using 1987 NGS GPS data as a baseline, my observations did not indicate differences above noise levels (2-10 cm vertical); thus subsidence was not yet detectable. This research has shown that gravity measurements are useful in monitoring ground-water conditions.

The successful application of a CRR model depends on how well we handle each phase of model calibration. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain a unique set of optimal parameters for a CRR model. Unless the best set of parameters associated with a given calibration data set can be found, it is impossible to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. In this dissertation, results that clearly establish the nature of the problem of multiple optima in CRR models are presented. Based on these results it is shown why currently used optimization procedures have little chance of successfully finding the optimal parameter sets. This understanding is then used to develop a new global optimization procedure, the Shuffled Complex Evolution (SCE) method, which can efficiently and effectively identify the optimal values for the model parameters. The efficiency and effectiveness of the SCE method is first demonstrated on some theoretical test functions. It is then used to calibrate a research version of the SMA-NWSRFS model--the SIXPAR model. The SCE method is compared to other available methods used in practice on the theoretical test functions and the SIXPAR model. Finally, the SCE method is applied to the full scale SMA-NWSRFS model using both synthetic data and real data. The test results clearly indicate that the SCE method is superior to other methods tested in this research.

Although water conservation programs in the arid Southwest have prompted prudent landscaping practices such as planting low water use trees, there is little data on the actual water use of most species. Few methods or models have been developed for measuring tree water use. The stem heat balance method is one such method. Predictive models of tree water use have been limited to applications of the Penman-Monteith (PM) equation with varying degrees of success. The purpose of this study was: to validate stem flow gauge accuracy in a greenhouse and a desert environment; to determine the actual water use of two landscape tree species in Tucson, Arizona; to determine water use coefficients for two tree species based on the crop coefficient concept; and to test and develop a predictive model of tree water use based on the Penman-Monteith equation. Water use of oak (Quercus virginiana 'Heritage') and mesquite (Prosopis alba 'Colorado') trees in containers was measured using a precision balance and stem flow gauges. Water use coefficients for each tree species were calculated as the ratio of water use per total leaf area and per projected canopy area to reference evapotranspiration (ETo) using the Penman combination equation. After accounting for tree growth, water use coefficients on a total leaf area basis were calculated to be 0.48 and 0.97 for the oaks and mesquites, respectively, and 1.36 and 1.56 for the oaks and mesquites, respectively, on a projected canopy area basis. These coefficients indicate that mesquites (so called xeric trees) use more water than oaks (so called mesic trees) under non-limiting conditions. Stomatal resistances (r(s)) were calculated using the PM equation, and ranged from 20 to 200 s cm⁻¹. Calibrations were developed between r(s) and net radiation for both species. Results of the PM model to predict daily tree water use ranged from -15 to +150 percent error, depending on the tree, indicating the need for accurate measurements of stomatal resistance in order to use the PM model. Results indicate that a shortened form of the PM equation requiring only vapor pressure deficit and r(s) would be sufficient to predict tree water use.

The motivation for this dissertation is to develop innovations in spatial, environmental data analyses, using kriging and kernel estimation, that form a basis for an eventual automation of the calculations. Special consideration should be given to the different requirements for environmental data as compared to the mining data generally used in the evaluation of kriging applications. It is common to use standard search neighborhoods in the applications of kriging. It is one object of this dissertation to develop variable search neighborhoods and to extend the use of these search neighborhoods to experimental variogram calculations. Other objectives include incorporating one dimensional kernel estimation into variogram calculation; and augmenting kriging with two and three dimensional kernel estimators. These three different areas require the development of programs to accomplish the following: (1) Generate elliptical neighborhoods with variable parameters in two dimensions and ellipsoidal neighborhoods with variable parameters in three dimensions; and calculate experimental variograms using these neighborhoods to limit the number of data pairs used and thereby reduce the effects of drift. (2) Calculate experimental variograms with a one dimensional kernel to separate the bin width from the number of points which is not possible with the standard experimental variogram. (3) Use two or three dimensional kernel estimators to provide an alternate to kriging.