Dissertations / Theses on the topic 'Hydrologic models – Orange river'

Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.

Hydrological responses vary from one catchment to another, depending on the nature of land use and cover changes. Modelling the hydrological responses to changes in land use and cover at different catchment spatial scales was the major focus of this study. This study assessed the hydrological responses attributed to changes in land use and extreme weather events resulting into increased sediment loading/concentration, rainfall-runoff generation/volume, streamflow fluctuation and modification of the river channel in the Malaba River Catchment, Eastern Uganda. The hydrological responses were assessed using hydrological models (IHACRES, SCS CN, and SHETRAN) to examine the effect of land use on soil physio-chemical properties susceptibility to rainfall-runoff generation and volume, frequency and severity of extreme weather events, changes in streamflow variations, sediment loading/concentration and river channel morphology. The preliminary study results showed that the frequency of extreme weather events reduced from 4-10 to 1-3 years over the catchment. The performance of the IHACRES model with a Nash-Sutcliffe Efficiency (NSE) of 0.89 showed that streamflow comparatively corresponded with the results obtained the drought indices in predicting the recorded events of severe drought (2005) and flood (1997). Changes in land use and cover types showed that the highest change in the gain of land was experienced from the agricultural land use (36.7 percent), and tropical forest (regeneration) (2.2 percent). The biggest losses in land were experienced in the wetlands (24.6 percent) and bushland and thickets (15.3 percent) land cover types. The SHETRAN model calibrated period had a NSE of 0.78 and 0.81 in the validation period showed satisfactory fits between the measured and simulated streamflow. The agricultural land use (crop growing) had a higher influence on the rainfall-runoff generation and increase in the streamflow than the tropical forest, and bushland cover types in the simulated period. Similarly, the curve number model estimated a comparatively higher surface rainfall-runoff volume generated from the agricultural land use (crop growing) (71,740 m3) than in the bushlands and thickets (42,872 m3) from a rainstorm followed by the tropical forest cover type. This was also reflected in the lower rates of saturated hydraulic conductivity from the agricultural land use (crop growing). The study also showed that human-induced sediment loading due to gold mining activities contributed a much higher impact on the concentration of suspended sediments and streamflow than sediments from rainfall-runoff from the sampled streams. The main contributor of human-induced sediments to the Malaba River were Nankuke River (130.6kg/annum), followed by Omanyi River (70.6kg/annum), and Nabewo River (66.8kg/annum). Human-induced sediment loading had a profound impact on the streamflow variations both in the dry and wet seasons from the sampled tributaries. Lastly, in regard to the effect of land use and cover types on the river channel morphology, tree plantation (cohesion=12, angle of internal friction=27) and bushland and thickets (cohesion=14, angle of internal friction=22) cover types had the most stable river banks compared to the wetland and agricultural land use and cover types that exhibited higher levels of sediment concentration.

This study investigated the effects of riparian vegetation on sediment transport rates and flow dynamics in the South Platte River just downstream of Fort Lupton, Colorado. FaSTMECH, a two-dimensional coupled fluvial and hydraulic model, was used to compute flow characteristics (velocity and depth) in addition to sediment mobility characteristics (shear stress and sediment flux) for four discharge levels ranging from 5% of bankfull flow to bankfull flow (Qbf). Estimates of a dimensionless drag coefficient (Cd) representative of the middle-aged bushy willows found on the river banks at the study site were used to create a spatially variable roughness in the model throughout the river reach. Model results show that during average annual flood events, vegetation on the river banks causes increased drag forces on the flow, leading to an increased proportion of flow being diverted into the main channel and resulting in higher velocities. The spatial distribution of shear stresses collapse under these conditions with an order of magnitude decreases over river banks and significant increases throughout the main channel. Sediment fluxes in the reach increase by nearly an order of magnitude with the presence of bank vegetation, however, the greatest differences occur during Qbf when the highest fraction of the sediment is mobile. Further analysis of vegetation effects was conducted by performing a sensitivity analysis by altering the representative non-dimensional vegetation drag coefficient by as much as +/- 400%. These alterations represent differences in vegetation density, height, orientation, leafy/leafless structure, age, rigidity, and vegetation type. Although there is a relationship between sediment fluxes and changes in Cd, there only exists a 14% increase in transport at Qbf between the two exterior limits of Cd.

A Disaggregation model was tested for arid land stream flow generating. The test was performed on data from Black River, near Fort Apache, Arizona. The model was tested in terms of preserving the relevant historical statistics on both monthly and daily levels, the monthly time series were disaggregated to a random observation of their daily components and the daily components were then reaggregated to yield monthly values. A computer model (DSGN) was developed to perform the model implementation. The model was written and executed on the Macintosh plus personal computer Data from two months were studied; the October data represented the low flow season, while the April data represented the high flow season. Twenty five years of data for each month was used. The generated data for the two months was compared with the historical data.

The Pacific Northwest is dependent on seasonal snowmelt for water resources that support a significant portion of its economy. Increased temperatures resulting from higher concentrations of atmospheric greenhouse gases may cause disruptions to these resources because of reductions in the annual snowpack and variations of the timing of snowmelt. This study reconstructs and applies a GIS-based distributed hydrologic model at a monthly scale to assess the effects of future climate change on runoff from the Upper Clackamas River Basin (located near Portland, Oregon). Historic flow data and snow measurements are used to calibrate and test the perfonnance of the hydro logic model for a contemporary period (1971-2000), and the model is run for two future scenarios (2010-2039 and 2070-2099) using IS92 climate change scenarios from two global climate circulation models (Hadley and Canadian Centre for Climate) as inputs. The results forecast that mean peak snowpack in the study area will drop dramatically (36% to 49% by 2010-2039, and 83% to 88% by 2070-2099), resulting in earlier runoff and diminished spring and summer flows. Increases to mean winter runoff by by the 2070-2099 period vary from moderate (13.7%) to large (46.4%), depending on the changes to precipitation forecasted by the global climate circulation models. These results are similar to those of other studies in areas dependent on snowpack for seasonal runoff, but the reductions to snowpack are more severe in this study than similar studies for the entire Columbia Basin, presumably because the elevations of much of the Upper Clackamas Basin are near the current mid-winter snow line.

Considerable interest lies in understanding the hydrologic response to climate change in the upper Deschutes Basin, particularly as it relates to groundwater fed streams. Much of the precipitation occurring in the recharge zone falls as snow. Consequently, the timing of runoff and recharge depend on accumulation and melting of the snowpack. Numerical modeling can provide insights into evolving hydrologic system response for resource management consideration. A daily mass and energy balance model known as the Deep Percolation Model (DPM) was developed for the basin in the 1990s. This model uses spatially distributed data and is driven with daily climate data to calculate both daily and monthly mass and energy balance for the major components of the hydrologic budget across the basin. Previously historical daily climate data from weather stations in the basin was used to drive the model. Now we use the University of Washington Climate Impact Group's 1/16th degree daily downscaled climate data to drive the DPM for forecasting until the end of the 21st century. The downscaled climate data is comprised from the mean of eight GCM simulations well suited to the Pacific Northwest. Furthermore, there are low emission and high emission scenarios associated with each ensemble member leading to two distinct means. For the entire basin progressing into the 21st century, output from the DPM using both emission scenarios as a forcing show changes in the timing of runoff and recharge as well as significant reductions in snowpack. Although the DPM calculated amounts of recharge and runoff varies between the emission scenario of the ensemble under consideration, all model output shows loss of the spring snowmelt runoff / recharge peak as time progresses. The response of the groundwater system to changing in the time and amount of recharge varies spatially. Short flow paths in the upper part of the basin are potentially more sensitive to the change in seasonality. However, geologic controls on the system cause this signal to attenuate as it propagates into the lower portions of the basin. This scale-dependent variation to the response of the groundwater system to changes in seasonality and magnitude of recharge is explored by applying DPM calculated recharge to an existing regional groundwater flow model.

One of the most important purposes of surface water resource management is to develop predictive models to assist in identifying and evaluating operational and structural measures for improving water quality. To better understand the effects of external and internal nutrient and organic loading and the effects of reservoir operation, a model is often developed, calibrated, and used for sensitivity and management simulations. The importance of modeling and simulation in the scientific community has drawn interest towards methods for automated calibration. This study addresses using an automatic technique to calibrate the water quality model CE-QUAL-W2 (Cole and Wells, 2013). CE-QUAL-W2 is a two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model for surface water bodies, modeling eutrophication processes such as temperature-nutrient-algae-dissolved oxygen-organic matter and sediment relationships. The numerical method used for calibration in this study is the particle swarm optimization method developed by Kennedy and Eberhart (1995) and inspired by the paradigm of birds flocking. The objective of this calibration procedure is to choose model parameters and coefficients affecting temperature, chlorophyll a, dissolved oxygen, and nutrients (such as NH4, NO3, and PO4). A case study is presented for the Karkheh Reservoir in Iran with a capacity of more than 5 billion cubic meters that is the largest dam in Iran with both agricultural and drinking water usages. This algorithm is shown to perform very well for determining model parameters for the reservoir water quality and hydrodynamic model. Implications of the use of this procedure for other water quality models are also shown.

Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient dynamics. Meanwhile, densification associated with urban growth will create more impervious surfaces which will alter sediment and nutrient loadings. Land and water managers often rely on models to develop possible future scenarios and devise management responses to these projected changes. We use the Soil and Water Assessment Tool (SWAT) to assess the sensitivities of stream flow, sediment, and nutrient loads in two urbanizing watersheds in Northwest Oregon, USA to various climate and urbanization scenarios. We evaluate the spatial patterns climate change and urban growth will have on water, sediment and nutrient yields. We also identify critical source areas (CSAs) and investigate how implementation of vegetative filter strips (VFS) could ameliorate the effects of these changes. Our findings suggest that: 1) Water yield is tightly coupled to precipitation. 2) Large increases in winter and spring precipitation provide enough sub-surface storage to increase summertime water yields despite a moderate decrease in summer precipitation. 3) Expansion of urban areas increases surface runoff and has mixed effects on sediment and nutrients. 4) Implementation of VFS reduces pollutant loads helping overall watershed health. This research demonstrates the usefulness of SWAT in facilitating informed land and water management decisions.

The recent high profile flooding events – that have occurred in many parts of the world – have drawn attention to the need for new and improved methods for water resources assessment, water management and the modelling of large-scale flooding events. In the case of the Zambezi Basin, a review of the 2000 and 2001 floods identified the need for tools to enable hydrologists to assess and predict daily stream flow and identify the areas that are likely to be affected by flooding. As a way to address the problem, a methodology was set up to derive catchment soil moisture statistics from Earth Observation (EO) data and to study the improvements brought about by an assimilation of this information into hydrological models for improving reservoir management in a data scarce environment. Rainfall data were obtained from the FEWSNet Web site and computed by the National Oceanic and Atmospheric Administration Climatic Prediction Center (NOAA/CPC). These datasets were processed and used to monitor rainfall variability and subsequently fed into a hydrological model to predict the daily flows for the Zambezi River Basin. The hydrological model used was the Geospatial Stream Flow Model (GeoSFM), developed by the United States Geological Survey (USGS). GeoSFM is a spatially semi-distributed physically-based hydrological model, parameterised using spatially distributed topographic data, soil characteristics and land cover data sets available globally from both Remote Sensing and in situ sources. The Satellite rainfall data were validated against data from twenty (20) rainfall gauges located on the Lower Zambezi. However, at several rain gauge stations (especially those with complex topography, which tended to experience high rainfall spatial variability), there was no direct correlation between the satellite estimates and the ground data as recorded in daily time steps. The model was calibrated for seven gauging stations. The calibrated model performed quite well at seven selected locations (R2=0.66 to 0.90, CE=0.51 to 0.88, RSR=0.35 to 0.69, PBIAS=−4.5 to 7.5). The observed data were obtained from the National Water Agencies of the riparian countries. After GeoSFM calibration, the model generated an integration of the flows into a reservoir and hydropower model to optimise the operation of Kariba and Cahora Bassa dams. The Kariba and Cahora Bassa dams were selected because this study considers these two dams as the major infrastructures for controlling and alleviating floods in the Zambezi River Basin. Other dams (such as the Kafue and Itezhi-Thezi) were recognised in terms of their importance but including them was beyond the scope of this study because of financial and time constraints. The licence of the reservoir model was limited to one year for the same reason. The reservoir model used was the MIKE BASIN, a professional engineering software package and quasi-steady-state mass balance modelling tool for integrated river basin and management, developed by the Denmark Hydraulic Institute (DHI) in 2003. The model was parameterised by the geometry of the reservoir basin (level, area, volume relationships) and by the discharge-level (Q-h) relationship of the dam spillways. The integrated modelling system simulated the daily flow variation for all Zambezi River sub-basins between 1998 and 2008 and validated between 2009 and 2011. The resulting streamflows have been expressed in terms of hydrograph comparisons between simulated and observed flow values at the four gauging stations located downstream of Cahora Bassa dam. The integrated model performed well, between observed and forecast streamflows, at four selected gauging stations (R2=0.53 to 0.90, CE=0.50 to 0.80, RSR=0.49 to 0.69, PBIAS=−2.10 to 4.8). From the results of integrated modelling, it was observed that both Kariba and Cahora Bassa are currently being operated based on the maximum rule curve and both remain focused on maximising hydropower production and ensuring dam safety rather than other potential influences by the Zambezi River (such as flood control downstream – where the communities are located – and environmental issues). In addition, the flood mapping analysis demonstrated that the Cahora Bassa dam plays an important part in flood mitigation downstream of the dams. In the absence of optimisation of flow releases from both the Kariba and Cahora Bassa dams, in additional to the contribution of any other tributaries located downstream of the dams, the impact of flooding can be severe. As such, this study has developed new approaches for flood monitoring downstream of the Zambezi Basin, through the application of an integrated modelling system. The modelling system consists of: predicting daily streamflow (using the calibrated GeoSFM), then feeding the predicted streamflow into MIKE BASIN (for checking the operating rules) and to optimise the releases. Therefore, before releases are made, the flood maps can be used as a decision-making tool to both assess the impact of each level of release downstream and to identify the communities likely to be affected by the flood – this ensures that the necessary warnings can be issued before flooding occurs. Finally an integrated flood management tool was proposed – to host the results produced by the integrated system – which would then be accessible for assessment by the different users. These results were expressed in terms of water level (m). Four discharge-level (Q-h) relationships were developed for converting the simulated flow into water level at four selected sites downstream of Cahora Bassa dam – namely: Cahora Bassa dam site, Tete (E-320), Caia (E-291) and Marromeu (E-285). However, the uncertainties in these predictions suggested that improved monitoring systems may be achieved if data access at appropriate scale and quality was improved.

The wet weather pattern since the early 1990's has created two problems for the people living in the Red River Valley (RRV): (1) wet field conditions for farmers and (2) more frequent major spring floods in the Red River system. Farmers in the region are increasingly adopting subsurface drainage practice to remove excess water from their fields to mitigate the first problem. However, it is not clear whether subsurface drainage will deteriorate or mitigate the spring flood situation, the second problem. The Soil and Water Assessment Tool (SWAT) model was applied to evaluate the impacts of tile drainage on the Red River's streamflows. The model was calibrated and validated against monthly streamflows at the watershed scale and against daily tile flows at the field scale. The locations and areas of the existing and potential tile drained (PTD) areas were identified using a GIS based decision tree classification method. The existing and maximum PTD areas were found to be about 0.75 and 17.40% of the basin area, respectively. At the field scale, the range of Nash-Sutcliffe efficiency (NSE) for model calibration and validation was 0.34-0.63. At the watershed scale, the model showed satisfactory performance in simulating monthly streamflows with NSE ranging from 0.69 to 0.99, except that the model under-predicted the highest spring flood peak flows in three years. The results of modeling a 100% tiled experimental field showed that about 30-40% of water yield was produced as tile flow. Surface runoff and soil water content decreased about 34% and 19%, respectively, due to tile drainage. However, the impact of subsurface drainage on evapotranspiration (ET) and water yield was mixed. ET slightly decreased in a wet year and slightly increased in a dry year, while the pattern for water yield was opposite to that of ET. The watershed-scaled modeling results showed that a tiling rate of 0.75-5.70% would not have significant effects on the monthly average streamflows in the Red River at Fargo. For the 17.40% tiling rate, the streamflow in the Red River at Fargo might increase up to 1% in April and about 2% in Fall (September to November), while decreasing up to 5% in the remaining months. This SWAT modeling study helped to better understand the impact of subsurface drainage on the water balance and streamflows in the Red River of the North basin. The findings will also help watershed managers in making decisions for the purpose of managing agricultural drainage development in the RRV and other snow dominated watersheds around the world.

A comprehensive hydrological study of large watersheds in Africa e.g. the Congo basin and the Nile basin has not been vigorously pursued for various reasons. One of the major reasons is the lack of adequate modeling tools that would not be very demanding in terms of input data needs and yet inclusive enough to cover such wide extents (over 3 million square kilometers for the Congo basin). Using a coupled run of the Community Atmospheric model (CAM3) and Community Land Model (CLM3) components of the Community Climate System of Models (CCSM), this study looks into the spatial and temporal variation of precipitation and river runoff in the Congo basin in the light of increasing trends in deforestation of the tropical forests. The effect of deforestation on precipitation and runoff is investigated by changing the land cover-type from the current configuration of broadleaf evergreen/deciduous, non-Artic grass and corn to a mostly grass type of vegetation. Discharge simulation for the river Congo is centered at the point of entrance to the Atlantic Ocean. Although the CLM3 does not presently simulate the observed river runoff to within at least one standard deviation it gives an opportunity to iteratively improve on the land surface parameterization with a possibility of future accurate prediction of mean monthly river runoffs under varying climate scenarios and land use practices. When forced with the National Center for Environment and Prediction (NCEP) re-analysis data the CLM3 runoff simulation results are relatively more stable and much closer to the observed. An improved CLM3 when coupled to CAM3 or other Global Climate Models is definitely a better tool for investigative studies on the regional hydrological cycle in comparison to the traditional methods. There was a slight reduction in rainfall in the first experiment which mimicked a severe form of deforestation and a slight increase in rainfall following low level of deforestation. These changes in rainfall were however statistically insignificant when compared to the control simulation. There was notable heterogeneity in the spatial distribution of the changes in rainfall following deforestation.

The Caledon River Basin is located on one of the most water-scarce region on the African continent. The water resources of the Caledon River Basin play a pivotal role in socio-economic activities in both Lesotho and South Africa but the basin experiences recurrent severe droughts and frequent water shortages. The Caledon River is mostly used for commercial and subsistence agriculture, industrial and domestic supply. The resources are also important beyond the basin’s boundaries as the water is transferred to the nearby Modder River. The Caledon River is also a significant tributary to the Orange-Senqu Basin, which is shared by five southern African countries. However, the water resources in the basin are under continuous threat as a result of rapidly growing population, economic growth as well as changing climate, amongst others. It is therefore important that the hydrological regime and water resources of the basin are thoroughly evaluated and assessed so that they can be sustainably managed and utilised for maximum economic benefits. Climate change has been identified by the international community as one of the most prominent threats to peace, food security and livelihood and southern Africa as among the most vulnerable regions of the world. Water resources are perceived as a natural resource which will be affected the most by the changing climate conditions. Global warming is expected to bring more severe, prolonged droughts and exacerbate water shortages in this region. The current study is mainly focused on investigating the impacts of climate change on the water resources of the Caledon River Basin. The main objectives of the current study included assessing the past and current hydrological characteristics of the Caledon River Basin under current state of the physical environment, observed climate conditions and estimated water use; detecting any changes in the future rainfall and evaporative demands relative to present conditions and evaluating the impacts of climate on the basin’s hydrological regime and water resources availability for the future climate scenario, 2046-2065. To achieve these objectives the study used observed hydrological, meteorological data sets and the basin’s physical characteristics to establish parameters of the Pitman and WEAP hydrological models. Hydrological modelling is an integral part of hydrological investigations and evaluations. The various sources of uncertainties in the outputs of the climate and hydrological models were identified and quantified, as an integral part of the whole exercise. The 2-step approach of the uncertainty version of the model was used to estimate a range of parameters yielding behavioural natural flow ensembles. This approach uses the regional and local hydrological signals to constrain the model parameter ranges. The estimated parameters were also employed to guide the calibration process of the Water Evaluation And Planning (WEAP) model. The two models incorporated the estimated water uses within the basin to establish the present day flow simulations and they were found to sufficiently simulate the present day flows, as compared to the observed flows. There is an indication therefore, that WEAP can be successfully applied in other regions for hydrological investigations. Possible changes in future climate regime of the basin were evaluated by analysing downscaled temperature and rainfall outputs from a set of 9 climate models. The predictions are based on the A2 greenhouse gases emission scenario which assumes a continuous increase in emission rates. While the climate models agree that temperature, and hence, evapotranspiration will increase in the future, they demonstrate significant disagreement on whether rainfall will decrease or increase and by how much. The disagreement of the GCMs on projected future rainfall constitutes a major uncertainty in the prediction of water resources availability of the basin. This is to the extent that according to 7 out of 9 climate models used, the stream flow in four sub-basins (D21E, D22B, D23D and D23F) in the Caledon River Basin is projected to decrease below the present day flows, while two models (IPSL and MIUB) consistently project enhanced water resource availability in the basin in the future. The differences in the GCM projections highlight the margin of uncertainty involved predicting the future status of water resources in the basin. Such uncertainty should not be ignored and these results can be useful in aiding decision-makers to develop policies that are robust and that encompass all possibilities. In an attempt to reduce the known uncertainties, the study recommends upgrading of the hydrological monitoring network within the Caledon River Basin to facilitate improved hydrological evaluation and management. It also suggests the use of updated climate change data from the newest generation climate models, as well as integrating the findings of the current research into water resources decision making process.

The world is undergoing rapid changes and the future is uncertain. The changes are related to modification of the landscape due to human activities, such as large and small scale irrigation, afforestation and changes to the climate system. Understanding and predicting hydrologic change is one of the challenges facing hydrologists today. Part of this understanding can be developed from observed data, however, there often too few observations and those that are available are frequently affected by uncertainties. Hydrological models have become essential tools for understanding historical variations of catchment hydrology and for predicting future possible trends. However, most developing countries are faced with poor spatial distributions of rainfall and evaporation stations that provide the data used to force models, as well as stream flow gauging stations to provide the data for establishing models and for evaluating their success. Hydrological models are faced with a number of challenges which include poor input data (data quality and poorly quantified human activities on observed stream flow data), uncertainties associated with model complexity and structure, the methods used to quantify model parameters, together with the difficulties of understanding hydrological processes at the catchment or subbasin. Within hydrological modelling, there is currently a trend of dealing with equifinality through the evaluation of parameter identifiability and the quantification of uncertainty bands associated with the predictions of the model. Hydrological models should not only focus on reproducing the past behaviour of a basin, but also on evaluating the representativeness of the surface and subsurface model components and their ability to simulate reality for the correct reasons. Part of this modelling process therefore involves quantifying and including all the possible sources of uncertainty. Uncertainty analysis has become the standard approach to most hydrological modelling studies, but has yet to be effectively used in practical water resources assessment. This study applied a hydrological modelling approach for understanding the hydrology of a large Tanzanian drainage basin, the Great Ruaha River that has many areas that are ungauged and where the available data (climate, stream flow and existing water use) are subject to varying degrees of uncertainty. The Great Ruaha River (GRR) is an upstream tributary of the Rufiji River Basin within Tanzania and covers an area of 86 000 km2. The basin is drained by four main tributaries; the Upper Great Ruaha, the Kisigo, the Little Ruaha and the Lukosi. The majority of the runoff is generated from the Chunya escarpment, the Kipengere ranges and the Poroto Mountains. The runoff generated feeds the alluvial and seasonally flooded Usangu plains (including the Ihefu perennial swamp). The majority of the irrigation water use in the basin is located where headwater sub‐basins drain towards the Usangu plains. The overall objective was to establish uncertain but behavioural hydrological models that could be useful for future water resources assessments that are likely to include issues of land use change, changes in patterns of abstraction and water use, as well the possibility of change in future climates.

Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. Reliable forecast of hydrologic variables, in general, is a preliminary requirement for appropriate planning of water resources and developing effective allocation policies. This study aims at developing new techniques with specific probabilistic features to improve the reliability of hydrologic forecasts, particularly the drought forecasts. The drought status in the future is determined by certain hydrologic variables that are basically estimated by the hydrologic models with rather simple to complex structures. Since the predictions of hydrologic models are prone to different sources of uncertainties, there have been several techniques examined during past several years which generally attempt to combine the predictions of single (multiple) hydrologic models to generate an ensemble of hydrologic forecasts addressing the inherent uncertainties. However, the imperfect structure of hydrologic models usually lead to systematic bias of hydrologic predictions that further appears in the forecast ensembles. This study proposes a post-processing method that is applied to the raw forecast of hydrologic variables and can develop the entire distribution of forecast around the initial single-value prediction. To establish the probability density function (PDF) of the forecast, a group of multivariate distribution functions, the so-called copula functions, are incorporated in the post-processing procedure. The performance of the new post-processing technique is tested on 2500 hypothetical case studies and the streamflow forecast of Sprague River Basin in southern Oregon. Verified by some deterministic and probabilistic verification measures, the method of Quantile Mapping as a traditional post-processing technique cannot generate the qualified forecasts as comparing with the copula-based method. The post-processing technique is then expanded to exclusively study the drought forecasts across the different spatial and temporal scales. In the proposed drought forecasting model, the drought status in the future is evaluated based on the drought status of the past seasons while the correlations between the drought variables of consecutive seasons are preserved by copula functions. The main benefit of the new forecast model is its probabilistic features in analyzing future droughts. It develops conditional probability of drought status in the forecast season and generates the PDF and cumulative distribution function (CDF) of future droughts given the past status. The conditional PDF can return the highest probable drought in the future along with an assessment of the uncertainty around that value. Using the conditional CDF for forecast season, the model can generate the maps of drought status across the basin with particular chance of occurrence in the future. In a different analysis of the conditional CDF developed for the forecast season, the chance of a particular drought in the forecast period can be approximated given the drought status of earlier seasons. The forecast methodology developed in this study shows promising results in hydrologic forecasts and its particular probabilistic features are inspiring for future studies.

[Truncated abstract] During the last fifty years mathematical models of catchment hydrology have been widely developed and used for hydrologic forecasting, design and water resources management. Most of these models need large numbers of parameters to represent the flow generation process. The model parameters are estimated through calibration techniques and often lead to ‘unrealistic’ values due to structural error in the model formulations. This thesis presents a new strategy for developing catchment hydrology models for representing streamflow and salinity generation processes. The strategy seeks to ‘learn from data’ in order to specify a conceptual framework that is appropriate for the particular space and time scale under consideration. Initially, the conceptual framework is developed by considering large space and time scales. The space and time scales are then progressively reduced and conceptual model complexity systematically increased until ultimately, an adequate simulation of daily streamflow and salinity is achieved. This strategy leads to identification of a few key physically meaningful parameters, most of which can be estimated a priori and with minimal or no calibration. Initially, the annual streamflow data from ten experimental catchments (control and cleared for agriculture) were analysed. The streamflow increased in two phases: (i) immediately after clearing due to reduced evapotranspiration, and (ii) through an increase in stream zone saturated area. The annual evapotranspiration losses from native vegetation and pasture, the ‘excess’ water (resulting from reduced transpiration after land use change), runoff and deep storage were estimated by a simple water balance model. The model parameters are obtained a priori without calibration. The annual model was then elaborated by analysing the monthly rainfall-runoff, groundwater and soil moisture data from four experimental catchments. Ernies (control, fully forested) and Lemon (53% cleared) catchments are located in zone with a mean annual rainfall of 725 mm. Salmon (control, fully forested) and Wights (100% cleared) are located in zone with mean annual rainfall of 1125 mm. Groundwater levels rose and the stream zone saturated area increased significantly after clearing. From analysis of this data it was evident that at a monthly time step the conceptual model framework needed to include a systematic gain/loss to storage component in order to adequately describe the observed lags between peak monthly rainfall and runoff.

A crescente produção de carga orgânica e de nutrientes em bacias hidrográficas tem levado, historicamente, rios a significativas mudanças nos padrões de qualidade da água, na diversidade e riqueza biológica e nos usos nobres de suas águas. Neste trabalho, a qualidade da água na bacia do rio Doce foi avaliada através de um modelo hidrológico integrado a um SIG, denominado SGAG (Sistema Georeferenciado de Apoio ao Gerenciamento). O sistema foi capaz de analisar o efeito de lançamentos múltiplos de efluentes na qualidade da água do rio Doce no período de estiagem. O modelo foi calibrado satisfatoriamente utilizando os dados de monitoramento ao longo do curso do rio Doce. Cenários de planejamento também foram estabelecidos, simulados e avaliados. As simulações mostraram que as medidas propostas para os cenários de planejamento promovem uma significativa redução das concentrações dos poluentes em comparação com o cenário atual, principalmente no cenário de tratamento universal. O modelo hidrológico em ambiente SIG proposto mostrou ser uma ferramenta promissora e simples para investigação da qualidade da água de bacias hidrográficas sujeitas a lançamentos pontuais de efluentes.
The increase of organic and nutrient loading in watersheds have led to significant changes in water quality patterns in rivers. In this work we evaluated the water quality of the Basin of River Doce basin through an hydrologic model integrated with GIS, called SGAG (Georeferencing Management Support System). The model was calibrated using water quality monitoring data throughout River Doce extension. Moreover planning scenarios were established, simulated and evaluated. The simulations indicated that the corrective measures in the planning scenarios promoted a significant decrease of the pollutant concentrations in comparison with the current situation. The hydrological model coupled with GIS environment have shown to be a simple and a promising tool to investigating water quality in basins subject to point sources of pollution.

Les bassins versants du pourtour méditerranéen français sont touchés par des pluies parfois intenses et à fort cumuls, qui peuvent engendrer des crues à cinétique rapide. Les derniers exemples majeurs en date sont ceux de l’Aude en 1999, du Gard en 2002 et du Var en 2010, dont les conséquences furent dramatiques. Ces travaux de thèse visent à évaluer une approche de modélisation complémentaire aux outils dont disposent déjà les Services de Prévision des Crues pour la prévision des crues à cinétique rapide : le couplage de modèles hydrologique et hydraulique, qui est a priori adapté pour la modélisation à l’échelle des grands bassins méditerranéens, de superficies supérieures à 1 000 km² (Ardèche, Cèze, Gardon, Vidourle…). Des éléments de réponses aux 4 questions suivantes sont recherchés : 1) le couplage est-il adapté pour la modélisation des débits atteints lors d’évènements passés, d’importance intermédiaire ? 2) le couplage est-il performant pour la modélisation des débits, cotes atteintes, et extension d’inondation, observés lors d’un épisode majeur? 3) comment envisager d’améliorer la modélisation des apports latéraux non-jaugés au modèle hydraulique, tout en adoptant une démarche adaptée à la prévision ? 4) le couplage est-il performant en prévision ? Le couplage employé combine le modèle hydrologique SCS-LR de la plateforme ATHYS (Bouvier et al., 2004), et le code de modélisation hydraulique 1D MASCARET (EDF-CETMEF, 2011). Il est appliqué au bassin versant du Gardon (2 040 km²), dans le sud de la France
The French catchments around the Mediterranean Sea are affected by intense rains, which can cause fast and flash floods. The last major events are the one of the Aude river in 1999, of the Gard area in 2002, and of the Var area in 2010, whose consequences were tragic. This PhD intends to assess a modeling strategy complementary to the tools that are already used by the regional flood warning services: the coupling of hydrologic and hydraulic models, which is a priori well-adapted for the modelling of catchments of large-scale areas (larger than 1 000 km²) around the Mediterranean Sea (such as the ones of the Ardèche river, the Cèze river, the Vidourle river, the Gardon river…). The works aim at bringing elements of responses to the following questions: 1) is the coupling adapted to the modelling of floods hydrographs of past events of moderate importance? 2) in case of an extreme event (like in September 2002), is the coupling effective for the modelling of discharges, of water levels, and of flood extension? 3) how can we improve the modelling of ungauged lateral inflows to the hydraulic model, while applying a method adapted to forecasting? 4) Is the coupling efficient at forecasting? The coupling used combines the SCS-LR hydrologic model of the ATHYS platform (Bouvier et al., 2004), and the MASCARET 1D hydraulic model (EDF-CETMEF, 2011). It is applied to the Gardon river basin (2 040 km²), in the South of France

This thesis describes the development of a decision support tool to be used in the operation of Vanderkloof Dam on the Orange River so that the supply of water to the lower Orange River can be optimised. The decision support tool is based on a hydrodynamic model that was customised to incorporate real time data recorded at several points on the river. By incorporating these data into the model the simulated flows are corrected to the actual flow conditions recorded on the river, thereby generating a best estimate of flow conditions at any given time. This information is then used as the initial conditions for forecast simulations to assess whether the discharge volumes and schedules from the dam satisfy the water demands of downstream users, some of which are 1400km or up to 8 weeks away. The various components of the decision support system, their functionality and their interaction are described. The details regarding the development of these components include: • The hydraulic model of the Orange River downstream of Vanderkloof Dam. The population and calibration of the model are described. • The modification of the code of the hydrodynamic engine so that real time recorded stage and flow data can be incorporated into the model • The development of a graphical user interface to facilitate the exchange of data between the real time network of flow gauging stations on the Orange River and the hydraulic model • The investigation into the effect of including the real time data on the simulated flows • Testing the effectiveness of the decision support system.
Thesis (M.Sc.)-University of Natal, Durban, 2002.

Submitted in fulfillment of the requirements of the Degree of Master of Technology: Civil Engineering, Durban University of Technology, 2014.
Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

The segment of the Pecos River that extends from Red Bluff Reservoir until it discharges to the Rio Grande/Bravo near Langtry was studied in this project. Hydrologic behavior of the basin was analyzed between 1981 and 2000, the first ten year period for calibration and the second ten year period for validation by using Water Evaluation and Planning Software (WEAP, SEI, 2006). Simulated streamflows were compared with naturalized streamflows (RJBCO, 2003) at two control points, one in the middle of the basin near Girvin and the other one is at the end of the basin near Langtry. The purpose of the project is to create a valid model for water availability simulations in the Pecos River Basin to be used for future water availability simulations considering climate change effects. The basin was divided into two parts in order to evaluate the results, the upper basin and the entire basin (below Red Bluff reservoir) according to the location of control gages. Simulated streamflows closely match the naturalized flows at the Girvin station in the upper basin. Although the results at the Langtry station for the entire basin are not as good as Girvin, the model still reproduces streamflows well enough to represent the hydrologic behavior of the basin, especially for the base flow. Considering the complex geological structure of the Pecos River Basin below Red Bluff Reservoir, the results can be considered satisfactory. The model can be used for future water availability predictions in the basin considering climate change effects.
text

Finally, the newly integrated model was firstly applied to analyze the relationship of land use and hydrologic regimes based on the land use maps in 1980 and 2000. Then the newly integrated model was applied to simulate the potential impacts of land use change on hydrologic regimes in the East River Basin under a series of hypothetical scenarios. The results show that ET has a positive relationship with Leaf Area Index (LAI) while runoff has a negative relationship with LAI in the same climatic zone, which can be elaborated by surface energy balance and water balance equation. Specifically, on an annual basis, ET of forest scenarios is larger than that of grassland or cropland scenarios. On the contrary, runoff of forest scenarios is less than that of grassland or cropland scenarios. On a monthly basis, for most of the scenarios, particularly the grassland and cropland scenarios, the most significant changes occurred in the rainy season. The results indicate that deforestation would cause increase of runoff and decrease of ET on an annual basis in the East River Basin. On a monthly basis, deforestation would cause significant decrease of ET and increase of runoff in the rainy season in the East River Basin.
In order to effectively simulate hydrologic impact of LUCC, an integrated model of ESEBS and distributed monthly water balance model has been developed in this study. The model is capable of considering basin terrain and the spatial distribution of precipitation and soil moisture. Particularly, the model is unique in accounting for spatial and temporal variations of vegetation cover and ET, which provides a powerful tool for studying the hydrologic impacts of LUCC. The model was applied to simulate the monthly runoff for the period of 1980-1994 for model calibration and for the period of 1995-2000 for validation. The calibration and validation results show that the newly integrated model is suitable for simulating monthly runoff and studying hydrologic impacts ofLUCC in the East River Basin.
Land use/cover change (LUCC) has significantly altered the hydrologic system in the East River (Dongjiang) Basin. Quantitative modeling of hydrologic impacts of LUCC is of great importance for water supply, drought monitoring and integrated water resources management. An integrated eco-hydrologic modeling system of Distributed Monthly Water Balance Model (DMWBM), Surface Energy Balance System (SEBS) was developed with aid of GIS/RS to quantify LUCC, to conduct physically-based ET (evapotranspiration) mapping and to predict hydrologic impacts of LUCC.
The physically-remote-sensing-based Surface Energy Balance System (SEBS) was employed to estimate areal actual ET for a large area rather than traditional point measurements . The SEBS was enhanced for application in complex vegetated area. Then the inter-comparison with complimentary ET model and distributed monthly water balance model was made to validate the enhanced SEBS (ESEBS). The application and test of ESEBS show that it has a good accuracy both monthly and annually and can be effectively applied in the East River Basin. The results of ET mapping based on ESEBS demonstrate that actual ET in the East River Basin decreases significantly in the last two decades, which is probably caused by decrease of sunshine duration.
These results are not definitive statements as to what will happen to runoff, ET and soil moisture regimes in the East River Basin, but rather offer an insight into the plausible changes in basin hydrology due to land use change. The integrated model developed in this study and these results have significant implications for integrated water resources management and sustainable development in the East River Basin.
To begin with, in order to evaluate LUCC, understand implications of LUCC and provide boundary condition for the integrated eco-hydrologic modeling, firstly the long-term vegetation dynamics was investigated based on Normalized Difference Vegetation Index (NDVI) data, and then LUCC was analyzed with post-classification methods and finally LUCC prediction was conducted based on Markov chain model. The results demonstrate that the vegetation activities decreased significantly in summer over the years. Moreover, there were significant changes in land use/cover over the past two decades. Particularly there was a sharp increase of urban and built-up area and a significant decrease of grassland and cropland. All these indicate that human activities are intensive in the East River Basin and provide valuable information for constructing scenarios for studying hydrologic impacts of LUCC.
Wang, Kai.
"December 2010."
Adviser: Yongqin Chen.
Source: Dissertation Abstracts International, Volume: 73-04, Section: A, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 203-227).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.

The research project described in this dissertation studies the modelling techniques employed for the Mlazi River in the context of flood analysis and flood forecasting in order to model flood inundation. These techniques are applicable to an environment where there is uncertainty due to a lack of historical input data for calibration and validation purposes. This uncertainty is best explained by understanding the process and data required to model flood inundation. In order to model flood inundation in real time, forecasted flood flows would be required as input to a hydraulic river model used for simulating flood inundation levels. During this process, forecasted flood flows would be obtained from a flood-forecasting model that would need to be calibrated and validated. The calibration process would require historical rainfall data correlating with streamflow data and subsequently, the validation process would require real time streamflow data. In the context of the Mlazi Catchment, there are only two stream gauges located in the upper subcatchments. Although these stream gauges have recorded data for 20 years, the streamflow data does not correlate with disaggregated daily rainfall data, of which there are records for at least 40 years. Therefore it would be difficult to develop the forecasting model based on the rainfall and streamflow data available. In this instance, a more realistic approach to modelling flood inundation involved the integration of GIS technology, a physically based hydrological model for flood analysis, a conceptual forecasting model for real time forecasting and a hydraulic model for computation of inundation levels. The integration of modelling techniques are better explained by categorising the process into three phases: Phase 1 Desktop catchment modelling: A continuous, physically based simulation model (HEC-HMS Model) was set up using GIS technology. The model applied the SCS-UH method for the estimation of peak discharges. Synthetic hyetographs for various recurrence intervals were used as input to the model. A sensitivity analysis was implemented and subsequently the HEC-HMS model was calibrated against output SCS-UH method and peak discharges simulated. The synthetic hyetographs together with results from the HEC-HMS model were used for validation of the Mlazi Meta Model (MMM) used for real time flood forecasting. Phase 2 Implementation of the Inundation Model: The hydraulic model (HEC-RAS) was created using a Digital Elevation Model (DEM). A field survey was conducted for the purpose of capturing the roughness coefficients and hydraulic structures, which were incorporated into the model and also for the confirmation of the terrain cross sections from the DEM. Flow data for the computation of levels of inundation were obtained from the HEC-HMS model. The levels of inundation for the natural channel of Mlazi River were simulated using the one dimensional steady state analysis, whereas for the canal overbank areas, simulation was conducted for unsteady state conditions. Phase 3 Creation of the Mlazi Meta Model (MMM): The MMM used for real time flood forecasting is a linear catchment model which consists of a semi-distributed three reservoir cell model (Pegram and Sinclair, 2002). The MMM parameters were initially adjusted using the HEC-HMS model so that it became representative of the Mlazi catchment. This approach sounds unreasonable because a model is being validated by another model but it gave the best initial estimate of the parameters rather than using trial and error. The MMM will be further updated using record radar data and streamflow data once all structures have been put in place. The confidence in the applicability of the HEC-HMS model is based on the intensive efforts applied in setting it up. Furthermore, the output results from the calibrated HEC-HMS model were compared with other reliable methods of computing design peak discharges and also validated with frequency analysis conducted on one of the subcatchments.
Thesis (M.Sc.)-University of Natal, Durban,2003.

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

A lumped conceptual rainfall-runoff model based on the MIKE 11/NAM modeling code has been applied to the 372 km2 Tsitika River Watershed. The model was constructed on the basis of readily accessible data of precipitation, temperature, land cover and topography, and was calibrated against the river discharge at the watershed outlet. Several validation tests were carried out and model performance evaluated in terms of the water balance error and the agreement of general hydrograph shape. Sensitivity analysis was conducted to explore the variability of simulation response produced by uncertainty associated with parameter values. In general, the model captured the dynamics of river discharge moderately well with most problems arising during the simulations of the snowmelt season. The differences between observations and model output were attributed to the insufficient spatial coverage of meteorological input data, errors in model structure resulting from a simplified model set-up, and errors caused by inevitable simplifications of temporal and spatial characteristics of the hydrologic behaviour of a very complex natural system.

Submitted in fulfillment of the requirements for the degree of Master of Engineering, Department of Civil Engineering and Surveying, Durban University of Technology, Durban, South Africa, 2016.
Due to the water scarcity in South Africa, new strategies in management planning are needed in order to sustain water resources. The increase of population and economic growth in South Africa has a negative effect on the water resources. Therefore, it should be well managed. The main concerns of the sustainability of water resources are hydropower, irrigation for agriculture, domestic and industries. Hence, the use of integrated water resources management in a single system which is built up by a river basin will help in water resources. This study was focused on water management issues: some of the principal causes of water shortages in UMdloti River are discussed. The current situation of water supply and demand at present is discussed. It also addressed some essential elements of reasonable, cooperative and sustainable water resources management solutions. Many developing countries are characterized as there is limited data availability, water scarcity and decrease of water levels in the dams. The eThekwini municipality is also having similar problems. Water resources have been modelled under this limited data using the hydrological modelling techniques by assessing the streamflow and observed data. The aim of the study was to address the issue of water management how water supply sources can be sustained to be manageable to meet the population growth demand considering the capacity of Hazelmere Dam demand downstream of the dam. Hydrological models, simulation, and decision making support systems were used to achieve all the research objectives. Hazelmere Dam has been modelled so that it can be used efficiently for the benefit of all users downstream of the dam for their economic and ecological benefits. Monthly reservoir inflow data for Hazelmere Dam was obtained from the Department of Water Affairs, South Africa. The nature of data is streamflow volume in mega liter (Ml) recorded for every month of the year. This was converted to mega cubic meter (Mm3) for use in the analysis herein. A period spanning 19 years of data (1994 – 2013) was used for the analysis. Six parametric probability distribution models were developed for estimating the monthly streamflow at Hazelmere Dam. These probability distribution functions include; Normal, Log-Normal (LN), Pearson III, Log-Pearson type III (LP3), Gumbel extreme value type1 (EVI) and Log-Gumbel (LG). It was observed that UMdloti River is smaller when compared with other rivers within the KwaZulu-Natal Province which could make it difficult to implement integrated water resources management. The hydro-meteorological data collected also has some limitations. The meteorological stations are far away to one another and this would make it difficult to attach their readings with the corresponding water basin. The comparison between the observed and simulated streamflow indicated that there was a good agreement between the observed and simulated discharge. Even though, the performance of the model was satisfactory, yet, it should not be generalized equally for all purposes. The erosion on the study area must be addressed by the stakeholders. It must be minimized in order to sustain the water resources of the UMdloti River. Erosion has a bad impact on the environment because it causes environmental degradation as well. Further investigations are recommended that account for the geological characteristics and the source of the base flow to make sure the rate of groundwater is sufficient for any future developments. Harnessing more energy from existing water sources within the frontier of the country is important in capacitating the South African Government’s commitment to reduction of the country’s greenhouse gas emissions and transition to a low-carbon economy while meeting a national target of 3,725 megawatts by 2030. This study also aimed to determine the amount of energy that can be generated from Hazelmere Dam on the uMdloti River, South Africa. Behavioral analyses of the Hazelmere reservoir were performed using plausible scenarios. Feasible alternative reservoir operation models were formulated and investigated to determine the best operating policy and power system configuration. This study determines the amounts of monthly and total annual energy that can be generated from Hazelmere reservoir based on turbines efficiencies of 75%, 85% and 90%. Optimization models were formulated to maximize hydropower generation within the constraints of existing abstractions, hydrological and system constraints. Differential evolution (DE) optimization method was adopted to resolve the optimization models. The methodology was applied for an operating season. The optimization models were formulated to maximize hydropower generation while keeping within the limits of existing irrigation demands. Differential evolution algorithm was employed to search feasible solution space for the best policy. Reservoir behavioural analysis was conducted to inspect the feasibility of generating hydropower from the Hazelmere reservoir under normal flow conditions. Optimization models were formulated to maximize hydropower generation from the dam. DE was employed to resolve the formulated models within the confines of the system constraints. It was found that 527.51 MWH of annual energy may be generated from the dam without system failure. Storage was maintained above critical levels while the reservoir supplied the full demands on the dam throughout the operating period indicating that the system yield is sufficient and there is no immediate need to augment the system.
M

abstract: The Colorado River Basin (CRB) is the primary source of water in the southwestern United States. A key step to reduce the uncertainty of future streamflow projections in the CRB is to evaluate the performance of historical simulations of General Circulation Models (GCMs). In this study, this challenge is addressed by evaluating the ability of nineteen GCMs from the Coupled Model Intercomparison Project Phase Five (CMIP5) and four nested Regional Climate Models (RCMs) in reproducing the statistical properties of the hydrologic cycle and temperature in the CRB. To capture the transition from snow-dominated to semiarid regions, analyses are conducted by spatially averaging the climate variables in four nested sub-basins. Most models overestimate the mean annual precipitation (P) and underestimate the mean annual temperature (T) at all locations. While a group of models capture the mean annual runoff at all sub-basins with different strengths of the hydrological cycle, another set of models overestimate the mean annual runoff, due to a weak cycle in the evaporation channel. An abrupt increase in the mean annual T in observed and most of the simulated time series (~0.8 °C) is detected at all locations despite the lack of any statistically significant monotonic trends for both P and T. While all models simulate the seasonality of T quite well, the phasing of the seasonal cycle of P is fairly reproduced in just the upper, snow-dominated sub-basin. Model performances degrade in the larger sub-basins that include semiarid areas, because several GCMs are not able to capture the effect of the North American monsoon. Finally, the relative performances of the climate models in reproducing the climatologies of P and T are quantified to support future impact studies in the basin.
Dissertation/Thesis
Masters Thesis Civil, Environmental and Sustainable Engineering 2018

Thesis (M.S.)--University of Tennessee, Knoxville, 2003.
Title from title page screen (viewed Mar. 22, 2004). Thesis advisor: Carol P. Harden. Document formatted into pages (x, 149 p. : col. ill., col. maps). Vita. Includes bibliographical references (p. 72-78).

D.Phil.
Land cover changes associated with growing human populations and expected changes in climatic conditions are likely to accelerate alterations in hydrological phenomena and processes on various scales. Subsequently, these changes could significantly influence the quantity and quality of water resources for both nature and human society. Documenting the distribution of land cover types within the Shire River catchment is the foundation for applications in this study of the hydrology of the Shire catchment. The aim of this study is to investigate the relationships between the measured land cover changes and hydrological regimes in the Shire River Catchment in Malawi. Maps depicting land cover dynamics for 1989 and 2002 were derived from multispectral and multi-temporal Landsat 5 (1989) and Landsat 7 ETM+ (2002) satellite remote sensing data for this catchment. Other spectral-independent data sets included the 90-m resolution Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM), Geographical Information System (GIS) layers of soils, geology and archived land cover. Core image-derived data sets such as individual Landsat bands, Normalized Difference Vegetation Index (NDVI), Principal Components Analysis and Tasseled Cap transformations were computed. From generated composite images, land cover classes were identified using a maximum likelihood algorithm. Eight land cover classes were mapped. A hierarchical multispectral shape classifier with an object conditional approach determined by the Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) legend structure was used to map land cover variables. LCCS was used as a basis for classification to achieve legend harmonization within Africa and on a global scale. Flexibility of the hierarchical system allowed incorporation of digital elevation objects, soil and underlying geological features as well as other available geographical data sets. This approach improved classification accuracy and can be adopted to discriminate land cover features at several scales, which are internally relatively homogeneous.In addition to compatibility with the FAO/LCCS classification system, the derived land cover maps have provided recent and improved classification accuracy, and added thematic detail compared to the existing 1992 land cover maps. Fieldwork was conducted to validate the land cover classes identified during classification. Accuracy assessment was based on the correlation between ground reference samples collected during field exercise and the satellite image classification. The overall mapping accuracy was 87%, with individual classes being mapped at accuracies of above 77% for both user and producer accuracy. The combination of Landsat images, vector data and detailed ground truthing information was used successfully to classify land cover of the Shire River catchment for years 1989 and 2002.

The change in total evaporation through alteration of vegetative cover is a major influence on catchment hydrology. The transformation of grassland and scrub habitats to commercial tree plantations, as well as the uncontrolled spread of invasive alien plants (lAPs) to ecologically sensitive systems, riparian zones in particular, are a threat to biodiversity and integrity of natural systems. Furthermore, critical low flow periods are of particular concern to water managers and local communities, as well as the associated impacts of potentially compromised water resources for rural livelihoods. The Working for Water (WfW) programme was implemented in 1995 by the Department of Water Affairs and Forestry and its main goals are to remove lAPs in order to improve water supply while at the same time providing employment to marginalised communities. In this study, the hydrological response to lAPs in the Upper Blyde River catchment is assessed. This is done by developing a classification structure for lAPs as a land use using detailed mapping available from WfW for use in a hydrological model, and then configuring and running the ACRU hydrological model for the Upper Blyde River catchment in Mpumalanga. In the classification, lAPs are represented as spatially explicit land use units in the ACRU model according to the type of habitat they invade, viz. riparian or non-riparian; as well as by type of plant, i.e. tree or shrub; and their area and density. The results obtained from simulating catchment hydrological responses using the ACRU model indicate that riparian lAPs have a great er impact on streamflow than do landscape invasions alone, specifically during periods of low flow. An increase in streamflow after removing lAPs from riparian and non-riparian habitats is a consistent outcome at both subcatchment and catchment scales. Using a spatially explicit method in order to model the hydrological response of different types of lAPs for different density classes in both riparian and non-riparian habitats is found to be a useful technique in determining the degree to which lAPs influence catchment streamflow. Recommendations for future research include focussing hydrological assessments of lAPs on critical flow periods and their impacts on water quality; investigation into the water use of invasive and indigenous vegetation for more accurate estimates from modelling exercises; and finally, applying the classification system for lAPs with other land use sensitive hydrological models for validation, and their wider application by incorporating methodologies into guidelines for use by WfW at national and provincial level.
Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.

In many of the rural areas of South Africa local communities rely on wetland resources for daily living. For a symbiotic relationship to exist between these communities and the wetlands, the wetlands must be utilised in a manner sustainable to both parties. To prevent exploitation thereof, a comprehensive understanding of the processes and functions of wetlands, of the values and needs of rural community members, and of the interactions between these entities is essential. This study focuses on research at three scales; the plot scale (10m\ the microcatchment scale (lkm2 ), represented by the Craigiebum wetland and microcatchment, and the catchment area upstream of the gauging weir X3H008, all of which exist in the Sand River catchment in the Mpumalanga and Limpopo Provinces of South Africa. Relationships between the geomorphological properties of the Craigiebum microcatchment, the wetland management practices of the local communities, and the hydrological properties of the microcatchment have been investigated. Various hydrological models, but in the main the ACRU model, have been adopted as tools to facilitate this research. Possible scenarios of changes in land use, rainfall and soil texture were performed at the plot scale and at the scale of the microcatchment, and changes in wetland extent were simulated and analysed at the scale of the catchment. Results of the modelling exercises simulating the effects of differences in soil texture higWight the positive effects of retention of fine particles within a wetland in a sandy environment. These results also depict greater rates of hydraulic conductivity, erosion and desiccation within coarse-textured soils than fmer textured soils. Low levels of fertility can also be attributed to the lack of fine particles present in the soils of the Craigiebum microcatchment wetland. Results of the modelling exercises that investigate the likely hydrological effects of a variety of land uses within the Craigiebum microcatchment verify accepted hydrological theory, as they highlight that more impervious areas produce more stormflow and lose more water to evaporation, and that the natural vegetation of the area contributes to streamflow regulation more than other land uses do. The exercises performed at the scale of the Sand River catchment do not provide conclusive evidence of the effects of changes in wetland extent, as the hydrological effects that other land uses in the area have appear to override the effects of the simulated wetland areas. Analysis of the sociological data captured highlights the great extent to which the local community depends on the Craigieburn wetland resources for a variety of livelihood strategies. Furthermore it illustrates the degree to which a reduction in wetland health negatively impacts upon the community. Viewed in conjunction, the hydrological, biophysical and sociological results highlight the degree to which changes in one aspect of the environment affect other aspects thereof, thereby highlighting the degree to which these aspects of the Craigieburn microcatchment are inextricably linked.
Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.

Wetlands are considered sensitive eco-tones that provide numerous goods and services, not only to the communities which are immediately dependent upon them, but also to the many downstream stakeholders who benefit from the hydrological influences that wetlands have on a catchment. The three main objectives of this study, the foci of which included an assessment of impacts of wetlands on catchment hydrological responses (viz. flood attenuation and streamflow regulation) in the Thukela catchment under varying geographical and climatic conditions, are: · A modification and validation of the ACRU Model’s Wetland Routine; · Assessing impacts of wetlands on hydrological responses from catchments in varying climatic regions under historical climatic conditions; and · Assessing impacts of wetlands on catchment hydrological responses for climate change scenarios by using outputs from a Regional Climate Model (RCM). The ACRU Model was selected to undertake the daily hydrological simulations, while historical climate data and climate information derived from the C-CAM Regional Climate Model were used as inputs into the model. These varying climatic inputs, as well as the changes in water fluxes between simulations with and without the wetlands routine switched on, enabled the author to assess the impacts of wetlands on catchment hydrological responses under varying climatic conditions. The ACRU wetland routine initially did not produce output in line with conceptualisation of wetlands processes. As a result of this, certain modifications had to be made to the model to ensure that the results obtained mimicked wetlands hydrological processes realistically. A validation was performed on the re-configured ACRU wetlands routine to show that the simulated results of impacts of wetlands on catchment hydrological responses were realistic when compared to findings from the literature review (e.g. in regard to streamflow regulation and flood attenuation). These validation results also show that the impacts of wetlands on catchment hydrological responses are dependent on the level of soil water saturation of the wetland at the start of a streamflow event and the volume of the streamflow event in relation to the relative size of the wetland. The results further illustrate that wetlands have a relatively small flood attenuation and streamflow regulation impact on mean annual catchment hydrology at the outlet of the 29 136 km2 Thukela catchment. However, mean monthly results show pronounced effects (20 – 30%) of flood attenuation in the summer months and streamflow regulation throughout the year, especially in the drier winter months. The climate change scenario results illustrate that the impact of wetlands on hydrological responses are virtually entirely masked by the impact of climate change, with only minor changes shown on outflows of the Thukela between climate change scenarios without and with wetlands.
Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.