The application of the monthly time step Pitman rainfall-runoff model to the Kafue River basin of Zambia
- Mwelwa, Elenestina Mutekenya
- Authors: Mwelwa, Elenestina Mutekenya
- Date: 2005
- Subjects: Kafue River (Zambia) , Kafue Flats (Zambia) , Floodplains -- Zambia , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:6032 , http://hdl.handle.net/10962/d1006171 , Kafue River (Zambia) , Kafue Flats (Zambia) , Floodplains -- Zambia , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models
- Description: This thesis presents a discussion on the study undertaken in the application of the monthly time step Pitman rainfall-runoff model to the Kafue River basin. The study constituted one of the initial steps in the capacity building and expansion of the application of hydrologic models in the southern African region for water resources assessment, one of the core areas of the Southern African FRIEND project (Flow Regimes from International Experimental Network Data). The research process was undertaken in four major stages, each stage working towards achieving the research objectives. The first stage was the preparation of spatial data which included the selection and delineation of sub-catchments and inclusion of spatial features required to run the Pitman model and transferring the spatial data into SPATSIM. The second stage was the preparation of input data, mainly rainfall, streamflow, evaporation, and water abstraction data. This information was then imported into SPATSIM, which was able to assist in the further preparation of data by assessment of the input data quality, linking of observed flows and spatial interpolation of point rainfall data to average catchment rainfall in readiness for running and calibration of the model. The third stage was the running and calibration of the Pitman model. Use was made of both the automatic calibration facility, as well as manual calibration by means of the time series graph display and analysis facility of SPATSIM. Model calibration was used to obtain the best fit and an acceptable correlation between the simulated and the observed flows and to obtain simulation parameter sets for sub-catchments and regions within the Kafue catchment. The fourth stage was the analysis and evaluation of the model results. This included verification of results over different time periods and validation and testing of parameter transfers to other catchments. This stage also included the evaluation of SPATSIM as a tool for applying the model and as a database for the processing and storage of water resources data. The study’s output includes: A comprehensive database of hydrometeorological, physical catchment characteristics, landuse and water abstraction information for the Kafue basin; calibrated Pitman model parameters for the sub-catchments within the Kafue basin; recommendations for future work and data collection programmes for the application of the model. The study has also built capacity by facilitating training and exposure to rainfall-runoff models (specifically the Pitman model) and associated software, SPATSIM. In addition, the dissemination of the results of this study will serve as an effective way of raising awareness on the application of the Pitman model and the use of the SPATSIM software within Zambia and the region. The overall Pitman model results were found to be satisfactory and the calibrated model is able to reproduce the observed spatial and temporal variations in streamflow characteristics in the Kafue River basin.
- Full Text:
- Authors: Mwelwa, Elenestina Mutekenya
- Date: 2005
- Subjects: Kafue River (Zambia) , Kafue Flats (Zambia) , Floodplains -- Zambia , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:6032 , http://hdl.handle.net/10962/d1006171 , Kafue River (Zambia) , Kafue Flats (Zambia) , Floodplains -- Zambia , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models
- Description: This thesis presents a discussion on the study undertaken in the application of the monthly time step Pitman rainfall-runoff model to the Kafue River basin. The study constituted one of the initial steps in the capacity building and expansion of the application of hydrologic models in the southern African region for water resources assessment, one of the core areas of the Southern African FRIEND project (Flow Regimes from International Experimental Network Data). The research process was undertaken in four major stages, each stage working towards achieving the research objectives. The first stage was the preparation of spatial data which included the selection and delineation of sub-catchments and inclusion of spatial features required to run the Pitman model and transferring the spatial data into SPATSIM. The second stage was the preparation of input data, mainly rainfall, streamflow, evaporation, and water abstraction data. This information was then imported into SPATSIM, which was able to assist in the further preparation of data by assessment of the input data quality, linking of observed flows and spatial interpolation of point rainfall data to average catchment rainfall in readiness for running and calibration of the model. The third stage was the running and calibration of the Pitman model. Use was made of both the automatic calibration facility, as well as manual calibration by means of the time series graph display and analysis facility of SPATSIM. Model calibration was used to obtain the best fit and an acceptable correlation between the simulated and the observed flows and to obtain simulation parameter sets for sub-catchments and regions within the Kafue catchment. The fourth stage was the analysis and evaluation of the model results. This included verification of results over different time periods and validation and testing of parameter transfers to other catchments. This stage also included the evaluation of SPATSIM as a tool for applying the model and as a database for the processing and storage of water resources data. The study’s output includes: A comprehensive database of hydrometeorological, physical catchment characteristics, landuse and water abstraction information for the Kafue basin; calibrated Pitman model parameters for the sub-catchments within the Kafue basin; recommendations for future work and data collection programmes for the application of the model. The study has also built capacity by facilitating training and exposure to rainfall-runoff models (specifically the Pitman model) and associated software, SPATSIM. In addition, the dissemination of the results of this study will serve as an effective way of raising awareness on the application of the Pitman model and the use of the SPATSIM software within Zambia and the region. The overall Pitman model results were found to be satisfactory and the calibrated model is able to reproduce the observed spatial and temporal variations in streamflow characteristics in the Kafue River basin.
- Full Text:
Revised parameter estimation methods for the Pitman monthly rainfall-runoff model
- Authors: Kapangaziwiri, Evison
- Date: 2008
- Subjects: Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models , Water supply -- South Africa , Water resources development -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:6033 , http://hdl.handle.net/10962/d1006172 , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models , Water supply -- South Africa , Water resources development -- South Africa
- Description: In recent years, increased demands have been placed on hydrologists to find the most effective methods of making predictions of hydrologic variables in ungauged basins. A huge part of the southern African region is ungauged and, in gauged basins, the extent to which observed flows represent natural flows is unknown, given unquantified upstream activities. The need to exploit water resources for social and economic development, considered in the light of water scarcity forecasts for the region, makes the reliable quantification of water resources a priority. Contemporary approaches to the problem of hydrological prediction in ungauged basins in the region have relied heavily on calibration against a limited gauged streamflow database and somewhat subjective parameter regionalizations using areas of assumed hydrological similarity. The reliance of these approaches on limited historical records, often of dubious quality, introduces uncertainty in water resources decisions. Thus, it is necessary to develop methods of estimating model parameters that are less reliant on calibration. This thesis addresses the question of whether physical basin properties and the role they play in runoff generation processes can be used directly in the estimation of parameter values of the Pitman monthly rainfall-runoff model. A physically-based approach to estimating the soil moisture accounting and runoff parameters of a conceptual, monthly time-step rainfall-runoff model is proposed. The study investigates the physical meaning of the model parameters, establishes linkages between parameter values and basin physical properties and develops relationships and equations for estimating the parameters taking into account the spatial and temporal scales used in typical model applications. The estimationmethods are then tested in selected gauged basins in southern Africa and the results of model simulations evaluated against historical observed flows. The results of 71 basins chosen from the southern African region suggest that it is possible to directly estimate hydrologically relevant parameters for the Pitman model from physical basin attributes. For South Africa, the statistical and visual fit of the simulations using the revised parameters were at least as good as the current regional sets, albeit the parameter sets being different. In the other countries where no regionalized parameter sets currently exist, simulations were equally good. The availability, within the southern African region, of the appropriate physical basin data and the disparities in the spatial scales and the levels of detail of the data currently available were identified as potential sources of uncertainty. GIS and remote sensing technologies and a widespread use of this revised approach are expected to facilitate access to these data.
- Full Text:
- Authors: Kapangaziwiri, Evison
- Date: 2008
- Subjects: Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models , Water supply -- South Africa , Water resources development -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:6033 , http://hdl.handle.net/10962/d1006172 , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Hydrology -- Mathematical models , Water supply -- South Africa , Water resources development -- South Africa
- Description: In recent years, increased demands have been placed on hydrologists to find the most effective methods of making predictions of hydrologic variables in ungauged basins. A huge part of the southern African region is ungauged and, in gauged basins, the extent to which observed flows represent natural flows is unknown, given unquantified upstream activities. The need to exploit water resources for social and economic development, considered in the light of water scarcity forecasts for the region, makes the reliable quantification of water resources a priority. Contemporary approaches to the problem of hydrological prediction in ungauged basins in the region have relied heavily on calibration against a limited gauged streamflow database and somewhat subjective parameter regionalizations using areas of assumed hydrological similarity. The reliance of these approaches on limited historical records, often of dubious quality, introduces uncertainty in water resources decisions. Thus, it is necessary to develop methods of estimating model parameters that are less reliant on calibration. This thesis addresses the question of whether physical basin properties and the role they play in runoff generation processes can be used directly in the estimation of parameter values of the Pitman monthly rainfall-runoff model. A physically-based approach to estimating the soil moisture accounting and runoff parameters of a conceptual, monthly time-step rainfall-runoff model is proposed. The study investigates the physical meaning of the model parameters, establishes linkages between parameter values and basin physical properties and develops relationships and equations for estimating the parameters taking into account the spatial and temporal scales used in typical model applications. The estimationmethods are then tested in selected gauged basins in southern Africa and the results of model simulations evaluated against historical observed flows. The results of 71 basins chosen from the southern African region suggest that it is possible to directly estimate hydrologically relevant parameters for the Pitman model from physical basin attributes. For South Africa, the statistical and visual fit of the simulations using the revised parameters were at least as good as the current regional sets, albeit the parameter sets being different. In the other countries where no regionalized parameter sets currently exist, simulations were equally good. The availability, within the southern African region, of the appropriate physical basin data and the disparities in the spatial scales and the levels of detail of the data currently available were identified as potential sources of uncertainty. GIS and remote sensing technologies and a widespread use of this revised approach are expected to facilitate access to these data.
- Full Text:
Evaluating uncertainty in water resources estimation in Southern Africa : a case study of South Africa
- Authors: Sawunyama, Tendai
- Date: 2009
- Subjects: Water supply -- South Africa , Water supply -- Africa, Southern , Hydrology -- South Africa , Hydrology -- Africa, Southern , Hydrologic models , Hydrology research -- South Africa , Hydrology research -- Africa, Southern , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:6035 , http://hdl.handle.net/10962/d1006176
- Description: Hydrological models are widely used tools in water resources estimation, but they are simple representations of reality and are frequently based on inadequate input data and uncertainties in parameter values. Data observation networks are expensive to establish and maintain and often beyond the resources of most developing countries. Consequently, measurements are difficult to obtain and observation networks in many countries are shrinking, hence obtaining representative observations in space and time remains a challenge. This study presents some guidelines on the identification, quantification and reduction of sources of uncertainty in water resources estimation in southern Africa, a data scarce region. The analyses are based on example sub-basins drawn from South Africa and the application of the Pitman hydrological model. While it has always been recognised that estimates of water resources availability for the region are subject to possible errors, the quantification of these uncertainties has never been explicitly incorporated into the methods used in the region. The motivation for this study was therefore to contribute to the future development of a revised framework for water resources estimation that does include uncertainty. The focus was on uncertainties associated with climate input data, parameter estimation (and recognizing the uncertainty due model structure deficiencies) methods and water use data. In addition to variance based measures of uncertainty, this study also used a reservoir yield based statistic to evaluate model output uncertainty, which represents an integrated measure of flow regime variations and one that can be more easily understood by water resources managers. Through a sensitivity analysis approach, the results of the individual contribution of each source of uncertainty suggest regional differences and that clear statements about which source of uncertainty is likely to dominate are not generally possible. Parameter sensitivity analysis was used in identifying parameters which are important withinspecific sub-basins and therefore those to focus on in uncertainty analysis. The study used a simple framework for evaluating the combined contribution of uncertainty sources to model outputs that is consistent with the model limitations and data available, and that allows direct quantitative comparison between model outputs obtained by using different sources of information and methods within Spatial and Time Series Information Modelling (SPATSIM) software. The results from combining the sources of uncertainties showed that parameter uncertainty dominates the contribution to model output uncertainty. However, in some parts of the country especially those with complex topography, which tend to experience high rainfall spatial variability, rainfall uncertainty is equally dominant, while the contributions of evaporation and water use data uncertainty are relatively small. While the results of this study are encouraging, the weaknesses of the methods used to quantify uncertainty (especially subjectivity involved in evaluating parameter uncertainty) should not be neglected and require further evaluations. An effort to reduce data and parameter uncertainty shows that this can only be achieved if data access at appropriate scale and quality improves. Perhaps the focus should be on maintaining existing networks and concentrating research efforts on making the most out of the emerging data products derived from remote sensing platforms. While this study presents some initial guidelines for evaluating uncertainty in South Africa, there is need to overcome several constraints which are related to data availability and accuracy, the models used and the capacity or willingness to adopt new methods that incorporate uncertainty. The study has provided a starting point for the development of new approaches to modelling water resources in the region that include uncertain estimates.
- Full Text:
- Authors: Sawunyama, Tendai
- Date: 2009
- Subjects: Water supply -- South Africa , Water supply -- Africa, Southern , Hydrology -- South Africa , Hydrology -- Africa, Southern , Hydrologic models , Hydrology research -- South Africa , Hydrology research -- Africa, Southern , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:6035 , http://hdl.handle.net/10962/d1006176
- Description: Hydrological models are widely used tools in water resources estimation, but they are simple representations of reality and are frequently based on inadequate input data and uncertainties in parameter values. Data observation networks are expensive to establish and maintain and often beyond the resources of most developing countries. Consequently, measurements are difficult to obtain and observation networks in many countries are shrinking, hence obtaining representative observations in space and time remains a challenge. This study presents some guidelines on the identification, quantification and reduction of sources of uncertainty in water resources estimation in southern Africa, a data scarce region. The analyses are based on example sub-basins drawn from South Africa and the application of the Pitman hydrological model. While it has always been recognised that estimates of water resources availability for the region are subject to possible errors, the quantification of these uncertainties has never been explicitly incorporated into the methods used in the region. The motivation for this study was therefore to contribute to the future development of a revised framework for water resources estimation that does include uncertainty. The focus was on uncertainties associated with climate input data, parameter estimation (and recognizing the uncertainty due model structure deficiencies) methods and water use data. In addition to variance based measures of uncertainty, this study also used a reservoir yield based statistic to evaluate model output uncertainty, which represents an integrated measure of flow regime variations and one that can be more easily understood by water resources managers. Through a sensitivity analysis approach, the results of the individual contribution of each source of uncertainty suggest regional differences and that clear statements about which source of uncertainty is likely to dominate are not generally possible. Parameter sensitivity analysis was used in identifying parameters which are important withinspecific sub-basins and therefore those to focus on in uncertainty analysis. The study used a simple framework for evaluating the combined contribution of uncertainty sources to model outputs that is consistent with the model limitations and data available, and that allows direct quantitative comparison between model outputs obtained by using different sources of information and methods within Spatial and Time Series Information Modelling (SPATSIM) software. The results from combining the sources of uncertainties showed that parameter uncertainty dominates the contribution to model output uncertainty. However, in some parts of the country especially those with complex topography, which tend to experience high rainfall spatial variability, rainfall uncertainty is equally dominant, while the contributions of evaporation and water use data uncertainty are relatively small. While the results of this study are encouraging, the weaknesses of the methods used to quantify uncertainty (especially subjectivity involved in evaluating parameter uncertainty) should not be neglected and require further evaluations. An effort to reduce data and parameter uncertainty shows that this can only be achieved if data access at appropriate scale and quality improves. Perhaps the focus should be on maintaining existing networks and concentrating research efforts on making the most out of the emerging data products derived from remote sensing platforms. While this study presents some initial guidelines for evaluating uncertainty in South Africa, there is need to overcome several constraints which are related to data availability and accuracy, the models used and the capacity or willingness to adopt new methods that incorporate uncertainty. The study has provided a starting point for the development of new approaches to modelling water resources in the region that include uncertain estimates.
- Full Text:
Uncertainties in modelling hydrological responses in gauged and ungauged sub‐basins
- Authors: Tumbo, Madaka Harold
- Date: 2015
- Subjects: Hydrologic models , Watersheds -- Tanzania , Water-supply -- Tanzania -- Great Ruaha River Watershed , Water resources development -- Tanzania -- Great Ruaha River Watershed , Rain and rainfall -- Mathematical models , Rain gauges -- Tanzania -- Great Ruaha River Watershed , Great Ruaha River Watershed (Tanzania)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:6053 , http://hdl.handle.net/10962/d1018568
- Description: 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.
- Full Text:
- Authors: Tumbo, Madaka Harold
- Date: 2015
- Subjects: Hydrologic models , Watersheds -- Tanzania , Water-supply -- Tanzania -- Great Ruaha River Watershed , Water resources development -- Tanzania -- Great Ruaha River Watershed , Rain and rainfall -- Mathematical models , Rain gauges -- Tanzania -- Great Ruaha River Watershed , Great Ruaha River Watershed (Tanzania)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:6053 , http://hdl.handle.net/10962/d1018568
- Description: 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.
- Full Text:
Quantification of water resources uncertainties in two sub-basins of the Limpopo River basin
- Authors: Oosthuizen, Nadia
- Date: 2018
- Subjects: Hydrologic models -- Limpopo River Watershed , Water-supply -- Limpopo River Watershed , Water-supply -- Management , Sustainable development , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Reservoirs -- Limpopo River Watershed
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/63267 , vital:28388
- Description: The demand for water is rapidly growing, placing more strain on access to the resources and subsequently its management. For sustainable management, there is a need to accurately quantify the available water resources. Unfortunately, the data required for such assessments are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In the absence of historical observed data, models are generally used to describe the different hydrological processes and generate data and information that will inform management and policy decision making. Ideally, any hydrological model should be based on a sound conceptual understanding of the processes in the basin and be backed by quantitative information for the parameterization of the model. Such data is however, often inadequate in many sub-basins necessitating the incorporation of the uncertainty related to the estimation process. Model parameter estimation and input data are significant sources of uncertainty that should be quantified. Also, in southern Africa water use data are unreliable because available databases consist of licensed information and actual use is generally unknown. In this study, the water resources of two sub-basins of the Limpopo River basin – the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe – are estimated. The study assessed how uncertainties in the Pitman model parameterisation and input water use data affect the estimation of surface water resources of the selected sub-basins. Farm reservoirs and irrigated areas data from various sources were collected and used to run the Pitman model. Results indicate that the total model output uncertainty is higher for the Shashe sub-basin which is more data scarce than the Mogalakwena sub-basin. The study illustrates the importance of including uncertainty in the water resources assessment process to provide baseline data for decision making in resource management and planning. The study reviews existing information sources associated with the quantification of water balance components and gives an update of water resources of the sub-basin. The flows generated by the model at the outlet of the basin were between 22.6 Mm3 and 24.7 Mm3 per month when incorporating uncertainty to the main physical runoff generating parameters. The total predictive uncertainty of the model increased to between 22.2 Mm3 and 25.0 Mm3 when anthropogenic water use data such as small farm and large reservoirs and irrigation were included. The flows generated for Shashe was between 11.7 Mm3 and 14.5 Mm3 per month when incorporating uncertainty to the main physical runoff generating parameters. The predictive uncertainty of the model changed to 11.7 Mm3 and 17.7 Mm3 after the water use uncertainty was added. However, it is expected that the uncertainty could be reduced by using higher resolution remote sensing imagery.
- Full Text:
- Authors: Oosthuizen, Nadia
- Date: 2018
- Subjects: Hydrologic models -- Limpopo River Watershed , Water-supply -- Limpopo River Watershed , Water-supply -- Management , Sustainable development , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Reservoirs -- Limpopo River Watershed
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/63267 , vital:28388
- Description: The demand for water is rapidly growing, placing more strain on access to the resources and subsequently its management. For sustainable management, there is a need to accurately quantify the available water resources. Unfortunately, the data required for such assessments are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In the absence of historical observed data, models are generally used to describe the different hydrological processes and generate data and information that will inform management and policy decision making. Ideally, any hydrological model should be based on a sound conceptual understanding of the processes in the basin and be backed by quantitative information for the parameterization of the model. Such data is however, often inadequate in many sub-basins necessitating the incorporation of the uncertainty related to the estimation process. Model parameter estimation and input data are significant sources of uncertainty that should be quantified. Also, in southern Africa water use data are unreliable because available databases consist of licensed information and actual use is generally unknown. In this study, the water resources of two sub-basins of the Limpopo River basin – the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe – are estimated. The study assessed how uncertainties in the Pitman model parameterisation and input water use data affect the estimation of surface water resources of the selected sub-basins. Farm reservoirs and irrigated areas data from various sources were collected and used to run the Pitman model. Results indicate that the total model output uncertainty is higher for the Shashe sub-basin which is more data scarce than the Mogalakwena sub-basin. The study illustrates the importance of including uncertainty in the water resources assessment process to provide baseline data for decision making in resource management and planning. The study reviews existing information sources associated with the quantification of water balance components and gives an update of water resources of the sub-basin. The flows generated by the model at the outlet of the basin were between 22.6 Mm3 and 24.7 Mm3 per month when incorporating uncertainty to the main physical runoff generating parameters. The total predictive uncertainty of the model increased to between 22.2 Mm3 and 25.0 Mm3 when anthropogenic water use data such as small farm and large reservoirs and irrigation were included. The flows generated for Shashe was between 11.7 Mm3 and 14.5 Mm3 per month when incorporating uncertainty to the main physical runoff generating parameters. The predictive uncertainty of the model changed to 11.7 Mm3 and 17.7 Mm3 after the water use uncertainty was added. However, it is expected that the uncertainty could be reduced by using higher resolution remote sensing imagery.
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Constraining simulation uncertainties in a hydrological model of the Congo River Basin including a combined modelling approach for channel-wetland exchanges
- Authors: Kabuya, Pierre Mulamba
- Date: 2021-04
- Subjects: Congo River Watershed , Watersheds -- Congo (Democratic Republic) , Hydrologic models , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Wetland hydrology
- Language: English
- Type: thesis , text , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/177997 , vital:42897 , 10.21504/10962/177997
- Description: Compared to other large river basins of the world, such as the Amazon, the Congo River Basin appears to be the most ungauged and less studied. This is partly because the basin lacks sufficient observational hydro-climatic monitoring stations and appropriate information on physiographic basin properties at a spatial scale deemed for hydrological applications, making it difficult to estimate water resources at the scale of sub-basins (Chapter 3). In the same time, the basin is facing the challenges related to rapid population growth, uncontrolled urbanisation as well as climate change. Adequate quantification of hydrological processes across different spatial and temporal scales in the basin, and the drivers of change, is essential for prediction and strategic planning to ensure sustainable management of water resources in the Congo River Basin. Hydrological models are particularly important to generate the required information. However, the shortness of the available streamflow records, lack of spatial representativeness of the available streamflow gauging stations and the lack of understanding of the processes in channel-wetland exchanges, are the main challenges that constrain the use of traditional approaches to models development. They also contribute to increased uncertainty in the estimation of water resources across the basin (Chapter 1 and 2). Given this ungauged nature of the Congo River Basin, it is important to resort to hydrological modelling approaches that can reasonably quantify and model the uncertainty associated with water resources estimation (Chapter 4) to make hydrological predictions reliable. This study explores appropriate methods for hydrological predictions and water resources assessment in ungauged catchments of the Congo River Basin. In this context, the core modelling framework combines the quantification of uncertainty in constraint indices, hydrological modelling and hydrodynamic modelling. The latter accounts for channel-wetland exchanges in sub-basins where wetlands exert considerable influence on downstream flow regimes at the monthly time scale. The constraint indices are the characteristics of a sub-basin’s long-term hydrological behaviour and may reflect the dynamics of the different components of the catchment water balance such as climate, water storage and different runoff processes. Currently, six constraint indices namely the mean monthly runoff volume (MMQ in m3 *106), mean monthly groundwater recharge depth (MMR in mm), the 10th, 50th and 90th percentiles of the flow duration curve expressed as a fraction of MMQ (Q10/MMQ, Q50/MMQ, Q90/MMQ) and the percentage of time that zero flows are expected (%Zero), are used in the modelling approach. These were judged to be the minimum number of key indices that can discriminate between different hydrological responses. The constraint indices in the framework help to determine an uncertainty range within which behavioural model parameters of the expected hydrological response can be identified. Predictive equations of the constraint indices across all climate and physiographic regions of the Congo Basin were based only on the aridity index because it was the most influential sub-basin attribute (Chapter 5) for which quantitative information was available. The degree of uncertainty in the constraint Q10/MMQ and Q50/MMQ indices is less than 41%, while it is somewhat higher for the mean monthly runoff (MMQ) and Q90/MMQ constraint indices. The established uncertainty ranges of the constraint indices were tested in some selected sub-basins of the Congo Basin, including the Lualaba (93 sub-basins), Sangha (24 sub-basins), Oubangui (19 sub-basins), Batéké plateaux (4 sub-basins), Kasai (4 sub-basins) and Inkisi (3 sub-basins). The results proved useful through the application of a 2-stage uncertainty approach of the PITMAN model. However, it comes out of this study that the application of the original constraint indices ranges (Chapter 5) generated satisfactory simulation results in some areas, while in others both small and large adjustments were required to fully capture some aspects of the observed hydrological responses (Chapter 6). Part of the reason is attributed to the availability and quality of streamflow data used to develop the constraint indices ranges (Chapter 5). The main issue identified in the modelling process was whether the changes made to the original constraints at headwater-gauged sub-basins can be applied to ungauged upstream sub-basins to match the observed flow at downstream gauging stations. Ideally, only gauged sub-basin’s constraints can be easily revised based on the observed flow. However, the refinement made to gauged sub-basins alone may fail to substantially affect the results if ungauged upstream sub-basins exert a major impact on defining downstream hydrological response. The majority of gauging stations used in this analysis are located downstream of many upstream ungauged sub-basins and therefore adjustments were required in ungauged sub-basins. These adjustments consist of shifting the full range of a constraint index either towards higher or lower values, depending on the degree to which the simulated uncertainty bounds depart from the observed flow. While this modelling approach seems effective in capturing many aspects of the hydrological responses with a reduced level of uncertainty compared to a previous study, it is recommended that the approach be extended to the remaining parts of the Congo Basin and assessed under current and future development conditions including environmental changes. A 2D hydrodynamic river-wetland model (LISFLOOD-FP) has been used to explicitly represent the inundation process exchanges between river channels and wetland systems. The hydrodynamic modelling outputs are used to calibrate the PITMAN wetland sub-model parameters. The five hydrodynamic models constructed for Ankoro, Kamalondo, Kundelungu, Mweru and Tshiangalele wetland systems have been partially validated using independent estimates of inundation extents available from Landsat imagery. Other sources of data such as remote sensing of water level altimetry, SAR images and wetland storage estimates may be used to improve the validation results. However, the important objective in this study was to make sure that flow volume exchanges between river channels and their adjacent floodplains were being simulated realistically. The wetland sub-model parameters are calibrated in a spreadsheet version of the PITMAN wetland routine to achieve visual correspondence between the LISFLOOD-FP and PITMAN wetland sub-model outputs (Storage volumes and channel outputs). The hysteretic patterns of the river-wetland processes were quantified using hysteresis indices and were associated with the spill and return flow parameters of the wetland sub-model and eventually with the wetland morphometric characteristics. One example is the scale parameter of the return flow function (AA), which shows a good relationship with the average surface slope of the wetland when the coefficient parameter (BB) of the same function is kept constant to a value of 1.25. The same parameter (AA) is a good indicator of the wetland emptying mechanism. A small AA indicates a wetland that slowly releases its flow, resulting in a highly delayed and attenuated hydrological response in downstream sub-basins. This understanding has a practical advantage for the estimation of the PITMAN wetland parameters in the many areas where it is not possible, or where the resources are not available, to run complex hydrodynamic models (Chapter 7). The inclusion of these LISFLOOD-FP informed wetland parameters in the basin-scale hydrological modelling results in acceptable simulations for the lower Lualaba drainage system. The small wetlands, like Ankoro and Tshiangalele, have a negligible impact on downstream flow regimes, whereas large wetlands, such as Kamalondo and Mweru, have very large impacts. In general, the testing of the original constraint indices in the region of wetlands and further downstream of the Lualaba drainage system has shown acceptable results. However, there remains an unresolved uncertainty issue related to the under and over-estimation of some aspects of the hydrological response at both Mulongo and Ankoro, two gauging stations in the immediate downstream of the Kamalondo wetland system. It is difficult to attribute this uncertainty to Kamalondo wetland parameters alone because many of the incremental sub-basins contributing to wetland inflows are ungauged. The issue at Mulongo is the under simulation of low flow, while the high flows at the Ankoro gauging station are over-simulated. However, the pattern of the calibrated constraint indices in this region (Chapter 8) shows that the under simulation of low flow at Mulongo cannot be attributed to incremental sub-basins (between Bukama, Kapolowe and Mulongo gauging stations), because their Q90/MMQ constraint indices are even slightly above the original constraint ranges, but maintain a spatial consistency with sub-basins of other regions. Similarly, sub-basins located between Mulongo, Luvua and Ankoro gauging stations have high flow indices slightly below the original constraint ranges and therefore they are unlikely to be responsible for the over simulation of high flow at the Ankoro gauging station. These facts highlight the need for a further understanding of the complex wetland system of Kamalondo. Short-term data collection and monitoring programme are required. Important tributaries that drain to this wetland need to be monitored by installing water level loggers and periodically collecting flow data and river bathymetry. This programme should lead to the development of rating curves of wetland input tributaries. This would partially solve the unresolved uncertainty issues at the Ankoro and Mulongo gauging stations. The integrated modelling approach offers many opportunities in the Congo Basin. The quantified and modelled uncertainty helps to identify regions with high uncertainty and allows for the identification of various data collection and management strategies that can potentially contribute to the uncertainty reduction. The quantified channel-wetland exchanges contribute to the improvement of the overall knowledge of water resources estimation within the regions where the effects of wetlands are evident even at the monthly time scale. In contrast, ignoring uncertainty in the estimates of water resources availability means that water resources planning and management decisions in the Congo Basin will continue to be based on inadequate information and unquantified uncertainty, thus increasing the risk associated with water resources decision making. , Thesis (PhD) -- Faculty of Science, Institute for Water Research, 2021
- Full Text:
- Authors: Kabuya, Pierre Mulamba
- Date: 2021-04
- Subjects: Congo River Watershed , Watersheds -- Congo (Democratic Republic) , Hydrologic models , Rain and rainfall -- Mathematical models , Runoff -- Mathematical models , Wetland hydrology
- Language: English
- Type: thesis , text , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/177997 , vital:42897 , 10.21504/10962/177997
- Description: Compared to other large river basins of the world, such as the Amazon, the Congo River Basin appears to be the most ungauged and less studied. This is partly because the basin lacks sufficient observational hydro-climatic monitoring stations and appropriate information on physiographic basin properties at a spatial scale deemed for hydrological applications, making it difficult to estimate water resources at the scale of sub-basins (Chapter 3). In the same time, the basin is facing the challenges related to rapid population growth, uncontrolled urbanisation as well as climate change. Adequate quantification of hydrological processes across different spatial and temporal scales in the basin, and the drivers of change, is essential for prediction and strategic planning to ensure sustainable management of water resources in the Congo River Basin. Hydrological models are particularly important to generate the required information. However, the shortness of the available streamflow records, lack of spatial representativeness of the available streamflow gauging stations and the lack of understanding of the processes in channel-wetland exchanges, are the main challenges that constrain the use of traditional approaches to models development. They also contribute to increased uncertainty in the estimation of water resources across the basin (Chapter 1 and 2). Given this ungauged nature of the Congo River Basin, it is important to resort to hydrological modelling approaches that can reasonably quantify and model the uncertainty associated with water resources estimation (Chapter 4) to make hydrological predictions reliable. This study explores appropriate methods for hydrological predictions and water resources assessment in ungauged catchments of the Congo River Basin. In this context, the core modelling framework combines the quantification of uncertainty in constraint indices, hydrological modelling and hydrodynamic modelling. The latter accounts for channel-wetland exchanges in sub-basins where wetlands exert considerable influence on downstream flow regimes at the monthly time scale. The constraint indices are the characteristics of a sub-basin’s long-term hydrological behaviour and may reflect the dynamics of the different components of the catchment water balance such as climate, water storage and different runoff processes. Currently, six constraint indices namely the mean monthly runoff volume (MMQ in m3 *106), mean monthly groundwater recharge depth (MMR in mm), the 10th, 50th and 90th percentiles of the flow duration curve expressed as a fraction of MMQ (Q10/MMQ, Q50/MMQ, Q90/MMQ) and the percentage of time that zero flows are expected (%Zero), are used in the modelling approach. These were judged to be the minimum number of key indices that can discriminate between different hydrological responses. The constraint indices in the framework help to determine an uncertainty range within which behavioural model parameters of the expected hydrological response can be identified. Predictive equations of the constraint indices across all climate and physiographic regions of the Congo Basin were based only on the aridity index because it was the most influential sub-basin attribute (Chapter 5) for which quantitative information was available. The degree of uncertainty in the constraint Q10/MMQ and Q50/MMQ indices is less than 41%, while it is somewhat higher for the mean monthly runoff (MMQ) and Q90/MMQ constraint indices. The established uncertainty ranges of the constraint indices were tested in some selected sub-basins of the Congo Basin, including the Lualaba (93 sub-basins), Sangha (24 sub-basins), Oubangui (19 sub-basins), Batéké plateaux (4 sub-basins), Kasai (4 sub-basins) and Inkisi (3 sub-basins). The results proved useful through the application of a 2-stage uncertainty approach of the PITMAN model. However, it comes out of this study that the application of the original constraint indices ranges (Chapter 5) generated satisfactory simulation results in some areas, while in others both small and large adjustments were required to fully capture some aspects of the observed hydrological responses (Chapter 6). Part of the reason is attributed to the availability and quality of streamflow data used to develop the constraint indices ranges (Chapter 5). The main issue identified in the modelling process was whether the changes made to the original constraints at headwater-gauged sub-basins can be applied to ungauged upstream sub-basins to match the observed flow at downstream gauging stations. Ideally, only gauged sub-basin’s constraints can be easily revised based on the observed flow. However, the refinement made to gauged sub-basins alone may fail to substantially affect the results if ungauged upstream sub-basins exert a major impact on defining downstream hydrological response. The majority of gauging stations used in this analysis are located downstream of many upstream ungauged sub-basins and therefore adjustments were required in ungauged sub-basins. These adjustments consist of shifting the full range of a constraint index either towards higher or lower values, depending on the degree to which the simulated uncertainty bounds depart from the observed flow. While this modelling approach seems effective in capturing many aspects of the hydrological responses with a reduced level of uncertainty compared to a previous study, it is recommended that the approach be extended to the remaining parts of the Congo Basin and assessed under current and future development conditions including environmental changes. A 2D hydrodynamic river-wetland model (LISFLOOD-FP) has been used to explicitly represent the inundation process exchanges between river channels and wetland systems. The hydrodynamic modelling outputs are used to calibrate the PITMAN wetland sub-model parameters. The five hydrodynamic models constructed for Ankoro, Kamalondo, Kundelungu, Mweru and Tshiangalele wetland systems have been partially validated using independent estimates of inundation extents available from Landsat imagery. Other sources of data such as remote sensing of water level altimetry, SAR images and wetland storage estimates may be used to improve the validation results. However, the important objective in this study was to make sure that flow volume exchanges between river channels and their adjacent floodplains were being simulated realistically. The wetland sub-model parameters are calibrated in a spreadsheet version of the PITMAN wetland routine to achieve visual correspondence between the LISFLOOD-FP and PITMAN wetland sub-model outputs (Storage volumes and channel outputs). The hysteretic patterns of the river-wetland processes were quantified using hysteresis indices and were associated with the spill and return flow parameters of the wetland sub-model and eventually with the wetland morphometric characteristics. One example is the scale parameter of the return flow function (AA), which shows a good relationship with the average surface slope of the wetland when the coefficient parameter (BB) of the same function is kept constant to a value of 1.25. The same parameter (AA) is a good indicator of the wetland emptying mechanism. A small AA indicates a wetland that slowly releases its flow, resulting in a highly delayed and attenuated hydrological response in downstream sub-basins. This understanding has a practical advantage for the estimation of the PITMAN wetland parameters in the many areas where it is not possible, or where the resources are not available, to run complex hydrodynamic models (Chapter 7). The inclusion of these LISFLOOD-FP informed wetland parameters in the basin-scale hydrological modelling results in acceptable simulations for the lower Lualaba drainage system. The small wetlands, like Ankoro and Tshiangalele, have a negligible impact on downstream flow regimes, whereas large wetlands, such as Kamalondo and Mweru, have very large impacts. In general, the testing of the original constraint indices in the region of wetlands and further downstream of the Lualaba drainage system has shown acceptable results. However, there remains an unresolved uncertainty issue related to the under and over-estimation of some aspects of the hydrological response at both Mulongo and Ankoro, two gauging stations in the immediate downstream of the Kamalondo wetland system. It is difficult to attribute this uncertainty to Kamalondo wetland parameters alone because many of the incremental sub-basins contributing to wetland inflows are ungauged. The issue at Mulongo is the under simulation of low flow, while the high flows at the Ankoro gauging station are over-simulated. However, the pattern of the calibrated constraint indices in this region (Chapter 8) shows that the under simulation of low flow at Mulongo cannot be attributed to incremental sub-basins (between Bukama, Kapolowe and Mulongo gauging stations), because their Q90/MMQ constraint indices are even slightly above the original constraint ranges, but maintain a spatial consistency with sub-basins of other regions. Similarly, sub-basins located between Mulongo, Luvua and Ankoro gauging stations have high flow indices slightly below the original constraint ranges and therefore they are unlikely to be responsible for the over simulation of high flow at the Ankoro gauging station. These facts highlight the need for a further understanding of the complex wetland system of Kamalondo. Short-term data collection and monitoring programme are required. Important tributaries that drain to this wetland need to be monitored by installing water level loggers and periodically collecting flow data and river bathymetry. This programme should lead to the development of rating curves of wetland input tributaries. This would partially solve the unresolved uncertainty issues at the Ankoro and Mulongo gauging stations. The integrated modelling approach offers many opportunities in the Congo Basin. The quantified and modelled uncertainty helps to identify regions with high uncertainty and allows for the identification of various data collection and management strategies that can potentially contribute to the uncertainty reduction. The quantified channel-wetland exchanges contribute to the improvement of the overall knowledge of water resources estimation within the regions where the effects of wetlands are evident even at the monthly time scale. In contrast, ignoring uncertainty in the estimates of water resources availability means that water resources planning and management decisions in the Congo Basin will continue to be based on inadequate information and unquantified uncertainty, thus increasing the risk associated with water resources decision making. , Thesis (PhD) -- Faculty of Science, Institute for Water Research, 2021
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