Assessing MODIS evapotranspiration data for hydrological modelling in South Africa
- Mazibuko, Sbongiseni Christian
- Authors: Mazibuko, Sbongiseni Christian
- Date: 2017
- Subjects: Evapotranspiration , Evapotranspiration -- Measurement , Hydrologic models , Hydrologic models -- South Africa , MODIS (Spectroradiometer)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/8009 , vital:21334
- Description: Evapotranspiration as a major component of the water balance has been identified as a key factor in hydrological modelling. Water management can be improved by means of increased use of reliable methods for estimating evapotranspiration. The limited availability of measured climate and discharge data sets, particularly in the developing world, restricts the reliability of hydrological models in these regions. Furthermore, rapid changes in hydrological systems with increasing development mean uncertainties in water resource estimation are growing. These changes are related to the modification of catchment hydrological processes with increasing human activity. Dealing with data uncertainty and quantifying the impacts of catchment activities are significant challenges that scientists in the field of hydrology face today. Uncertainties in hydrometeorological data are associated with poor observation networks that provide data at point scales which are not adequately representative of the inherent heterogeneity within catchment processes. Using uncertain data in model applications reduces the predictive power of hydrological models as well as the ability to validate the model outcomes. This study examines the potential of using remote sensing-based evapotranspiration data to reduce uncertainty in the climatic forcing data and constraining the output of a rainfall-runoff hydrological model. It is common to use fixed seasonally variable potential evapotranspiration (PET) instead of temporally varying PET data as inputs to standard rainfall-runoff models. Part of the reason is that there are relatively few stations available to measure a variety of meteorological input data needed to compute PET, as well as the apparent lack of sensitivity of rainfall-runoff models to different types of PET inputs. As hydrometeorological data become more readily available through the use of earth observation systems, it is important to determine whether rainfall-runoff models are sensitive to time-varying PET derived from these earth observations systems. Further potential includes the use of actual evapotranspiration (ETa) from this type of data to constrain model outputs and improve model realism. It is assumed that a better representation of evapotranspiration demands could improve the efficiency of models, and this study explores some of these issues. The study used evapotranspiration estimates (PET and ETa) from the MOD16 global product with one of the most widely used hydrological models in South Africa. The investigation included applying the Pitman model in a number of case study catchments located in different climatic regions of the country. The main objectives of the study included (i) the establishment of behavioural model parameter sets that generate acceptable hydrological response under both naturalised and present-day conditions, (ii) the use of time-varying PET estimates derived from MOD16 data to force the model, and (iii) the use of MOD16 ETa estimates to constrain model-simulated ETa. Before examining the use of different PET forcing data in the model, a two-step modelling approached was employed both a single-run and an uncertainty version of the Pitman model. During the first step (using a single-run version), available information on catchment physical properties and regionalised groundwater recharge together with model calibration principles were used to develop model functionality understanding and establish initial parameter sets. The outcomes from the first step were used to define uncertain parameter ranges for the use in the uncertainty version of the Pitman model (second step). Further, catchment water uses were quantified to ensure comparability with present-day flow conditions represented by the stream flow records. The effects of forcing the Pitman model with MOD16-based time-varying PET data inputs were evaluated using static and dynamic sensitivity analysis approaches. In the static approach, parameter sets calibrated using fixed seasonal distributions of PET data remain unchanged when forcing the model with other forms of PET, whereas in the dynamic method, the model is recalibrated with changing PET inputs. In both approaches, model sensitivity was assessed by comparing objective function statistics of reference flow simulations with those simulations incorporating changing PET data inputs. The use of the MOD16 ETa data to constrain model- simulated evapotranspiration losses was conducted by calibrating the parameters such that the simulated-ETa matched the evapotranspiration loss estimated from the MOD16 data. Despite issues around model equifinality and significant uncertainty within water use information, the Pitman model simulations were generally satisfactory and compared with observed stream flow data where available. The use of time-varying PET data does not improve the efficiency of the model when both static and dynamic sensitivity approaches are used. This was highly expected with the static approach where fixed model parameter sets do not account for the changes in evapotranspiration demands. However, with the dynamic approach, it was difficult to conclude why the model efficiency did not improve given the flexibility of the model to achieve appropriate parameter sets to different forms of PET. The study noted that the insensitivity of the model to changes in PET demands could be due to uncertainties in the model structure and MOD16 data. Attempts to constrain the model-simulated actual evapotranspiration with MOD16 ETa estimates were hampered by large errors in the MOD16 data and resulted in the non-closure of the catchment annual water balance, even when likely errors in the other components of the water balance were accounted for. There is still a great deal of work that needs to be done to reduce uncertainties associated with the use of earth observation data in hydrological modelling. This study has identified some of the specific gaps within the application of evapotranspiration data from earth observation information. While the MOD16 data applied with the Pitman model did not achieve improved simulations, the study has demonstrated the enormous potential of the data product in the future should the identified uncertainties be resolved. Lastly, the investigation highlighted some of the possible model structural uncertainties specifically associated with the simplified soil-moisture accounting routines within the model. It is possible that amending the model structure through investigating the dynamics of the relationship between soil moisture and evapotranspiration losses would assist in the improved utilisation of earth observation products related to the MOD16 ET data.
- Full Text:
- Date Issued: 2017
- Authors: Mazibuko, Sbongiseni Christian
- Date: 2017
- Subjects: Evapotranspiration , Evapotranspiration -- Measurement , Hydrologic models , Hydrologic models -- South Africa , MODIS (Spectroradiometer)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/8009 , vital:21334
- Description: Evapotranspiration as a major component of the water balance has been identified as a key factor in hydrological modelling. Water management can be improved by means of increased use of reliable methods for estimating evapotranspiration. The limited availability of measured climate and discharge data sets, particularly in the developing world, restricts the reliability of hydrological models in these regions. Furthermore, rapid changes in hydrological systems with increasing development mean uncertainties in water resource estimation are growing. These changes are related to the modification of catchment hydrological processes with increasing human activity. Dealing with data uncertainty and quantifying the impacts of catchment activities are significant challenges that scientists in the field of hydrology face today. Uncertainties in hydrometeorological data are associated with poor observation networks that provide data at point scales which are not adequately representative of the inherent heterogeneity within catchment processes. Using uncertain data in model applications reduces the predictive power of hydrological models as well as the ability to validate the model outcomes. This study examines the potential of using remote sensing-based evapotranspiration data to reduce uncertainty in the climatic forcing data and constraining the output of a rainfall-runoff hydrological model. It is common to use fixed seasonally variable potential evapotranspiration (PET) instead of temporally varying PET data as inputs to standard rainfall-runoff models. Part of the reason is that there are relatively few stations available to measure a variety of meteorological input data needed to compute PET, as well as the apparent lack of sensitivity of rainfall-runoff models to different types of PET inputs. As hydrometeorological data become more readily available through the use of earth observation systems, it is important to determine whether rainfall-runoff models are sensitive to time-varying PET derived from these earth observations systems. Further potential includes the use of actual evapotranspiration (ETa) from this type of data to constrain model outputs and improve model realism. It is assumed that a better representation of evapotranspiration demands could improve the efficiency of models, and this study explores some of these issues. The study used evapotranspiration estimates (PET and ETa) from the MOD16 global product with one of the most widely used hydrological models in South Africa. The investigation included applying the Pitman model in a number of case study catchments located in different climatic regions of the country. The main objectives of the study included (i) the establishment of behavioural model parameter sets that generate acceptable hydrological response under both naturalised and present-day conditions, (ii) the use of time-varying PET estimates derived from MOD16 data to force the model, and (iii) the use of MOD16 ETa estimates to constrain model-simulated ETa. Before examining the use of different PET forcing data in the model, a two-step modelling approached was employed both a single-run and an uncertainty version of the Pitman model. During the first step (using a single-run version), available information on catchment physical properties and regionalised groundwater recharge together with model calibration principles were used to develop model functionality understanding and establish initial parameter sets. The outcomes from the first step were used to define uncertain parameter ranges for the use in the uncertainty version of the Pitman model (second step). Further, catchment water uses were quantified to ensure comparability with present-day flow conditions represented by the stream flow records. The effects of forcing the Pitman model with MOD16-based time-varying PET data inputs were evaluated using static and dynamic sensitivity analysis approaches. In the static approach, parameter sets calibrated using fixed seasonal distributions of PET data remain unchanged when forcing the model with other forms of PET, whereas in the dynamic method, the model is recalibrated with changing PET inputs. In both approaches, model sensitivity was assessed by comparing objective function statistics of reference flow simulations with those simulations incorporating changing PET data inputs. The use of the MOD16 ETa data to constrain model- simulated evapotranspiration losses was conducted by calibrating the parameters such that the simulated-ETa matched the evapotranspiration loss estimated from the MOD16 data. Despite issues around model equifinality and significant uncertainty within water use information, the Pitman model simulations were generally satisfactory and compared with observed stream flow data where available. The use of time-varying PET data does not improve the efficiency of the model when both static and dynamic sensitivity approaches are used. This was highly expected with the static approach where fixed model parameter sets do not account for the changes in evapotranspiration demands. However, with the dynamic approach, it was difficult to conclude why the model efficiency did not improve given the flexibility of the model to achieve appropriate parameter sets to different forms of PET. The study noted that the insensitivity of the model to changes in PET demands could be due to uncertainties in the model structure and MOD16 data. Attempts to constrain the model-simulated actual evapotranspiration with MOD16 ETa estimates were hampered by large errors in the MOD16 data and resulted in the non-closure of the catchment annual water balance, even when likely errors in the other components of the water balance were accounted for. There is still a great deal of work that needs to be done to reduce uncertainties associated with the use of earth observation data in hydrological modelling. This study has identified some of the specific gaps within the application of evapotranspiration data from earth observation information. While the MOD16 data applied with the Pitman model did not achieve improved simulations, the study has demonstrated the enormous potential of the data product in the future should the identified uncertainties be resolved. Lastly, the investigation highlighted some of the possible model structural uncertainties specifically associated with the simplified soil-moisture accounting routines within the model. It is possible that amending the model structure through investigating the dynamics of the relationship between soil moisture and evapotranspiration losses would assist in the improved utilisation of earth observation products related to the MOD16 ET data.
- Full Text:
- Date Issued: 2017
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:
- Date Issued: 2015
- 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:
- Date Issued: 2015
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:
- Date Issued: 2009
- 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:
- Date Issued: 2009
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