Understanding and modelling of surface and groundwater interactions

Abstract

The connections between surface water and groundwater systems remain poorly understood in many catchments throughout the world and yet they are fundamental to effectively managing water resources. Managing water resources in an integrated manner is not straightforward, particularly if both resources are being utilised, and especially in those regions that suffer problems of data scarcity. This study explores some of the principle issues associated with understanding and practically modelling surface and groundwater interactions. In South Africa, there remains much controversy over the most appropriate type of integrated model to be used and the way forward in terms of the development of the discipline; part of the disagreement stems from the fact that we cannot validate models adequately. This is largely due to traditional forms of model testing having limited power as it is difficult to differentiate between the uncertainties within different model structures, different sets of alternative parameter values and in the input data used to run the model. While model structural uncertainties are important to consider, the uncertainty from input data error together with parameter estimation error are often more significant to the overall residual error, and essential to consider if we want to achieve reliable predictions for water resource decisions. While new philosophies and theories on modelling and results validation have been developed (Beven, 2002; Gupta et al., 2008), in many cases models are not only still being validated and compared using sparse and uncertain datasets, but also expected to produce reliable predictions based on the flawed data. The approach in this study is focused on fundamental understanding of hydrological systems rather than calibration based modelling and promotes the use of all the available 'hard' and 'soft' data together with thoughtful conceptual examination of the processes occurring in an environment to ensure as far as possible that a model is generating sensible results by simulating the correct processes. The first part of the thesis focuses on characterising the 'typical' interaction environments found in South Africa. It was found that many traditional perceptual models are not necessarily applicable to South African conditions, largely due to the relative importance of unsaturated zone processes and the complexity of the dominantly fractured rock environments. The interaction environments were categorised into four main 'types' of environment. These include karst, primary, fractured rock (secondary), and alluvial environments. Processes critical to Integrated Water Resource Management (IWRM) were defined within each interaction type as a guideline to setting a model up to realistically represent the dominant processes in the respective settings. The second part of the thesis addressed the application and evaluation of the modified Pitman model (Hughes, 2004), which allows for surface and groundwater interaction behaviour at the catchment scale to be simulated. The issue is whether, given the different sources of uncertainty in the modelling process, we can differentiate one conceptual flow path from another in trying to refine the understanding and consequently have more faith in model predictions. Seven example catchments were selected from around South Africa to assess whether reliable integrated assessments can be carried out given the existing data. Specific catchment perceptual models were used to identify the critical processes occurring in each setting and the Pitman model was assessed on whether it could represent them (structural uncertainty). The available knowledge of specific environments or catchments was then examined in an attempt to resolve the parameter uncertainty present within each catchment and ensure the subsequent model setup was correctly representing the process understanding as far as possible. The confidence in the quantitative results inevitably varied with the amount and quality of the data available. While the model was deemed to be robust based on the behavioural results obtained in the majority of the case studies, in many cases a quantitative validation of the outputs was just not possible based on the available data. In these cases, the model was judged on its ability to represent the conceptualisation of the processes occurring in the catchments. While the lack of appropriate data means there will always be considerable uncertainty surrounding model validation, it can be argued that improved process understanding in an environment can be used to validate model outcomes to a degree, by assessing whether a model is getting the right results for the right reasons. Many water resource decisions are still made without adequate account being taken of the uncertainties inherent in assessing the response of hydrological systems. Certainly, with all the possible sources of uncertainty in a data scarce country such as South Africa, pure calibration based modelling is unlikely to produce reliable information for water resource managers as it can produce the right results for the wrong reasons. Thus it becomes essential to incorporate conceptual thinking into the modelling process, so that at the very least we are able to conclude that a model generates estimates that are consistent with, and reflect, our understanding (however limited) of the catchment processes. It is fairly clear that achieving the optimum model of a hydrological system may be fraught with difficulty, if not impossible. This makes it very difficult from a practitioner's point of view to decide which model and uncertainty estimation method to use. According to Beven (2009), this may be a transitional problem and in the future it may become clearer as we learn more about how to estimate the uncertainties associated with hydrological systems. Until then, a better understanding of the fundamental and most critical hydrogeological processes should be used to critically test and improve model predictions as far as possible. A major focus of the study was to identify whether the modified Pitman model could provide a practical tool for water resource managers by reliably determining the available water resource. The incorporation of surface and groundwater interaction routines seems to have resulted in a more robust and realistic model of basin hydrology. The overall conclusion is that the model, although simplified, is capable of representing the catchment scale processes that occur under most South African conditions