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 
Published: April 2021

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.