Data-scarce Catchment Research Articles

Assessing two methods of defining rainfall intensity and duration thresholds for shallow landslides in data-scarce catchments of the Colombian Andean Mountains

Rainfall thresholds are intensity-duration relations that are supposedly able to distinguish precipitation events that may or may not trigger landslides. The most common method for defining rainfall thresholds relies on observed landslides and the corresponding values of rainfall intensity and duration that caused each failure. Alternative methods to define rainfall thresholds, using physically-based models, recently gained importance, as they may provide complementary information to other methods. Still, their applicability in most of the world's regions, including the mountainous basins of the Colombian Andes, has not been demonstrated or validated. In this study, we evaluated the applicability of the physically-based model TRIGRS to define rainfall intensity and duration thresholds in individual basins from the Colombian Andes. We obtained rainfall thresholds using two different methods and compared them with landslide-triggering rainfall events in two distinct basins, namely La Arenosa and La Liboriana. Furthermore, we used a (presumably incomplete) landslide database from Medellín to rebuild the rainfall events associated with individual landslides and compared them with the physically-based thresholds. The rainfall thresholds calculated in the three study areas and the applicability of the methods in data-scarce environments were assessed. Results showed that both methods for defining rainfall intensity and duration thresholds have merits and represent potential tools for improving or complementing landslide early warning systems, especially in data-scarce regions.

Intensity duration frequency relationship of maximum rainfall in a data scarce urbanized environment: A case study of the Guma Catchment in Sierra Leone

Rainfall intensity for a particular frequency and duration is one of the most important parameter for the hydrologic design of dam, reservoirs, storm sewers, culverts and many other hydraulic structures. This can be obtained from Intensity-Duration-Frequency (IDF) relationship, which is determined by frequency analysis of rainfall data. The goal of this research is to develop rainfall intensity-duration frequency relationship for the data scarce urbanized Guma catchment in Freetown the capital city of Sierra Leone using the Gumbel and Pearson Type III distributions. To achieve this goal, daily rainfall data for the period of 1991 to 2018 for the Guma catchment rainfall station was obtained from the Guma Valley Water Company and were converted to shorter duration (hourly) using the Indian Meteorological Department (IMD) method. Twenty-eight maximum daily rainfall events were converted to hourly rainfall events. Frequency analysis was conducted to develop the rainfall intensity-duration-frequency relationships using the Gumbel and Pearson Type III distributions. The Kolmogorov-Smirnov (K-S) goodness of fit test was utilized to determine which of the distributions have a better fit at 5 and 10% significant levels. The frequency analysis results show that the Gumbel distribution gives higher intensity for all return periods and durations than the Pearson Type III distribution. The result of the K-S goodness of fit test shows that all of the data fit the Gumbel distribution for different return periods (2, 5, 10, 25, 50 and 100 years) at the level of significance of which yield and which yield while the data do not give good fit using the Pearson Type III distribution at both levels of significance for the different return periods. Key words: Rainfall, Intensity-duration-frequency relationships, probability distributions, goodness of fit test.

Rainfall-runoff simulation using satellite rainfall in a scarce data catchment

This study investigates the performance of TRMM and PERSIANN satellite rainfall data as input in a reliable rainfall-runoff model in order to provide information to the flood early warning for the Voshmgir Dam, Iran. Calibration of both continuous daily rainfall-runoff and an event-based flood was done using HEC-HMS Nelder-Mead (NM) method. Furthermore, simulations based on daily versus 3-hourly TRMM were compared to evaluate the effect of input time-step of rainfall-runoff model. Results show that the deficit and constant loss method is able to successfully predict the observed runoff. In addition, the Green-Ampt method using 3-hourly TRMM data showed a good capability to simulate daily peak discharges. The current study demonstrates the suitability of HEC-HMS for continuous and event-based runoff simulation in a complex watershed. Therefore, this research will have a significant contribution to the future development of water resources planning in this catchment in particular and in other data-scarce catchments.

A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchment

Real-time streamflow modeling is a challenging endeavor in regions where real-time ground-based hydro-meteorological observations are scarce. Nevertheless, with the emergence of satellite remote sensing, some of the essential hydro-meteorological datasets such as rainfall and soil moisture are available in near real-time, which can circumvent the problem. In such scenario, machine learning (ML) approaches can be used to model streamflow even with limited real-time data. In view of this, the effectiveness of integrating satellite-based near real-time rainfall, i.e., Tropical Rainfall Measuring Mission based (3B42RT V7), and Advance Scatterometer (ASCAT) based soil moisture observations through Support Vector Machine (SVM) for simulating streamflow in a poorly gauged catchment in India has been investigated. Results show that 3B42RT forced streamflow performed poorly compared to streamflow simulated using the observed gridded rainfall. This inferior performance is mainly attributed to errors in 3B42RT rainfall product. Hence, a recently developed SVM based error reduction model using ASCAT soil moisture data is used to correct 3B42RT. The obtained corrected satellite rainfall outperformed 3B42RT in streamflow simulations but still it is affected by notable errors. To further improve the streamflow estimates, ASCAT soil moisture is used as a predictor variable along with 3B42RT and the corrected satellite rainfall separately into SVM based streamflow modelling. Results indicate that this integration considerably improves the streamflow estimates with respect to the use of 3B42RT and corrected satellite rainfall individually. This is attributed to the crucial role of antecedent soil moisture for streamflow generation. Among all the tested scenarios, streamflow derived by the integration of soil moisture and corrected satellite rainfall showed the highest performance. Hence, it can be said that the proposed approach has considerable potential to model real time streamflow in the chosen poorly gauged catchment and possibly beyond. However, the approach needs detailed evaluation before applying in other such catchments.

Improving the integrated hydrological simulation on a data-scarce catchment with multi-objective calibration

Abstract The process-based hydrological model Soil and Water Assessment Tool ensures the simulation's reliability by calibration. Compared to the commonly applied single-objective calibration, multi-objective calibration benefits the spatial parameterization and the simulation of specific processes. However, the requirements of additional observations and the practical procedure are among the reasons to prevent the wider application of the multi-objective calibration. This study proposes to consider three groups of objectives for the calibration: multisite, multi-objective function, and multi-metric. For the study catchment with limited observations like the Yuan River Catchment (YRC) in China, the three groups corresponded to discharge from three hydrometric stations, both Nash–Sutcliffe efficiency (NSE) and inversed NSE for discharge evaluation, and MODIS global terrestrial evapotranspiration product and baseflow filtered from discharge as metrics, respectively. The applicability of two multi-objective calibration approaches, the Euclidean distance and nondominated sorting genetic algorithm II, was analyzed to calibrate the above-mentioned objectives for the YRC. Results show that multi-objective calibration has simultaneously ensured the model's better performance in terms of the spatial parameterization, the magnitude of the output time series, and the water balance components, and it also reduces the parameter and prediction uncertainty. The study thus leads to a generalized, recommended procedure for catchments with data scarcity to perform the multi-objective calibration.

Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments

The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

Potential of SAR-Derived Flood Maps for Hydrodynamic Model Calibration in Data Scarce Regions

AbstractSynthetic aperture radar (SAR) observations of real world flood extents are often the only source of information in data scarce catchments and the only reliable resource for channels with s...

Integrated Hydrologic and Hydrodynamic Models to Improve Flood Simulation Capability in the Data-Scarce Three Gorges Reservoir Region

One-dimensional hydrodynamic modeling approaches are useful for flood simulations; however, most studies often neglect intermediate discharges due to difficulties in obtaining the associated data. Herein, we produced the XAJ-H1DM model by coupling the Xinanjiang (XAJ) model, without the Muskingum module, with a one-dimensional hydrodynamic (H1DM) model, using regionalization approaches to test their practicality. Another model, named H1DM-XAJ, was also produced by orderly calibrating the H1DM and XAJ models to achieve improved flood simulations in poorly gauged catchments. The flood simulation capabilities of the four models (including the single XAJ and H1DM models) were investigated and compared at a daily time scale in the Three Gorges Reservoir Region, China. The results show that the regionalization approaches can be successfully used in the application of the integrated hydrologic and hydrodynamic model in ungauged intermediate catchments. Further, the coupled models produced markedly improved estimates of peak discharge and runoff volume compared to the single models. Moreover, the ability of the coupled models to simulate the peak water level and hydrograph, which hydrological models lack, is significantly better than that of the single H1DM model. The framework presented can be applied in other data-scarce catchments worldwide for better understanding of the hydrodynamic processes.

Recharge Estimation Using CMB and Environmental Isotopes in the Verlorenvlei Estuarine System, South Africa and Implications for Groundwater Sustainability in a Semi-Arid Agricultural Region

Groundwater recharge remains one of the most difficult hydrogeological variables to measure accurately, especially for semi-arid environments where the recharge flux is much smaller than in humid conditions. In this study, groundwater recharge was estimated using chloride mass balance (CMB) in the Verlorenvlei catchment, South Africa where the effects of recent severe drought conditions in an already semi-arid environment have impacted both agricultural activity as well as the RAMSAR-listed Verlorenvlei estuarine system. Chloride, 18O and 2H tracers were used to improve understanding of the groundwater flow patterns and allowed the fresh parts of the groundwater system, defined by Ca2+-HCO3− groundwater types, to be separated from those where additional salts were being introduced through groundwater mixing, and thus characterized as Na+-Cl− groundwater types. Recharge rates calculated from CMB in the fresh parts of the system were between 4.2–5.6% and 11.4–15.1% of mean annual precipitation for the headwater valley and mountains of the Krom Antonies and are largely consistent with previous studies. However, much lower recharge rates in the valleys where agriculture is dominant contrasts with previous results, which were higher, since groundwater-mixing zones were not recognised. Although the chloride concentration in precipitation is based on only one year of data between 2015 and 2016, where 2015 had on average 28% less precipitation than 2016, the results provide a snapshot of how the system will respond to increasing drought frequency in the future. The results suggest that low rates of groundwater recharge under dry spell conditions will impact on low flow generations which are required to sustain the Verlorenvlei estuarine lake system. Overall, the study highlights the importance of combining hydrochemical tracers such as bulk chloride and stable isotopes with numerical modelling in data-scarce catchments to fully understand the nature of hydrological resilience.

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.

Hydrological modelling in data-scarce catchments: Black Volta basin in West Africa

A lumped version of the conceptual HBV–IWS version (Hydrologiska Byrans Vattenbalansavdelning) hydrological model was applied on the Black Volta Catchment in the West African sub-region (a data-scarce catchment) to investigate the performance of the model in the study area. In assessing the robustness of the model and how well it performs in the study area, different optimization methods were used. The Robust Parameter Estimation (ROPE) algorithm was used to generate and calibrate 10,000 best parameter sets in a computer framework with initially set model parameter ranges. The model performed well with an average NS of 0.75 and 0.6 for calibration and validation respectively for subcatchments Lawra and Chache. The performance also improved when a higher weight was given to the low-flows during the optimization. Results also show that the model reacts well to precipitation signals. For a proper management of the water resources and hydropower in the West Africa sub-region and for good policy decisions and investments in water related projects, a robust hydrological model would be essential and crucial for the Black Volta Catchment. The results of this study will be an essential input to a further study in assessing the effects of climate change on the hydrology of the West African sub-region.

Hydrological impacts of climate change on a data-scarce Greek catchment

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model’s capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration.

Techniques for calibration and validation of SWAT model in data scarce arid and semi-arid catchments in South Africa

Study regionThis study was conducted in Soutloop River Catchment, Northern Cape, South Africa. Study focusAlthough hydrologic models play a critical role in the management of natural resources in arid areas, their application is challenged by the scarcity of data for calibration and validation. Therefore, this study aimed at to configure, calibrate and validate SWAT model in a data-scarce catchment by using the regionalization with physical similarity approach. This approach uses dual calibration and validation procedure, i.e., one in the donor catchment (by using SWAT-CUP (SWAT Calibration and Uncertainty Programs) and the other on the study catchment (by manual calibration and verification). New hydrological insights for the regionBased on the sensitivity analysis, sixteen parameters were calibrated by SWAT-CUP. The result from the uncertainty analysis indicated acceptable values of both the R-factor (0.8****Values are the averages of the calibration and validation procedures.) and P-factor (0.7**). The model performance evaluation also showed acceptable ranges of values (e.g., NS was 0.76** and R2 was 0.78**). However, the main calibration and validation process was conducted outside the target catchment, though it was assumed that the donor and target catchments have similar hydrological responses. Therefore, the study suggested further inspection methods to minimize the model uncertainty in the study catchment. This study enables researchers to exploit the river eco-regional classifications of South Africa to apply hydrologic models to estimate the components of water balance in arid/semi-arid catchments.

A method to study antibiotic emission and fate for data-scarce rural catchments

Estimations of antibiotic emission and fate and thereby ecological risk in rural catchments still lack feasible methods due to data scarcity. This study developed a new framework to evaluate the emission and fate of typical antibiotics for data-scarce catchments with uncertainty analysis. We estimated antibiotic discharge through questionnaire surveys; predicted antibiotic fate in air, water, soil, and sediment phases using a multimedia fugacity model; and analyzed the uncertainties of predicted environmental concentrations (PECs) and ecological risks of antibiotics. The developed method was tested in the Meijiang River catchment in China, and the uncertainty was systematically analyzed. Results showed that the discharge of tetracycline antibiotics (TCs) in the studied watershed was 8.56 t/a, with approximately 93% from veterinary medicine. TCs existed dominantly in the soil phase, accounting for 87.3% of total discharge. TC levels at the equilibrium states were the highest in sediment and soil, followed by water and air. The emission levels of TCs may cause slight risk to algae, daphniids, and fish in the receiving water based on the ecological risk evaluation of PECs. Despite of some uncertainties, the developed method provided an effective alternative to evaluate the ecological risks of antibiotics in catchments where sufficient monitored data are unavailable.

An efficient representation of glacier dynamics in a semi-distributed hydrological model to bridge glacier and river catchment scales

Glacierised river catchments have been shown to be highly sensitive to climate change, while large populations depend on the water resources originating from them. Hydrological models are used to aid water resource management, yet their treatment of glacier processes is either rudimentary in large-scale applications or linked to fully distributed glacier models that prevent larger model domains. Also, data scarcity in mountainous catchments has hampered the implementation of physically based approaches over entire river catchments. A fully integrated glacier dynamics module was developed for the hydrological model SWIM (SWIM-G) that takes full account of the spatial heterogeneity of mountainous catchments but keeps in line with the semi-distributed disaggregation of the hydrological model. The glacierised part of the catchment is disaggregated into glaciological response units that are based on subbasin, elevation zone and aspect classes. They seamlessly integrate into the hydrological response units of the hydrological model. Robust and simple approaches to ice flow, avalanching, snow accumulation and metamorphism as well as glacier ablation under consideration of aspect, debris cover and sublimation are implemented in the model, balancing process complexity and data availability. The fully integrated model is also capable of simulating a range of other hydrological processes that are common for larger mountainous catchments such as reservoirs, irrigation agriculture and runoff from a diverse soil and vegetation cover. SWIM-G is initialised and calibrated to initial glacier hypsometry, glacier mass balance and river discharge. While the model is intended to be used in medium to large river basins with data-scarce and glacierised headwaters, it is here validated in the data-scarce catchment of the Upper Aksu River, Kyrgyzstan/NW China and in the relatively data-abundant catchment of the Upper Rhone River, Switzerland.

Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria

Abstract. The main objective of this study was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data from the Global Land Evaporation Amsterdam Model (GLEAM_v3.0a) and from the Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) for the Ogun River Basin (20 292 km2) located in southwestern Nigeria. Three potential evapotranspiration (PET) equations (Hargreaves, Priestley–Taylor and Penman–Monteith) were used for the SWAT simulation of AET. The reference simulations were the three AET variables simulated with SWAT before model calibration took place. The sequential uncertainty fitting technique (SUFI-2) was used for the SWAT model sensitivity analysis, calibration, validation and uncertainty analysis. The GLEAM_v3.0a and MOD16 products were subsequently used to calibrate the three SWAT-simulated AET variables, thereby obtaining six calibrations–validations at a monthly timescale. The model performance for the three SWAT model runs was evaluated for each of the 53 subbasins against the GLEAM_v3.0a and MOD16 products, which enabled the best model run with the highest-performing satellite-based AET product to be chosen. A verification of the simulated AET variable was carried out by (i) comparing the simulated AET of the calibrated model to GLEAM_v3.0b AET, which is a product that has different forcing data than the version of GLEAM used for the calibration, and (ii) assessing the long-term average annual and average monthly water balances at the outlet of the watershed. Overall, the SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool. The 95 % uncertainty of the SWAT-simulated variable bracketed most of the satellite-based AET data in each subbasin. A validation of the simulated soil moisture dynamics for GS1 was carried out using satellite-retrieved soil moisture data, which revealed good agreement. The SWAT model (GS1) also captured the seasonal variability of the water balance components at the outlet of the watershed. This study demonstrated the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged large river basin with reasonable accuracy. The novelty of the study is the use of these freely available satellite-derived AET datasets to effectively calibrate and validate an eco-hydrological model for a data-scarce catchment.

Event-based uncertainty assessment of sediment modeling in a data-scarce catchment

Uncertainties in watershed modeling need to be addressed to increase predictive accuracy and enhance model interpretation. Nevertheless, event-based characteristics incorporating management concerns were rarely considered in the sediment modeling within an uncertainty assessment framework, which might lead to biased decision makings in watershed management. In this study, the event-based likelihood measure was developed to improve the understanding and prediction of the sediment dynamics in a data-scarce catchment in the Three Gorge Reservoir Region (TGRR), China. The uncertainty assessment is based on the Generalized Likelihood Uncertainty Estimation (GLUE) approach applied to the Hydrological Simulation Program - Fortran (HSPF) model. The impact of the Nash-Sutcliffe efficiency (NSE) as the traditionally used likelihood measure was also investigated as a comparison. The results showed that the event-based likelihood measure had advantages in judging critical parameters. Sensitive parameters in the soil erosion and channel transport and their impacts on different phrases of sedimentograph were recognized. The event-based likelihood measure was much more discriminating by efficiently eliminating unsatisfactory parameter sets. Better validity in efficiently reducing the parameter uncertainty and a higher identifiability was achieved by the newly developed likelihood measure. The event-based likelihood measure resulted in more progressive reductions in predictive uncertainty in terms of uncertainty bounds and evaluation criteria. The proposed uncertainty assessment framework yields improved predictions and an increased understanding of sediment response behaviors in a data-limited environment.

Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

Deterioration of upland soils, demographic growth, and climate change all lead to an increased utilization of wetlands in East Africa. This considerable pressure on wetland resources results in trade-offs between those resources and their related ecosystem services. Furthermore, relationships between catchment attributes and available wetland water resources are one of the key drivers that might lead to wetland degradation. To investigate the impacts of these developments on catchment-wetland water resources, the Soil and Water Assessment Tool (SWAT) was applied to the Kilombero Catchment in Tanzania, which is like many other East African catchments, as it is characterized by overall data scarcity. Due to the lack of recent discharge data, the model was calibrated for the period from 1958–1965 (R2 = 0.86, NSE = 0.85, KGE = 0.93) and validated from 1966–1970 (R2 = 0.80, NSE = 0.80, KGE = 0.89) with the sequential uncertainty fitting algorithm (SUFI-2) on a daily resolution. Results show the dependency of the wetland on baseflow contribution from the enclosing catchment, especially in dry season. Main contributions with regard to overall water yield arise from the northern mountains and the southeastern highlands, which are characterized by steep slopes and a high share of forest and savanna vegetation, respectively. Simulations of land use change effects, generated with Landsat images from the 1970s up to 2014, show severe shifts in the water balance components on the subcatchment scale due to anthropogenic activities. Sustainable management of the investigated catchment should therefore account for the catchment–wetland interaction concerning water resources, with a special emphasis on groundwater fluxes to ensure future food production as well as the preservation of the wetland ecosystem.

Regionalising MUSLE factors for application to a data-scarce catchment

Abstract. The estimation of soil loss and sediment transport is important for effective management of catchments. A model for semi-arid catchments in southern Africa has been developed; however, simplification of the model parameters and further testing are required. Soil loss is calculated through the Modified Universal Soil Loss Equation (MUSLE). The aims of the current study were to: (1) regionalise the MUSLE erodibility factors and; (2) perform a sensitivity analysis and validate the soil loss outputs against independently-estimated measures. The regionalisation was developed using Geographic Information Systems (GIS) coverages. The model was applied to a high erosion semi-arid region in the Eastern Cape, South Africa. Sensitivity analysis indicated model outputs to be more sensitive to the vegetation cover factor. The simulated soil loss estimates of 40 t ha−1 yr−1 were within the range of estimates by previous studies. The outcome of the present research is a framework for parameter estimation for the MUSLE through regionalisation. This is part of the ongoing development of a model which can estimate soil loss and sediment delivery at broad spatial and temporal scales.

A management-oriented water quality model for data scarce catchments

Due to the degeneration of water quality globally, water quality models could increasingly be utilised within water resource management. However, a lack of observed data as well as financial resources often constrain the number of potential water quality models that could practically be utilised. This study presents the Water Quality Systems Assessment Model (WQSAM). WQSAM directly utilises flow data generated by systems models to drive water quality simulations. The model subscribes to requisite simplicity by constraining the number of variables simulated as well as the processes represented to only those most important to water quality management, in this case, nutrients and salinity. The model application to the upper Olifants River catchment in South Africa is described. WQSAM was able to use the limited observed data to simulate representative frequency distributions of water quality, and the approach used within WQSAM was shown to be suitable for application to data scarce catchments.