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The Agricultural Catchments Research Unit (ACRU) model is a daily time step physical-conceptual agrohydrological model with various applications, design hydrology being one of them. Model verification is a measure of model performance and streamflow, soil water content and sediment yield simulated by the ACRU model have been extensively verified against observed data in southern Africa and internationally. The primary objective of this study was to verify simulated runoff volume, peak discharge and sediment yield against observed data from small catchments, under both bare fallow conditions and sugarcane production, which were located at La Mercy in South Africa. The study area comprised 4 research catchments, 101, 102, 103 and 104, monitored both under bare fallow conditions and sugarcane production, with different management practices per catchment. Observed data comprised: daily rainfall, maximum and minimum temperature, A-pan evaporation and runoff for the period 1978–1995, and peak discharge and sediment yield for the period 1984–1995. The data were checked for errors and and inconsistent records excluded from analysis. Runoff volume, peak discharge and sediment yield were simulated with the ACRU model and verified against the respective observed data. In general, the correlations between observed and simulated daily runoff volumes and peak discharge were acceptable (i.e. slopes of regression lines close to unity, R2 ≥ 0.6 and the Nash–Sutcliffe coefficient of efficiency close to unity). Similarly, the correlation between observed and simulated sediment yield was also good. From the results obtained, it is concluded that the ACRU model is suitable for the simulation of runoff volume, peak discharge and sediment yield from catchments under both bare fallow and sugarcane land cover in South Africa.

Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, afected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results.

A number of severe flooding events have occurred both in South Africa and internationally in recent years. Furthermore, changes in both the intensity and frequency of extreme rainfall events have been documented, both locally and internationally, associated with climate change. The recent loss of life, destruction of infrastructure, and associated economic losses caused by flooding, compounded by the probability of increased rainfall variability in the future, highlight that design flood estimation (DFE) techniques within South Africa are outdated and in need of revision. A National Flood Studies Programme (NFSP) has recently been initiated to overhaul DFE procedures in South Africa. One of the recommendations in the NFSP is the further development of a continuous simulation modelling (CSM) system for DFE in South Africa. The focus of this paper is a review of CSM techniques for DFE, to guide further development for application in South Africa. An introduction to DFE, and particularly the CSM approach, is presented, followed by a brief overview of DFE techniques used in South Africa, leading into a more detailed summary of CSM for DFE within South Africa to date. This is followed by a review of the development and application of CSM methods for DFE internationally, with a focus on the United Kingdom and Australia, where methods have been developed with the intention of national scale implementation. It is important to highlight that there is a plethora of CSM methods available internationally and this review is not exhaustive; it focuses on and identifies some of the strengths and weaknesses of several popular methods, particularly those intended for national scale application, as the intended outcome from this review is to identify a path towards the development of a usable national scale CSM system for DFE in South Africa. Emphasis on a usable method is important, considering the reality that, despite promising results, numerous benefits, and national scale methods being developed, it appears that the CSM method for DFE is rarely used in practice.

Soil information is increasingly sought after for hydrological modelling, as the importance of soil in the hydrological cycle is understood better. In this paper the output of a digital soil mapping exercise was used as the soil input into a distributed hydrological model (ACRU) for a test site within the Stevenson-Hamilton Research Supersite, Kruger National Park (South Africa). The aim was to determine the effect of parameterising a hydrological model with increased levels of soil information, at different scales. To accommodate this aim, ACRU was run in 3 different modes, each with increasing levels of input, on 3 catchments, including a 1 st , 2 nd and 3 rd order catchment. The outputs evaluated included both streamflow and soil water content at selected soil profiles. Simulation accuracy increased with higher levels of soil input, as well as with increasing catchment size. The improved accuracy with increased soil input underscores the value of detailed soil information in modelling, while the improved results with increased catchment size show that the optimal scale for including soil information has not yet been reached.

The Agricultural Catchments Research Unit (ACRU) model is a daily time step physical-conceptual agrohydrological model with various applications, design hydrology being one of them. Model verification is a measure of model performance and streamflow, soil water content and sediment yield simulated by the ACRU model have been extensively verified against observed data in southern Africa and internationally. The primary objective of this study was to verify simulated runoff volume, peak discharge and sediment yield against observed data from small catchments, under both bare fallow conditions and sugarcane production, which were located at La Mercy in South Africa. The study area comprised 4 research catchments, 101, 102, 103 and 104, monitored both under bare fallow conditions and sugarcane production, with different management practices per catchment. Observed data comprised: daily rainfall, maximum and minimum temperature, A-pan evaporation and runoff for the period 1978-1995, and peak discharge and sediment yield for the period 1984-1995. The data were checked for errors and and inconsistent records excluded from analysis. Runoff volume, peak discharge and sediment yield were simulated with the ACRU model and verified against the respective observed data. In general, the correlations between observed and simulated daily runoff volumes and peak discharge were acceptable (i.e. slopes of regression lines close to unity, [R.sup.2] [greater than or equal to]0.6 and the Nash-Sutcliffe coefficient of efficiency close to unity). Similarly, the correlation between observed and simulated sediment yield was also good. From the results obtained, it is concluded that the ACRU model is suitable for the simulation of runoff volume, peak discharge and sediment yield from catchments under both bare fallow and sugarcane land cover in South Africa. KEYWORDS ACRU bare fallow peak discharge sediment yield streamflow sugarcane

Design flood estimation (DFE) is essential in the planning and design of hydraulic structures. In South Africa, outdated methods are widely applied for DFE. In this paper the potential of a continuous simulation modelling (CSM) approach to DFE in South Africa, using the daily time-step ACRU agrohydrological model, is investigated. The paper focuses on the links and similarities between the SCS-SA and ACRU models and the subsequent preliminary investigations that were undertaken to account for and incorporate the land cover classes, including land management practices and hydrological condition, of the SCS-SA model into the ACRU CSM approach. The approach to this study was to investigate how design volumes simulated by the SCS-SA model for various land management practices or conditions could be simulated by the ACRU model. Since peak discharge estimation in both models is directly dependent on simulated volumes, this preliminary study focused only on design runoff volumes, with subsequent investigations on peak discharge required in future research. In the absence of observed data, design runoff volumes and changes in design runoff volumes, as simulated by the SCS-SA model, were used as a substitute for observed data, i.e., as a reference, to achieve similar design runoff volumes and changes in design volumes in the ACRU model. This was achieved by adjusting relevant input parameters in the ACRU model to represent the change in management practice or hydrological condition, as represented in the SCS-SA model. Following a sensitivity analysis of relevant ACRU parameters, calibration of 2 selected parameters against SCS-SA CN values for selected land cover classes was performed. A strong linear relationship (R2 = 0.94) between these ACRU parameters and SCS-SA CNs for selected land cover classes was found and consequently specific rules and equations were developed to represent SCS-SA land cover classes in ACRU. Recommendations are made to further validate and verify the approach and to further the development of a CSM system for DFE in South Africa.

The skill and accuracy of the quantitative precipitation forecasts by CCAM, UM and NCEP-MRF models are verified using various statistical scores at the Mgeni catchment in KwaZulu-Natal, South Africa. The CCAM model is capable of identifying a rainfall event, but with a tendency of under-estimating its magnitude. The UM model is capable of distinguishing rainy days from non-rainy days, but with a significant over-estimation of rainfall amount. There is no significant difference between the 1 and 2 day lead time UM forecasts. Statistical comparisons show that there is an acceptable skill in the CCAM forecasts, but the forecast skill of the UM model is low and unreliable. The role of the initial hydrological conditions in affecting the accuracy of CCAM and UM streamflows forecasts was significant. The results show that the under-estimation of the CCAM forecasts was reduced from −44% to −10%, while the over-estimation in the UM forecasts was reduced from 291% to only 59% when the ACRU agrohydrological model was initialised with observed rainfalls up to the previous day at each forecast run within the study period. The combined use of the CCAM and UM models by a “weighted averaging” had little effect in improving the skill as it is overshadowed more by the over-estimation of the UM forecasts than the under-estimation of the CCAM forecasts. Results obtained for a continuous period of 92 days showed that the NCEP-MRF rainfall forecasts were significantly over-predicted. The NCEP-MRF rainfall forecast is found to be totally unskillful, although the skill was seen to slightly increase with decreasing lead time.

Water managers face the daunting task of balancing limited water resources with over-subscribed water users among competing demands. They face the additional challenge of taking water planning decisions in an uncertain environment with limited and sometimes inaccurate observed and simulated hydrological data. Within South African watersheds, spatial parameterization data for hydrological models are now available at two different basin management resolutions (termed quaternary and quinary). Currently, water management decisions in the Crocodile River watershed are often made at a more coarse resolution, which may exclude crucial insights into the data. This research has the following aims (1) to explore whether model performance is improved by parameterization using a more detailed quinary-scale watershed data and (2) to explore whether quinary-scale models reduce uncertainty in allocation or restriction decisions to provide better informed water resources management and decision outcomes. This study used the Agricultural Catchments Research Unit (ACRU) agro-hydrological watershed model, to evaluate the effects of spatial discretization at the quaternary and quinary scales on watershed hydrological response and runoff within the Crocodile River basin. Model performance was evaluated using statistical comparisons of results using traditional goodness-of-fit measures such as the coefficient of efficiency ( C eff ), root mean square of the error and the coefficient of determination ( R 2 ) to compare simulated monthly flows and observed flows in six subcatchments. Traditional interpretation of these goodness-of-fit measures may be inadequate as they can be subjectively interpreted and easily influenced by the number of data points, outliers and model bias. This research utilizes a recently released model evaluation program (FITEVAL) which presents probability distributions of R 2 and C eff derived by bootstrapping, graphical representation of observed and simulated stream flows, incorporates statistical significance to detect the sufficiency of the R 2 and C eff and determines the presence of outliers and bias. While analyses indicate that the ACRU model performs marginally better when parameterized and calibrated at the quinary scale, the measurements at both scales show significant variability in predictions for both high and low flows that are endemic to southern African hydrology. The improved evaluation methods also allow for the analysis of data collection errors at monitoring sites and help determine the effect of data quality on adaptive water planning management decisions. Given that many water resource challenges are complex adaptive systems, these expanded performance analysis tools help provide deeper insights into matching watershed decision metrics and model-derived predictions.

A case study was conducted in the Orari catchment near Timaru, New Zealand, to assess the potential hydrological impacts of irrigated agriculture on catchmentscale hydrological processes. Impacts on catchment hydrology are quantified as the additional evapotranspiration losses caused by irrigated agriculture in comparison to dryland evapotranspiration. Different land-use scenarios with varying irrigation intensities are investigated with the ACRU agro-hydrological modeling system (Agricultural Catchments Research Unit (ACRU), Department of Agricultural Engineering, University of KwaZulu-Natal, Republic of South Africa (http://www.beeh.unp.ac.za/acru/). ACRU was applied to calculate components of the water balance at a daily time step for hydrological response units delineated for the lower, irrigated part of the Orari catchment. Irrigation water demand and application were simulated using a soil-moisture-driven demand model and compared with a dataset of observed irrigation takes. Simulations based on current irrigation demand indicated that irrigation resulted in a 17% increase of average annual evapotranspiration over the whole lower Orari catchment, compared to dryland (nonirrigated) agriculture. This figure could go up to 37% if all crop and dryland pastoral areas in the lower Orari catchment were to be be converted to irrigated dairy farms. These increases show large spatial and temporal variations. For a complete dairy conversion, the catchment total peak summer monthly irrigation application rate could go as high as 20% of the total (rainfall and upstream) input into the catchment water balance. The related changes in evapotranspiration due to land-use intensification can be as high as 20% of the total monthly water balance, equivalent to a 60% increase of total catchment evapotranspiration when compared to dryland evapotranspiration. For the most intensely irrigated areas, evapotranspiration could increase to as high as 200% when compared to dryland farming. Irrigation and evaporative irrigation losses play significant roles in the Orari catchment water balance under current irrigation and even more so under intensified future irrigation scenarios. Man-made changes to the catchment water balance affect downstream groundwater and lowland surfacewater systems and related ecosystem services. This highlights the need for prudent management of Canterbury's intensively used hydrological systems to balance the costs and benefits of land-use intensification.