Implementing contour bank farming practices into the J2000 model to improve hydrological and erosion modelling in semi-arid Western Cape Province of South Africa

Abstract

Contour bank farming is a well-known agricultural management technique in areas which are characterised by intensive and erosive rainfalls. Contour banks are designed to reduce the flow velocity of overland flow and to intercept water before it concentrates in rills, thereby reducing the risk of soil erosion and land degradation. By their structure, contour banks noticeably impact surface runoff pattern both temporally and spatially. Also subsurface flow may be affected by contour banks.For example, if contour banks intersect the A-and B-horizon of the soil, it can cause significant infiltration of water into the C-horizon, which if saline, can generate saline interflow to downslope areas. Although these aspects have been highlighted in previous research efforts, the quantitative and qualitative impacts of contours on runoff generation and associated erosion dynamics or salinisation are rarely considered in process-based hydrological modelling approaches. In this study an approach was developed to improve distributed hydrological and erosion modelling by integrating contour banks in the delineation and routing of Hydrological Response Units. Applying the distributed and process-based hydrological model J2000 which was modified with a contour bank and erosion module it could be shown that the implementation of contour banks improved the model performance significantly.

Distributive rainfall–runoff modelling to understand runoff-to-baseflow proportioning and its impact on the determination of reserve requirements of the Verlorenvlei estuarine lake, west coast, South Africa

“…The vegetation and land-use dataset that was used for the sub-catchment (CSIR, 2009) included five different land-use classes: (1) wetlands and waterbodies, (2) cultivated (temporary, commercial, dryland), (3) shrubland and low fynbos, (4) thicket, bushveld, bush clumps and high fynbos and (5) cultivated (permanent, commercial, irrigated). Each different land-use class was assigned an albedo, root depth and seal grade value based on previous studies (Steudel et al, 2015) (Table S2). The leaf area index (LAI) and vegetation height vary by growing season with different values of each for the particular growing season.…”

“…The rainfall-runoff model used was J2000 (Krause, 2001;Kralisch and Krause, 2006), as this model had previously been set up in the region and model variables were well established (e.g. Bugan, 2014;Steudel et al, 2015). While the estuarine lake's importance is well documented (Martens et al, 1996;Wishart, 2000), the lake's reserve is not well understood, due to the lack of streamflow and baseflow estimates for the main feeding tributaries of the system.…”

Estimation of groundwater recharge via percolation outputs from a rainfall/runoff model for the Verlorenvlei estuarine system, west coast, South Africa

“…325 Within the J2000 model, the maximum soil infiltration rate is set for different seasons, where during 326 dry conditions the maximum soil infiltration rate is higher than in wet conditions. The maximum 327 infiltration rate of the soil was set as 100 mm/day during the dry season and 40 mm/day during the wet 328 season, based on previous models constructed in the area (Steudel et al, 2015). If throughfall exceeds 329 this maximum rate, the surplus water is fed to the depression storage.…”

“…6) is used to determine how 364 sensitive estimated input values for different parameters are, with regard to the outputs (Krause et al, 365 2006;Nepal, 2012). The fully distributed HRU based JAMS/J2000 model was applied to a number of 366 semi-arid catchments, as well as the nearby Berg River catchment (Steudel et al, 2015). 367…”

“…In this study, calibration was completed by comparison of model outputs to gauging data from the 369 Before the automated calibration was conducted, the initial parameterization of the J2000 model was 374 carried out by adapting and transferring model parameter values from the neighbouring Berg River 375 catchment (Steudel et al, 2015). These parameter values were then integrated into the automated 376 optimization tool, OPTAS (Fischer, 2013), which identifies optimal parameter value sets based on 377 multi-criteria analysis (MCA) ( Table 2).…”