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[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI [ADD1_IRSTEA]Gestion intégrée de la ressource et des infrastructures; International audience; Agriculture in developing countries will have to transform and increase production by an estimated 70% in order to meet demands by 2050. Although well-managed commercial farms offer little manoeuvring space for increasing agricultural water productivity, smallholder farms usually operate at low input costs and therefore provide ample opportunities to reduce the potential yield gap through agricultural intensification. The aim of this paper is to analyse and compare yields and water productivities obtained in field and modelling experiments in Ethiopia (maize, garlic, onion), South Africa (tomato) and Tunisia (tomato, potato, wheat). Innovative agricultural practices were introduced on smallholder farms: irrigation scheduling and NPS Zn fertilization in Ethiopia; high-yielding cultivar, drip irrigation, mulching and organic amendments in South Africa; and crop water modelling in Tunisia. In general, crop yields increased up to eight times with innovative practices compared to current conventional farming practices. Crop water productivities were fairly stable within the same experiments, but increased with innovations, indicating that intensive farming can be more environmentally sustainable than conventional farming. Intensive farming systems in a resource-rich environment (high radiation levels, relatively fertile, deep and well-drained soils), combined with technology transfer and capacity building could be seen as viable strategies to secure food for smallholders and communities in African rural areas, as well as to improve water utilization in water-scarce catchments.

Knowledge of soil depth spatial variability is important for land use management especially in dryland agriculture regions, which rely on climate and soils to provide adequate water and nutrients during the growing season. Soil spatial variability can be predicted from legacy soil data through machine learning techniques producing quantitative soil maps requiring minimal resources. South Africa has a country wide 1:250,000 scale resource map known as the Land Type Survey (LTS) which includes soil properties such as soil depth, soil class, root limiting layer, clay content, and texture. Each LTS polygon (land type), is comprised of unique soil – terrain patterns and is therefore, not a true soil map. This study aims to disaggregate the LTS into a farm-scale soil depth class map through a two-step disaggregation approach. First, landform elements were predicted through a pattern recognition algorithm known as geomorphons. Geomorphons, together with the original LTS were overlaid to produce polygons with unique distributions of soil. The polygons were disaggregated further to produce a raster map of soil depth classes through a soil map disaggregation algorithm known as DSMART. The first most probable class raster achieved an accuracy of 68% and for the two most probable class rasters, an accuracy of 91% was achieved. The two-step approach proved necessary for producing a farm-scale soil map. The result of this study is significant as it produced a soil depth class map from a national resource map at a scale and resolution (10 m) suitable for farm management.

CITATION: Watson, A., et al. 2018. Estimation of groundwater recharge via percolation outputs from a rainfall / runoff model for the Verlorenvlei estuarine system, west coast, South Africa. Journal of Hydrology, 558:238-254.

Clay mineralogy of the fluvial plains reflects the complexity of geological setting, material weathering and transport. Clay minerals produced in the weathering process are closely associated with the parent rock and serve as an indicator of the material sources. Soil clay mineralogy of South African river valleys is insufficiently studied and this research can make a meaningful contribution towards advancing the understanding the history of planation surfaces in Africa and their evolution. Here we examine the valley of the Letaba river – one of the many Limpopo tributaries originating in the Drakensberg escarpment and flowing through the basement complex of the Lowveld geomorphic province. Nine soil profiles were selected from a total of 37 profiles represented by Ustifluvents, Haplustepts, and Haplustalfs for mineralogical study of the The upstream section of the study area is dominated by kaolinite associated with the general dominance of granite and granitic gneiss lithology in the upper reaches of the river. The occurrence of mafic (dolerite) dikes along the river course immediately shows in soils as presence of smectite trail in the clay fraction. As the river cuts through the greenstone belt the clay mineralogy of the fluvial terraces immediately reflects this change as an increase in talc and chlorite proportions and the appearance of vermiculite. The traces of talc originating from greenstones are present throughout the valley within the middle reaches of the river and may be showing the contribution of small streams and tributaries transporting the material from the water divide to the valley of the Letaba. The changes in soil clay mineralogy of alluvial soils across the lithological boundaries indicate the prevalence of locally-sources material over long-distance-transported sediment in aggradation of these terraces. The analysis of clay mineralogy in fluvial environments may be a good indicator of local sediment source and short distance transport contribution towards formation of fluvial terraces in the valley of the Letaba river system. This contradicts the pediplane theory of Lowveld planation and emphasizes the role of localized erosion/sedimentation processes.

River systems that support high biodiversity profiles are conservation priorities worldwide. Understanding river ecosystem thresholds to low-flow conditions is important for the conservation of these systems. While climatic variations are likely to impact the streamflow variability of many river courses into the future, understanding specific river flow dynamics with regard to streamflow variability and aquifer baseflow contributions is central to the implementation of protection strategies. While streamflow is a measurable quantity, baseflow has to be estimated or calculated through the incorporation of hydrogeological variables. In this study, the groundwater components within the J2000 rainfall–runoff model were distributed to provide daily baseflow and streamflow estimates needed for reserve determination. The modelling approach was applied to the RAMSAR-listed Verlorenvlei estuarine lake system on the west coast of South Africa, which is under threat due to agricultural expansion and climatic fluctuations. The sub-catchment consists of four main tributaries, Krom Antonies, Hol, Bergvallei and Kruismans. Of these, Krom Antonies was initially presumed the largest baseflow contributor, but was shown to have significant streamflow variability attributed to the highly conductive nature of the Table Mountain Group sandstones and Quaternary sediments. Instead, Bergvallei was identified as the major contributor of baseflow. Hol was the least susceptible to streamflow fluctuations due to the higher baseflow proportion (56 %) as well as the dominance of less conductive Malmesbury shales that underlie it. The estimated flow exceedance probabilities indicated that during the 2008–2017 wet cycle average lake inflows exceeded the average evaporation demand, although yearly rainfall is twice as variable in comparison to the first wet cycle between 1987 and 1996. During the 1997–2007 dry cycle, average lake inflows are exceeded 85 % of the time by the evaporation demand. The exceedance probabilities estimated here suggest that inflows from the four main tributaries are not enough to support Verlorenvlei, with the evaporation demand of the entire lake being met only 35 % of the time. This highlights the importance of low-occurrence events for filling up Verlorenvlei, allowing for regeneration of lake-supported ecosystems. As climate change drives increased temperatures and rainfall variability, the length of dry cycles is likely to increase into the future and result in the lake drying up more frequently. For this reason, it is important to ensure that water resources are not over-allocated during wet cycles, hindering ecosystem regeneration and prolonging the length of these dry cycle conditions.

Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality digital elevation models (DEMs). Digital geomorphic mapping (DGM) approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units as well as a range of spatial scales, therefore, offer great potential for improved topographic and landscape analysis. One such approach is the mapping of landform elements using the concept of the Geomorphon (geomorphological phonotypes). The output of the geomorphon approach is the stratification of the landscape into ten unique but recognisable landform elements: peak, ridge, shoulder, spur, and slope, hollow, foot slope, valley, depression and flat. Equally appealing is the way the model self-adapts to local topography using a line-of-sight principle enabling better matching of landform elements to computational spatial scale. The purpose of this paper is to observe the effects that different pixel resolution (grain size) and digital elevation model source (DEM) would have on the replication of observed geomorphic spatial patterns and representation of terrain selected parameters within the landscape. This paper provides a comprehensive exploratory assessment of digital terrain representation and relief classification using an automated geomorphometric mapping approach, by evaluating three different digital surface models (SUDEM, SRTM, ASTER GDEM2) and different spatial resolution (30 m & 90 m) for an 11,200 ha catchment in KwaZulu-Natal, South Africa. To test the self-adapting ability of the geomorphon approach under regional conditions, we use 4750 gridded terrain samples to quantitatively analyse how the choice of terrain model and scale influence the extraction, generalisation and representation of digitally-derived terrain attributes such as slope, elevation and terrain unit feature extent. We further show how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities of selected elementary soil association distribution, soil texture and soil depth. We also introduce the results of a Similarity Index used to gauge the degree of recall and precision between the different geomorphic landscape features. Finally, the findings of the regional geomorphon-soil relationships are presented in a readily interpretable and qualitative manner, providing a “quasi-landscape signature” for potential localised geomorphons. The application of the study findings may be beneficial to practitioners looking to align or refine modelled terrain classification approaches with expert perception and formalised heuristic approaches.

Landform elements (LFEs) are commonly used in soil science to demark pedological boundaries and as a first indication of soil spatial variability. A novel LFE classification system known as geomorphons, has been shown to be able to overcome limitations of other automated LFE classifiers. The pattern recognition algorithm classifies the 10 most common LFEs, is computationally efficient, and is robust to changes in scale. However, due to their novelty, research into geomorphons has been limited. This study aimed to stratify the soil landscape through an aggregated geomorphon at the farm-scale (1:25 000) in the Western Cape, South Africa (33.25° S and 18.20° E). Twenty-four geomorphons were created at different resolutions and their association with soil classes were compared. The best fitting geomorphon was aggregated into a 5-unit system corresponding to the South African national resource inventory. The aggregation was based on a decision tree corresponding to soil type. The 5-unit system was evaluated on how well the system stratified soil associations, soil lightness, soil electrical conductivity (EC), soil organic carbon, effective rooting depth (ERD), depth to lithology, gravel, sand, silt, and clay. The prediction potential was compared between the original geomorphon, the aggregated geomorphon, and a manually delineated LFE system. It was found that the aggregated geomorphon stratified all soil attributes except EC. Additionally, the aggregated geomorphon predicted 6 out of 9 soil properties with the greatest accuracy (RMSE). This study shows that aggregating geomorphons can stratify the soil landscape even at the farm-scale and can be used as an initial indication of the soil spatial variability. This has implications in resource poor areas where an additional soil survey is not feasible or can be used to aid in the disaggregation of existing soil-terrain datasets.

The water-energy-food nexus has emerged as a useful concept to understand the multiple interdependencies that exist between the water, energy and food sectors. The nexus is an ambitious attempt to work across disciplines and scales to understand the workings of these complex systems. It is, however, criticised for being more of a general framework than a practical methodology because of the vast amount of data it would need to make real-life contributions to sustainable development. We show how the nexus approach, when used within a farm budget model, can transform the problem focus in water governance. By changing the relationship among water, energy and food production of a farm, profitability is significantly changed. The water-energy-food nexus debate is discussed within the context of the South African water sector, particularly the Breede River Catchment. Working from within the farm budget model, we demonstrate the impact of moving from an irrigation canal system that requires electricity for pumping, to a gravity-fed piped irrigation system in the Middle Breede River. The finding is that the water-energy-food nexus has the potential to unlock groundbreaking solutions to complex problems in agricultural water management when used in appropriate modelling systems.Significance: The water-energy-food nexus approach can lead to an entirely new framing of water governance problems and therefore solutions to these problems. The water-energy-food nexus when used in farm budget models can identify ways of altering farm profitability. By addressing the energy cost of farming through an irrigation pipeline system in parts of the Breede Catchment Area, farm profitability could significantly increase. A gravity-fed closed pipeline system in parts of the Breede River can improve water availability and reduce farm and management costs.

River systems that support high biodiversity profiles are conservation priorities world-wide. Understanding river eco-system thresholds to low flow conditions is important for the conservation of these systems. While climatic variations are likely to impact the streamflow variability of many river courses into the future, understanding specific river flow dynamics with regard to streamflow variability and aquifer baseflow contributions are central to the implementation of protection strategies. While streamflow is a measurable quantity, baseflow has to be estimated or calculated through the incorporation of hydrogeological variables. In this study, the groundwater components within the J2000 rainfall/runoff model were distributed to provide daily baseflow and streamflow estimates needed for ecological reserve determination. The modelling approach was applied to the RAMSAR-listed Verlorenvlei estuarine lake system on the west coast of South Africa which is under threat due to agricultural expansion and climatic fluctuations. The sub-catchment consists of four main tributaries, the Krom Antonies, Hol, Bergvallei and Kruismans. Of these, the Krom Antonies tributary was initially presumed the largest baseflow contributor, but was shown to have significant streamflow variability, attributed to the highly conductive nature of the Table Mountain Group sandstones and quaternary sediments. The Bergvallei tributary was instead identified as the major contributor of baseflow. The Hol tributary was the least susceptible to streamflow fluctuations due to the higher baseflow proportion (56 %), as well as the dominance of less conductive Malmesbury shales which underlie this tributary. The estimated flow exceedance probabilities indicated that during the wet cycle (2007–2017) the average inflow supported the evaporative demands if the lake was at 40 % capacity, while during the dry cycle (1997–2008), only 15 % of the lake’s capacity would be met. The exceedance probabilities estimated in this study suggest that inflows from the four main tributaries are not enough to support the lake during dry cycles, with the evaporation demand of the entire lake being met only 38 % of the time. This study highlighted the importance of low occurrence events for filling up the lake, allowing for regeneration of lake supported ecosystems. While the increased length of dry cycles are likely to result in the lake drying up more frequently, it is important to ensure that water resources are not overallocated during wet cycles, hindering ecosystem regeneration and prolonging the length of these dry cycle conditions.

Soil and stream salinisation is a major environmental problem because it reduces the productivity of landscapes and degrades water quality. The Berg River (South Africa) has been exhibiting a trend of increasing salinity levels, which has primarily been attributed to the manifestation of dryland salinity. Dryland salinity occurs as a result of changes in land use (indigenous vegetation to agriculture and/or pasture), which cause a change in the water and salt balance of the landscape, consequently mobilising stored salts. The quantification of salinity fluxes at the catchment scale is an initial step and integral part of developing dryland salinity mitigation measures. The objective of this study was to quantify the salinity fluxes in the Sandspruit catchment, a tributary catchment of the Berg River. This included the quantification of salt storage, salt input (rainfall) and salt output (in run‑off). The results of the catchment salt balance computations indicate that the Sandspruit catchment is exporting salts, i.e. salt output exceeds salt input, which may have serious implications for downstream water users. Interpolated regolith salt storage generally exhibited increasing storage with decreasing ground elevation. A salinity hotspot was identified in the lower reaches of the catchment. It is envisaged that the data presented in this study may be used to classify the land according to the levels of salinity present; inform land management decisions; and provide a guide and framework for the prioritisation of areas for intervention and the choice and implementation of salinity management options. The data which were generated may also be used to calibrate hydrosalinity models.Online Supplementary Material: Quantifying the catchment salt balance: An important component of salinity assessments

Digital elevation model (DEM) data are elemental in deriving primary topographic attributes which serve as input variables to a variety of hydrologic and geomorphologic studies. There is however still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape characterisation. While elevation data for South Africa are available from several major sources and resolutions: Shuttle Radar Topographic Mission (SRTM), Earth ENV and Stellenbosch University DEM (SUDEM). Limited research has been conducted in a local context comparing the extraction of terrain attributes to high resolution Digital Terrain Data (DTM) such as LiDAR (Light Detection and Ranging) that are becoming increasing available. However, the utility of LiDAR to topographic analyses presents its own challenges in terms of operational-relevant resolution, processing demands and limited spatial coverage. There is a need to quantify the impact that generalisation approaches have on simplifying detailed DEMs and to compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localized topographic variables. In this regional study, we analyse the accuracy on selected local terrain attributes: elevation, slope and topographic wetness index derived from DEMs from varying sources, at different spatial resolutions and using three generalisation algorithms, namely: mean cell aggregation, nearest neighbour and hydrological corrected topo-to-raster. We show that topographic variable extraction is highly dependent on DEM source and generalisation approach and while higher resolution DEMs may represent the “true“ surface more accurately, they do not necessarily offer the best results for all extracted variables. Our results highlight the caveats of selecting DEMs not “fit-for-purpose” for topographic analysis and offer a simple yet effective solution for reconciling the selection of DEMs based on neighbourhood size resolution prior to terrain analyses and topographic feature characterization .