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Rationale
Prediction of water quality requires an understanding of the hydrologic, physical, chemical, and biological processes in both the landscape and water bodies (Linsley et al., 1988). SWAT can be used to effectively model connectivity because it captures controls of land surface and subsurface properties (Feng et al., 2013).
Hypothesis and Objectives of the Study
Contributions of the Thesis to the Knowledge Base
A new method to calculate crop yield that differs from that used in the SWAT model was developed and incorporated into the ACRU-NPS model. Suggestions for future improvements to the ACRU-NPS model were given based on the understanding gained from the various observations and sampling conducted in the Mkabela catchment.
Outline of the Thesis
The first perspective considers the impact of connectivity on nutrient and sediment migration in the Mkabela catchment based on field and catchment observations. From the results of the simulation, the effects of reserve sites, wetlands and dams on the migration of nutrients and sediments in the watershed were clearly visible.
Mkabela Catchment Location
- Climate
- Geology and soils
- Vegetation and land use
The geology consists of Cambrian-age sandstones of the Natal Group and relatively small patches of sedimentary rocks of Dwyka and Ecca in the north (Le Roux et al., 2006; Fey, 2010). According to Le Roux et al., (2006) the Westleigh and Longlands soil types are overlain by Natal Group sandstones, while the Avalon soil type is overlain by Dwyka Group sedimentary rocks and Natal Group sandstones.
Hydrological Processes and Scale Issues
- Plot or local scale simulations
- Field scale simulations
- Catchment scale simulations
Understanding upscaling issues is paramount, as hydrological processes that are characteristic of the local (point) scale also affect the catchment scale (Beven, 1993; Bloeschl, 1997). The individual activities of the farmer at the field level will eventually affect watercourses at the level of the entire watershed.
Effect of Scale on NPS Processes: Small Plot to Catchment-scale
Index-based approaches are used to rank the vulnerability of sites to nutrient loss by considering source and transport factors with adjustments designed for local conditions (Newham and Drewry, 2006). Transport factors include leaching, erosion, runoff and the 'connectivity' or land connection to the waterway.
Connectivity Concept
- Landform connectivity
- Hydrological connectivity
- Controls and thresholds
The nature of their interaction (e.g. in highly connected systems, changes in the upper parts of the catchment appear relatively quickly). The results showed that a large part of the watershed was hydrologically separated from the canal network during the growing season, and most of it.
Nested Catchment Monitoring, Materials and Methods
- Local scale
- Field scale
- Catchment scale
The number of samples and the sampling rate of the ISCO sampler were changed from the parameters conditioned on the CR 200 data logger shown in Table 4.1. The sampling strategy in the Mkabela catchment was to take infrequent samples during stable flows (low flows) and frequent samples during rapidly changing flows (events).
Laboratory Procedures and Analysis
- Field sampling and analyses for nutrients and sediments
- Field sampling and analyses for stable water isotopes
A simple mass balance mixing model was developed using the end-member δ18O values as the combined discharge of the impounded tributaries (QDO); the contribution to the streamflow of the land unit between the impoundments and the bridge stations (QLUi) and the discharge from the bridge station (QB1 and QB2). Recognizing that QB1 = QDO + QLU1, the ratio of discharge from the contributing sub-basin to the total discharge at the bridge station can be expressed as a function of the isotope ratios, as in Equation 4.5:.
Development of the modified ACRU-NPS model
4.5) It was hypothesized that the eastern, impoundment inflow, upstream of Bridge 1, gave a similar evaporated isotope signal to that of the discharge from the Dam Out stations. A watershed can range in size from 0.01 to 50 km2 and can be easily connected to other sub-basin facilities via a stream (Smithers and Schulze, 1995).
Mkabela Hydrological Processes Response Zones and Connectivity Processes
- Hydrological processes response zones and connectivity
- Hillslope hydropedological transects
- Transects on Avalon hillslopes (A1-A2 and B1-B2)
- Transects on Glencoe hillslopes (C1- C2)
- Transects on Cartref hillslopes (D1- D2)
- Transects on Hutton hillslopes (E1-E2)
- Longitudinal transects along the river reaches
- Nutrient and sediment source connectivity and controls
The general characteristics of the reach process zones in the Mkabela catchment are shown in Table 5.1. The water table sits beneath and in the soft plinth B horizon of the Avalon soils for significant periods during high rainfall.
Plot Scale Geophysics, Soil Water and Nutrient Dynamics
- Geophysics
- Soil water and nutrient dynamics
Once in the groundwater, the fertilizer can be hazardous to the environment for a long period of time due to the large amounts of water present. Similar migration of dissolved nutrients in the soil column (i.e., NO3 and soluble-P movement) is expected to follow the same trend as groundwater. Soil moisture and nutrient dynamics are expected to behave in the same way when discharge is observed at the streams.
This can be attributed to the ease with which sandy soils saturate so that water can infiltrate quickly due to the larger pores in the soil particles. After the rainfall, all three watermarks at Nest 5 were responsive to the infiltrated rainwater in the soil column.
ACRU-NPS Model Processes
- Nutrient processes
- Nitrogen processes
- Phosphorus processes
- Sediment processes
- Crop growth, water and nitrogen stress processes
Mixing of NO3 and soluble P with precipitation in the soil surface layer during an event; In the ACRU-NPS model, ammonification of organic N from surface animal waste contributes to a soluble surface NH4+ pool in the surface soil layer. The zero-order nitrification process is independent of the amount of NH3 in the soil layer, i.e.
NO3- is usually not adsorbed to soil particles and is therefore the most abundant form of N in moving water. It is subtracted from labile P in topsoil and added to P in surface residues (Knisel and Davis, 1999).
ACRU-NPS Crop BaseLAI Values
- Length of crop growth stages
- Numerical determination of crop BaseLAI values
The stress factor decreases rapidly and is effectively zero (without LAI or growth increment) for any day when uptake is less than 60% of demand (Lorentz et al., 2012). 56 provide general lengths for four different growth stages and a total growth period for different types of climates and locations (Allen et al., 1998). During crop development and the late season phase, however, Kc varied linearly between Kc at the end of the previous phase (Kc prev) and Kc at the beginning of the next phase (Kc next), which is Kc end in the case of the late season phase.
Considering that LAI for the optimal growth curve will have the same shape as that for the optimal values of the Kc curve, for sugarcane. LAI LAI previous LAI LAI. where i = number of days within the growing season [1.. length of the growing season];.
ACRU-NPS Processes at Control Structures and Connectivity Modelling
- Processes at control structures
- Farm dams and wetlands
- Riparian buffer strips
- Connectivity aspects in NPS pollution migration in the Mkabela
The sediment load stored in the dam or wetland is determined by a mass balance of the inflow sediment load and the change in sediment load in the water body due to settling of sediments. The initial concentration of nutrients in the water mass is calculated by dividing the initial mass of nutrient by the initial amount of water. Deposition loss in the water mass can be expressed as a mass flux across the surface area of the sediment-water interface (ie, flux = settling velocity concentration).
Sugarcane fields located in the headwater areas of Mkabela catchment have waterways that deliver water, sediments and associated nutrients to the upland channels. The ACRU-NPS model was applied to simulate hydrology and NPS pollution in the Mkabela catchment.
Model Input Parameters
- Climatic data
- Initial soil and nutrient parameters
- Fertilizer and manure applications
- Other agricultural practices
Evaporation in the ACRU-NPS model has a significant impact on water cycle simulations (Lorentz et al., 2012). Daily meteorological data series were used to generate reference potential evapotranspiration using the FAO approach to the Penman-Monteith equation (Allen et al., 1998). Appendix B lists the calculated values of nutrient parameters for different soil types in the Mkabela watershed.
Before 2003, the farmers in the Mkabela catchment only used superphosphates, potassium chlorides and lime from ammonium nitrates as fertilizers in the sugarcane fields. This assumption was necessary because the different nutrient contents in the farmyard compost used in Mkabela are not known.
Model Calibration and Validation
- Model evaluation criteria
- Calibration of runoff, nutrient and sediment yields
- Runoff and root zone water balance
- Nitrate (NO 3 )
- Phosphorous (Soluble-P)
- Sediments
- Calibration of sugarcane yields
- Validation of sugarcane yields
Calibration of the ACRU-NPS model focused mainly on the hydrological part of the model that adjusts the most sensitive parameters. During calibration, the final value of ABRESP (i.e., the fraction of . However, the results of the statistical tests performed on the measured and simulated soluble-P show that the values are not significantly different at the 95% confidence level.
The scattergram between the measured and simulated soluble-P loads for the studied rainfall events shows underprediction for some of the observations (Figure 7.9). Field experiments and laboratory studies have shown that "the amount of nitrogen available to the crop varies markedly between soils and is likely influenced by factors such as climate, aeration, moisture availability, organic matter and soil depth" (Moberly and Meyer). 1984).
Connectivity Influences on Nutrient and Sediment Migration
- Field and catchment scale nutrient and isotope events
- Field nutrient and isotope events results
- Catchment nutrient and isotope transect results
- Mass balance mixing model
This soluble-P in the groundwater from the upper reaches of the catchment is later diluted downstream in Flue 2 resulting in low soluble-P concentrations. By the end of the storm event, base flow contribution to the total discharge was 92 % while that from the surface runoff was 8. Downstream of the reservoir outlets, the isotope values at the Bridge 1 and Bridge 2 stations reflect a mixture of upstream inflows from the sheltered tributaries (Dam1 and Dam2) as well as contributions from the soil units between the reservoirs and the Bridge sampling stations (groundwater).
In winter, the majority of releases at Dam1 In originated from the subsurface, as confirmed by an isotopic signature similar to the groundwater signal. This resulted in higher SS concentrations up to ~300 mg/l in the dams by the end of the summer season.
ACRU-NPS Crop Yields and Pollutant Loads Simulations
- Simulation scenarios in the Mkabela Catchment
- Sugarcane crop yields from sub-catchments
- Crop yields from zero, quarterbase and halfbase fertilizer application
- Crop yields from base and doublebase fertilizer application rates
- Crop yields from variable irrigation rates with base fertilizer
- Movement of water, nutrient and sediment loads through control
- Discharge through control features
- Transport of nutrient and sediment loads through control features
This then leads to the least available nutrients for the sugarcane crop which leads to the lowest crop yields (Figure 8.13). The retention capacities for the base scenario (see Appendix H for details) obtained from the ACRU-NPS model simulations in the Mkabela catchment are summarized below;. In the ACRU-NPS model, N, P and sediment losses in buffer strips are associated with strip width.
The simulated flow (SIMSQ) in the ACRU-NPS model consists of storm flow and base flow. In the ACRU-NPS simulations crop yields did not change with changes in soil loss/soil erosion.
