4. METHODS OF MODELLING SOIL MOISTURE
4.1 Land Surface Model (PyTOPKAPI)
based mass and continuity equations under the estimate of the kinematic wave model (Vischel et al., 2008).
Figure 4.1 A schematic of the water transfer in a typical PyTOPKAPI model cell (Sinclair and Pegram, 2012)
The model is forced by time varying estimates of total evaporation and rainfall for each model cell (Sinclair and Pegram, 2012). The total evaporation forcing is based on the modification of the FAO56 reference crop evaporation, whereas the rainfall forcing is applied in the TRMM 3B42RT product, which is automatically downloaded from the NASA server and processed locally (Sinclair and Pegram, 2010).
There are six fundamental assumptions on which the TOPKAPI model is based on (Vischel et al., 2008):
i. Precipitation is spatially and temporally constant over the integration domain;
ii. Unless the soil is already saturated, all precipitation falling on the soil infiltrates;
iii. The slope of the groundwater table corresponds with the slope of the ground;
iv. The saturated hydraulic conductivity in the soil surface layer is constant with depth;
v. Local transmissivity; and
vi. During the transition phase, the variation in water content in time is constant in
The TOPKAPI Model has been adapted to South African conditions. A land surface model was developed, with the final product being the freely available open source software package, known as the PyTOPKAPI (Sinclair and Pegram, 2013). The PyTOPKAPI Model is rainfall-runoff model used to examine the soil moisture dynamics at different scales, ranging from catchment to national scale (Sinclair and Pegram, 2013b).
The PyTOPKAPI Model uses three sets of input data, which consist of the meteorological, static and remote sensing data sets, as seen in Figure 4.2 (Sinclair and Pegram, 2010). The meteorological input data include the calculation of reference crop total evaporation and require parameters, such as relative humidity, temperature, solar radiation flux and wind speed (Sinclair and Pegram, 2010). The static input data required are the digital elevation models, land cover and soil properties. The input remote sensing data required are the rainfall and NDVI products on a three-hour temporal scale. In addition, the solar radiation flux from the meteorological input data is a satellite-based product and can be considered as a remote sensing product (Sinclair and Pegram, 2012).
The PyTOPKAPI is an automated modelling system, which produces national estimates of total evaporation and soil moisture at a three-hour time step over South Africa on a 0.125o spatial grid (Sinclair and Pegram, 2010).
Figure 4.2 Data-flow diagram showing sources of the dynamic and static data to produce the main information streams (Sinclair and Pegram, 2010).
The purpose of the computation is to obtain a Saturated Soil Index (SSI), which is defined as the percentage of the soil voids occupied by water.
𝑆𝑆𝐼 = 100 ∗ ( Ɵ−Ɵ𝑟
Ɵ𝑠𝑎𝑡−Ɵ𝑟) (4.1)
Where Ɵ is the volumetric soil moisture, Ɵr is the residual soil moisture and Ɵsat is the saturated soil moisture.
In the original design, Liu and Todini (2002) created the model in such a manner, that whether the soil store was saturated or not, all rainfall during the computational interval was added to the soil store. At the end of the interval, an inventory was made and if the store was full, the excess was assigned to overland and channel stores. Then, based on the content of the soil store, the drainage was sent to the downslope store (Sinclair and Pegram, 2013a).
This resulted in two effects: the soil store was always depleted at the end of the computation interval and surface runoff only occurred once the soil store was full, independent of rainfall intensity (Sinclair and Pegram 2013).
In the adaption of the TOPKAPI Model to South African conditions, the first effect was cancelled by modifying the continuity equation, so that there was ponding in the overland store, which resulted in the soil store remaining full after computation (Sinclair and Pegram, 2013a). The second effect was dealt with the introduction of the Green-Ampt infiltration model, which gave the PyTOPKAPI Model the ability to generate rapid overland flows in response to intense rainfall events (Sinclair and Pegram 2013).
The benefits of using this model are that the soil moisture estimates obtained have a temporal resolution of three hours and a spatial resolution grid ≈ 12.5 km. This finer temporal and spatial resolution better accounts for the heterogeneity of soil moisture, compared to the current global remote sensing of soil moisture products.
There have been research studies that reviewed the estimation of soil moisture through land surface modelling.
i. Sinclair and Pegram (2010) conducted a study, which aimed to compare soil moisture estimates from two independent sources over South Africa. The first soil moisture estimate was provided by automated real-time estimates from the TOPKAPI hydrological model. The second set of soil moisture estimates was from the ASCAT remote sensing product.
ii. A similar study was conducted by Vischel et al. (2008) in the Liebenbergslvei Catchment in South Africa, which compared soil moisture estimates derived from the TOPKAPI Model with European remote sensing satellite-based soil moisture estimates. Both studies concluded that the modeled soil moisture measurements correlated well, when compared to remote sensing soil moisture measurements.
iii. More recent studies, conducted by Sinclair and Pegram (2013), evaluated the sensitivity of the PyTOPKAPI Model to systematic bias in the variables of soil properties, evapotranspiration and rainfall. This was achieved by analysing 7200 sites within South Africa for a two-and-a-half year simulation period. The study concluded that the model is robust to errors in forcing parameters.
iv. A study conducted by Mengistu et al. (2014) involved the validation of variables of the PyTOPKAPI Model. The aims of the study were to provide data for the soil moisture mapping of South Africa, using the PyTOPKAPI Model. The purpose was to provide accurate field and satellite estimates of total evaporation and soil moisture for the calibration of the model and to evaluate the spatial variability of soil moisture at a catchment scale.