## 4 Discussion  
### General Discussion  
In this paper, we demonstrated that it is now feasible to observe several water balance components at field scales using satellite data, complemented by modelling at finer scales. Evapotranspiration, soil moisture, and volumes of water in farm dams can all be observed, and there are often several approaches to estimate the same property, e.g., [31]. Soil moisture has always been difficult, as most of the remote sensing can only investigate the top few centimetres of soil, and this is mostly at very large scales [41; 62]. As a result, the future might be using satellite observation of the top layers, augmented by soil moisture modelling (as presented here and [62]), or using downscaling ([PERSON], [PERSON], [PERSON], _Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative_, submitted to Journal of Hydrology). This also has the potential resolve the differences between the energy balance and crop-factor-based approaches highlighted in this paper. However, given that at least daily rainfall data exists in most places (and in Australia, several detailed gridded products exist), closure of the water balance is eminent: rainfall inputs, evapotranspiration outputs, and changes in storage can all be observed at a fine-gridded scale. Assuming runoff losses in irrigated systems are negligible, closing the water balance at the field and farm scale is within reach. Before we reach this point there are still several challenges, particularly if this needs to be extended to many global locations. The first is the availability of input data at sufficient detail in time and space. While the satellite data are widely available, and ET estimates can be derived without any ancillary data, this is not the case for several of the other water balance elements, as was demonstrated here. For example, the current implementation to quantify farm dam volumes [27] relies on the 1-m digital elevation model (DEM) that is available for parts of Australia. Applying the same algorithm to a coarser scale DEM will result in a significant deterioration of the accuracy as the actual depth of the farm dam cannot be estimated accurately. In the soil moisture modelling presented here, several  
Figure 11: Time-depth graphs of the Relative Water Content (-) across the soil profile for the three selected study points (top: location 1, middle: location 2, bottom: location 3). Note that “Depth” is in elevation above Australian sea level (ASL). assumptions had to be made simply because detailed data for soil properties and irrigation applications were not available. However, despite these assumptions, modelling combined with satellite information will deliver estimates of sufficient accuracy that water balance calculations can be performed. For example, the modelling of soil moisture suggests that an increase of roughly 0.2 \(\times\) 0.2 \(\times\) 0.25 \(\times\) 10,000 = 100 m\({}^{3}\) ha\({}^{-1}\) as a result of the rainfall in November, or approximately 10-15% of the monthly rainfall total, with similar amounts for the earlier month. This explains some of the mismatch in Tables 2 and 3, as this means that the \"effective rainfall\" contributing to the deficit is a lot lower, hence explaining the additional water use in many months. The second challenge leading from the above analysis is the assumption that runoff is negligible, or that all rainfall is somehow used for crop production. Clearly this is not the case, as Tables 2 and 3 and the above analysis demonstrate. With limited data on field scale runoff losses reported, this currently can only be resolved by modelling, or estimating using the water balance closure as a constraint. As runoff will vary globally depending on soil type, crop cover, and rainfall intensity, this remains a problem that can only be approached using modelling.