8The spatial and temporal impacts of climate change on irrigation water requirements and yield 9 for sugarcane grown in Swaziland have been assessed, by combining the outputs from a 10 general circulation model (HadCM3), a sugarcane crop growth model and a GIS. The 11 CANEGRO model (embedded with the DSSAT program) was used to simulate the baseline 12 and future cane net annual irrigation water requirements (IR net ) and yield (t ha -1 ) using a 13 reference site and selected emissions scenario (SRES A2 and B2) for the 2050s (including 14 CO 2 -fertilisation effects). The simulated baseline yields were validated against field data from 15 1980-1997. An aridity index was defined and used to correlate agroclimate variability against 16 irrigation need to estimate the baseline and future irrigation water demand (volumetric). To 17 produce a unit weight of sucrose equivalent to current optimum levels of production, future 18 irrigation needs were predicted to increase by 20-22%. With CO 2 -fertilisation, the impacts of 19 climate change are offset by higher crop yields, such that IR net is predicted to increase by 9%. 20The study showed that with climate change, the current peak capacity of existing irrigation 21 schemes could fail to meet the predicted increases in irrigation demand in nearly 50% of years 22 assuming unconstrained water availability. 23

The Surface Energy Balance Algorithm for Land (SEBAL) is one of the remote sensing (RS) models that are increasingly being used to determine evapotranspiration (ET). SEBAL is a widely used model, mainly due to the fact that it requires minimum weather data, and also no prior knowledge of surface characteristics is needed. However, it has been observed that it underestimates ET under advective conditions due to its disregard of advection as another source of energy available for evaporation. A modified SEBAL model was therefore developed in this study. An advection component, which is absent in the original SEBAL, was introduced such that the energy available for evapotranspiration was a sum of net radiation and advected heat energy. The improved SEBAL model was termed SEBAL-Advection or SEBAL-A. An important aspect of the improved model is the estimation of advected energy using minimal weather data. While other RS models would require hourly weather data to be able to account for advection (e.g., METRIC), SEBAL-A only requires daily averages of limited weather data, making it appropriate even in areas where weather data at short time steps may not be available. In this study, firstly, the original SEBAL model was evaluated under advective and non-advective conditions near Rocky Ford in southeastern Colorado, a semi-arid area where afternoon advection is common occurrence. The SEBAL model was found to incur large errors when there was advection (which was indicated by higher wind speed and warm and dry air). SEBAL-A was then developed and validated in the same area under standard surface conditions, which were described as healthy alfalfa with height of 40-60 cm, without water-stress. ET OPEN ACCESSRemote Sens. 2015, 7 15047 values estimated using the original and modified SEBAL were compared to large weighing lysimeter-measured ET values. When the SEBAL ET was compared to SEBAL-A ET values, the latter showed improved performance, with the ET Mean Bias Error (MBE) reduced from −17.1% for original SEBAL to 2.2% for SEBAL-A and the Root Mean Square Error (RMSE) reduced from 25.1% to 10.9%, respectively. It was therefore concluded that the developed SEBAL-A model was capable of accounting for advection and therefore suitable for arid and semi-arid regions where advection is common.

Because the Surface Energy Balance Algorithm for Land (SEBAL) tends to underestimate ET when there is advection, the model was modified by incorporating an advection component as part of the energy usable for crop evapotranspiration (ET). The modification involved the estimation of advected energy, which required the development of a wind function. In Part I, the modified SEBAL model (SEBAL-A) was developed and validated on well-watered alfalfa of a standard height of 40-60 cm. In this Part II, SEBAL-A was tested on different crops and irrigation treatments in order to determine its performance under varying conditions. The crops used for the transferability test were beans (Phaseolus vulgaris L.), wheat (Triticum aestivum L.) and corn (Zea mays L.). The estimated ET using SEBAL-A was compared to actual ET measured using a Bowen Ratio Energy Balance (BREB) system. Results indicated that SEBAL-A estimated ET fairly well for beans and wheat, only showing some slight underestimation of a Mean Bias Error (MBE) of −0.7 mm·d (10.7%) and NSCE of 0.82. The SEBAL-A model showed less or no improvement on corn that was either water-stressed or at early stages of growth. The errors incurred under these conditions were not due to advection not accounted for but rather were due to the nature of SEBAL and SEBAL-A being single-source energy balance models and, therefore, not performing well over heterogeneous surfaces. Therefore, it was concluded that SEBAL-A could be used on a wide range of crops if they are not water stressed. It is recommended that the SEBAL-A model be further studied to be able to accurately estimate ET under dry and sparse surface conditions.