The assessment of potential evaporation or reference combined evaporation and transpiration is among the most important components for many hydro-climatic applications such as irrigation design and management, water balance assessment studies, and assessment of aridity classification indices. Aridity classification indices such as UNEP, Thornthwaite and others are usually employed at large scale applications and require respective estimations of potential or reference combined evaporation and transpiration. The major problem in such applications is not only the limited availability of stations per se but also the limitation of many stations to provide data for a complete set of parameters (i.e., precipitation, temperature, solar radiation, wind speed, humidity). A complete set of climate parameters is prerequisite for accurate estimations of potential or reference combined evaporation and transpiration using the most advanced methods, which are expressions of energy balance (e.g., ASCE-standardized method, successor method of Penman-Monteith FAO-56). Unfortunately, large scale applications of aridity indices suffer from this limitation and the common solution is to use temperature-based formulas. The most popular and historical temperature-based formula is the one of Thornthwaite, which was developed to support the respective aridity classification index. The popularity of this formula is based on the minimum requirement of mean monthly temperature and latitude at the location of interest. Considering the above, this study aims to develop a global database of local correction factors for the original Thornthwaite formula that will better support all hydro-climatic applications but mostly to support large scale applications of aridity indices, which are highly prone to data limitations. The hypothesis that is tested in this work is that a local correction factor that integrates the local mean effect of wind speed, humidity and solar radiation can improve the performance of the original Thornthwaite formula and to convert it at the same time to a formula of reference combined evaporation and transpiration for short reference crop. The global database of local correction factors was developed using gridded climate data of the period 1950-2000 at 30 arc-sec resolution (~1 km at the equator) from freely available climate geodatabases. The correction factors were produced as partial weighted averages of monthly ratios between the benchmark ASCE-standardized method for short reference crop versus the original formula of Thornthwaite by giving more weight to the warmer months and by excluding colder months of Epr<45 mm month-1 where monthly ratios are highly unstable with unrealistic values. The validation of the correction factors was made using raw data from 525 stations of Europe, California-USA and Australia that cover periods mostly after 2000 and up to 2020. The validation procedure showed significant improvement in the estimations of reference combined evaporation and transpiration using the corrected Thornthwaite formula that led to a 19.4% reduction of RMSE for monthly and a 55% reduction of RMSE for annual estimations compared to the original formula. The variation of the correction factor was also investigated in different major Köppen climate classes and it was found that tends to increase in drier and warmer territories. The five major Köppen groups were ordered as follows B > C > A > D > E considering the magnitude of the correction factors values. The corrected and original Thornthwaite formulas were also evaluated by their use in UNEP and Thornthwaite aridity indices using as a benchmark the respective indices estimated by the ASCE-standardized method. The analysis was made using the validation data of the stations and the results showed that the corrected Thornthwaite formula increased by 18.3% the accuracy of detecting identical aridity classes with ASCE-standardized method for the case of UNEP classification, and by 10.4% for the case of Thornthwaite classification in comparison to the original formula. The performance of the corrected formula was extremely improved especially in the case of non-humid classes of both aridity indices. The overall results showed that the correction factors produced in this study can improve the performance of the original Thornthwaite formula providing better estimations of the aridity classification indices.

Agriculture is an important source of income for many countries in the Global South, where it may account for as much as 25% of GDP. Precipitation is crucial for agriculture in countries like Ghana, where ~95% of farming is rainfed. Accurate rainfall observations are limited in Ghana. The sparse rain gauge network and the lack of weather radars make remote sensing methods a potentially attractive alternative source of rainfall data. Radar satellites, such as Sentinel-1, emit radiation that passes through the atmosphere and is scattered back to the satellite by the Earth’s surface. The backscatter measured by the satellite is correlated with the wetness of the soil but the existence of vegetation hinders straightforward quantification of soil moisture. By choosing sites with a simple and, more or less, constant phenology, it may be possible to eliminate the effect of vegetation on backscatter. Soccer field may qualify as sites with such a simple and constant phenology. The main objective of this study is to use the Sentinel-1 data over soccer fields and assess them as rainfall detectors. A machine learning approach will be used to reach this objective. This research assessed the stability and the generalization capabilities of a classification model (rain/no rain). The model was trained with and applied to different locations and periods (2019 & 2020). Ground observations from 53 Ghanaian (TAHMO) and 1 Greek stations were used. Soccer fields in Ghana and Greece were selected and their suitability as rainfall detectors was checked based on the correlation between modeled soil moisture and backscatter strength. The rain/no rain classification of the soccer fields was made with a stacked classifier that was trained and validated with both spaceborne and ground data. The classifier was tested on six different datasets from Greece and Ghana 2019 and 2020. The stability of the model was assessed by a Leave-p out cross-validation approach. The generalization in space was tested by using different environments. The generalization in time was tested by using different time periods. The results showed that the classification was stable. The minimum and maximum performances for the different testing datasets were 0.43 to 0.85. The median performance of the algorithm in Ghana for 2020 is 67%. The stacked classifier was found to have the best performance compared to other classifiers. Finally, the performance of the stacked classifier was competitive in comparison with the performance of the well-known IMERG algorithm. The study showed that there is a potential for using radar backscatter from suitable fields to detect rainfall. The classifier is stable and can be generalized in time and space under certain conditions.