Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models

Modelling rainfall, an important element of the hydrological cycle, is of crucial importance in hydrology, water resources engineering, irrigation scheduling and environmental issues. In the current study, the monthly rainfall amounts were simulated through the least squares support vector machine (LSSVM), the model tree (MT) and the geostatistical kriging approaches using geographical inputs. So, the latitude, longitude, altitude and the periodicity component were introduced as model inputs for simulating monthly rainfall values. Long-term rainfall records from 73 weather stations covering different climatic contexts of Iran were used for developing and evaluating the proposed methodology. Two modelling scenarios, namely the at station and pooled scenarios, were carried out for assessing the capability of the applied models in modelling rainfall values. First, two different heuristic methods, the LSSVM and MT methods, were compared with each other. The results obtained revealed that the LSSVM model produced more accurate results than the MT for the at station scenario. Both methods gave better results in arid/semi-arid regions than humid stations. In the pooled scenario, the rainfall data of humid and arid training stations were pooled and used for calibration and then tested at each station. In this scenario, the LSSVM was superior to the MT in modelling long-term monthly rainfall. The LSSVM and MT models were then compared with the geostatistical kriging method in both scenarios. It was observed that the kriging method generally performed better than the heuristic methods in spatial modelling of rainfall.