ClimaSG: A weather generator for crop modelling and water requirements studies

Abstract

A new stochastic weather generator, ClimaSG, is presented and evaluated under a wide range of climates. The model requires monthly means data as inputs to generate daily weather data. Validation tests revealed that ClimaSG performed well in capturing monthly rainfall variability but showed some limitations for generating extreme temperatures. Solar radiation, air humidity, and wind speed were reproduced acceptably. The weather generator was particularly accurate in predicting mean and extreme values of ET0, which indicates that ClimaSG may be used as the basis for calculating crop water requirements and for irrigation design and planning purposes. The suitability of synthetic weather series generated with ClimaSG for crop modelling applications was further assessed by running the model OliveCan for low density rainfed (LD) and super high density irrigated (SHD) olive orchards for several locations in Spain. Yield distributions simulated with synthetic weather rarely differed significantly from those obtained with actual weather data, although the coefficient of variation (CV) was usually underestimated. Considering its low input requirements and satisfactory performance, ClimaSG represents a promising source of weather data for different practical applications.