• Energy Research

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics.

Global warming may induce significant alterations to the rainfall regimes, especially in the Mediterranean basin, which can be considered as a hot-spot for climate change. Several previous studies focused on the variations in annual rainfall and extreme values, while rainfall seasonal variations were less explored. Rainfall seasonality is a critical climate factor affecting the evolution of natural vegetation, water resource availability, and water security. Rainfall seasonality anomalies may have a high impact, especially in areas of the Mediterranean basin where water supplied during the wet season is used to offset rainfall shortages in the dry season. In southern Italy, the occurrence of long water deficit periods and extremely concentrated rainy seasons could limit water uses and cause serious effects on crop yield and, consequently, on food production.This study aims at exploring potential variations in rainfall seasonality over the last 100 years across three regions of southern Italy (Campania, Sardinia, and Sicily) through a dynamic approach proposed by Feng et al. (2013). The study area is characterized by a Mediterranean climate, where the hydrological year consists of a net alternation of two seasons: a cold-rainy period (wet season), usually including fall-winter months, and a hot-dry period (dry season), typically including spring-summer months. The analysis proposed involves the determination of time-variant values of rainfall magnitude and frequency of the two seasons (wet and dry).Daily rainfall values, recorded between 1916 and 2023, are gathered from hundreds of rain gauge stations distributed over the three regions. A pre-processing procedure was applied for data quality check, data reconstruction in years with less than 80% of missing data, and rain gauge selection; then, only rain gauge datasets with adequate data availability (i.e., more than 70 complete years, with at least 15 years in the last two decades, 15 years in the pre-World War II period, and without significant data interruptions) were retained and used for data analyses. Rainfall depth over each season is idealized as an exponentially distributed independent random variable with mean values h (mm), whereas the seasonal rainfall occurrence is modelled as a Poisson process with rate l (d-1). Rainfall seasonality at each rain gauge was defined annually, considering different indices: the Dimensionless Seasonality Index (DSI); the seasonal rainfall depth and the seasonal values of h and l; the wet season timing (i.e., centroid of the season) and duration. The reference period was divided into different equal-size and non-overlapping subperiods.Differences in the various rainfall seasonality indices and their distributions among the various gauges, regions, and subperiods were analyzed, also investigating the influence of some climatic and topographic factors (i.e., temperature, gauge distance from the sea and elevation). A trend analysis based on Mann-Kendall's and Sen's Slope Method with statistical significance at 95% level of confidence, was also carried out considering a limited subset of gauges with the largest data availability for each region.