A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R²), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day⁻¹) and RMSE (0.326 mm day⁻¹) were also obtained.

This study focuses on the assessment of the impact of climate change on the efficiency of ambient air aeration and the dynamics of Rhyzopertha dominica, which is a major pest that infests wheat stored in Tunisia. To project future climate conditions for the period 2041–2070, two climate models, namely MPI-ESM1.2 and CNRMCM5.1, were used under two representative concentration pathways (RCP4.5 and RCP8.5). The study examined the historical and projected feasibility of aeration in six natural regions located in northwestern Tunisia, where wheat is the main crop, and investigated the potential consequences of climate change on the dynamics of R. dominica. Using a heat and mass transfer model in an unaerated wheat silo, the temperature and moisture distributions in the grain mass were used to predict the development, reproduction, and survival of R. dominica. The results revealed a decline in favorable aeration hours due to climate change, resulting in an average reduction of 25% across the six regions. This reduction in aerated hours could significantly affect the effectiveness of aeration as a means of pest control. The mean difference comparisons, based on Tukey’s honestly significant difference (HSD) test, revealed a significant effect of climate change on the developmental parameters of R. dominica for the period 2041–2070 compared to the period 1970–1997. According to the insect dynamic model, future climate scenarios are expected to induce changes in the development duration, reproduction, and survival rates of R. dominica. The model predicts a (i) 10–15% extension in the development duration, (ii) 20–30% decrease in reproduction, and (iii) 5–10% decrease in survival relative to the historical period. These results underscore the critical importance of implementing adaptive pest management strategies for stored wheat.

The Mediterranean region is increasingly recognized as a climate change hotspot but is highly underrepresented in hydrological climate change studies. This study aims to investigate the climate change effects on the hydrology of Lakhmass catchment in Tunisia. Lakhmass catchment is a part of the Medium Valley of Medjerda in northwestern Tunisia that drains an area of 126 km². First, the Hydrologiska Byråns Vattenbalansavdelning light (HBV-light) model was calibrated and validated successfully at a daily time step to simulate discharge during the 1981–1986 period. The Nash Sutcliffe Efficiency and Percent bias (NSE, PBIAS) were (0.80, +2.0%) and (0.53, −9.5%) for calibration (September 1982–August 1984) and validation (September 1984–August 1986) periods, respectively. Second, HBV-light model was considered as a predictive tool to simulate discharge in a baseline period (1981–2009) and future projections using data (precipitation and temperature) from thirteen combinations of General Circulation Models (GCMs) and Regional Climatic Models (RCMs). We used two trajectories of Representative Concentration Pathways, RCP4.5 and RCP8.5, suggested by the Intergovernmental Panel on Climate Change (IPCC). Each RCP is divided into three projection periods: near-term (2010–2039), mid-term (2040–2069) and long-term (2070–2099). For both scenarios, a decrease in precipitation and discharge will be expected with an increase in air temperature and a reduction in precipitation with almost 5% for every +1 °C of global warming. By long-term (2070–2099) projection period, results suggested an increase in temperature with about 2.7 °C and 4 °C, and a decrease in precipitation of approximately 7.5% and 15% under RCP4.5 and RCP8.5, respectively. This will likely result in a reduction of discharge of 12.5% and 36.6% under RCP4.5 and RCP8.5, respectively. This situation calls for early climate change adaptation measures under a participatory approach, including multiple stakeholders and water users.