The Mediterranean region is one of the most responsive areas to climate change and was identified as a major “hot-spot” based on global climate change analyses. This study provides insight into local climate changes in the Mediterranean region under the scope of the InTheMED project, which is part of the PRIMA programme. Precipitation and temperature were analyzed in an historical period and until the end of this century for five pilot sites, located between the two shores of the Mediterranean region. We used an ensemble of 17 Regional Climate Models, developed in the framework of the EURO-CORDEX initiative, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Over the historical period, the temperature presents upward trends, which are statistically significant for some sites, while precipitation does not show significant tendencies. These trends will be maintained in the future as predicted by the climate models projections: all models indicate a progressive and robust warming in all study areas and moderate change in total annual precipitation, but some seasonal variations are identified. Future changes in droughts events over the Mediterranean region were studied considering the maximum duration of the heat waves, their peak temperature, and the number of consecutive dry days. All pilot sites are expected to increase the maximum duration of heat waves and their peak temperature. Furthermore, the maximum number of consecutive dry days is expected to increase for most of the study areas.
<p>Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.</p>
Infiltration and illegal inflow into foul sewer systems can cause different problems such as a decrease in the performance of treatment plants, the surcharge of pipelines and more frequent overflows, which cause negative impacts on the environment. Water companies are increasingly been driven to address these problems by reducing infiltrations and identifying the sources of illegal inflows. Overall, the traditional techniques applied in these cases are expensive and time consuming and many times only partially efficient. Examples are the use of CCTV inspections, smoke tests and the installation of a large set of sensors to collect continuous data such as flow rates, water levels, temperature or concentrations of pollutants. The aim of this study is to apply two types of inverse numerical techniques to identify the source location of illegal inflows into wastewater systems based on information collected at the outlet of the drained basin and a calibrated numerical model of the sewer network. In this work, the numerical model is developed using the Storm Water Management Model (SWMM) software distributed by the Environmental Protection Agency (USA). We considered a realistic foul sewer system with known dimensional and hydrological characteristics. Synthetic case studies are set up to test the inverse approaches. Assuming a hypothetical rainfall event and an illegal inflow released at a certain location in the sewer system, the numerical model is run forward to obtain the flow hydrograph at the network outlet. This information is then used as available observations to perform the inverse modelling. The first investigated technique is an artificial neural network (ANN) of the feed-forward type. It will be trained to recover the inflow source using the simulation results of SWMM driven by a large set of rainfall events and inflows located at different positions in the sewer network. Once trained, the ANN will be used to identify the location of the inflow based the observed flood wave. The second procedure derives from Kalman filter techniques: the Ensemble Smoother with Multiple Data Assimilation (ES-MDA). Also in this case, the method, starting from the known rainfall event and the observed flow hydrograph, is used to locate the inflow source. In addition to the results of the synthetic case obtained by means of the two procedures, the field applicability to real case studies will be discussed.
<p>The Konya province in the Central Anatolia Region of Turkey features a semi-arid climate with cold winters and hot, dry summers. Although the annual precipitation of the Konya Closed Basin is about 350 mm, the basin is considered one of the main agricultural regions of Turkey. Given the effects of drought on crop yields and food security, evaluation of drought risks is crucial. This study aims to describe historical as well as future drought characteristics of the Konya basin by means of two widely used meteorological drought indices: the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI). The indices were calculated for different timescales (6&#8211;24-month timescale) to better assess agricultural drought conditions. For the SPEI index, the potential evapotranspiration (PET) was calculated using the Hargreaves and Samani method, commonly used in arid and semi-arid weather conditions. The analysis was performed over the period 1980-2020 using precipitation and temperature data from 18 weather stations located within Konya Closed Basin. Based on drought classification by SPI and SPEI, values equal to or lower than -2 are considered extreme droughts. The results show that the number of extreme climatic drought periods at the considered stations within the Konya basin based on SPI is higher than that based on SPEI. The findings also reveal that both SPEI and SPI characterize a general increase in drought severity, areal extent, and frequency over 2000-2010 compared to those during 1980-1990, mostly because of the decreasing precipitation and to a lesser extent rising potential evapotranspiration. To assess future drought frequencies, the drought indices were calculated using precipitation and temperature data provided by 17 regional climate models from the EUROCORDEX project. The results for both RCP 4.5 and RCP 8.5 scenarios show significantly more frequent extreme and severe droughts, particularly for the second half of the 21st century. Overall, this study implies that SPEI may be more appropriate than SPI to monitor drought periods under climate change since potential evapotranspiration increases in a warmer climate.</p> <p>This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union&#8217;s Horizon 2020 research and innovation program under grant agreement No 1923.</p>
<p>Groundwater is a strategic reserve that is often used to meet water demands in dry seasons and during drought periods. However, the over-exploitation of this vital resources can jeopardize its sustainability. Projected climate change is expected to further exacerbate the situation in many regions of the world. Therefore, it is essential for decision makers to have simple tools to model groundwater flow and to assist in aquifer management. These tools can reduce the computational cost of complex physics-based models, without undermining the reliability of the results. The aim of this work is to develop a surrogate model capable of simulating groundwater flow in the Konya closed basin, a major agricultural region located in central Turkey. The model is used to analyze different future water demand scenarios and evaluate the possible effects of climate change and agricultural policies on groundwater. This aquifer is one of the pilot sites investigated within the &#8220;Innovative and Sustainable Groundwater Management In the Mediterranean (InTheMed)&#8221; project, which is part of the PRIMA programme. An Artificial Neural Network (ANN) was trained to provide groundwater levels at 30 monitoring points for the period 2020-2039 accounting for different climate and agricultural scenarios. The surrogate model replaces a full numerical surface-subsurface flow model implemented in MODFLOW and calibrated using field data recorded in the period 2000-2019. To define the dataset that feeds the ANN, two multiplicative coefficients were considered: one applied to the historical precipitation and the other to crop water demand. The two coefficients and the current month were considered as input features of the ANN, while the piezometric heads at the 30 monitoring points were the outputs. A dataset of 100 combinations of precipitation and crop coefficients was generated using the Latin Hypercube Sampling method, assuming an increase/decrease range in terms of precipitation equal to +/- 40% and water demand equal to +/- 25%. For each combination of the coefficients, the full numerical model was run starting from January 2020 to obtain piezometric heads at the 30 monitoring points with a monthly time discretization. The final dataset was used to train (70%), validate (15%) and test (15%) the network, highlighting a very good performance of the ANN for all three phases. The fully trained network was used to predict groundwater levels considering three different precipitation scenarios for the period 2020-2039: - 20% of the observed precipitation, no reduction of the observed precipitation and + 20% of the observed precipitation. For each precipitation scenario, the water demand was considered in the range -/+ 20%.</p> <p>This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA programme supported by the European Union&#8217;s HORIZON 2020 research and innovation programme under grant agreement No 1923.</p>
<p>Climate change presents a serious problem for water resources (WR) and the shallow aquifers are strongly affected. This type of WR presents fundamental importance in certain regions, due to their accessibility and sometimes, for their quality, it is preferred to surface water sources, often polluted. It is also, affected by overexploitation problems, which contribute to the destruction of the sustainability of the aquifer system. This study considers the Grombalia aquifer in Tunisia which has suffered from climate change&#8217;s impact in recent years due to water resources scarcity. Aim of the present research is to evaluate the impact of climate change on this aquifer that is one of the pilot sites in the European project InTheMed. First, a collection of historical temperature, precipitation and groundwater level data in the period 1976-2020 was carried out. Then, starting from the few available geological cross sections, a two-dimensional numerical model of the aquifer was developed in MODFLOW. The groundwater numerical model reproduces the whole basin, from the recharge area to the outlet in the Mediterranean Sea. The area is characterized by agricultural intensive activities and high-water demand. For this reason, the model required a calibration of hydraulic parameters, recharge and pumping rate. After the calibration, the numerical model was able to estimate the groundwater flow across the entire watershed of Grombalia aquifer. To evaluate the impact of climate change on the future groundwater availability, the model was driven using future precipitation and temperature projections. The water abstractions were assumed to remain unchanged in the future and equal to the condition of existing wells at 2020. To describe the future climate, 17 combinations of Regional Climate Models (RCM) and General Circulation Models (GCMs), developed within the EURO-CORDEX initiative, were used. The simulations were performed for the period 2006-2100, and according to the RCP4.5 and RCP8.5 scenarios. Before their use, the climate projections were downscaled and bias corrected with reference to the historical temperature and precipitation data. The results are evaluated in terms of local variations of the groundwater level and their uncertainty is expressed with reference to the variability of the 17 RCM-GCM combinations.</p><p>Acknowledgments&#160;<br>This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union&#8217;s Horizon 2020 research and innovation program under grant agreement No 1923.&#160;</p>
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