An increase in severe precipitation events of higher intensity are expected to occur in the southeastern Mediterranean due to intensification of the hydrological cycle caused by climate change. Results of the climate change model’s precipitation data for the period 1970–2100 show a decreasing trend of daily precipitation but of higher intensity. Post-flood field investigation from a severe rainfall event in a small ungauged basin located in northwest Crete produced a validated flow hydrograph, and in combination with two high-resolution digital elevation models (DEMs), were used in the 1D/2D HEC-RAS (Hydrologic Engineering Center’s River Analysis System model), in order to determine the flooded area extent. Lateral structures were designed along the stream’s overbanks, hydraulically connecting the 1D streamflow with the 2D flow areas behind levees. Manning’s roughness coefficient and the weir coefficient were the most crucial parameters in the estimation of floodplain extent. The combined 1D/2D hydraulic model provides more detailed results than the 1D model with regards to the floodplain extent at the peak outflow, maximum flood depths, and wave velocities. Furthermore, modeling with a DEM at 2 m spatial resolution showed more precise water depth output and inundated floodplains. Scenarios of increasing peak precipitation for the same event precipitation depth were used to identify the flood extent due to an increase in daily rainfall recorded by adjacent meteorological stations. These simulation results can be useful in flood risk mapping and informing civil protective measures in flood basin management, for an effective adaptation to increased flood risk caused by a changing climate.

We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management–or with some omissions–, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.

Wildfires are a complex and underexplored aspect of the Earth system, significantly affecting climate, as they emit greenhouse gases and aerosols that alter the Earth’s radiative balance. This study utilizes the EC-Earth3 Earth System Model to investigate how interannual variability in biomass burning emissions influences variability in total aerosol optical depth (AOD), as well as carbon monoxide (CO) and ozone (O₃) tropospheric columns. We demonstrate that fluctuations in biomass burning emissions impact AOD, CO, and O₃ variability at regional and global scales, emphasizing the need for improved understanding of wildfires and their climate effects.

Our study focuses on investigating the present-day influence of wildfires on the global atmosphere. To achieve this, we utilized four observational biomass burning (BB) emissions datasets for present-day simulations employing the TM5 Chemical Transport Model (CTM). To assess how different emissions estimates influence the model’s ability to simulate the atmosphere, we compared the following datasets over the period 2003–2015: GFED4s, GFASv1.2, FEERv1.0-G1.2 and QFEDv2.6r1. Our study aims to investigate the role of wildfires in affecting important trace gases and aerosols. Their impact on atmospheric composition and their interactions with solar radiation affect the radiative balance at the Earth’s surface and, consequently, temperature trends in the troposphere.

The current study focuses on the interconnection between wildfires and the atmosphere and more precisely on the radiative effect of wildfire emissions on a global scale. Specifically, the effective radiative forcing (ERF) of present-day wildfire emissions relative to pre-industrial conditions is determined. Atmosphere-only simulations were performed using EC-Earth3, and three wildfire-emission datasets were introduced: BB4CMIP6 and two reconstructed alternatives, one derived from the BB4CMIP6 dataset and one derived from the fire model LPJ-LMfire. Our simulations indicate that the main drivers of ERF are the changes in cloud cover and surface albedo caused by the present-day wildfire emissions.