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Water Infrastructure (WI), incorporating water supply, wastewater, and stormwater systems, is vulnerable to climate change impacts that can disrupt their functionality; thus, WI needs to be adapted to climate change. In 2021, the European Commission (EC) released the technical guidelines on "Climate-proofing Infrastructure" that include mitigation and adaptation strategies; these guidelines, and the relevant guides that followed, focus mainly on climate change aspects without examining sufficiently the engineering features of WI that are described mainly in the relevant hydro-environmental research; this research is vast and includes various terminologies and methods for all aspects of climate change adaptation. The adaptation procedure of WI to climate change is improved when this research is known to guidelines' developers. In the present work, to facilitate this knowledge transfer, we typologize the hydro-environmental research via its classification into five categories that are based on the EC guidelines and then perform a literature review that we present as follows: firstly, we introduce and typologize the climate hazards for WI systems and identify the most common of them in the Mediterranean region that we classify into seven groups; then, we classify the hydro-environmental research into five categories based on the EC guidelines, present the main aspects for each of these categories, discuss the future research; and finally, we summarize the conclusions. [ABSTRACT FROM AUTHOR]

Soils form the basis for agricultural production and other ecosystem services, and soil management should aim at improving their quality and resilience. Within the SoilCare project, the concept of soil-improving cropping systems (SICS) was developed as a holistic approach to facilitate the adoption of soil management that is sustainable and profitable. SICS selected with stakeholders were monitored and evaluated for environmental, sociocultural, and economic effects to determine profitability and sustainability. Monitoring results were upscaled to European level using modelling and Europe-wide data, and a mapping tool was developed to assist in selection of appropriate SICS across Europe. Furthermore, biophysical, sociocultural, economic, and policy reasons for (non)adoption were studied. Results at the plot/farm scale showed a small positive impact of SICS on environment and soil, no effect on sustainability, and small negative impacts on economic and sociocultural dimensions. Modelling showed that different SICS had different impacts across Europe—indicating the importance of understanding local dynamics in Europe-wide assessments. Work on adoption of SICS confirmed the role economic considerations play in the uptake of SICS, but also highlighted social factors such as trust. The project's results underlined the need for policies that support and enable a transition to more sustainable agricultural practices in a coherent way. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The European Space Agency (ESA), through the Climate Change Initiative (CCI), is currently providing nearly 4 decades of global satellite‐observed, fully homogenized soil moisture data for the uppermost 2–5 cm of the soil layer. These data are valuable as they comprise one of the most complete remotely sensed soil moisture data sets available in time and space. One main limitation of the ESA CCI soil moisture data set is the limited soil depth at which the moisture content is represented. In order to address this critical gap, we (a) estimate and calibrate the Soil Water Index using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then (b) leverage machine learning techniques and physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration. We use this calibration to assess the root‐zone soil moisture for the period 2001–2018. The results are compared against the European Centre for Medium‐Range Weather Forecasts, ERA5 Land, and the Famine Early Warning Systems Network Land Data Assimilation System reanalyses soil moisture data sets, showing a good agreement, mainly over mid latitudes. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large‐scale soil moisture‐related studies. Key Points: We use machine learning to regionalize the calibration of the Soil Water Index based on soil, climate, and vegetation descriptorsThe results are compared to reanalysis data sets, indicating added value to the results of the machine learning calibration [ABSTRACT FROM AUTHOR]

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data. [ABSTRACT FROM AUTHOR]

Billions of people rely on groundwater as an accessible source for drinking water and irrigation, especially in times of drought. Its importance will likely increase with a changing climate. It is still unclear, however, how climate change will impact groundwater systems globally and thus the availability of this vital resource. This study investigates uncertainties in groundwater recharge projections using a multi-model ensemble of eight global hydrological models (GHMs) that are driven by the bias-adjusted output of four global circulation models (GCMs). Preindustrial and current groundwater recharge values are compared with recharge for different global warming (GW) levels as a result of three representative concentration pathways (RCPs). Results suggest that the uncertainty range is extensive, and projections with confidence can only be made for specific regions of the world. In some regions, reversals of groundwater recharge trends can be observed with global warming. On average, a consistent median increase of groundwater recharge in northern Europe of 19 % and a decrease of 10 % in the Amazon at 3 °C GW compared to preindustrial levels are simulated. In the Mediterranean, a 2 °C GW leads to a reduction of GWR of 38 %. Because most GHMs do not include CO2 driven vegetation processes, we investigate how, including the effect of evolving CO2 concentrations into the calculation of future groundwater recharge impacts the results. In some regions, the inclusion of these processes leads to differences in groundwater recharge changes of up to 100 mm year-1. Overall, models that include CO2 driven vegetation processes simulate less severe decreases of groundwater recharge and in some regions even increases instead of decreases. In regions where GCMs predict decreases in precipitation, and groundwater availability is most important, the model agreement among GHMs with dynamic vegetation is lowest in contrast to GHMs without, which show a high agreement. [ABSTRACT FROM AUTHOR]

Abstract: The Paris Agreement of the United Nations Framework Convention on Climate Change aims not only at avoiding +2°C warming (and even limit the temperature increase further to +1.5°C), but also sets long‐term goals to guide mitigation. Therefore, the best available science is required to inform policymakers on the importance of and the adaptation needs in a +1.5°C warmer world. Seven research institutes from Europe and Turkey integrated their competencies to provide a cross‐sectoral assessment of the potential impacts at a pan‐European scale. The initial findings of this initiative are presented and key messages communicated. The approach is to select periods based on global warming thresholds rather than the more typical approach of selecting time periods (e.g., end of century). The results indicate that the world is likely to pass the +1.5°C threshold in the coming decades. Cross‐sectoral dimensions are taken into account to show the impacts of global warming that occur in parallel in more than one sector. Also, impacts differ across sectors and regions. Alongside the negative impacts for certain sectors and regions, some positive impacts are projected. Summer tourism in parts of Western Europe may be favored by climate change; electricity demand decreases outweigh increases over most of Europe and catchment yields in hydropower regions will increase. However, such positive findings should be interpreted carefully as we do not take into account exogenous factors that can and will influence Europe such as migration patterns, food production, and economic and political instability. [ABSTRACT FROM AUTHOR]

Understanding the Mediterranean rangelands degradation trends is a key element of mitigating their vulnerability and enhancing their resilience. Climate change and its inherent effects on mean temperature and the precipitation variability can regulate the magnitude, frequency and duration of droughts and aridity with a profound effect on ecosystem productivity. Here we investigate the effects of climate change to project the development of vegetation in the Mediterranean rangelands by (i) estimating the relative Standardized Precipitation Index and a modification of the United Nations Environment Programme Aridity Index to classify climate variability, and (ii) modelling vegetation response to climate using the Food and Agriculture Organisation crop-water production function. Climate model data are obtained from nine general circulation models under Relative Concentration Pathways 2.6 and 8.5 of the fifth phase of the Coupled Model Intercomparison Project. After correcting climate model data for biases, results for two 40-year future study periods are compared with the baseline period 1961-2000 within a domain that includes the European Mediterranean. We show that a gradual but robust increase of aridity and drought frequency is estimated for most of the Mediterranean region, impacting rangeland vegetation yields. Projected drought and aridity disturbances may well represent permanent shifts to a warmer and more frequently dry status. This alternative stability of climatic pressure lies outside the limits of ecosystem resilience and may indicate that in some cases vegetation will either adapt to the new conditions or be succeeded by more water-stress tolerant species. Results raise concerns about the fate of the Mediterranean rangelands and the effectiveness of mitigation measures. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies. [ABSTRACT FROM AUTHOR]

Climate model outputs feature systematic errors and biases that render them unsuitable for direct use by the impact models, especially when hydrological parameters are studied. To deal with this issue many bias correction techniques have been developed to adjust the modelled variables against observations. For the most common applications, adjustment concerns only precipitation and temperature whilst for others more driving parameters (including radiation, wind speed, humidity, air pressure) are bias adjusted. Bias adjusting only a part of the variables required as biophysical model input could affect the physical consistency among input variables and is poorly studied. In this work we quantify the individual effect of bias correction of each climate variable on global scale hydrological simulations of the recent past. To this end, a partial correction bias assessment experiment is conducted. Six climate parameters (precipitation, temperature, radiation, humidity, surface pressure and wind speed) from a set of three Global Climate Models are tested. The examined hydrological indicators are mean and extreme (low and high) runoff production. A methodology for the classification of the bias correction effects is developed and applied. Global hotspots of hydrological sensitivity to GCM biases at the global scale are derived, for both mean and extreme runoff. Our results show that runoff is mostly affected by the biases in precipitation, temperature, specific humidity and radiation (in this order) and suggest that bias correction should be applied in priority to these parameters. Surface pressure and wind speed had a minor effect on runoff simulations for the majority of the land surface. Low runoff has an increased sensitivity to the GCM biases compared to mean and high runoff, underlying the importance of bias correction for the study of low flow conditions and relevant hydrological extremes, such as droughts. [ABSTRACT FROM AUTHOR]

Bias correction of climate variables has become a standard practice in Climate Change Impact (CCI) studies. While various methodologies have been developed, their majority assumes that the statistical characteristics of the biases between the modeled data and the observations remain unchanged in time. However, it is well known that this assumption of stationarity cannot stand in the context of a climate. Here, a method to overcome the assumption of stationarity and its drawbacks is presented. The method is presented as a pre-post processing procedure that can potentially be applied with different bias correction methods. The methodology separates the stationary and the non-stationary components of a time series, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method prevents the distortion and allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation, but also the higher and lower percentiles of the climate variable. Daily temperature time series obtained from five Euro CORDEX RCM models are used to illustrate the improvements of this method. [ABSTRACT FROM AUTHOR]

Climate models project a much more substantial warming than the 2 °C target under the more probable emission scenarios, making higher-end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under C4 °C of global warming for the European region. Five major European catchments were analysed in terms of future drought climatology and the impact of C2 °C versus C4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce, leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the C2 to the C4° of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational data set for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the Global Climate Model (GCM). [ABSTRACT FROM AUTHOR]

The characteristics of cyclones over Eastern Mediterranean that cause precipitation in the island of Crete are studied examining potential forecasting applications. The datasets used for this study are ECMWF reanalysis (ERA-Interim) for the time period 1979-2011 and local precipitation records from a dense rain gauge network in Crete. The Melbourne University algorithm (MS-scheme) is used for identification of the cyclones' characteristics and tracks from Mean Sea Level Pressure (MSLP) fields. Cyclone pressure, intensity and other characteristics are examined in different seasons in combination with three rain severity categories. Results show that the most cyclones that affect Crete approach from the southwest direction during the spring period while during the other seasons they originate from north-northwest direction. Decrease in pressure as well as increase in cyclone intensity, depth, radius and propagation velocity result in an increased rain accumulation. Probability maps of the cyclone tracks over a gridded mesh of the domain of interest provide short term forecast of an affecting cyclone pathway. The rain diagnostic potential of the estimated probability maps reach 50-80% scores in the statistical tests of sensitivity, specificity and accuracy. [ABSTRACT FROM AUTHOR]