Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physicallybased predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling

This paper presents a framework to assess the vulnerability of the electrical power grid (EPG) to extreme weather events. The paper presents a methodology based on the Extra-Trees classifier and historical weather data to identify the EPG assets that are most likely to be affected in future extreme weather conditions under various climate change scenarios. The developed methodology considers the EPG different asset classes (lines, towers, poles, transformers, substations.) and identifies the weather parameters that are most relevant to their vulnerability. The paper presents results concerning wind speed, wind gusts, soil type, and altitude, which are used to train a model that predicts the probability of an asset being damaged based on the future weather parameters. The methodology was developed has been applied to a dataset of historical events in Portugal, from the major Portuguese DSO, thus assessing the future vulnerability of the EPG under three different scenarios of climate change. The developed methodology is a successful tool, that would not only help prevent occurrences of faults/failures in the Electrical Power Grid and its recovery from these occurrences, but also to have a better perception of a geographically safe future expansion of infrastructures. In this way it contributes to a continuous, non-faulty EPG operation, fulfilling society's demands by generating maps that identify the most vulnerable areas for each future climate scenario.

Climate change scenarios based on integrations of the Hadley Centre regional climate model HadRM2 are used to determine the change in the flow regime of the river Rhine by the end of the 21st century. Two scenarios are formulated: Scenario 1 accounting for the temperature increase (4.8degreesC on average over the basin) and changes in the mean precipitation, and Scenario 2 accounting additionally for changes in the temperature variance and an increase in the relative variability of precipitation. These scenarios are used as input into the RhineFlow hydrological model, a distributed water balance model of the Rhine basin that simulates river flow, soil moisture, snow pack and groundwater storage with a 10 d time step. Both scenarios result in higher mean discharges of the Rhine in winter (approx. + 30%), but lower mean discharges in summer (approx. -30%), particularly in August (approx. -50%). RhineFlow simulations also indicate that the variability of the 10 d discharges increases significantly, even if the variability of the climatic inputs remains unchanged. The annual maximum discharge increases in magnitude throughout the Rhine and tends to occur more frequently in winter, thus suggesting an increasing risk of winter floods. This is especially pronounced in Scenario 2. At the Netherlands-German border, the magnitude of the 20 yr maximum discharge event increases by 14% in Scenario 1 and by 29% in Scenario 2; the present-day 20 yr event tends to reappear every 5 yr in Scenario 1 and every 3 yr in Scenario 2. The frequency of occurrence of low and very low flows increases, in both scenarios alike.