Climate change effects on crop evapotranspiration, Pannonian basin

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Ten principles to integrate the water-energy-land nexus with climate services for co-producing local and regional integrated assessments

The water-energy-land nexus requires long-sighted approaches that help avoid maladaptive pathways to ensure its promise to deliver insights and tools that improve policy-making. Climate services can form the foundation to avoid myopia in nexus studies by providing information about how climate change will alter the balance of nexus resources and the nature of their interactions. Nexus studies can help climate services by providing information about the implications of climate-informed decisions for other economic sectors across nexus resources. First-of-its-kind guidance is provided to combine nexus studies and climate services. The guidance consists of ten principles and a visual guide, which are discussed together with questions to compare diverse case studies and with examples to support the application of the principles

Meteorological and Ancillary Data Resources for Climate Research in Urban Areas

An increasing plethora of both meteorological and ancillary data are presently available for climate research and applications in urban areas. The data are often held by local or national institutions (i.e., meteorological services, universities or environmental agencies). This paper outlines a total number of 33 datasets, organized into three main categories of meteorological data resources (14 datasets) and four categories of ancillary data resources (19 datasets), selected for their potential to support urban climate studies, but also for their free accessibility. Such a collection cannot be exhaustive, but we aim to draw the attention of the scientific community to relevant datasets, freely available at temporal and spatial resolutions appropriate for urban climatology. Each dataset contains information about its availability, limitations, and examples of research in urban areas

Statistical Gap-Filling of SEVIRI Land Surface Temperature

A reliable and practically useable method for gap filling in hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI LST) data using ERA5 Land Skin Temperature (ERA5ST) co-variate and additional easily accessible data (elevation, time, solar radiation info) is proposed. The suggested approach provides estimates to all weather conditions and it is based on a probabilistic model via modern regression models. We have tested two classes of regression models of different complexity and flexibility, namely multiple linear regression (MLR), and generalized additive model (GAM). This analysis uses as main input the hourly LST data set over Romania, through 2016 and 2017, extracted from MSG-SEVIRI images, which is an operational product of the Land Surface Analysis–Satellite Application Facility (LSA-SAF). The comparison between the estimated LST and the original LST values shows that GAM model, that takes into account the distance between missing LST locations and the nearest non-missing locations (GAM2), provides the best results, hence this was used to fill the gaps from the analyzed remote sensing product. Considering the fact that the best covariate (ERA5ST) has global coverage and it is available at high spatial resolution and temporal resolution, the proposed approach could be also used to perform the gap-filling of other existing LST remote sensing products

Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology

Challenges in Applied Human Biometeorology

Facing the impacts of climate change and urbanization, adaptation and resilience to climate extremes have become important issues of global concern [...