For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large‐scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies.

We produced the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for 2000–2019, named MeteoSerbia1km. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea-level pressure, and total precipitation. In addition to daily summaries, we produced monthly and annual summaries, and daily, monthly, and annual long-term means. Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology, based on using the nearest observations and distances to them as spatial covariates, together with environmental covariates to make a random forest model. The accuracy of the MeteoSerbia1km daily dataset was assessed using nested 5-fold leave-location-out cross-validation. All temperature variables and sea-level pressure showed high accuracy, although accuracy was lower for total precipitation, due to the discontinuity in its spatial distribution. MeteoSerbia1km was also compared with the E-OBS dataset with a coarser resolution: both datasets showed similar coarse-scale patterns for all daily meteorological variables, except for total precipitation. As a result of its high resolution, MeteoSerbia1km is suitable for further environmental analyses.

Gaps in comprehension of demographic change in the region of ex-Yugoslavia after 1990, caused by a lack of reliable data series, frequent change of borders, and distinctive historical and cultural tradition in comparison to other post-communist societies, motivated us to contribute to the understanding of the spatial diffusion of recent profound fertility changes in South-Eastern Europe. We analysed changes in the spatial pattern and distribution of typical fertility indicators of the second demographic transition at the sub-national level in Serbia in order to find out whether these demographic shifts could be interpreted to be similar to those in Central and Eastern Europe. We found that differences in economic, historical, and cultural development between sub-regions of the country strongly affect spatial patterns of fertility change. Also, this paper suggests that the sub- regions forerunners of the first demographic transition could be considered as the cores of diffusion for the second demographic transition.

The assessment of the potential use of renewable energy resources requires reliable and precise data inputs for sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids of potential insolation and solar duration in order to determine the location of potential solar power plants in Montenegro. Grids with a 25-m spatial resolution of potential solar radiation and duration were produced based on observational records and publicly available high-resolution digital elevation model provided by the European Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m? and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy.