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Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

One of the main contributors to air pollution is particulate matter (PMxy), which causes several Covid-19 related diseases such as respiratory problems and cardiovascular disorders. Therefore, the spatial and temporal trend analysis of particulate matter and the mass concentration of all aerosol particles ≤2.5 μm in diameter (PM2.5) have become critical to control the risk factors of co-morbidity of a patient. Lockdown plays a significant role in reducing Covid-19 cases as well as air pollution, including particulate matter concentration. This study aims to analyse the effect of the lockdown on controlling air pollution in metropolitan cities in India through various statistical modelling approaches. Most research articles in the literature assume a linear relationship between responses and covariates and take independent and identically distributed error terms in the model, which may not be appropriate for analysing such air pollution data. In this study, a pattern analysis of PM2.5 daily emissions in different main activity zones during 2019 and 2020 was performed. The seasonal effect was also taken into account when measuring the lockdown effect. The PM2.5 values at the unobserved location were predicted using three popular spatial interpolation techniques: (i) inverse distance weight (IDW), (ii) ordinary kriging (OK), and (iii) random forest regression kriging (RFK), and their root mean square error (RMSE) was compared. Subsequently, the spatio-temporal intervention of lock-down on air pollution was estimated using the difference-in-difference (DID) estima-tor. In winter, the transport zones, namely Anand Vihar and ITO airport, were the most affected regions. The northwestern part of Delhi is the most sensitive zone in terms of air pollution. Due to the lockdown, the weekly PM2.5 emission decreased by 62.15%, the mass concentration of all aerosol particles ≤10 μm in diameter (PM10) decreased by 53.14%, and the air quality index (AQI) improved by 22.40%. A proposal is made to adopt corrective measures to maintain the air pollution index, taking into account the spatial and temporal variability in the responses.

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.

Background Accurate measurements of aboveground biomass (AGB) are essential for understanding the planet’s carbon balance. The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-preserved remnants, characterized by mountainous terrain with significant orographic contrasts along its elevation gradient. This diverse landscape creates a variety of biophysical factors that strongly influence the spatial distribution of AGB. This study aims to estimate AGB using a hybrid geostatistical methodology, regression kriging simulation (RKS), to analyze AGB spatial distribution at a local scale (84 plots, each 0.01 ha) across a small forest fragment covering the entire tree-covered area (8777 ha). Building on traditional regression kriging method, this study introduces an innovative approach by incorporating Gaussian simulation to interpolate residuals, allowing RKS to account for uncertainties in the estimation process and create new results. This allows us to clearly distinguish exogenous ecological processes from endogenous ones before reaching the model’s final estimate. Results Four regression kriging models were created, and the best-performing model used the Enhanced Vegetation Index and direct solar radiation (DSR), achieving an R 2 of 55%. A Gaussian simulation was performed to interpolate the residuals of this model. The final results indicate that RKS provides accurate AGB estimates (RMSE = 1.333 Mg/0.01 ha and R 2 of 77%). Additionally, the inclusion of DSR as a new predictor variable enhances the precision of AGB estimates. The analysis showed that 63% of the sample pairs exhibited measurable spatial dependence. Conclusions Regression kriging simulation is proposed using Gaussian simulation, altering the classical application of regression kriging. For this, a case study was conducted in the Atlantic Forest of Serra do Mar to estimate the spatial distribution of tree biomass in a forest fragment of this region. We demonstrate that the proposed method better captures the heterogeneity of the region and produces more comprehensive results than regression kriging. Regression kriging simulation estimates tree biomass by considering the actual fluctuations of the spatial distribution of tree biomass in the region, taking into account exogenous and endogenous ecological processes, addressing random noise, and allowing the creation of dynamic maps for use by environmental managers.

Understanding the spatial variation of soil properties is essential for monitoring land capabilities as well as the sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables, i.e., terrain attributes and remote sensing (RS) indices, across 1500 km2 of Mashhad plain lands. To achieve this purpose, a total of 180 soil samples (0–10 cm) were taken. The random forest (RF) model combined with ordinary kriging (OK), as well as regression kriging (RK), were applied to relate environmental variables and the studied soil properties. The results revealed that RF-OK was the best model with R2 and RMSE for silt (0.89% and 0.10%), followed by calcium carbonate equivalent (0.88% and 3.30%), clay (0.87% and 2.26%), soil organic carbon (0.86% and 0.24%), sand (0.84% and 4.21%), and pH (0.82% and 5.42%). The RS covariates, including band 5 (B5), modified soil-adjusted vegetation index (MSAVI), difference vegetation index (DVI), band 2 (B2), carbonate rock index (CRI2), gypsum index (GI), and enhanced vegetation index (EVI), and terrain attributes, including topographic wetness index (TWI) and elevation (EL), and topographic position index (TPI), were the most important variables in modeling different soil properties. RF-OK showed the prediction and uncertainty maps related to high precision and low standard deviation in most study areas, which indicate low overfitting and overtraining in modeling processes. In general, the RF-OK model, with low cost and high accuracy, can be applicable to use for predicting different soil properties, as well as spatial information acquired from an effort to maps to managing agriculture in areas at different conditions. Finally, this method can be applied to other regions of similar properties and for similar purposes.

Background In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded new tools, techniques, and technologies that have led to improvements in forest management and forest productivity assessments. Our aim was to estimate and map the basal area and volume of Eucalyptus stands through the integration of forest inventory, remote sensing, parametric, and nonparametric methods of spatial prediction. Methods This study was conducted in 20 5-year-old clonal stands (362 ha) of Eucalyptus urophylla S.T.Blake x Eucalyptus camaldulensis Dehnh. The stands are located in the northwest region of Minas Gerais state, Brazil. Basal area and volume data were obtained from forest inventory operations carried out in the field. Spectral data were collected from a Landsat 5 TM satellite image, composed of spectral bands and vegetation indices. Multiple linear regression (MLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) methods were used for basal area and volume estimation. Using ordinary kriging, we spatialised the residuals generated by the spatial prediction methods for the correction of trends in the estimates and more detailing of the spatial behaviour of basal area and volume. Results The ND54 index was the spectral variable that had the best correlation values with basal area ( r  = − 0.91) and volume ( r  = − 0.52) and was also the variable that most contributed to basal area and volume estimates by the MLR and RF methods. The RF algorithm presented smaller basal area and volume errors when compared to other machine learning algorithms and MLR. The addition of residual kriging in spatial prediction methods did not necessarily result in relative improvements in the estimations of these methods. Conclusions Random forest was the best method of spatial prediction and mapping of basal area and volume in the study area. The combination of spatial prediction methods with residual kriging did not result in relative improvement of spatial prediction accuracy of basal area and volume in all methods assessed in this study, and there is not always a spatial dependency structure in the residuals of a spatial prediction method. The approaches used in this study provide a framework for integrating field and multispectral data, highlighting methods that greatly improve spatial prediction of basal area and volume estimation in Eucalyptus stands. This has potential to support fast growth plantation monitoring, offering options for a robust analysis of high-dimensional data.

Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms. The Heihe area of Heilongjiang Province is one of the key forest areas and forest-fire-prone areas in China. Based on daily historical forest-fire data from 1995 to 2015, daily meteorological data, topographic data and basic geographic information data, the main forest-fire driving factors were first analyzed by using RF importance characteristic evaluation and logistic stepwise regression. Then, the prediction models were established by using the two machine learning methods. Furthermore, the goodness of fit of the models was tested using the receiver operating characteristic test method. Finally, the fire-risk grades were divided by applying the kriging method. The results showed that 11 driving factors were significantly correlated with forest-fire occurrence, and days after the last rain, daily average relative humidity, daily maximum temperature, daily average water vapor pressure, daily minimum relative humidity and distance to settlement had a high correlation with the risk of forest-fire occurrence. The prediction accuracy of the two algorithms in regard to fire points was higher than that for nonfire points. The overall prediction accuracy and goodness of fit of the RF and BPNN algorithms were similar. The two methods were both suitable for forest-fire occurrence prediction. The high-fire-risk zones were mainly concentrated in the northwestern and central parts of the Heihe area.

Understanding the spatial variability of precipitation is essential for water resource management and climate adaptation, especially in arid and semi-arid regions with strong spatiotemporal heterogeneity. Traditional geostatistical methods, such as ordinary kriging, often struggle to capture nonlinear relationships between rainfall and spatial coordinates. This study focuses on comparing ML–RK methods for spatial interpolation using only latitude and longitude as predictors, rather than developing a full rainfall prediction model. As, machine learning techniques integrated with regression kriging (RK) have wide applications for capturing complex spatial patterns. Therefore, this study evaluates RK combined with six regression models including Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Network (NN), Elastic Net (EN), and Polynomial Regression (PR). In this research, we used monthly and decadal averages of precipitation from 42 meteorological stations (2001–2021) of Pakistan. For assessing optimal spatial structure, four theoretical variogram models including exponential, circular, spherical, and linear–were tested using Leave-One-Out Cross-Validation. Here, the performance of the variogram was assessed using RMSE and MAE. Outcomes associated with this research show that RF-RK consistently outperformed other combinations of ML-RK. Consequently, the combination of ensemble learning and geostatistical interpolation effectively captured both nonlinear relationships and spatial dependencies. The resulting high-resolution rainfall maps can support climate adaptation planning, irrigation scheduling, and sustainable management of water resources in data-scarce regions such as Pakistan.

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

In this paper, we compared the efficiency of advanced deterministic and stochastic geo-statistical techniques to predict spatial patterns of fish density in the tropical monsoonal estuary, Zuari, using the following environmental descriptors: temperature, salinity, dissolved oxygen, transparency and geographic coordinates. The methods applied in this study were multiple linear regression, Cubist, support vector regression, random forest regression, universal kriging and regression kriging. Fish abundance and environmental data were collected from September, 2013 to August, 2016 in 48 sampling stations distributed along the estuarine gradient. Ranking procedure of various regression methods showed that the Cubist model was the best performing model based on prediction accuracy in the development phase and prediction consistency in the validation phase. Latitude, temperature, salinity and dissolved oxygen had positive influence on fish abundance, while longitude and transparency showed negative impacts. This study offers scope for refining the employed currently models to predict spatial densities of fish populations using a wide range of available biotic and abiotic variables, which will enable to develop an efficient management framework for tropical monsoonal estuaries.