Fine-resolution Data Research Articles

Integrating both restoration and regeneration potentials into real-world forest restoration planning: A case study of Hong Kong

Forest restoration is a vital nature-based solution for mitigating climate change and land degradation. To ensure restoration effectiveness, the costs and benefits of alternative restoration strategies (i.e., active restoration vs. natural regeneration) need to be evaluated. Existing studies generally focus on maximum restoration potential, neglecting the recovery potential achievable through natural regeneration processes, leading to incomplete understanding of the true benefits and doubts about the necessity of active restoration. In this study, we introduce a multi-stage framework incorporating both restoration and regeneration potential into prioritized planning for ecosystem restoration. We used the vegetated landscape of Hong Kong (covering 728 km2) as our study system due to its comprehensive fine-resolution data and unique history of vegetation recovery, making it an ideal candidate to demonstrate the importance of this concept and inspire further research. We analyzed vegetation recovery status (i.e., recovering, degrading, and stable) over the past decade based on the canopy height data derived from multi-temporal airborne LiDAR. We assessed natural regeneration potential and maximum restoration potential separately, producing spatially-explicit predictions. Our results show that 44.9% of Hong Kong's vegetated area has showed evidence of recovery, but remaining gains through natural regeneration are limited, constituting around 4% of what could be attained through active restoration. We further estimated restoration priority by maximizing the restoration gain. When prioritizing 5% of degraded areas, the increment in canopy height could be up to 10.9%. Collectively, our findings highlight the importance of integrating both restoration and regeneration potential into restoration planning. The proposed framework can aid policymakers and land managers in optimizing forest restoration options and promoting the protection and recovery of fragile ecosystems.

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

ESM data downscaling: a comparison of super-resolution deep learning models

Climate projections at fine spatial resolutions are required to conduct accurate risk assessment for critical infrastructure and design adaptation planning. Generating these projections using advanced Earth system models (ESM) requires significant computational resources. To address this issue, various statistical downscaling techniques have been introduced to generate fine-resolution data from coarse-resolution simulations. In this study, we evaluate and compare five deep learning-based downscaling techniques, namely, super-resolution convolutional neural networks, fast super-resolution convolutional neural network ESM, efficient sub-pixel convolutional neural network, enhanced deep residual network (EDRN), and super-resolution generative adversarial network (SRGAN). These techniques are applied to a dataset generated by the Energy Exascale Earth System Model (E3SM), focusing on key surface variables such as surface temperature, shortwave heat flux, and longwave heat flux. Models are trained and validated using paired fine-resolution (0.25∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}) and coarse-resolution (1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}) monthly data obtained from a 9-year simulation. Next, blind testing is performed using monthly data obtained from two different years outside of the training and validation set. To evaluate the efficiency of each technique, different statistical metrics are used, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The results show that EDRN outperforms other algorithms in terms of PSNR, SSIM, and MSE, but struggles to capture fine-scale features in the data. In contrast, SRGAN, a generative model that uses perceptual loss, excels in capturing fine details at boundaries and internal structures, resulting in lower LPIPS than other methods.

Farmer Perceptions of Land Cover Classification of UAS Imagery of Coffee Agroecosystems in Puerto Rico

Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers.

Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data

Land surface temperature (LST) is an important factor in studies of surface energy fluxes between the Earth's surface and atmosphere. The Landsat LST product has been applied widely due to its fine spatial resolution and high data quality. Frequent cloud coverage, however, results in different degrees of gaps in the Landsat LST images, restricting greatly their application in practical cases requiring continuous LST time-series. Gap filling is an important technique used to reconstruct the missing data in the Landsat LST images. Using only temporally neighboring data from the same sensor, however, current gap filling methods struggle to deal with large spatial gaps and large temporal changes in LST. To solve these challenging issues, in this paper, multi-scale data (i.e., the 2 km, all-sky, hourly GOES LST product) were proposed for gap filling of Landsat LST images, and a spatial-temporal random forest (STRF) method was developed. The GOES LST product can provide effective information on LST in the gaps of the target Landsat LST data, with great potential to decreasing the uncertainty in gap filling when large LST change exists or the gap size is large. Through experiments in six regions of the USA, the proposed STRF method was shown to be more accurate than three typical gap filling and spatio-temporal fusion methods, and particularly advantageous for cases involving large temporal changes or large spatial gaps in LST. Specifically, STRF produces an average root mean square error (RMSE) of 1.6444 K and average correlation coefficient (CC) of 0.8653 for the six regions, which are 0.1133 K smaller and 0.0176 larger than the most competitive benchmark method (i.e., the spatial random forest (SRF) method). Conceptually, by spatial-temporal fusion of multi-scale data in gap filling, STRF provides a new paradigm for both spatial-temporal fusion and gap filling. Furthermore, the proposed STRF has the potential to be applied to various gap filling tasks in other situations.

Analysis of trends in productivity metrics in assessing land degradation: A case study in the Campania region of southern Italy

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application

In recent years, developing intelligent Earth System Models (ESMs), which integrate artificial intelligence (AI) models to replace ESMs' sub-modules, has shown promising results in enhancing both the accuracy and efficiency of traditional ESMs. A crucial determinant in this process is the quality of training data, which determines what knowledge AI can learn. Delving into specifics, while training with sub-module simulation data bolsters computational swiftness, it can come at the cost of accuracy. On the other hand, training with finer-resolution data (e.g., high-resolution model simulation and observational data) augments accuracy but introduces challenges when coupling AI into ESMs, such as instability or even crashing, and deteriorating accuracy over time. Notably, sub-module simulation and finer-resolution data can complement each other and provide different knowledge to AI. Therefore, our study proposes a novel AI-RepSub framework for replacing sub-modules, which employs the incremental learning (IL) method to learn from multiple data types and preserve previously acquired knowledge when fitting new data. To verify the AI-RepSub's performance, this study chooses to substitute the convection trigger function (CTF) in the Community Earth System Model (CESM), developing a hybrid CESM with an intelligent CTF module (CESM-iCTF). Our online evaluations indicate that CESM-iCTF outperforms CESM in CTF accuracy, computational efficiency, and North Atlantic Oscillation (NAO) hindcast skill while maintaining stability. To the best of our knowledge, this represents the first exploration into AI sub-modules driven by multi-source data in the field of intelligent ESMs, thereby demonstrating the potential of AI to serve as robust replacements for ESMs' sub-modules.

An enhanced method for automated end-use classification of household water data

Abstract An accurate estimation of residential end uses of water is helpful in developing efficient water systems. If not obtainable through direct metering, this information can be gathered by disaggregating and classifying household-level water-use data. However, most automated techniques require fine-resolution data (e.g., 1 s) and end-use parameters which may be unavailable to water utilities. To fill the above gap, this study presents a method for the automated disaggregation and classification of indoor water-use data collected at the 1-min temporal resolution, and by exclusively relying on the end-use parameter values available in the literature. Specifically, the features of each water-use event detected at the household level are compared against the most common event features for the selected end-use category. The results obtained by testing the method with real data collected at 14 households in two different countries (Italy and the Netherlands) confirm its potential in disaggregating and classifying water end-use events with an average accuracy higher than 90% and an average (normalized) root-mean-square lower than 0.06 despite the lack of information about end uses in individual households. This demonstrates that end-use detection is possible even with data whose resolution is closer to that of most commercial water meters.

CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories

Abstract. Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1∘ × 0.1∘) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

Assessing the Monthly Trends in Precipitable Water Vapor over the Indian Subcontinent

This study estimates the trends in precipitable water vapor (PWV), atmospheric moisture budget (AMB), and the factors influencing them: air temperature, evapotranspiration (ET), convective available potential energy (CAPE), and vertical velocity (Omega) over the Indian subcontinent using ERA5 reanalysis data sets between 1980 and 2020. PWV is examined across three atmospheric layers (1000–850 hPa: lower layer; 850–500 hPa: middle layer; 500–300 hPa: upper layer), and the entire atmospheric column (EAC; 1000–300 hPa). The observed PWV trends exhibit variability within the EAC, ranging from −0.53 to 1.25 mm/decade across the study area, with the middle layer showing the most pronounced variation (–0.44 to 0.83 mm/decade), followed by the lower layer (0.10 to 0.45 mm/decade), and the upper layer (–0.02 to 0.23 mm/decade). These fluctuations in PWV are attributed to changes in air temperature, ET, CAPE, and Omega. This investigation, however, underscores the necessity of delving into the impacts of these influencing factors on PWV using finer resolution data, to enhance our comprehension of its spatial and temporal dynamics in the region. Furthermore, the annual AMB analysis reveals a declining trend in the study region. These findings collectively contribute to the understanding of regional water-energy cycles and the recent shifts in atmospheric dynamics.

Global fine-resolution data on springtail abundance and community structure

Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

Integrating APSIM model with machine learning to predict wheat yield spatial distribution

AbstractTraditional simulation models are often point based; thus, more research is needed to emphasize spatial simulation, providing decision‐makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine‐resolution data from coarse‐resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the artificial neural network, creating a hybrid modeling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modeling workflows with the spatial APSIM next‐generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg N ha−1 to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89) between the spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha−1) relative to the potential yield.

Blending of Landsat and Sentinel images using Multi-sensor fusion

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Adult thermoregulatory behaviour does not provide, by itself, an adaptive explanation for the reflectance–climate relationship (Bogert's pattern) inIberianbutterflies

AbstractThe association of darker, less reflective insect wings with cooler environments (Bogert's rule) is thought to be related to adult thermoregulation, but the adaptive explanation and the implications for sensitivity to climate warming are yet to be tested. We re‐evaluate the pattern for butterflies using finer resolution data than in previous approaches, both geographically and morphologically, and test its correlation with recent evidence of impacts of warming on butterflies.We compared reflectance–climate relationships at different grid sizes, selected the best subset of reflectance measurements and tested the contribution of the species basking mode, the phylogenetic structure of the data and the correlation between reflectance and published abundance or altitudinal shifts. We used standardised RGB (Red, Green, Blue) values from 222 species from the Iberian Peninsula, and regional mean temperature and precipitation data from the study area (10 and 50‐km resolutions) and Europe (50‐km resolution).Correlations between reflectance and temperature increased at finer geographical and morphological resolutions. However, the butterfly basking mode did not improve the statistical explanation of the pattern. Reflectance shows a strong phylogenetic structure, while variance partitioning indicated a poor pure contribution of the climate variables in the reflectance–climate correlation.Overall, mean temperature and precipitation were only modest predictors of butterfly reflectance. No correlation between reflectance and recent abundance or altitudinal shifts was found using the hypothesised best estimates of reflectance. Although significant correlations between butterfly shading and altitudinal shifts were found for two of the reflectance measurements, this is interpreted as weak, probably artifactual evidence on the predictive power of this relationship.The strong phylogenetic pattern of the reflectance and the low fraction of the reflectance measures analysed suggest that tests for alternative explanations are still needed to shed light on the meaning of the colour–environment relationships in butterflies, which probably are of a complex nature. From an adaptive point of view, unravelling the basis of Bogert's pattern in butterflies requires a closer, habitat‐level approach and alternative variables to adult thermoregulatory behaviour to be tested.

Farmland biodiversity benefits from small woody features

Although positive effects on biodiversity of woody features in agricultural landscapes are widely recognized, questions remain as to which landscape context to prioritize their implementation and in what proportion. To investigate the response of farmland biodiversity to small woody features (SWF) density in different landscape compositions (cropland, grassland, mixed), we analyzed fine-resolution data from standardized monitoring schemes on 111 birds, 22 bats, and 25 bush cricket's species, at national scale (relying on 3772, 834 and 727 monitoring points) over the period 2015–2019, in metropolitan France. We used Generalized Additive Mixed Models to analyze population and community responses, through different metrics (abundance, species diversity, and functional composition). We found a positive response exhibited by the three taxa to SWF, more especially in cropland where the SWF density is the lowest relative to grassland and mixed landscapes. Also, our results suggested a non-linear response common to the three taxa, with an increased benefit up to at least 6 % of SWF density in cropland, and beyond for most of the metrics but to a lesser extent (e.g., maximum abundance reached at 7–12 % SWF). We note, however, that some species among farmland bird specialists are negatively impacted by SWF. Overall, we emphasize the benefits to promote woody features in agricultural landscapes, notably in cropland, to support biodiversity and its associated ecosystem functions. Our study provides crucial empirical evidence to the recommendations from previous studies and the relevance of the EU Biodiversity Strategy for 2030 to dedicate at least 10 % of farmland to high-diversity landscape features.

Multi-temporal downscaling of daily to sub-daily streamflow for flash flood watersheds at ungauged stations using a hybrid framework

Unprecedented flash floods (FFs) in urban regions have been rising due to the increasing intensity and magnitude of heavy rainfall resulting from human-induced climate and land-use changes. Modelling of FF along different spatiotemporal scales is extremely complex since FF models require multi-resolution forcing and observed information. In the absence of sub-daily, and multi-site streamflow data, multi-temporal downscaling (MTD) plays a crucial role for FF modeling. While a wide range of methods are available for the spatio-temporal downscaling of climate data, the applicability of the MTD strategy for streamflow has not been investigated yet. The current study proposed a MTD methodology for yielding the daily to sub-daily streamflow of gauged and ungauged stations using adaptive emulator modelling concepts. The proposed MTD framework for ungauged stations is a hybrid model that draws on conceptual and machine learning-based approaches to analyze catchment behavior. The study selected the Peachtree Creek watershed (United States) since it frequently experiences FFs. Results suggest that model-derived sub-daily streamflow had minimal uncertainty in capturing hydrological signatures and nearly 95% accuracy in predicting flow attributes over ungauged stations. The proposed framework can be useful for planning, mitigation, and management, where the fine resolution data is required.

Assessing the performance of techniques for disaggregating daily rainfall in South Africa

Design flood estimation (DFE) methods are used to limit the risk of failure and ensure the safe design of hydrological and related infrastructure, and to inform water resources management. In order to improve DFE methods which are based on event or continuous simulation rainfall–runoff models, it is generally necessary to use sub-daily rainfall data. However, sub-daily rainfall gauges are relatively sparse and have shorter record lengths than daily rainfall gauges in South Africa. Rainfall temporal disaggregation (RTD) techniques can be used to produce finer resolution data from coarser resolution daily rainfall data. Several RTD approaches have been developed and are used in South Africa. However, there is a need to review and assess the performance of the available RTD methods. This paper contains an overview of selected RTD approaches and the performance of the methods at selected sites in South Africa, for disaggregating daily rainfall into 15-min intervals. Temporal distributions of rainfall were represented by dimensionless Huff curves, which served as the basis for comparison of observed and disaggregated rainfall. In a pilot study it was found that the SCS-SA (Soil Conservation Service model South Africa) distributions and the Knoesen model approaches performed considerably better than the other approaches. The RTD approaches were further assessed using data from 14 additional rainfall stations. For the additional stations, the Knoesen model and SCS-SA disaggregated rainfall generally provided the most realistic temporal distributions.

Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States

The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks.