Reply on RC2 DOI Creative Commons
Jan Magnusson

Published: Dec. 1, 2024

Abstract. We present a high-resolution hydrometeorological and snow dataset from the alpine Dischma watershed its surroundings in eastern Switzerland, including station measurements of variables such as depth catchment runoff. This is particularly suited for different modelling experiments using distributed process-based models, physics-based hydrological models. Additionally, data highly useful testing various assimilation schemes developing models representing snow-forest interactions. The covers seven water years 1 October 2016 to 30 September 2023. complete domain spans an area 333 km² with altitudes ranging 1250 3228 meters. basin, outlet at 1671 m elevation, occupies 42.9 km². Included are (100 m) hourly meteorological (air temperature, relative humidity, wind speed direction, precipitation, well long- shortwave radiation), land cover characteristics (primarily forest properties), digital elevation model. Noteworthy, includes acquisitions obtained airborne lidar photogrammetry surveys, constituting most extensive spatial European Alps. Along these gridded datasets, we provide daily quality-controlled recordings sites, biweekly equivalent two locations, runoff stream temperature observations watershed. compiled this study will be further our ability forecast conditions high-alpine headwater catchments that sensitive ongoing climate change.

Language: Английский

High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland DOI Creative Commons
Jan Magnusson, Yves Bühler, Louis Quéno

et al.

Earth system science data, Journal Year: 2025, Volume and Issue: 17(2), P. 703 - 717

Published: Feb. 21, 2025

Abstract. We present an hourly hydrometeorological and snow dataset with 100 m spatial resolution from the alpine Dischma watershed its surroundings in eastern Switzerland, including station measurements of variables such as depth catchment runoff. This is particularly suited for different modelling experiments using distributed process-based models, physics-based hydrological models. Additionally, data are highly useful testing various assimilation schemes developing models representing snow–forest interactions. The covers 7 water years 1 October 2016 to 30 September 2023. complete domain spans area 333 km2 altitudes ranging 1250 3228 m. Basin, outlet at 1671 elevation, occupies 42.9 km2. Included high-resolution (100 m) meteorological (air temperature, relative humidity, wind speed direction, precipitation, long- shortwave radiation) a numerical weather predication model rain radar, land cover characteristics (primarily forest properties), digital elevation model. Notably, includes acquisitions obtained airborne lidar photogrammetry surveys, constituting most extensive derived techniques European Alps. Along these gridded datasets, we provide daily quality-controlled recordings seven sites, biweekly equivalent two locations, runoff stream temperature observations watershed. compiled this study will be further develop our ability forecast conditions high-alpine headwater catchments that sensitive ongoing climate change. All available download https://doi.org/10.16904/envidat.568 (Magnusson et al., 2024).

Language: Английский

Citations

0
Local atmospheric vapor pressure deficit as microclimate index to assess tropical rainforest riparian restoration success DOI
Bruno Moreira Felippe, Ana Cláudia dos Santos Luciano, Fábio Ricardo Marin

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 973, P. 179146 - 179146

Published: March 18, 2025

Language: Английский

Citations

0
Ten practical guidelines for microclimate research in terrestrial ecosystems DOI Creative Commons
Pieter De Frenne, Rémy Beugnon, David H. Klinges

et al.

Methods in Ecology and Evolution, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Abstract Most biodiversity dynamics and ecosystem processes on land take place in microclimates that are decoupled from the climate as measured by standardised weather stations open, unshaded locations. As a result, microclimate monitoring is increasingly being integrated many studies ecology evolution. Overviews of protocols measurement methods related to needed, especially for those starting field achieve more generality standardisation studies. Here, we present 10 practical guidelines ground‐based research terrestrial microclimates, covering best practices initial conceptualisation study data analyses. Our encompass significance microclimates; specifics what, where, when how measure them; design studies; optimal approaches analysing sharing future use collaborations. The paper structured chronological guide, leading reader through each step necessary conduct comprehensive study. At end, also discuss further avenues development this field. With these monitoring, hope stimulate advance evolution, under pressing need account buffering or amplifying abilities contrasting microhabitats context global change.

Language: Английский

Citations

2
Embracing plant–plant interactions—Rethinking predictions of species range shifts DOI
Pieter Sanczuk, Dries Landuyt, Emiel De Lombaerde

et al.

Journal of Ecology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Abstract Interactions among plants are changing across the globe resulting from a multitude of changes in environment. Obtaining accurate predictions plant species' range dynamics requires us to account for plant–plant interactions, but this remains challenging using existing species distribution modelling (SDM) techniques. Advanced SDM techniques facilitate integration interactions based on species‐to‐species associations. However, uncharted environmental conditions which formerly derived correlations potentially no longer hold, more process‐based alternative is expected become increasingly relevant. We first review most common that integrate and then present concept novel map product: spatial interaction index (PII) depicting link between focal species’ performance trait signature interacting vegetation. The latest developments remote sensing increasing availability vegetation plot data PII mapping trait–environment relationships. Synthesis : holds potential advance next‐generation biogeographical analyses as it can serve pivotal missing covariate layer necessary into applications. This product adds flexibility ecologists’ toolbox analyse shifts formation communities response multiple changes.

Language: Английский

Citations

1
Holocene summer temperature reconstruction from plant sedaDNA and chironomids from the northern boreal forest DOI Creative Commons
Roseanna J. Mayfield, Dilli P. Rijal, Peter D. Heintzman

et al.

Quaternary Science Reviews, Journal Year: 2024, Volume and Issue: 345, P. 109045 - 109045

Published: Oct. 31, 2024

Language: Английский

Citations

1
Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques DOI Creative Commons

Aleksandr Gevorgian,

Giovanni Pernigotto, Andrea Gasparella

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3365 - 3365

Published: July 9, 2024

The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data the dynamic nature local weather conditions, which complicate GHI measurement. scarcity precise impedes development energy models, impacting both economic environmental outcomes. To address these prediction, this paper focuses on various locations Europe Asia Minor, predominantly regions. Advanced machine learning techniques, including random forest (RF) extreme gradient boosting (XGBoost) regressors, are employed effectively predict GHI. Additionally, training distribution based cloud opacity values integrating synthetic significantly enhance predictive accuracy, R2 scores ranging from 0.91 0.97 across multiple locations. Furthermore, substantial reductions root mean square error (RMSE), absolute (MAE), bias (MBE) underscore improved reliability predictions. Future research should refine generation, optimize additional meteorological parameter integration, extend methodology new regions, test predicting tilted (GTI). studies expand considerations beyond opacity, incorporating sky cover sunshine duration accuracy reliability.

Language: Английский

Citations

1
FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities DOI Creative Commons
Bin Wang, Cameron Proctor, Zhiliang Yao

et al.

Methods in Ecology and Evolution, Journal Year: 2024, Volume and Issue: 15(9), P. 1540 - 1552

Published: July 9, 2024

Abstract Light availability (LAv) dictates a variety of biological and ecological processes across range spatiotemporal scales. Quantifying the spatial pattern LAv in three‐dimensional (3D) space can promote understanding microclimates that are critical to fine‐scale species distribution. However, there is still lack tools robust evaluate heterogeneity forests. Here, we propose Forest Analyzer python package ( FLApy ), an open‐source computational tool designed for analysis intra‐forest variation multiple freely invoked by Python, facilitating processing LiDAR point cloud data into 3D container constructed voxels, as well traversal calculations related regime high performance synthetic hemispherical algorithm. Furthermore, incorporates 37 indicators, enabling users expediently export visualize patterns evaluation at two scales (voxel scale 3D‐cluster scale) study purposes. To validate efficacy , employed simulated dataset simulates forests (varying canopy closure). real world forest, executed standard workflow utilizing drone‐derived from three subtropical evergreen broad‐leaved forest dynamics plots within Ailao Mountain Reserve. Our findings underscore series indices derived provide characterization light diverse settings. Additionally, when juxtaposed with conventional monitoring techniques, metrics offered demonstrated better generality our field assessments. offers ecologists solution rapid quantification understory 3D‐regimes scales, addressing disparity between traditional manual approaches precision required contemporary studies. Moreover, provides support establishment expansion based on micro‐environments, enhancing largely uncharted structural patterns. Anticipated outcomes suggest will enhance knowledge concerning climatic conditions context, proving pivotal delineation microhabitats development detailed 3D‐scale distribution models.

Language: Английский

Citations

0
Comparison of Landsat-8 and Sentinel-2 Imagery for Modeling Gross Primary Productivity of Tea Ecosystem DOI
Ali Raza, Yongguang Hu, Yongzong Lu

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 76(6), P. 1585 - 1605

Published: Oct. 21, 2024

Accurately estimating gross primary productivity (GPP) is essential for understanding and managing carbon dynamics within an ecosystem. This study investigates the potential of imagery approaches, specifically utilizing Landsat‑8 Sentinel‑2 data, to model GPP in tea ecosystem subtropical region China. While extensive research has focused on cereal crop ecosystems, plantations, despite their global significance as a cash crop, have received limited attention regarding modeling. To address this gap, field campaign was carried out using eddy covariance (EC) system monitor net exchange (NEE) plantations at scale. Pruning recognized crucial management practice growth plants, leading significant variations NEE its components (ecosystem respiration (RES)). Consequently, we selected pruning period, from February June modeling GPP. Traditionally, vegetation photosynthesis models (VPMs) based data required parameterization, posing challenges data-limited scenarios. In study, developed parametric indices such normalized difference index (NDVI) scaled photochemical reflectance (sPRI), which describe both plant structure physiology EC Landsat-8/Sentinel‑2 data. Results indicate that while NDVI partially captures variation (R2 = 0.60) 0.71) imagery, incorporating sPRI significantly enhances agreement between modeled observed (Landsat-8 : R2 0.77, Sentinel-2 0.80). Furthermore, comparing estimates derived (GPPEC) with those Sentinel (GPPSentinel) Landsat (GPPLandsat) reveals GPPSentinel closely aligns GPPEC 0.80), outperforming GPPLandsat various evaluation (index Agreement, Kling-Gupta efficiency, mean bias error, relative percent).

Language: Английский

Citations

0
Reply on RC1 DOI Creative Commons
Jan Magnusson

Published: Dec. 1, 2024

Abstract. We present a high-resolution hydrometeorological and snow dataset from the alpine Dischma watershed its surroundings in eastern Switzerland, including station measurements of variables such as depth catchment runoff. This is particularly suited for different modelling experiments using distributed process-based models, physics-based hydrological models. Additionally, data highly useful testing various assimilation schemes developing models representing snow-forest interactions. The covers seven water years 1 October 2016 to 30 September 2023. complete domain spans an area 333 km² with altitudes ranging 1250 3228 meters. basin, outlet at 1671 m elevation, occupies 42.9 km². Included are (100 m) hourly meteorological (air temperature, relative humidity, wind speed direction, precipitation, well long- shortwave radiation), land cover characteristics (primarily forest properties), digital elevation model. Noteworthy, includes acquisitions obtained airborne lidar photogrammetry surveys, constituting most extensive spatial European Alps. Along these gridded datasets, we provide daily quality-controlled recordings sites, biweekly equivalent two locations, runoff stream temperature observations watershed. compiled this study will be further our ability forecast conditions high-alpine headwater catchments that sensitive ongoing climate change.

Language: Английский

Citations

0
Reply on RC2 DOI Creative Commons
Jan Magnusson

Published: Dec. 1, 2024

Abstract. We present a high-resolution hydrometeorological and snow dataset from the alpine Dischma watershed its surroundings in eastern Switzerland, including station measurements of variables such as depth catchment runoff. This is particularly suited for different modelling experiments using distributed process-based models, physics-based hydrological models. Additionally, data highly useful testing various assimilation schemes developing models representing snow-forest interactions. The covers seven water years 1 October 2016 to 30 September 2023. complete domain spans an area 333 km² with altitudes ranging 1250 3228 meters. basin, outlet at 1671 m elevation, occupies 42.9 km². Included are (100 m) hourly meteorological (air temperature, relative humidity, wind speed direction, precipitation, well long- shortwave radiation), land cover characteristics (primarily forest properties), digital elevation model. Noteworthy, includes acquisitions obtained airborne lidar photogrammetry surveys, constituting most extensive spatial European Alps. Along these gridded datasets, we provide daily quality-controlled recordings sites, biweekly equivalent two locations, runoff stream temperature observations watershed. compiled this study will be further our ability forecast conditions high-alpine headwater catchments that sensitive ongoing climate change.

Language: Английский

Citations

0