WeChat Mini Program
Old Version Features

Reconstructing Spatial Variability of Forest Soil Water Characteristic by Using a Combination of Electrical Resistivity Tomography and Local Soil Water Content Measurements

Ursula Noell, Erkki Hemmens,Bernd Ahrends,Susanne Stadler,Stefan Fleck, Klibw-gw working group

crossref(2023)

Cited 0|Views4
Abstract
Effective management of groundwater resources requires conclusive evidence and understanding of forest management effects (tree species selection, harvest intensities, forest rotation periods) on groundwater recharge. The high spatial variability of forest soil characteristic hampers an area representative measurement of forest soil moisture distribution and flow processes in the unsaturated zone. This results in high uncertainties in the detection of tree species difference of water balance and groundwater recharge in forests. We attempt to delineate this heterogeneity by combining different investigation methods and forest stands of different tree species. From 2019 – 2022 we investigated a Norway spruce stand (Picea abies (L.) KARST.) in the Solling mountains (AMT 7.3°C, AMP 1168 mm). The observation shows higher moisture contents close to the trees, where the root density is highest. We calculated a site-specific function relating electrical resistivity to soil water content and used this to reconstruct moisture changes down to a depth of one meter below the rooting zone. Recharge seems to happen not only in winter but also in summer after intense precipitation events. During a severe spring drought in 2020, the water content dropped markedly in the rooting zone. In 2022 we started the observation of the water balance in a lowland Scots pine stand (Pinus sylvestris) with locally regenerating red oak (Quercus rubra) in the shrub layer. The geophysical monitoring using electrical resistivity tomography discovered again lower resistivity indicating higher moisture content close to the trees where root density is highest. The application of different inversion smoothness constraints revealed differences in resulting electrical resistivity values, showing the non-uniqueness of the inversion results. This presents a challenge, relating single point soil water measurements to ERT 3D inversion results and calls for the need to construct a site-specific Archie function by using simultaneous water content measurements at the site rather than laboratory measurements. The investigations will continue in stands of Douglas fir (Pseudotsuga mentiesii), red oak (Quercus rubra), common oak (Quercus robur) and European beech (Fagus sylvatica).The project is funded by the Forest Climate Fond under the joint leadership of BMUV and BMEL (Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection and the Federal Ministry of Food and Agriculture (KLIBW-GW:FKZ: 2220WK39B4 and 2220WK39B4)).
More
Translated text
Key words
Soil Moisture,Soil Water Content
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined