High-resolution operational soil moisture monitoring for forests in central Germany

被引:0
|
作者
Vorobevskii, Ivan [1 ]
Luong, Thi Thanh [1 ]
Kronenberg, Rico [1 ]
Petzold, Rainer [2 ]
机构
[1] TUD Dresden Univ Technol, Fac Environm Sci, Chair Meteorol, D-01737 Tharandt, Germany
[2] Saxony Forest State Enterprise, Competence Ctr Forest & Forestry, D-01796 Pirna, Germany
关键词
EVAPORATION; DROUGHT; SIMULATIONS; CLIMATE;
D O I
10.5194/hess-28-3567-2024
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The forests of central Germany (Saxony, Saxony-Anhalt, and Thuringia) are vital components of the local ecosystems, the economy, and recreation. However, in recent years, these forests have faced significant challenges due to prolonged climate-change-induced droughts, causing water shortages, tree stress, and pest outbreaks. One of the key components of the forests' vitality and productivity is the availability of soil moisture. Given the anticipated increase in the frequency and severity of drought events, there is a growing demand for accurate and real-time soil moisture information. This underscores the need for development of an appropriate monitoring tool to make forest management strategies more effective.The article introduces an operational high-resolution soil moisture monitoring framework for the forests in central Germany. The key components of this system include the advanced LWF-BROOK90 1D water balance model, a large database of the National Federal Forest Inventory, high-resolution forest soil maps, real-time climate data from the German Meteorological Service, and a web information platform for the presentation of daily updated results. This system informs the public and empowers forest managers and other decision-makers to take targeted, local measures for sustainable forest management, aiding in both drought mitigation and long-term forest health in the face of climate change. The validation of the system using soil moisture measurements from 51 stations with various sensor depths (up to 100 cm) showed an overall good agreement (0.76 median Pearson correlation), which was found to be higher for deciduous rather than coniferous forests. Finally, the framework is discussed against the background of the main limitations of existing monitoring systems and how operational soil moisture measurements contribute to better interpretation of simulations.
引用
收藏
页码:3567 / 3595
页数:29
相关论文
共 50 条
  • [41] Central vascular access guided by high-resolution ultrasonography for invasive intraanesthetic monitoring
    Duran-Briones, Gerardo
    [J]. CIRUGIA Y CIRUJANOS, 2010, 78 (05): : 418 - 421
  • [42] Magnetostratigraphy and high-resolution lithostratigraphy of the Permian-Triassic boundary interval in Central Germany
    Szurlies, M
    Bachmann, GH
    Menning, M
    Nowaczyk, NR
    Käding, KC
    [J]. EARTH AND PLANETARY SCIENCE LETTERS, 2003, 212 (3-4) : 263 - 278
  • [43] High-resolution ground-penetrating radar monitoring of soil moisture dynamics: Field results, interpretation, and comparison with unsaturated flow model
    Steelman, Colby M.
    Endres, Anthony L.
    Jones, Jon P.
    [J]. WATER RESOURCES RESEARCH, 2012, 48
  • [44] Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product
    Singh, Gurjeet
    Das, Narendra N.
    Colliander, Andreas
    Entekhabi, Dara
    Yueh, Simon H.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2023, 298
  • [45] High-resolution European daily soil moisture derived with machine learning (2003-2020)
    Sungmin, O.
    Orth, Rene
    Weber, Ulrich
    Park, Seon Ki
    [J]. SCIENTIFIC DATA, 2022, 9 (01)
  • [46] The Merit of Estimating High-Resolution Soil Moisture Using Combined Optical, Thermal, and Microwave Data
    Li, Ji
    Leng, Guoyong
    Peng, Jian
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 20
  • [47] Estimating high-resolution soil moisture by combining data from a sparse network of soil moisture sensors and remotely sensed MODIS LST information
    Gemitzi, Alexandra
    Kofidou, Maria
    Falalakis, George
    Fang, Bin
    Lakshmi, Venkat
    [J]. HYDROLOGY RESEARCH, 2024, 55 (09): : 905 - 920
  • [48] A roadmap for high-resolution satellite soil moisture applications - confronting product characteristics with user requirements
    Peng, Jian
    Albergel, Clement
    Balenzano, Anna
    Brocca, Luca
    Cartus, Oliver
    Cosh, Michael H.
    Crow, Wade T.
    Dabrowska-Zielinska, Katarzyna
    Dadson, Simon
    Davidson, Malcolm W. J.
    de Rosnay, Patricia
    Dorigo, Wouter
    Gruber, Alexander
    Hagemann, Stefan
    Hirschi, Martin
    Kerr, Yann H.
    Lovergine, Francesco
    Mahecha, Miguel D.
    Marzahn, Philip
    Mattia, Francesco
    Musial, Jan Pawel
    Preuschmann, Swantje
    Reichle, Rolf H.
    Satalino, Giuseppe
    Silgram, Martyn
    Van Bodegom, Peter M.
    Verhoest, Niko E. C.
    Wagner, Wolfgang
    Walker, Jeffrey P.
    Wegmuller, Urs
    Loew, Alexander
    [J]. REMOTE SENSING OF ENVIRONMENT, 2021, 252
  • [49] A Framework for High-Resolution Soil Moisture Extraction Using SCATSAT-1 Scatterometer Data
    Murugan, Deepak
    Maurya, Ajay Kumar
    Garg, Akanksha
    Singh, Dharmendra
    [J]. IETE TECHNICAL REVIEW, 2020, 37 (02) : 147 - 156
  • [50] Prediction of High-Resolution Soil Moisture Using Multi-source Data and Machine Learning
    Sudhakara, B.
    Bhattacharjee, Shrutilipi
    [J]. DISTRIBUTED COMPUTING AND INTELLIGENT TECHNOLOGY, ICDCIT 2024, 2024, 14501 : 282 - 292