IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i7d10.1007_s11269-022-03155-8.html
   My bibliography  Save this article

Seasonal Uncertainty Estimation of Surface Nuclear Magnetic Resonance Water Content using Bootstrap Statistics

Author

Listed:
  • Uttam Singh

    (Indian Institute of Technology Roorkee)

  • Pramod Kumar Sharma

    (Indian Institute of Technology Roorkee)

Abstract

A calibration procedure that fits the observed modeled data is used to determine the parameters of a hydrological model. As a result, the model parameters are highly uncertain. Estimation and the impact of uncertainty on model parameters have long been a source of debate. The bootstrap statistics method assesses uncertainty in surface nuclear magnetic resonance (surface NMR) water content and transverse relaxation time. The fundamental issue associated with the surface NMR data is that the quality of the surface NMR data is reduced in the presence of ambient electromagnetic and environmental noise. The bootstrap statistics is particularly well suited for estimating the uncertainty of the data set. We demonstrate that a bootstrap resampling of the observed synthetic data can provide an uncertainty estimate that closely fits the known uncertainty using synthetic forward modeled data with various noise levels, i.e., 5nV, 15nV, 30nV, and 50nV. The thickness of bootstrapped profile represents the uncertainty in the water content and relaxation time profiles. The thickness of the bootstrapped water content profile increases with an increase in noise level in the synthetic NMR data sets. Also, the thickness of the profiles increases along with the subsurface depth. Finally, we present seasonal field surface NMR data sets collected during the pre-monsoons and post-monsoon seasons under realistic ambient noise conditions. The surface NMR model was run for a 500–500 bootstrap to assess the pre-monsoon and post-monsoon uncertainty. This method is computationally extensive but straightforward to apply, and it provides valuable uncertainty estimates for both relaxation time and water content results for smooth-mono surface NMR models.

Suggested Citation

  • Uttam Singh & Pramod Kumar Sharma, 2022. "Seasonal Uncertainty Estimation of Surface Nuclear Magnetic Resonance Water Content using Bootstrap Statistics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2493-2508, May.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:7:d:10.1007_s11269-022-03155-8
    DOI: 10.1007/s11269-022-03155-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03155-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03155-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vinit Sehgal & Mukesh Tiwari & Chandranath Chatterjee, 2014. "Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2793-2811, August.
    2. Haibo Chu & Jiahua Wei & Yuan Jiang, 2021. "Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2617-2632, June.
    3. Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lili Wang & Yanlong Guo & Manhong Fan, 2022. "Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4535-4555, September.
    2. Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    3. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    4. Zhuoqi Wang & Yuan Si & Haibo Chu, 2022. "Daily Streamflow Prediction and Uncertainty Using a Long Short-Term Memory (LSTM) Network Coupled with Bootstrap," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4575-4590, September.
    5. Haibo Chu & Jiahua Wei & Yuan Jiang, 2021. "Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2617-2632, June.
    6. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    7. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
    8. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
    9. Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
    10. Pruethsan Sutthichaimethee & Harlida Abdul Wahab, 2021. "A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand s Environmental Law: Enriching the Path Analysis-VARIMA-OVi Model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 398-411.
    11. Lihui Zhang & Riletu Ge & Jianxue Chai, 2019. "Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm," Energies, MDPI, vol. 12(1), pages 1-19, January.
    12. Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    13. Jin Hyuck Kim & Jang Hyun Sung & Shamsuddin Shahid & Eun-Sung Chung, 2022. "Future Hydrological Drought Analysis Considering Agricultural Water Withdrawal Under SSP Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 2913-2930, July.
    14. Haibo Chu & Jianmin Bian & Qi Lang & Xiaoqing Sun & Zhuoqi Wang, 2022. "Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    15. Maha Shabbir & Sohail Chand & Farhat Iqbal, 2022. "A Novel Hybrid Method for River Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 253-272, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:36:y:2022:i:7:d:10.1007_s11269-022-03155-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.