IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i10p1856-d948392.html
   My bibliography  Save this article

Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems

Author

Listed:
  • Michael S. O’Donnell

    (U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave, Bldg. C, Fort Collins, CO 80526, USA)

  • Daniel J. Manier

    (U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Ave, Bldg. C, Fort Collins, CO 80526, USA)

Abstract

Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R 2 = 0.51), annual herbaceous plant cover (R 2 = 0.687), exposed soil (R 2 = 0.656) and fire occurrence (R 2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.

Suggested Citation

  • Michael S. O’Donnell & Daniel J. Manier, 2022. "Spatial Estimates of Soil Moisture for Understanding Ecological Potential and Risk: A Case Study for Arid and Semi-Arid Ecosystems," Land, MDPI, vol. 11(10), pages 1-37, October.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1856-:d:948392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/10/1856/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/10/1856/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    2. Changming Fang & Pete Smith & John B. Moncrieff & Jo U. Smith, 2005. "Erratum: Similar response of labile and resistant soil organic matter pools to changes in temperature," Nature, Nature, vol. 436(7052), pages 881-881, August.
    3. Tenreiro, Tomás R. & García-Vila, Margarita & Gómez, José A. & Jimenez-Berni, José A. & Fereres, Elías, 2020. "Water modelling approaches and opportunities to simulate spatial water variations at crop field level," Agricultural Water Management, Elsevier, vol. 240(C).
    4. Lü, Haishen & Zhu, Yonghua & Skaggs, Todd H. & Yu, Zhongbo, 2009. "Comparison of measured and simulated water storage in dryland terraces of the Loess Plateau, China," Agricultural Water Management, Elsevier, vol. 96(2), pages 299-306, February.
    5. Berti, Antonio & Tardivo, Gianmarco & Chiaudani, Alessandro & Rech, Francesco & Borin, Maurizio, 2014. "Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy," Agricultural Water Management, Elsevier, vol. 140(C), pages 20-25.
    6. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
    7. Changming Fang & Pete Smith & John B. Moncrieff & Jo U. Smith, 2005. "Similar response of labile and resistant soil organic matter pools to changes in temperature," Nature, Nature, vol. 433(7021), pages 57-59, January.
    8. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    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. Vasile Brătian & Ana-Maria Acu & Camelia Oprean-Stan & Emil Dinga & Gabriela-Mariana Ionescu, 2021. "Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    2. Rahman A. Prasojo & Karunika Diwyacitta & Suwarno & Harry Gumilang, 2017. "Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers)," Energies, MDPI, vol. 10(8), pages 1-18, August.
    3. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    4. Kayode Ayankoya & Andre P. Calitz & Jean H. Greyling, 2016. "Real-Time Grain Commodities Price Predictions in South Africa: A Big Data and Neural Networks Approach," Agrekon, Taylor & Francis Journals, vol. 55(4), pages 483-508, October.
    5. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    6. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    7. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    8. Jinquan Li & Junmin Pei & Changming Fang & Bo Li & Ming Nie, 2024. "Drought may exacerbate dryland soil inorganic carbon loss under warming climate conditions," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    9. Marijana Zekić-Sušac & Marinela Knežević & Rudolf Scitovski, 2021. "Modeling the cost of energy in public sector buildings by linear regression and deep learning," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(1), pages 307-322, March.
    10. Xu Yang & Dongsheng Chu & Haibo Hu & Wenbin Deng & Jianyu Chen & Shaojun Guo, 2024. "Effects of Land-Use Type and Salinity on Soil Carbon Mineralization in Coastal Areas of Northern Jiangsu Province," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    11. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
    12. Man Sing Wong & Tingneng Wang & Hung Chak Ho & Coco Y. T. Kwok & Keru Lu & Sawaid Abbas, 2018. "Towards a Smart City: Development and Application of an Improved Integrated Environmental Monitoring System," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    13. Zekić-Sušac Marijana & Scitovski Rudolf & Has Adela, 2018. "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 4(2), pages 57-66, November.
    14. Ayankoya, Kayode & Calitz, Andre P. & Greyling, Jean H., 2016. "Real-Time Grain Commodities Price Predictions in South Africa: A Big Data and Neural Networks Approach," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 55(4), December.
    15. Paolo Berta & Paolo Paruolo & Stefano Verzillo & Pietro Giorgio Lovaglio, 2020. "A bivariate prediction approach for adapting the health care system response to the spread of COVID-19," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    16. Shivaram Subramanian & Pavithra Harsha, 2021. "Demand Modeling in the Presence of Unobserved Lost Sales," Management Science, INFORMS, vol. 67(6), pages 3803-3833, June.
    17. Agnese Maria Di Brisco & Enea Giuseppe Bongiorno & Aldo Goia & Sonia Migliorati, 2023. "Bayesian flexible beta regression model with functional covariate," Computational Statistics, Springer, vol. 38(2), pages 623-645, June.
    18. Ming Yin & Feiya Lu & Xingxuan Zhuo & Wangzi Yao & Jialong Liu & Jijiao Jiang, 2024. "Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 344-365, March.
    19. Guo, Lin & Zhang, Ben, 2019. "Mining structural influence to analyze relationships in social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 301-309.
    20. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).

    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:gam:jlands:v:11:y:2022:i:10:p:1856-:d:948392. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.