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Short and Medium Range Forecast of Soil Moisture for the Different Climatic Regions of India Using Temporal Networks

Author

Listed:
  • Riya Dutta

    (Indian Institute of Technology Kharagpur)

  • Rajib Maity

    (Indian Institute of Technology Kharagpur)

  • Parul Patel

    (Indian Space Research Organization)

Abstract

Spatio-temporal evolution of soil moisture is a complex process and controlled by several factors including hydro-meteorological forcings. This study borrows a recently developed concept of temporal networks to capture the time-varying association between hydro-meteorological forcings and spatio-temporal evolution of soil moisture. Climate change and dynamic terrestrial environment cause slow but continuous change in the characteristics of hydro-meteorological forcings leading to variation in spatio-temporal distribution of soil moisture. Keeping this in the focus of the study, temporal networks based time-varying modelling framework is adapted for one-month to one-season (three-months) in advance prediction of monthly soil moisture for entire Indian mainland. Results indicate that the association among the hydro-meteorological forcings varies with both space and time. With the increase in prediction lead-time, the strength of association with the variables, such as pressure, wind and temperature, decreases and that with the variables like leaf area index remains informative. Among different seasons, the model shows superior performance for the monsoon and post monsoon periods. Next, the soil moisture based extremes are assessed by utilizing two deficit indices and two wetness indices. The model performance is highly satisfactory but varies over space and seasons, with a marginally better performance for the wetness indices. Overall, given the vast spatial extent of the Indian mainland, the proposed model performs robustly for almost all the climatic regions and may be promising for other parts of the world as well.

Suggested Citation

  • Riya Dutta & Rajib Maity & Parul Patel, 2022. "Short and Medium Range Forecast of Soil Moisture for the Different Climatic Regions of India Using Temporal Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 235-251, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03025-9
    DOI: 10.1007/s11269-021-03025-9
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    References listed on IDEAS

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    1. Andrea D’Aniello & Luigi Cimorelli & Luca Cozzolino & Domenico Pianese, 2019. "Correction to: The Effect of Geological Heterogeneity and Groundwater Table Depth on the Hydraulic Performance of Stormwater Infiltration Facilities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4669-4669, October.
    2. Fabio V. Difonzo & Costantino Masciopinto & Michele Vurro & Marco Berardi, 2021. "Shooting the Numerical Solution of Moisture Flow Equation with Root Water Uptake Models: A Python Tool," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2553-2567, June.
    3. Gokmen Tayfur & Luca Brocca, 2015. "Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3519-3533, August.
    4. Andrea D’Aniello & Luigi Cimorelli & Luca Cozzolino & Domenico Pianese, 2019. "The Effect of Geological Heterogeneity and Groundwater Table Depth on the Hydraulic Performance of Stormwater Infiltration Facilities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1147-1166, February.
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    Cited by:

    1. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.

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