Trend analysis and variability of satellite-based soil moisture data for the Lower Bhavani basin, Tamil Nadu using Google Earth Engine
Article Main
Abstract
Soil moisture is a significant hydrological component that is dynamic in nature. The variation in soil moisture in the basin scale would affect the vegetation, ecology and environment. Soil moisture trend analysis aids in providing the variation of soil moisture over the basin. The present study aimed to analyse the soil moisture trend in Lower Bhavani basin, Tamil Nadu from 2003-2022. Satellite-based soil moisture Global Land Data Assimilation System (GLDAS) data was extracted from the Google Earth Engine (GEE) platform to analyse the variation and trend over the period of time. The highest and lowest soil moisture was observed during monsoon and summer months and its percentage variation was studied. Using Man-Kendall test and Sen’s slope, trend analysis was calculated for two decades (2003-2012 and 2013-2022). In 2003-2012, an increasing trend of soil moisture was observed during winter (October to February); from 2013-2022, an increasing trend was observed during both winter (October to February) and monsoon seasons (June to September). The remaining season did not follow any trend, and there was no decreasing trend in soil moisture. The trend analysis of the study will help to monitor and manage the environmental system across the Lower Bhavani basin.
Article Details
Article Details
GLDAS, Google Earth Engine, Soil moisture, Trend analysis
Brown, I., Poggio, L., Gimona, A. & Castellazzi, M. (2011). Climate change, drought risk and land capability for agriculture: implications for land use in Scotland. Regional Environmental Change, 11(3), 503-518. https://doi.org/10.1007/s10113-010-0163-z.
Craig, P. P., Gadgil, A. & Koomey, J. G. (2002). What can history teach us? A retrospective examination of long-term energy forecasts for the United States. Annual Review of Energy and the Environment, 27(1), 83-118.
Danneberg, J. (2012). Changes in runoff time series in Thuringia, Germany–Mann-Kendall trend test and extreme value analysis. Advances in Geosciences, 31, 49-56. https://doi.org/10.5194/adgeo-31-49-2012.
Dawood, M. (2017). Spatio-statistical analysis of temperature fluctuation using Mann–Kendall and Sen’s slope approach. Climate dynamics, 48(3-4), 783-797. https://doi.org/10.1007/s00382-016-3110-y.
Djebou, D. C. S. & Singh, V. P. (2015). Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy. Ecological Modelling, 309, 10-21. https://doi.org/10.1016/j.ecolmodel.2015.03.022.
Fang, H., Beaudoing, H. K., Teng, W. L. & Vollmer, B. E. (2009). Global Land data assimilation system (GLDAS) products, services and application from NASA hydrology data and information services center (HDISC). In ASPRS 2009 Annual Conference.
González-Zamora, Á., Almendra-Martín, L., de Luis, M. & Martínez-Fernández, J. (2021). Influence of soil moisture vs. climatic factors in Pinus halepensis growth variability in Spain: A study with remote sensing and modeled data. Remote Sensing, 13(4), 757. https://doi.org/10.3390/rs13040757.
Janani, N., Kannan, B., Nagarajan, K., Thiyagarajan, G. & Duraisamy, M. R. (2023). Soil moisture mapping for different land-use patterns of lower Bhavani river basin using vegetative index and land surface temperature. Environment, Development and Sustainability, 1-17. https://doi.org/10.1007/s10668-022-02896-1.
Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D., Seyfried, M., ... & Du, J. (2010). Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing, 48(12), 4256-4272.
Kendall, M.G., (1975) Rank correlation methods. Griffin, London.
Kim, S., Paik, K., Johnson, F. M. & Sharma, A. (2018). Building a flood-warning framework for ungauged locations using low resolution, open-access remotely sensed surface soil moisture, precipitation, soil, and topographic information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 375-387.
Kumar, K. A., Reddy, G. O., Masilamani, P., Turkar, S. Y. & Sandeep, P. (2021). Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites. Advances in Space Research, 67(1), 298-315. https://doi.org/10.1016/j.asr.2020.10.003.
Liu, Y., Liu, Y. & Wang, W. (2019). Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis. Remote Sensing of Environment, 220, 1-18. https://doi.org/10.1016/j.rse.2018.10.026.
Mallick, J., Talukdar, S., Alsubih, M., Salam, R., Ahmed, M., Kahla, N. B. & Shamimuzzaman, M. (2021). Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theoretical and Applied Climatology, 143, 823-841. https://doi.org/10.1007/s00704-020-03448-1.
Mondal, A., Kundu, S. & Mukhopadhyay, A. (2012). Rainfall trend analysis by Mann-Kendall test: A case study of north-eastern part of Cuttack district, Orissa. International Journal of Geology, Earth and Environmental Sciences, 2(1), 70-78.
Nyikadzino, B., Chitakira, M. & Muchuru, S. (2020). Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic. Physics and Chemistry of the Earth, Parts A/B/C, 117, 102870. https://doi.org/10.1016/j.p ce.2020.102870.
Rajendran, S. (2014). Drought Mitigation in Tamil Nadu. Economic and Political weekly. 49(25).
Sen, P. K. (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat As, 63, 1379–1389.
Yadav, R., Tripathi, S. K., Pranuthi, G. & Dubey, S. K. (2014). Trend analysis by Mann-Kendall test for precipitation and temperature for thirteen districts of Uttarakhand. Journal of Agrometeorology, 16(2), 164-171. https://doi.org/10.54 386/jam.v16i2.1507.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) © Author (s)
