Correcting and Improving the Performance of Soil Moisture-based Product (SM2RAIN-ASCAT) Over Iran at Daily and Monthly Time Scales

Document Type : Research Paper

Authors

1 Water Engineering Dept./ Imam Khomeini International University, Qazvin, Iran

2 Assistant Professor in Water Engineering Department/ Imam Khomeini International University

3 Director of Research, Hydrology Group of the Research Institute for Geo-Hydrological Protection, Perugia, Italy

Abstract

Estimating precipitation plays an important role in different studies, including meteorological, hydrological, flood simulation, and drought monitoring. SM2RAIN-ASCAT is the newest attempt to estimate precipitation using surface soil moisture and inverse solving the soil-water balance equation. This research addressed the performance of the SM2RAIN-ASCAT dataset over diverse climate regions of Iran at daily and monthly time scales in 54 synoptic stations located in Iran (2007-2018). In addition, improving the efficiency of this product using Quantile Mapping bias correction method is another objective of this study. Results showed that the performance of SM2RAIN-ASCAT at the monthly time scale was better than the daily time scale, and in most parts of Iran, the correlation coefficient between observed and estimated datasets was relatively high. At the monthly time step, 67% of the studied stations had a CC value higher than 0.65. Moreover, findings indicated that this product tended to overestimate in Iran's north and west parts. Based on the RMSE value at the monthly time scale, SM2RAIN-ASCAT performed well at extra-arid and arid regions compared to the Mediterranean and humid climate zones. Also, removing bias from the raw dataset increased the efficiency of SM2RAN-ASCAT in most parts of the studied areas. Based on contingency table metrics, the bias-corrected dataset's skills in detecting rainy days and false alarm ratio (FAR) improved significantly. For instance, the FAR metric averagely improved 26 and 45 % at daily and monthly time scales, respectively. Finally, results indicated that SM2RAIN-ASCAT is a valuable dataset for estimating monthly precipitation, especially in arid-desert to extra-arid climates. The accuracy of this dataset increases using bias correction in different climates.

Keywords

References

Abdollahi, B., Hosseini-Moghari, S. M. and Ebrahimi, K. (2017). Assessment of Satellite Precipitation Data from TRMM 3B42RT V7 and CMORPH in Order to Estimate Precipitation in Gorganrood Basin-Iran. Iran-Watershed Management Science & Engineering, 11(36), 55–69. (In Farsi)
Amini, S., Azizian, A. and Daneshkar Arasteh, P. (2020). Improving the Performance of Global Rainfall Forecasting Systems in Different Climate Areas of Iran Using Quantile Mapping Method. Iranian Journal of Soil and Water Research, 51(9), 2275–2291. (In Farsi)
Aminyavari, S., Saghafian, B. and Delavar, M. (2018). Evaluation of TIGGE ensemble forecasts of precipitation in distinct climate regions in Iran. Advances in Atmospheric Sciences, 35(4), 457–468.
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., et al. (2015). PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69–83. https://doi.org/10.1175/BAMS-D-13-00068.1
Azizian, A. and Amini, S. (2020). The Effect of Climate and Topographic Conditions on the Performance of PERSIANN Family Products over Iran. Iran-Water Resources Research, 16(1), 86–101. (In Farsi). 
Borga, M. (2002). Accuracy of radar rainfall estimates for streamflow simulation. Journal of Hydrology, 267(1), 26–39.
Brocca, L, Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., et al. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119(9), 5128–5141.
Brocca, Luca, Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., et al. (2019). SM2RAIN-ASCAT (2007-2018): Global daily satellite rainfall data from ASCAT soil moisture observations. Earth System Science Data, 11(4), 1583–1601. https://doi.org/10.5194/essd-11-1583-2019
Gebremicael, T. G., Mohamed, Y. A., Zaag, P. van der, Gebremedhin, A., Gebremeskel, G., Yazew, E. and Kifle, M. (2019). Evaluation of multiple satellite rainfall products over the rugged topography of the Tekeze-Atbara basin in Ethiopia. International Journal of Remote Sensing, 40(11), 4326–4345. https://doi.org/10.1080/01431161.2018.1562585
Gupta, R., Bhattarai, R. and Mishra, A. (2019). Development of Climate Data Bias Corrector (CDBC) Tool and Its Application over the Agro-Ecological Zones of India. Water, 11(1102).
Hamill, T. M., Engle, E., Myrick, D., Peroutka, M., Finan, C., and Scheuerer, M. (2017). The U.S. national blend of models for statistical post processing of probability of precipitation and deterministic precipitation amount. Monthly Weather Review, 145(9), 3441–3463.
Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
Javanmard Ghasab, M., Delavar, M., and Morid, S. (2018). Medium-Term Forecast Evaluation of TIGGE Numerical Weather Prediction Models for Karun Basin. Iran-Water Resources Research, 14(3), 1–14. (In Farsi). 
Katiraie-Boroujerdy, P. S., Rahnamay Naeini, M., Akbari Asanjan, A., Chavoshian, A., Hsu, K., Sorooshian, S. (2020). Bias Correction of Satellite-Based Precipitation Estimations Using Quantile Mapping Approach in Different Climate Regions of Iran. Remote Sensing, 12 (13).
Kirchner, J. W. (2009). Catchments as simple dynamical systems: catchment characterization, rainfall-runoff modeling, and doing hydrology backward. Water Resources Research, 45(2).
Kucera, P. A., Ebert, E. E., Turk, F. J., Levizzani, V., D., K., Tapiador, F. J., et al. (2013). Precipitation from space: Advancing earth system science. Bulletin of the American Meteorological Society, 94(3), 365–375.
Maggioni, V. and Massari, C. (2018). On the performance of satellite precipitation products in riverine flood modeling: A review. Journal of Hydrology, 558, 214–224.
Mohsan, M., Acierto, R. A., Kawasaki, A. and Zin, W. W. (2018). Preliminary assessment of GPM satellite rainfall over Myanmar. Journal of Disaster Research, 13(1), 22–30. https://doi.org/10.20965/jdr.2018.p0022
Owusu, C., Adjei, K. A., and Odai, S. N. (2019). Evaluation of Satellite Rainfall Estimates in the Pra Basin of Ghana. Environmental Processes, 6(1), 175–190. https://doi.org/10.1007/s40710-018-0344-1
Panofsky, H. A., and Brier, G. W. (1965). Some Applications Of Statistics to Meteorology. Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences.
Pearson, K. (1896). Mathematical Contributions to the Theory of Evolution.–On a Form of Spurious Correlation Which May Arise When Indices Are Used in the Measurement of Organs. Proceedings of the Royal Society of London, 60, 489–498.
Rahimi, J., Ebrahimpour, M., and Khalili, A. (2013). Spatial changes of extended De Martonne climatic zones affected by climate change in Iran. Theoretical and Applied Climatology, 112(3–4), 409–418.
Shabanpour, F., Bazrafshan, J. and Araghinejad, S. (2020). Evaluation of the Effect of Bias Correction Methods on the Skill of Seasonal Precipitation Forecasts of CFSv2 Climate Model. Iranian Journal of Soil and Water Research.
Stampoulis, D. and Anagnostou, E. (2012). Evaluation of global satellite rainfall products over Continental Europe. Journal of Hydrometeorology, 13(2), 588–603. https://doi.org/10.1175/JHM-D-11-086.1
Stanski, H. R., Wilson, L. J. and Burrows, W. R. (1989). Survey of Common Verification Methods in Meteorology (2nd ed.). World Meteorological Organization.
Taromi, M., Azizian, A. and Brocca, L. (2020). Estimation of Precipitation Using Satellite-based Surface Soil Moisture (SSM) in Semi-Arid and Humid Regions of Iran. Iranian Journal of Soil and Water Research, 51(6), 1427-1440. (In Farsi). 
Themeßl, M. J., Gobiet, A. and Heinrich, G. (2012). Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climate Change, 112, 449–468.
 
 
Iranian Journal of Soil and Water Research
Volume 52, Issue 4
June 2021
Pages 917-932

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