Spatially-Varying Statistical Soil Moisture Profile Model by Coupling Memory and Forcing using Hydrologic Soil Groups to Estimate Vertical Soil Moisture Profile

<p>This study reports a recently developed spatially varying Statistical Soil Moisture Profile (SSMP) model, which is able to impart spatial transferability and to couple memory and forcing to estimate the vertical Soil Moisture Content (SMC) profile (Pal et al., 2016; Pal and Maity, 2018). The availability of satellite estimated surface soil moisture maps (Pal et al., 2017) and the potential of the coupling approach to integrate it, form the motivation to develop the SSMP model to prepare a fine resolution, 3-dimensional soil moisture profile for large areas by incorporating spatial transferability. The SSMP model uses only surface soil moisture (0-5 cm) values and incorporates the Hydrologic Soil Group (HSG) information to ensure the spatial transferability by capturing the spatial variations of vertical SMC profile with change in soil properties. The extensive daily soil moisture data for the study is obtained from 171 stations from three networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information of all the selected stations are extracted from the Web Soil Survey (WSS) database. The justified incorporation of the HSGs can be observed during model development through the forcing coefficient values. The values of forcing coefficients are higher for HSG A having a high infiltration rate whereas, the same is lower for HSG D with lower rate of infiltration. Thus, the forcing coefficients are at least able to differentiate the infiltration trend through a comparative analysis within the HSGs. The efficacy of the proposed SSMP model in terms of spatial transferability (as claimed) is evaluated by applying it to the new locations of the corresponding HSG. The observed model performances during model development as well as spatial validation are promising for all four depth pairs (5-10, 10-20, 20-51 and 51-102 cm) of all four HSGs considering the complexity involved in the problem statement itself. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the proposed SSMP model to develop a vertical soil moisture profile map over a large area.</p><p><strong>References:</strong></p><p>Pal M., Maity, R. and Dey, S., (2016), Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing, Water Resources Management, Springer, 30(6), 1973-1986, DOI: 10.1007/s11269-016-1263-4.</p><p>Pal M., Rajib Maity, M. Suman, S.K. Das, P. Patel and H.S. Srivastava (2017), Satellite based   Probabilistic Assessment of Soil Moisture using C-band Quad-polarized RISAT 1 data, IEEE Transactions on Geoscience and Remote Sensing, 55(3), 1351-1362, DOI: 10.1109/TGRS.2016.2623378.</p><p>Pal, M., and Maity, R. (2018), Development of a Spatially-Varying Statistical Soil Moisture Profile Model by Coupling Memory and Forcing using Hydrologic Soil Groups, Journal of Hydrology, Elsevier, 570 (2019), 141-155, https://doi.org/10.1016/j.jhydrol.2018.12.042.</p><p> </p><p> </p><p> </p>