Abstract
Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained from LSM do not reflect the contribution of irrigation to the soil moisture estimates. However, the satellite retrievals of soil moisture estimates do register the signature of the irrigation effects. It is suggested that the soil moisture estimates obtained from LSM may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites. The present study evaluates the improvement of soil moisture estimates obtained from Noah LSM by ingesting them with the satellite-derived Advanced Scatterometer (ASCAT) soil moisture retrievals over the Indian domain for the year 2012. The above ingesting of soil moisture estimates is performed using the land information system (LIS). The improved soil moisture estimates are validated with the in situ India Meteorological Department (IMD) soil moisture observations and also with the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis data. The percentages of grid points over the Indian domain where the improvement parameter shows positive values are 59.14% (winter), 69.17% (pre-monsoon), 43.59% (monsoon), and 77.53% (post-monsoon). Furthermore, the forecast impact parameter also indicates the positive impact of data assimilation. Also, 12 of the 22 stations show reduced RMSE soil moisture error after data assimilation is performed while only 6 of the 22 stations show higher correlation coefficient in soil moisture without data assimilation, when validated with the in situ IMD soil moisture observations. The study has also evaluated the irrigation impact of ASCAT in the assimilated soil moisture using triple collocation (TC) method. For the TC analysis, the model-based Global Land Data Assimilation System (GLDAS), Catchment Land Surface Model (CLSM), and MERRA (Modern-Era Retrospective analysis for Research and Applications) Land data set together with soil moisture model outputs with and without ASCAT assimilation are used to calculate the error and correlation coefficient of each of the two set of triplets. The results of the TC analysis further conclusively show the positive impact of irrigation effects in the ASCAT-assimilated soil moisture model output.
Highlights
Soil moisture plays a vital role in the exchange of moisture and energy fluxes at the landatmosphere boundary
It is suggested that the soil moisture estimates obtained from Land Surface Models (LSMs) may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites (Kumar et al, 2015)
It is well known that Noah LSM’s soil moisture estimate is completely devoid of any effects of irrigation while the satellite derived soil moisture does register the signature of the irrigation effects
Summary
Soil moisture plays a vital role in the exchange of moisture and energy fluxes at the landatmosphere boundary. It is suggested that the soil moisture estimates obtained from LSM may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites (Kumar et al, 2015). The above assimilation would contribute to reduced uncertainties in the LSM soil moisture estimates to yield a much improved soil moisture estimate. Such studies that ingest soil moisture obtained from LSM with satellite retrievals exist in the literature (Kumar et al, 2015; Nair and Indu, 2019), there are very few instances where such studies have been carried out over India
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