No abstract

No abstract

This study focuses on the probabilistic estimation of monthly soil moisture variation by considering (a) the influence of hydrometeorological forcing to model the temporal variation and (b) the information of Hydrological Soil Groups (HSGs) and Agro-Climatic Zones (ACZs) to capture the spatial variation. The innovative contributions of this study are: (i) development of a Combined Hydro-Meteorological (CHM) index to extract the information of different influencing hydrometeorological variables, (ii) consideration of soil-hydrologic characteristics (through HSGs) and climate regime-based zoning for agriculture (through ACZs), and (iii) quantification of uncertainty range of the estimated soil moisture. Usage of Supervised Principal Component Analysis (SPCA) in the development of the CHM index helps to eliminate the ''curse of dimensionality,'' typically arises in the multivariate analysis. The usage of SPCA also ensures the maximum possible association between the developed CHM index and soil moisture variation. The association between these variables is modeled through their joint distribution which is obtained by using the theory of copula. The proposed approach is also spatially transferable, since the information on HSGs and ACZs is considered. The ''leave-one-out'' cross-validation (LOO-CV) approach is adopted for stations belong to a particular HSG to examine the spatial transferability. The simulated soil moisture values are also compared with a few existing soil moisture data sets, derived from different Land Surface Models (LSMs) or retrieved from different satellite-based missions. The potential of the proposed approach is found to be promising and even applicable to crop land also, though with a lesser degree of efficiency as compared to bare land conditions.

Soil moisture is an important parameter in hydrometeorological as well as terrestrial geochemical processes. Near surface soil moisture is found to be critical for crop yield, occurrence of drought, soil erosion, regional weather prediction etc. However, in situ measurement of this important variable is difficult because of its high spatial and temporal variability. Variability of soil moisture can be attributed to heterogeneity in soil properties and distribution of hydrometeorological factors like precipitation, temperature, relative humidity etc. In this article, a hydrometeorological approach to probabilistically simulate soil moisture, at the monthly scale using a combined hydrometeorological (CHM) index, is proposed. A principal component analysis (PCA)-based approach is adopted to derive the CHM index from several meteorological variables. The joint probability distribution between CHM index and soil moisture is determined by a bivariate copula function. The proposed model is able to estimate soil moisture along with the quantification of associated uncertainty for a new location having a hydrometeorological data set and information on predominant soil type at that location. Simulated soil moisture is compared with soil moisture simulated by H96 Climate Prediction Center (CPC) model, which is based on the leaky bucket model. Advantages of proposed model for 10 soil moisture-monitoring stations in India are discussed.

An application of a proposed hydrometeorological approach for probabilistic simulation of soil moisture is carried out. The time series of in-situ soil moisture and meteorological variables at monthly scale from a few monitoring stations having different soil-hydrologic properties across India are utilized. Preliminary investigation with both precipitation and near-surface air-temperature as meteorological variables to establish that the strength of association between soil moisture and precipitation is more significant as compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in-situ observed soil moisture, CPC model output and with soil moisture data of the Climate Change Initiative (CCI) project. The parameter of the developed model is linked to the soil-hydrologic characteristics through Hydrologic Soil Group (HSG) classification. Higher values of model parameter (dependence parameter (θ) for the selected copula) correspond to HSG A and B having higher soil porosity, whereas, lower values correspond to HSG B and C having lower soil porosity.