This study aims to compare simulated soil moisture anomalies derived from different versions of the Global Land Data Assimilation System (GLDAS), the standardized precipitation index (SPI), and a new multisatellite surface soil moisture product over southern South America. The main motivation is the need for assessing the reliability of GLDAS variables to be used in the characterization of soil state and its variability at the regional scale. The focus is on the southeastern part of South America (SESA), which is part of the La Plata basin, one of the largest basins of the world, where agriculture is the main source of income. The results show that GLDAS data capture soil moisture anomalies and their variability, taking into account regional and seasonal dependencies and showing correspondence with other proxies used to characterize soil states. Over large portions of the domain, and particularly over SESA, the correlation with the SPI is very high, with the second version of GLDAS, version 2 (GLDAS-2 v2), exhibiting the highest values regardless of the season. Similar results were obtained by comparing the surface soil moisture anomalies from the GLDAS land surface model (LSM) against the satellite estimations for a shorter period of time. This work documents that the precipitation dataset used to force each LSM and the choice of the LSM are of major relevance for representing soil conditions in an adequate manner. The results are considered to support the use of GLDAS as an indicator of soil moisture states and for developing new soil moisture–monitoring indices that can be applied, for example, in the context of agricultural production management.

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ABSTRACT:The aim of this study is to identify regions of strong land surface -atmosphere coupling for the austral summer over South America. To accomplish this, a statistical methodology is applied to estimate the interactions of soil moisture with evapotranspiration and precipitation derived from the Global Land Data Assimilation System (GLDAS) dataset. Possible impacts of El Niño Southern Oscillation (ENSO) on the coupling strength are also examined. Particular emphasis is set over two sub-regions of interest: Southeastern South America (SESA) and the continental part of the South Atlantic Convergence Zone (SACZ). Positive and significant soil moisture-precipitation feedbacks are found over parts of SACZ and in the southern part of South America. Instead, significant negative feedback is found over SESA. The influence of ENSO over the soil moisture-precipitation coupling strength signal is evident over tropical regions. Plausible physical mechanisms involved in the land surface-atmosphere interactions, the influence of ENSO and that of precipitation persistence over extratropical regions on the results, are discussed. The implications of this analysis on monthly to seasonal forecast are also examined. Despite that this methodology cannot be used to establish a precise causal-effect relationship, this study gives a valuable first order approximation of land surface-atmosphere interactions over South America that complements pre-existing work.

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The importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socioeconomic losses, mainly in regions of large populations and where agriculture is a key value for the economies, such as southern South America (SSA). To improve the understanding of the performance and uncertainties of seasonal soil moisture and precipitation forecasts over SSA, this study aims to 1) perform a general assessment of the Climate Forecast System, version 2 (CFSv2), soil moisture and precipitation forecasts against observations and soil moisture simulations based on GLDAS, version 2.0; 2) evaluate the ability of CFSv2 to represent wet and dry events through the forecasted standardized precipitation index (SPI) and standardized soil moisture anomalies (SSMA); and 3) analyze the capability of a statistical methodology (merging observations and forecasts) in representing a severe drought event. Results show that both SPI and SSMA forecast skill are regionally and seasonally dependent. In general, a fast degradation of the forecasts skill is observed as the lead time increases, resulting in almost no added value with regard to climatology at lead times longer than 3 months. Additionally, a better performance of the SSMA forecasts is observed compared to SPI calculated using three months of precipitation (SPI3), with a higher skill for dry events against wet events. The CFSv2 forecasts are able to represent the spatial patterns of the 2008/09 severe drought event, although it shows crucial limitations regarding the identification of drought onset, duration, severity, and demise, considering both meteorological (SPI) and agricultural (SSMA) drought conditions.

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Abstract. Surface soil moisture (SSM) dry-downs have been employed to compare independent data sources on the dynamics of water in soils, including such remote sensing, land surface models and in-situ measurements, which are often difficult to contrast with standard methodologies. The soil drying approach summarizes the soil response to climate as well as surface conditions during a dry period. In this work it is estimated as the SSM e-folding decay, named as dry-down time scale. This is the first assessment over eastern Cordoba, Argentina, a region with a very high cultivated land fraction that was subject of important agricultural changes in the last decades. SMOS SSM product (derived from microwave measurements at L band) is validated with in-situ SSM measurements provided by the National Commission for Space Activities during 2012–2018. Both products agree in showing that the austral spring season has the largest number of dry-down events for the whole period. The dry-down time scale sensitivity to the chosen detection method as well as the data sampling frequency is larger in summer than in spring. A faster soil drying in SMOS than in In-situ SSM is found, likely as a consequence of the shallower sensing depth of the first. This dependency seems to be more important than the temporal sampling frequency in the SSM data.