Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: A. Celeste Saulo x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All Modify Search
Pablo C. Spennemann
,
Juan A. Rivera
,
Marisol Osman
,
A. Celeste Saulo
, and
Olga C. Penalba

Abstract

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.

Full access
Pablo C. Spennemann
,
Juan A. Rivera
,
A. Celeste Saulo
, and
Olga C. Penalba

Abstract

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.

Full access