Soil moisture evaluation over the Argentine Pampas using models, satellite estimations and in-situ measurements

Spennemann, Pablo C.; Long, María Elena Fernández; Gattinoni, Natalia Noemí; Cammalleri, Carmelo; Naumann, Gustavo

doi:10.1016/j.ejrh.2020.100723

Cited by 23 publications

(10 citation statements)

References 26 publications

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“…Several types of soil moisture datasets as the model output are used in previous studies (Qin et al, 2009;Chen et al, 2016), such as Global Land Data Assimilation System (GLDAS; Rodell et al, 2004), Coupled Model Intercomparison Project phase 5 (CMIP5; Berg et al, 2017;Feng et al, 2017), and various reanalysis data sets (e.g. ERA-Interim and MERRA V2) (Modanesi et al, 2020;Spennemann et al, 2020;Zhou et al, 2020). In GLDAS, Land Surface Models (LSMs) and hydrological models were driven by meteorological forcing to simulate soil moisture of multilayers with different depths (Bi et al, 2016;Yuan and Quiring, 2017).…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Chen

et al. 2021

Front. Earth Sci.

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Under the global warming, as the typical arid region of Central Asia, the Xinjiang Uygur Autonomous Region (Xinjiang) has been experienced the remarkable warming and increased precipitation based on large previous studies. The arid and semiarid ecosystem of Xinjiang is very sensitive and vulnerable to climate change and water resource variations. However, the sparse and highly unevenly distributed in-situ stations in this region provide limited data for understanding of the soil moisture variations. In this study, the spatial and temporal changes and variations of soil moisture were explored at annual and seasonal time scales during the period of 2000–2017. The soil moisture data are from the Global Land Data Assimilation System (GLDAS) models, including four GLDAS 1 models: CLM, Mosaic, VIC and Noah 2.7 and one GLDAS 2.1 model: Noah 3.3. Major results show that 1) Noah 3.3 and VIC have the significant positive trends of annual soil moisture with the values of 2.64°mm/a and 0.98°mm/a. The trend of CLM is significant negative. The other two models Mosaic and Noah 2.7 have the weak positive trends. The temporal variations of seasonal soil moisutre are similar the annual soil moisture for each of the model. 2) For the spatial characteristics of the soil mositure variations, CLM displays the negative trends over large part of Xinjiang. Mosaic and VIC have the similar spatial characteristics of the linear trends. Noah 3.3 has the significant positive trends over almost Xinjiang which is different with Noah 2.7. All the five models have the positive trends over KLM. Our results have a better understanding of the soil moisture variations across Xinjiang, and they also enhance the reconginzing of the complex hydrological circulation in the arid regions.

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Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Chen

et al. 2021

Front. Earth Sci.

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“…This model has been proven to have a very good fit with observed data (Spennemann et al ., 2020; Veliz et al ., 2016) and has been used in Argentina for the estimation of soil moisture with different objectives (Pántano et al ., 2017; Pinto et al ., 2017; Penalba et al ., 2019).…”

Section: Methodsmentioning

confidence: 99%

“…In Argentina, Spennemann et al . (2020) have recently examined different LSMs and satellite estimations of soil moisture, including GLDAS‐Noah (GLDAS version 2.1 [Ek et al ., 2003; Rodell et al ., 2004]), ERA Interim‐TESSEL (van den Hurk et al ., 2000), GDO‐LISFLOOD (de Roo et al ., 2000) and the BHOA bucket model (for its acronym in Spanish: “Balance Hidrológico Operativo para el Agro”; Fernández‐Long et al ., 2012; Fernández‐Long, 2017). These authors found that, in general, the soil moisture estimations are able to represent the main characteristics of the local soil moisture variability.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the seasonal variation of soil moisture in Argentina

Fernandez-Long

Peretti

Carnelos

et al. 2020

Intl Journal of Climatology

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Soil moisture is an essential variable for both the agricultural production and the functioning of natural ecosystems, as well as one of the main variables regulating the climate system. Like precipitation, soil moisture has a marked annual cycle that depends mainly on the behaviour of precipitation and evapotranspiration. In this study, we developed a methodology that allows describing and characterizing the annual cycle of soil moisture in Argentina. For this purpose, we estimated soil moisture at 121 sites of Argentina by means of a simple hydrological balance (the BHOA, an acronym for its name in Spanish: "Balance Hidrológico Operativo para el Agro") and analysed its intra-annual variability by means of different indices. This methodology allowed recognizing five different annual behaviours, with a consistent geographical pattern throughout Argentina. The characterization was based mainly on the time of occurrence of the annual maximum and minimum values of soil moisture, and secondly by the annual soil moisture content and seasonality. The northwest and part of the centre of Argentina present the minimum soil moisture values in spring and the maximum ones in autumn, while the east and south present the minimum values in summer and the maximum ones in winter. In general, sites with higher soil water content were found to have lower seasonality and vice-versa.

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“…In Latin America, the "Sistema Integral Regional de Información Satelital (SIRIS)" is an international collaboration among the Inter-American Development Bank and the Space Agencies from Argentina, Bolivia, Chile, Ecuador, Mexico, Paraguay, Peru, and Uruguay [22] to provide satellite products over Latin America, including SMAP SM. However, very few agricultural validation sites in Latin America and the Caribbean have been included for validation [23], [12], [24], primarily, because of the lack of mid-and long-term and/or reliable datasets of in situ SM. Due to the importance of agriculture in the region [25], [26], [27], more long-term datasets describing agricultural regions of Latin America and the Caribbean are needed to improve SM estimates in the region.…”

Section: Introductionmentioning

confidence: 99%

Assessment of NASA SMAP Soil Moisture Products for Agricultural Regions in Central Mexico: An Analysis Based on the THEXMEX Dataset

Monsiváis-Huertero¹,

Constantino-Recillas

Hernandez-Sanchez

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate knowledge of soil moisture (SM) is crucial in hydrological, micrometeorological, and agricultural applications; however, the SM estimation is particularly challenging in agricultural regions due to high spatial variability and dynamic vegetation conditions. The need of information about SM conditions is even more evident for developing countries with limited monitoring infrastructure. Satellite SM products are a useful tool as proxy of SM conditions on the ground, but they need to be evaluated for specific regions. In this study, we assess the quality of the Soil Moisture Active Passive (SMAP) SM retrievals at 36,

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Soil moisture evaluation over the Argentine Pampas using models, satellite estimations and in-situ measurements

Cited by 23 publications

References 26 publications

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Temporal and Spatial Variations of Soil Moisture Over Xinjiang Based on Multiple GLDAS Datasets

Characterization of the seasonal variation of soil moisture in Argentina

Assessment of NASA SMAP Soil Moisture Products for Agricultural Regions in Central Mexico: An Analysis Based on the THEXMEX Dataset

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