Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

Nair, Akhilesh S.; Indu, J.

doi:10.3390/rs8120976

Cited by 28 publications

(13 citation statements)

References 63 publications

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“…The land surface model (LSM) simulations also provide estimates of the GW storage. However, these model simulations only represent the naturally occurring variability (Nair & Indu, 2016, 2018, 2019) in the GW storage. In this study, we utilize the LSM estimates of GW storage to obtain the estimate of the nonclimatic contribution to GW variability.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Groundwater Sustainability and Identifying Factors Inducing Groundwater Depletion in India

Nair

Indu

2021

Geophysical Research Letters

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The alarming decline in groundwater (GW) storage threatens the sustainable development of the water‐energy‐food linkages of a country. India has experienced severe dry spells during the years 2002, 2004, 2009, and 2012. However, the relative contribution of factors affecting GW depletion (such as variability in precipitation rate and groundwater extraction) still remains unknown. Here, we first evaluate the current GW condition by implementing reliability, resilience, and vulnerability analysis on the 5,988 monitoring well observations spanning over a period of two decades. Our study further improves the existing studies by isolating the quantity of GW depletion due to precipitation and nonprecipitation (GW extraction) factors using a multivariate regression model. Our study indicates that the nonprecipitation factor is most responsible for GW depletion in North India at a rate of 3.5 cm per year.

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Section: Methodsmentioning

confidence: 99%

Assessment of Groundwater Sustainability and Identifying Factors Inducing Groundwater Depletion in India

Nair

Indu

2021

Geophysical Research Letters

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“…The ISMN has frequently been used to asses the impact of assimilating satellite observations into hydrological models (Khaki et al, 2019;Nair et al, 2020a;Gruber et al, 2015;Shin et al, 2016;Li et al, 2020b;Wang et al, 2020b), land surface models (Nair and Indu, 2016;Zhao and Yang, 2018;Nair et al, 2020b) and carbon models (Scholze et al, 2016). On a more methodological level, Zhang et al (2019a) assessed a new data assimilation scheme against ISMN observations.…”

Section: Drought Monitoringmentioning

confidence: 99%

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Dorigo¹

2021

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In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a Ground-based soil moisture measurements of land surface variables are indispensable in the process of developing, validating, and advancing spatially contiguous data sets derived from satellites or models (Loew et al., 2017;Gruber et al., 2020). Although the first systematic measurements of soil moisture started well before the satellite era in the former Soviet Union to support agricultural decision making (Robock et al., 2000), it was not until the early 2000s that soil moisture monitoring networks started being widely established as part of hydrological and meteorological observing capacities. Particularly, the launch of the Soil Moisture Ocean Salinity (SMOS) mission of the European Space Agency (ESA) in 2009 (Kerr et al., 2016), and the launch of the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA) in 2015 (Entekhabi et al., 2010), boosted the establishment of new research networks (Colliander et al., 2017).While all networks are a valuable asset for assessing the skill of soil moisture products under various conditions and scales, their usage is hampered by the diversity of sensors, data formats, quality control, and accessibility mechanisms. The need of bringing together and harmonising available soil moisture data was recognised by the international soil moisture community and expedited by the Global Energy and Water cycle Exchanges (GEWEX) project of the World Climate Research Programme (WCRP) with support of the Commission on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observation (GEO). In the advent of the SMOS mission, ESA decided to provide the financial impetus to establish a global reference database of in situ soil moisture measurements for the purpose of satellite product development and validation. As a result, the International Soil Moisture Network (ISMN) went online in 2010 (Dorigo et al., 2011b, a).The primary objective of the ISMN is to collect in situ soil moisture data sets shared by various data organisations on a voluntary basis and make them available in a harmonized format through a centralised free and open web portal (ismn.earth).While 10 years after its launch the core objective of the ISMN remains valid, its functionality has expanded since then. This new functionality includes the integration of advanced quality control methods (Dorigo et al., 2013), the provision of additional metadata and ancillary variables (e.g., precipitation, soil and air temperature), ongoing automation, the ...

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“…The output product includes volumetric SM of the top surface layer (1-5 cm) alongside with quality information and metadata. Some examples of SMOPS applications in SM assimilation into land surface models include Nair and Indu (2016) and Yin et al (2015Yin et al ( , 2019.…”

Section: B Soil Moisture Operational Products Systemmentioning

confidence: 99%

Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

Gavahi

Abbaszadeh

Moradkhani

et al. 2020

Journal of Hydrometeorology

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Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed data, namely SMOPS, and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm, was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the Southeastern United States. The model-based estimates are then compared against the corresponding US Drought Monitoring System archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents, however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM-DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.

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Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture

Cited by 28 publications

References 63 publications

Assessment of Groundwater Sustainability and Identifying Factors Inducing Groundwater Depletion in India

Assessment of Groundwater Sustainability and Identifying Factors Inducing Groundwater Depletion in India

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Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

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