scholarly journals L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records

2021 ◽  
Vol 13 (13) ◽  
pp. 2480
Author(s):  
Robin van der Schalie ◽  
Mendy van der Vliet ◽  
Nemesio Rodríguez-Fernández ◽  
Wouter A. Dorigo ◽  
Tracy Scanlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Passive Microwave ◽  
Climate Data ◽  
Land Surface Models ◽  
Surface Models ◽  
L Band ◽  
The Impact ◽  
Microwave Sensors

The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.

L-band soil moisture retrievals using microwave based temperature and filtering. Towards model-independent climate data records

2021 ◽  
Author(s):  
Robin van der Schalie ◽  
Mendy van der Vliet ◽  
Nemesio Rodríguez-Fernández ◽  
Wouter Dorigo ◽  
Tracy Scanlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Historical Record ◽  
Passive Microwave ◽  
Climate Data ◽  
Mean Values ◽  
Model Independent ◽  
L Band ◽  
And Function ◽  
The Impact

<p>The CCI Soil Moisture dataset (CCI SM, Dorigo et al., 2017) is the most extensive climate data record (CDR) of satellite soil moisture to date and is based on observations from multiple active and passive microwave satellite sensors. It provides coverage all the way back to 1978 and is updated yearly both in terms of algorithm and temporal coverage. In order to maximize its function as a CDR, both long term consistency and (model-)independence are high priorities in its development. </p><p>Two important satellite missions integrated into the CCI SM are the ESA Soil Moisture and Ocean Salinity mission (SMOS, Kerr et al., 2010) and the NASA Soil Moisture Active Passive mission (SMAP, Entekhabi et al., 2010). These missions distinguish themselves with their unique L-band (1.4 GHz) radiometers, which are theoretically more suitable for soil moisture retrieval than the prior available higher frequencies like C- X- and Ku-band (6.9 to 18.0 GHz). </p><p>However, these L-band missions are lacking onboard sensors for observations from higher frequencies Ku-, K- and Ka-band, which are normally used within the Land Parameter Retrieval Model (Owe et al., 2008), the baseline algorithm for passive microwave retrievals within the CCI SM, for retrieving the effective temperature (Holmes et al., 2009) and providing filters for snow/frozen conditions (Van der Vliet et al., 2020). Therefore, the retrievals from the current L-band missions make use of temperature and filters derived from global Land Surface Models (LSM, Van der Schalie et al., 2016). For a CDR that should function as an independent climate benchmark, this is a strong disadvantage.</p><p>Within this study the aim is to evaluate the impact of replacing the LSM based input for L-band soil moisture retrievals with one that comes from passive microwave observations. We use an inter-calibrated dataset existing of 6 different sensors that cover the complete SMOS and SMAP historical record (and further), consisting of AMSR2, AMSR-E, TRMM, GPM, Fengyun-3B and Fengyun-3D. These satellites are merged together using a minimization function that also penalizes errors in the Microwave Polarization Difference Index (MPDI) for a higher level of stability compared to using traditional linear regressions.</p><p>As currently the 6 am L-band retrievals are seen as the most reliable, and are currently the only ones used within the CCI, the main focus will be on the effects of using the 1:30 am observations from the inter-calibrated dataset as input. However, to make the method also applicable for daytime observations, the 6 pm retrievals have also been tested using an average of 1:30 pm and 1:30 am (next day) observations.   </p><p>This evaluation will provide an overview of the differences, giving insight on how this affects coverage, mean values, standard deviations and their inter-correlation. Secondly, we will test the resulting quality against both in situ observations and ERA5. A similar performance of this new dataset shows this is a good way to standardize input on temperature and filtering within the CCI SM, further improving its consistency and function as a model-independent CDR.</p>

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

Groundwater recharge estimated by land surface models: an evaluation in the conterminous U.S.

2020 ◽  
Author(s):  
Bailing Li ◽  
Matthew Rodell ◽  
Christa Peters-Lidard ◽  
Jessica Erlingis ◽  
Sujay Kumar ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Pacific Northwest ◽  
Land Surface ◽  
Land Surface Models ◽  
Deep Soil ◽  
Flow Models ◽  
Surface Models ◽  
Systematic Biases ◽  
Preferential Flows

AbstractEstimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

scholarly journals A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

2012 ◽  
Vol 16 (11) ◽  
pp. 3973-3988 ◽  
Author(s):  
M. Guimberteau ◽  
A. Perrier ◽  
K. Laval ◽  
J. Polcher
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Capacity ◽  
Land Surface Models ◽  
Data Set ◽  
Wetness Index ◽  
Surface Models ◽  
The Us ◽  
Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations.

scholarly journals A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over US and Illinois with SECHIBA forced by NLDAS

2012 ◽  
Vol 9 (4) ◽  
pp. 5039-5083
Author(s):  
M. Guimberteau ◽  
A. Perrier ◽  
K. Laval ◽  
J. Polcher
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Capacity ◽  
Comprehensive Approach ◽  
Land Surface Models ◽  
Data Set ◽  
Wetness Index ◽  
Surface Models ◽  
Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and mesoscale. The model is forced by NLDAS data set at eighth degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters are performed and show that the roots extraction parameter has the largest impact on soil moisture, others parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new computation of evapotranspiration including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the simulation of soil moisture when this effect is taken into account. Finally, uncertainties in forcing precipitation to simulate a realistic soil moisture are addressed and it is shown that soil moisture observations can be rather different depending on the method to measure field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations. Excepted for one station, the monthly mean correlation is around 0.9 between observation and simulation.

Sensitivity of wind and soil moisture to the land surface component in 30-year continuous simulations over the Iberian Peninsula

2021 ◽  
Author(s):  
Elena García-Bustamante ◽  
J. Fidel González-Rouco ◽  
Jorge Navarro ◽  
Ana Palomares Losada ◽  
Almudena García-García ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hot Spots ◽  
Land Surface ◽  
Soil Moisture Content ◽  
Anthropogenic Emissions ◽  
Soil Processes ◽  
Land Surface Models ◽  
Surface Models ◽  
The Impact ◽  
Impacts Of Climate Change

<p>During the last three decades significant trends compatible with a changing climate due to increased anthropogenic emissions have been observed. Changes are remarkable not only at global but also at regional scales. The Euro-Mediterranean sector has been identified as one of the hot spots potentially subject to critical impacts of climate change already manifest. In this work several long continuous (non re-initialized) WRF simulations at a high resolution (9 km) over peninsular Iberia that make use of a set of different land surface schemes have been performed. In doing so we categorize the impact of using alternate land surface models in long (30 years) continuous simulations since only such running approach allows to preserve the memory of the soil processes. Thus, we explore changes in the soil moisture content aiming at the detection of plausible evidences of drying trends, specially for the south of Spain. In addition we investigate a 30-year climatology of wind speed in the search of a potential stilling phenomenon already documented over several European and worldwide regions.</p>

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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