scholarly journals Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland

2016 ◽  
Vol 20 (10) ◽  
pp. 3987-4004 ◽  
Author(s):  
Raghavendra B. Jana ◽  
Ali Ershadi ◽  
Matthew F. McCabe
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Water Cycle ◽  
Cosmic Ray ◽  
Moisture Distribution ◽  
Observation Data ◽  
Cold Temperatures ◽  
Intermediate Scale ◽  
Semi Arid

Abstract. Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale mismatch when compared to coarser-resolution satellite-based soil moisture or evaporation estimates. The Cosmic Ray Neutron Probe (CRNP) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here, we present a study to assess the utility of CRNP soil moisture observations in validating model evaporation estimates. The CRNP soil moisture product from a pasture in the semi-arid central west region of New South Wales, Australia, was compared to evaporation derived from three distinct approaches, including the Priestley–Taylor (PT-JPL), Penman–Monteith (PM-Mu), and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson's correlations, quantile–quantile (Q–Q) plots, and analysis of variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly 2 years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q–Q plots and ANOVA illustrate that the root-zone soil moisture represented by the CRNP measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were present, while SEBS model estimates displayed a disconnect from the soil moisture distribution in summers with long dry spells. Importantly, no single evaporation model matched the statistical distribution of the measured soil moisture for the entire period, highlighting the challenges in effectively capturing evaporative flux response within changing landscapes. One of the outcomes of this work is that the analysis points to the feasibility of using intermediate-scale soil moisture measurements to evaluate gridded estimates of evaporation, exploiting the independent, yet physically linked nature of these hydrological variables.

scholarly journals Examining the relationship between intermediate scale soil moisture and terrestrial evaporation within a semi-arid grassland

2016 ◽  
Author(s):  
Raghavendra B. Jana ◽  
Ali Ershadi ◽  
Matthew F. McCabe
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Water Cycle ◽  
Cosmic Ray ◽  
Moisture Distribution ◽  
Observation Data ◽  
Cold Temperatures ◽  
High Soil Moisture ◽  
Semi Arid

Abstract. Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale-mismatch when compared to coarser-resolution satellite-based soil moisture or evaporation estimates. The Cosmic Ray Soil Moisture Observing System (COSMOS) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here we present an examination of the links observed between COSMOS soil moisture retrievals and evaporation estimates over a pasture in the semi-arid central-west region of New South Wales, Australia. The COSMOS soil moisture product was compared to evaporation derived from three distinct approaches, including the Priestley-Taylor (PT-JPL), Penman-Monteith (PM-Mu) and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson’s Correlations, Quantile-Quantile (Q-Q) plots, and Analysis of Variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly two years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q-Q plots and ANOVA illustrate that the root-zone soil moisture represented by the COSMOS measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were present, while SEBS model estimates displayed a disconnect from the soil moisture distribution in summers with long dry spells. Importantly, no single evaporation model matched the statistical distribution of the measured soil moisture for the entire period, highlighting the challenges in effectively capturing evaporative flux response within changing landscapes.

Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

2020 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks-Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Land Surface ◽  
Root Zone ◽  
Cosmic Ray ◽  
Soil Parameters ◽  
Surface Model ◽  
Field Scale ◽  
Spatio Temporal

<p>Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.</p>

scholarly journals Cosmic ray neutrons provide an innovative technique for estimating intermediate scale soil moisture

2018 ◽  
Vol 114 (7/8) ◽  
Author(s):  
Thigesh Vather ◽  
Colin Everson ◽  
Michael Mengistu ◽  
Trenton Franz
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Cosmic Ray ◽  
Time Domain Reflectometry ◽  
Data Set ◽  
Intermediate Scale ◽  
Spatial Estimates ◽  
Set Up

Soil moisture is an important hydrological parameter, which is essential for a variety of applications, thereby extending to numerous disciplines. Currently, there are three methods of estimating soil moisture: ground-based (in-situ) measurements; remote sensing based methods and land surface models. In recent years, the cosmic ray probe (CRP), which is an in-situ technique, has been implemented in several countries across the globe. The CRP provides area-averaged soil moisture at an intermediate scale and thus bridges the gap between in-situ point measurements and global satellite-based soil moisture estimates. The aim of this study was to test the suitability of the CRP to provide spatial estimates of soil moisture. The CRP was set up and calibrated in Cathedral Peak Catchment VI. An in-situ soil moisture network consisting of time-domain reflectometry and Echo probes was created in Catchment VI, and was used to validate the CRP soil moisture estimates. Once calibrated, the CRP was found to provide spatial estimates of soil moisture, which correlated well with the in-situ soil moisture network data set and yielded an R2 value of 0.845. The use of the CRP for soil moisture monitoring provided reliable, accurate and continuous soil moisture estimates over the catchment area. The wealth of current and potential applications makes the CRP very appealing for scientists and engineers in various fields. Significance: • The cosmic ray probe provides spatial estimates of surface soil moisture at an intermediate scale of 18 hectares. • A single cosmic ray probe can replace a network of conventional in-situ instruments to provide reliable soil moisture estimates. • The cosmic ray probe is capable of estimating soil moisture in previously problematic areas (saline soil, wetlands, rocky soil). • Cosmic ray probes can provide data for hydro-meteorologists interested in land–atmosphere interactions. • The cosmic ray probe estimates can be promising for remote sensing scientists for product calibration and validation.

scholarly journals Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale

2014 ◽  
Vol 18 (11) ◽  
pp. 4363-4379 ◽  
Author(s):  
R. Rosolem ◽  
T. Hoar ◽  
A. Arellano ◽  
J. L. Anderson ◽  
W. J. Shuttleworth ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
High Frequency ◽  
Mixed Forest ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Data Availability ◽  
Surface Model ◽  
Moisture Profiles ◽  
Semi Arid

Abstract. Above-ground cosmic-ray neutron measurements provide an opportunity to infer soil moisture at the sub-kilometer scale. Initial efforts to assimilate those measurements have shown promise. This study expands such analysis by investigating (1) how the information from aboveground cosmic-ray neutrons can constrain the soil moisture at distinct depths simulated by a land surface model, and (2) how changes in data availability (in terms of retrieval frequency) impact the dynamics of simulated soil moisture profiles. We employ ensemble data assimilation techniques in a "nearly-identical twin" experiment applied at semi-arid shrubland, rainfed agricultural field, and mixed forest biomes in the USA. The performance of the Noah land surface model is compared with and without assimilation of observations at hourly intervals, as well as every 2 days. Synthetic observations of aboveground cosmic-ray neutrons better constrain the soil moisture simulated by Noah in root-zone soil layers (0–100cm), despite the limited measurement depth of the sensor (estimated to be 12–20cm). The ability of Noah to reproduce a "true" soil moisture profile is remarkably good, regardless of the frequency of observations at the semi-arid site. However, soil moisture profiles are better constrained when assimilating synthetic cosmic-ray neutron observations hourly rather than every 2 days at the cropland and mixed forest sites. This indicates potential benefits for hydrometeorological modeling when soil moisture measurements are available at a relatively high frequency. Moreover, differences in summertime meteorological forcing between the semi-arid site and the other two sites may indicate a possible controlling factor to soil moisture dynamics in addition to differences in soil and vegetation properties.

scholarly journals Assimilation of near-surface cosmic-ray neutrons improves summertime soil moisture profile estimates at three distinct biomes in the USA

2014 ◽  
Vol 11 (5) ◽  
pp. 5515-5558 ◽  
Author(s):  
R. Rosolem ◽  
T. Hoar ◽  
A. Arellano ◽  
J. L. Anderson ◽  
W. J. Shuttleworth ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Mixed Forest ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Moisture Profile ◽  
The Usa ◽  
Moisture Profiles ◽  
Semi Arid

Abstract. Aboveground cosmic-ray neutron measurements provide an opportunity to infer soil moisture at the sub-kilometer scale. Initial efforts to assimilate those measurements have shown promise. This study expands such analysis by investigating (1) how the information from aboveground cosmic-ray neutrons can constrain the soil moisture at distinct depths simulated by a land surface model, and (2) how changes in data availability (in terms of retrieval frequency) impact the dynamics of simulated soil moisture profiles. We employ ensemble data assimilation techniques in a "nearly-identical twin" experiment applied at semi-arid shrubland, rainfed agricultural field, and mixed forest biomes in the USA The performance of the Noah land surface model is compared without and with assimilation of observations at hourly intervals and every 2 days Synthetic observations of aboveground cosmic-ray neutrons better constrain the soil moisture simulated by Noah in root zone soil layers (0–100 cm) despite the limited measurement depth of the sensor (estimated to be 12–20 cm). The ability of Noah to reproduce a "true" soil moisture profile is remarkably good regardless of the frequency of observations at the semi-arid site. However, soil moisture profiles are better constrained when assimilating synthetic cosmic-ray neutrons observations hourly rather than every 2 days at the cropland and mixed forest sites. This indicates potential benefits for hydrometeorological modeling when soil moisture measurements are available at relatively high frequency. Moreover, differences in summertime meteorological forcing between the semi-arid site and the other two sites may indicate a possible controlling factor to soil moisture dynamics in addition to differences in soil and vegetation properties.

scholarly journals Groundwater influence on soil moisture memory and land–atmosphere fluxes in the Iberian Peninsula

2019 ◽  
Vol 23 (12) ◽  
pp. 4909-4932 ◽  
Author(s):  
Alberto Martínez-de la Torre ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Soil Moisture ◽  
Iberian Peninsula ◽  
Water Table ◽  
Land Surface ◽  
River Flow ◽  
Climate Model ◽  
Moisture Distribution ◽  
Convective Precipitation ◽  
Shallow Water Table ◽  
Semi Arid

Abstract. Groundwater plays an important role in the terrestrial water cycle, interacting with the land surface via vertical fluxes through the water table and distributing water resources spatially via gravity-driven lateral transport. It is therefore essential to have a correct representation of groundwater processes in land surface models, as land–atmosphere coupling is a key factor in climate research. Here we use the LEAFHYDRO land surface and groundwater model to study the groundwater influence on soil moisture distribution and memory, and evapotranspiration (ET) fluxes in the Iberian Peninsula over a 10-year period. We validate our results with time series of observed water table depth from 623 stations covering different regions of the Iberian Peninsula, showing that the model produces a realistic water table, shallower in valleys and deeper under hilltops. We find patterns of shallow water table and strong groundwater–land surface coupling over extended interior semi-arid regions and river valleys. We show a strong seasonal and interannual persistence of the water table, which induces bimodal memory in the soil moisture fields; soil moisture “remembers” past wet conditions, buffering drought effects, and also past dry conditions, causing a delay in drought recovery. The effects on land–atmosphere fluxes are found to be significant: on average over the region, ET is 17.4 % higher when compared with a baseline simulation with LEAFHYDRO's groundwater scheme deactivated. The maximum ET increase occurs in summer (34.9 %; 0.54 mm d−1). The ET enhancement is larger over the drier southern basins, where ET is water limited (e.g. the Guadalquivir basin and the Mediterranean Segura basin), than in the northern Miño/Minho basin, where ET is more energy limited than water limited. In terms of river flow, we show how dry season baseflow is sustained by groundwater originating from accumulated recharge during the wet season, improving significantly on a free-drain approach, where baseflow comes from water draining through the top soil, resulting in rivers drying out in summer. Convective precipitation enhancement through local moisture recycling over the semi-arid interior regions and summer cooling are potential implications of these groundwater effects on climate over the Iberian Peninsula. Fully coupled land surface and climate model simulations are needed to elucidate this question.

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 Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

2021 ◽  
Vol 13 (4) ◽  
pp. 680
Author(s):  
Lei Wang ◽  
Wen Zhuo ◽  
Zhifang Pei ◽  
Xingyuan Tong ◽  
Wei Han ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arabian Peninsula ◽  
Vegetation Coverage ◽  
Observation Data ◽  
Desert Locust ◽  
Meteorological Observations ◽  
Earth Observation Data ◽  
Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.

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.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

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