Evaluating the value of a network of cosmic-ray probes for improving land surface modelling

Abstract. Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased.

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

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

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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Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions.

10.5194/ismc2021-81 ◽

2021 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Richard Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Small Scale ◽

Surface Model ◽

Land Surface Models ◽

Field Scale ◽

Surface Models

<p>Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.</p>

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Using satellite-based estimates of evapotranspiration and groundwater changes to determine anthropogenic water fluxes in land surface models

Geoscientific Model Development ◽

10.5194/gmd-8-3021-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 3021-3031 ◽

Cited By ~ 20

Author(s):

R. G. Anderson ◽

M.-H. Lo ◽

S. Swenson ◽

J. S. Famiglietti ◽

Q. Tang ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Climate Models ◽

Management Practice ◽

Water Volume ◽

Land Surface Models ◽

Community Land Model ◽

Irrigation Intensity ◽

Surface Models ◽

Data Latency

Abstract. Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks including data latency, accurately prescribing irrigation intensity, and a lack of conservation of water volume for models using a prescribed soil moisture approach. In this study, we parameterize irrigation fluxes using satellite observations of evapotranspiration (ET) compared to ET from a suite of land surface models without irrigation. We then incorporate the irrigation flux into the Community Land Model (CLM) and use a systematic trial-and-error procedure to determine the ground- and surface-water withdrawals that are necessary to balance the new irrigation flux. The resulting CLM simulation with irrigation produces ET that matches the magnitude and seasonality of observed satellite ET well, with a mean difference of 6.3 mm month−1 and a correlation of 0.95. Differences between the new CLM ET values and satellite-observed ET values are always less than 30 mm month−1 and the differences show no pattern with respect to seasonality. The results reinforce the importance of accurately parameterizing anthropogenic hydrologic fluxes into land surface and climate models to assess environmental change under current and future climates and land management regimes.

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Incorporating remote sensing ET into Community Land Model version 4.5

10.5194/hess-2016-696 ◽

2017 ◽

Author(s):

Dagang Wang ◽

Guiling Wang ◽

Dana T. Parr ◽

Weilin Liao ◽

Youlong Xia ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Correction Method ◽

Energy Budgets ◽

Land Surface Models ◽

Community Land Model ◽

Model Version ◽

Surface Models ◽

Time Invariant ◽

The Relationship

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite of the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten using the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly reduces the biases existing in CLM. The improvement differs with region, being more significant in eastern CONUS than western CONUS, with the most striking improvement over the southeast CONUS. This regional dependence reflects primarily the regional dependence in the degree to which the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. method). The bias correction method provides an alternative way to improve the performance of land surface models, which could lead to more realistic drought evaluations with improved ET and soil moisture estimates.

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State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4927-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4927-4958 ◽

Cited By ~ 22

Author(s):

Hongjuan Zhang ◽

Harrie-Jan Hendricks Franssen ◽

Xujun Han ◽

Jasper A. Vrugt ◽

Harry Vereecken

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Model Parameters ◽

Land Surface Models ◽

Evaluation Period ◽

Community Land Model ◽

Surface Models ◽

State Augmentation ◽

Dual Estimation

Abstract. Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.

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Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2509-2017 ◽

2017 ◽

Vol 21 (5) ◽

pp. 2509-2530 ◽

Cited By ~ 13

Author(s):

Roland Baatz ◽

Harrie-Jan Hendricks Franssen ◽

Xujun Han ◽

Tim Hoar ◽

Heye Reemt Bogena ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

Hydraulic Parameters ◽

Soil Map ◽

Soil Hydraulic Parameters ◽

Soil Hydraulic

Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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Using satellite-based estimates of evapotranspiration and groundwater changes to determine anthropogenic water fluxes in land surface models

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3565-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3565-3592 ◽

Cited By ~ 2

Author(s):

R. G. Anderson ◽

M.-H. Lo ◽

S. Swenson ◽

J. S. Famiglietti ◽

Q. Tang ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Climate Models ◽

Management Practice ◽

Water Flux ◽

Water Volume ◽

Land Surface Models ◽

Community Land Model ◽

Irrigation Intensity ◽

Surface Models

Abstract. Irrigation is a widely used water management practice that is often poorly parameterized in land surface and climate models. Previous studies have addressed this issue via use of irrigation area, applied water inventory data, or soil moisture content. These approaches have a variety of drawbacks including data latency, accurately prescribing irrigation intensity, and conservation of water volume for soil moisture approach. In this study, we parameterize irrigation fluxes using satellite observations of evapotranspiration (ET) against ET from a suite of land surface models without irrigation. We then apply this water flux into the Community Land Model (CLM) and use an iterative approach to estimate groundwater recharge and partition the water flux between groundwater and surface water. The ET simulated by CLM with irrigation matches the magnitude and seasonality of observed satellite ET well, with a mean difference of 6.3 mm month−1 and a correlation of 0.95. Differences between the new CLM ET values and observed ET values are always less than 30 mm month−1 and the differences show no pattern with respect to seasonality. The results reinforce the importance of accurately parameterizing anthropogenic hydrologic fluxes into land surface and climate models to assess environmental change under current and future climates and land management regimes.

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Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models

Water ◽

10.3390/w12010046 ◽

2019 ◽

Vol 12 (1) ◽

pp. 46 ◽

Cited By ~ 1

Author(s):

Prabhakar Shrestha ◽

Clemens Simmer

Keyword(s):

Soil Moisture ◽

Surface Energy ◽

Energy Flux ◽

Land Surface ◽

Small Scale ◽

Land Surface Models ◽

Surface Energy Flux ◽

Community Land Model ◽

Flux Partitioning ◽

Surface Models

An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.

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On the utility of land surface models for agricultural drought monitoring

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3451-2012 ◽

2012 ◽

Vol 16 (9) ◽

pp. 3451-3460 ◽

Cited By ~ 42

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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4649-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4649-4665 ◽

Cited By ~ 12

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.

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