Assessment of irrigation physics in a land surface modeling framework using non-traditional and human-practice datasets

Abstract. Irrigation increases soil moisture, which in turn controls water and energy fluxes from the land surface to the planetary boundary layer and determines plant stress and productivity. Therefore, developing a realistic representation of irrigation is critical to understanding land–atmosphere interactions in agricultural areas. Irrigation parameterizations are becoming more common in land surface models and are growing in sophistication, but there is difficulty in assessing the realism of these schemes, due to limited observations (e.g., soil moisture, evapotranspiration) and scant reporting of irrigation timing and quantity. This study uses the Noah land surface model run at high resolution within NASA's Land Information System to assess the physics of a sprinkler irrigation simulation scheme and model sensitivity to choice of irrigation intensity and greenness fraction datasets over a small, high-resolution domain in Nebraska. Differences between experiments are small at the interannual scale but become more apparent at seasonal and daily timescales. In addition, this study uses point and gridded soil moisture observations from fixed and roving cosmic-ray neutron probes and co-located human-practice data to evaluate the realism of irrigation amounts and soil moisture impacts simulated by the model. Results show that field-scale heterogeneity resulting from the individual actions of farmers is not captured by the model and the amount of irrigation applied by the model exceeds that applied at the two irrigated fields. However, the seasonal timing of irrigation and soil moisture contrasts between irrigated and non-irrigated areas are simulated well by the model. Overall, the results underscore the necessity of both high-quality meteorological forcing data and proper representation of irrigation for accurate simulation of water and energy states and fluxes over cropland.

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Assessment of Irrigation Physics in a Land Surface Modeling Framework using Non-Traditional and Human-Practice Datasets

10.5194/hess-2017-51 ◽

2017 ◽

Cited By ~ 1

Author(s):

Patricia M. Lawston ◽

Joseph A. Santanello, Jr. ◽

Trenton E. Franz ◽

Matthew Rodell

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Plant Stress ◽

Sprinkler Irrigation ◽

Cosmic Ray ◽

Surface Model ◽

Modeling Framework ◽

Irrigation Intensity ◽

Human Practice

Abstract. Irrigation increases soil moisture, which in turn controls water and energy fluxes from the land surface to the planetary boundary layer and determines plant stress and productivity. Therefore, developing a realistic representation of irrigation is critical to understanding land-atmosphere interactions in agricultural areas. Irrigation parameterizations are becoming more common in land surface models and are growing in sophistication, but there is difficulty in assessing the realism of these schemes, due to limited observations (e.g., soil moisture, evapotranspiration) and scant reporting of irrigation timing and quantity. This study uses the Noah land surface model run at high resolution within NASA's Land Information System to assess the physics of a sprinkler irrigation simulation scheme and model sensitivity to choice of irrigation intensity and greenness fraction datasets over a small, high resolution domain in Nebraska. Differences between experiments are small at the interannual scale, but become more apparent at seasonal and daily time scales. In addition, this study uses point and gridded soil moisture observations from fixed and roving Cosmic Ray Neutron Probes and co-located human practice data to evaluate the realism of irrigation amounts and soil moisture impacts simulated by the model. Results show that field-scale heterogeneity resulting from the individual actions of farmers is not captured by the model and the amount of irrigation applied by the model exceeds that applied at the two irrigated fields. However, the seasonal timing of irrigation and soil moisture contrasts between irrigated and non-irrigated areas are simulated well by the model. Overall, the results underscore the necessity of both high-quality meteorological forcing data and proper representation of irrigation for accurate simulation of water and energy states and fluxes over cropland.

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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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Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Water ◽

10.3390/w11030542 ◽

2019 ◽

Vol 11 (3) ◽

pp. 542 ◽

Cited By ~ 3

Author(s):

Mohammed Dabboor ◽

Leqiang Sun ◽

Marco Carrera ◽

Matthew Friesen ◽

Amine Merzouki ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Soil Texture ◽

Land Surface ◽

Goodness Of Fit ◽

Retrieval Algorithm ◽

Surface Model ◽

Land Surface Scheme ◽

Sar Imagery ◽

Surface Scheme

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

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

10.5194/egusphere-egu2020-10419 ◽

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

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.

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Multi-scale assimilation of SMAP data: comparison between land surface and land surface-subsurface model

10.5194/egusphere-egu2020-465 ◽

2020 ◽

Author(s):

Haojin Zhao ◽

Roland Baatz ◽

Carsten Montzka ◽

Harry Vereecken ◽

Harrie-Jan Hendricks Franssen

Keyword(s):

Parameter Estimation ◽

Soil Moisture ◽

Data Assimilation ◽

Simulation Model ◽

Land Surface ◽

Cosmic Ray ◽

Remotely Sensed ◽

Surface Model ◽

Systematic Bias

Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration. &#160;

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Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0186.1 ◽

2019 ◽

Vol 20 (5) ◽

pp. 793-819 ◽

Cited By ~ 12

Author(s):

Joseph A. Santanello Jr. ◽

Patricia Lawston ◽

Sujay Kumar ◽

Eli Dennis

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Land Surface ◽

Initial Conditions ◽

Land Surface Model ◽

Surface Model ◽

Coupled Models ◽

Wide Range ◽

Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

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Improving estimates of UK soil moisture using the JULES land surface model with COSMOS-UK measurements in the LaVEnDAR data assimilation framework

10.5194/egusphere-egu2020-11566 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Ewan Pinnington ◽

Richard Ellis ◽

Eleanor Blyth ◽

Simon Dadson ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Soil Texture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

Soil Physics ◽

Soil Moisture Sensors ◽

The Uk

Soil moisture predictions are increasingly important in hydrological, ecological and agricultural applications. In recent years the availability of wide-area assessments of current and future soil-moisture states has grown, yet few studies have combined model-based assessments with observations beyond the point scale. Here we use the JULES land surface model together with COSMOS-UK data to evaluate the extent to which data assimilation can improve predictions of soil moisture across the United Kingdom.COSMOS-UK is a network of soil moisture sensors run by UKCEH. The network provides soil moisture measurements at around 50 sites throughout the UK using innovative Cosmic Ray Neutron Sensors (CRNS). Half hourly measurements of the meteorological variables that the Joint UK Land Environment Simulator (JULES) requires as driving data are also recorded at COSMOS-UK sites, allowing us to run JULES at observation locations. This provides a unique opportunity to compare soil moisture outputs from JULES with CRNS observations; these measurements have a footprint of up to 12 ha (approx 30 acres) and are therefore better scale matched with JULES outputs than those from point sensors.We have used the Land Variational Ensemble Data Assimilation Framework (LaVEnDAR) to combine soil moisture estimates from JULES with daily CRNS observations from one year at a number of COSMOS-UK sites. We show that this results in improved soil moisture predictions from JULES over several years. This has been achieved by optimising parameters in the pedo-transfer function used to derive JULES soil physics parameters from soil texture information. Using data assimilation with LaVEnDAR in this way allows us to explore the relationships between soil moisture estimates, soil physics parameters and soil texture, as well as improving the agreement between JULES model outputs and observations.

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Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1617-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1617-1641

Author(s):

Ewan Pinnington ◽

Javier Amezcua ◽

Elizabeth Cooper ◽

Simon Dadson ◽

Rich Ellis ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Mean Squared Error ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Surface Model ◽

Squared Error ◽

Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

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Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

10.5194/hess-2020-303 ◽

2020 ◽

Author(s):

Ewan Pinnington ◽

Javier Amezcua ◽

Elizabeth Cooper ◽

Simon Dadson ◽

Rich Ellis ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Mean Squared Error ◽

Land Surface Model ◽

Cosmic Ray ◽

Pedotransfer Functions ◽

Surface Model ◽

Squared Error ◽

Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear, because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure we find improved estimates of soil moisture for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these COSMOS probes is much more representative of the 1 km model grid than traditional point based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain we find an average 22 % reduction in root-mean squared error, a 16 % reduction in unbiased root-mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

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Building a coupled data assimilation system for the atmosphere, land-surface and subsurface on the catchment scale

10.5194/egusphere-egu21-7439 ◽

2021 ◽

Author(s):

Bernd Schalge ◽

Barbara Haese ◽

Bastian Waldowski ◽

Natascha Brandhorst ◽

Emilio Sanchez ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Coupled Model ◽

Cosmic Ray ◽

Atmospheric Model ◽

Surface Model ◽

Catchment Scale ◽

Near Surface ◽

Fully Coupled

We present a data assimilation (DA) system for the atmosphere-land-surface-subsurface system on the catchment scale. The Neckar catchment in SW-Germany served as the specific case where the DA in combination with the coupled atmosphere-land surface-subsurface model TSMP was used. TSMP couples the atmospheric model COSMO, the land-surface model CLM and the hydrological model ParFlow to the DA framework PDAF. We will discuss how the ensemble system is set up in order to work properly and what issues we faced during our initial testing. For the atmosphere we found that it is important to have a good ensemble of lateral forcings as changing internal parameters for various parametrizations does not introduce sufficient variability on its own due to the rather small size of our domain. For the sub-surface the choice of parameters becomes most important and as such parameter estimation will be a valuable tool for improving DA results significantly. Finally, we are showing some first DA results with our system concerning soil moisture with two different assimilation methods with a fully coupled model setup. In the first assimilation scenario in-situ soil moisture data measured by cosmic ray probes are assimilated, while in the second assimilation scenario remotely sensed near surface soil moisture is assimilated. The first results are encouraging and we discuss additional planned simulation scenarios with the fully coupled atmosphere-land surface-subsurface modelling system as well as plans to test strongly coupled DA, where measurements are used to update states across compartments, possibly resulting in additional accuracy gain compared to traditional uncoupled DA.&#160;

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