A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture

Root-zone soil moisture at the regional scale has always been a missing element of the hydrological cycle. Knowing its value could be a great help in estimating evapotranspiration, erosion, runoff, permeability, irrigation needs, etc. The recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture content of deeper layers using a physically-based formulation. Previous studies have proved the effectiveness of SMAR in estimating root-zone soil moisture, yet there is still room for improvement in its application. For example, the soil water loss function (i.e. deep percolation and evapotranspiration), assumed to be a linear function in the SMAR model, may produce approximations in the estimation of water losses in the second soil layer. This problem becomes more critical in soils with finer textures. In this regard, the soil moisture profile data from two research sites (AMMA and SCAN) were investigated. The results showed that after a rainfall event, soil water losses decrease following a power pattern until they reach a minimum steady state. This knowledge was used to modify SMAR. In particular, SMAR was modified (MSMAR) by introducing a non-linear soil water loss function that allowed for improved estimates of root zone soil moisture.

How to cite: Faridani, F., A. Farid, H. Ansari, S. Manfreda, A modified version of the SMAR model for estimating root-zone soil moisture from time series of surface soil moistureWater SA, Vol. 43 No. 3 July 2017 (doi: 10.4314/wsa.v43i3.14), 2017.  [pdf]

Estimating spatial and temporal variation of root zone soil moisture across a temperate forested catchment with remote sensing data and simple hydrologic models

Satellite-based near-surface (0–2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm−3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm−3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = −0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

How to cite: Baldwin, D., Manfreda, S., Keller, K., and Smithwick, E.A.H., Predicting root zone soil moisture with soil properties and satellite near-surface moisture data at locations across the United StatesJournal of Hydrology, 546, 393-404, (doi: 10.1016/j.jhydrol.2017.01.020), 2017.  [pdf]

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

Satellite-based near-surface (0–2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [ cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [ cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

How to cite: Baldwin, D., Manfreda, S., Keller, K., and Smithwick, E.A.H., Predicting root zone soil moisture with soil properties and satellite near-surface moisture data at locations across the United StatesJournal of Hydrology, 546, 393-404, (doi: 10.1016/j.jhydrol.2017.01.020), 2017.  [pdf]

MY SIRR: Minimalist agro-hydrological model for Sustainable IRRigation management—Soil moisture and crop dynamics

The paper introduces a minimalist water-driven crop model for sustainable irrigation management using an eco-hydrological approach. Such model, called MY SIRR, uses a relatively small number of parameters and attempts to balance simplicity, accuracy, and robustness. MY SIRR is a quantitative tool to assess water requirements and agricultural production across different climates, soil types, crops, and irrigation strategies. The MY SIRR source code is published under copyleft license. The FOSS approach could lower the financial barriers of smallholders, especially in developing countries, in the utilization of tools for better decision-making on the strategies for short- and long-term water resource management.

How to cite: Albano, R., S. Manfreda, G. Celano, MYSIRR: Minimalist agro-hYdrological model for Sustainable IRRigation management – soil moisture and crop dynamicsSoftwareX, 6, 107–117, (doi: 10.1016/j.softx.2017.04.00), 2017. [pdf]

Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model

Estimation of root-zone soil moisture (RZSM) at regional scales is a critical issue in surface hydrology that could be a great help for estimating evapotranspiration, erosion, runoff, and irrigation requirements, etc. A significant number of satellites [soil moisture and ocean salinity (SMOS), special sensor microwave imager (SSM/I), advanced microwave scanning radiometer-EOS (AMSR-E), tropical rainfall measuring mission/microwave imager (TRMM/TMI), etc.] retrieve surface soil moisture (SSM) using passive microwave remote sensing. This information can be used to derive RZSM using a new mathematical filter. In particular, the recently developed soil moisture analytical relationship (SMAR) can relate the surface soil moisture to the moisture of deeper layer using a relationship derived from a soil water balance equation where infiltration is estimated based on the relative fluctuations of soil moisture in the surface soil layer. In the present paper, the SMAR model is tested on two research databases in Africa and North America [African monsoon multidisciplinary analysis (AMMA) and soil climate analysis network (SCAN), respectively], where field measurements at different depths are available. Furthermore, the TRMM/ TMI Satellite is selected to retrieve the satellite SSM data of the studied regions using the land parameter retrieval model (LPRM). Both remotely sensed SSM and field measurements are used within the SMAR model to explore their ability in reproducing the RZSM and also to explore the existing difference in model parameterization moving from one dataset to the other. The SMAR model is applied using three different schemes: (1) with parameters calibrated using surface field measurements, (2) with parameters calibrated using remotely sensed SSM as input, and finally (3) using the remotely sensed SSM with the same parameters calibrated in Scheme 1. In all cases, SMAR parameters have been calibrated using a genetic algorithm optimizing the root-mean square error (RMSE) between SMAR prediction and measured RZSM. The results show that remotely sensed data may be coupled with the SMAR model to provide a good description of RZSM dynamics, but it requires a specific parameterization respect to Scheme 1. Nevertheless, it is surprising to observe that two of the four parameters of the model related to the soil texture are relatively stable moving from remote-sensed to field data.

How to cite: Farid Faridani, Alireza Farid, Hossein Ansari and Salvatore Manfreda, Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model, Journal of Irrigation and Drainage Engineering (doi: 10.1061/(ASCE) IR.1943-4774.0001115), 2016. [pdf]  

Field test of a multi-frequency electromagnetic induction sensor for soil moisture monitoring in southern Italy test sites

Soil moisture is a variable of paramount importance for a number of natural processes and requires the capacity to be routinely measured at different spatial and temporal scales (e.g., hillslope and/or small catchment scale). The electromagnetic induction (EMI) method is one of the geophysical techniques potentially useful in this regard. Indeed, it does not require contact with the ground, it allows a relatively fast survey of hillslope, it gives information related to soil depth greater than few centimetres and it can also be used in wooded areas. In this study, apparent electrical conductivity (EC a ) and soil moisture (SM) measurements were jointly carried out by using a multi-frequency EMI sensor (GEM-300) and Time Domain Reflectometry (TDR) probes, respectively. The aim was to retrieve SM variations at the hillslope scale over four sites, characterized by different land-soil units, located in a small mountainous catchment in southern Italy. Repeated measurements of ECa carried out over a fixed point showed that the signal variability of the GEM-300 sensor (Std. Err. [0.02–0.1 mS/m]) was negligible. The correlation estimated between point EC a and SM, measured with both portable and buried TDR probes, varied between 0.24 and 0.58, depending on the site considered. In order to reduce the effect of small-scale variability, a spatial smoothing filter was applied which allowed the estimation of linear relationships with higher coefficient of correlation (r 0.46–0.8). The accuracy obtained in the estimation of the temporal trend of the soil moisture spatial averages was in the range 4.5–7.8% v/v and up to the 70% of the point soil moisture variance was explained by the EC a signal. The obtained results highlighted the potential of EMI to provide, in a short time, sufficiently accurate estimate of soil moisture over large areas that are highly needed for hydrological and remote sensing applications.

How to cite: G. Calamita, A. Perrone, L. Brocca, B. Onorati, S. Manfreda, Field test of a multi-frequency electromagnetic induction sensor for soil moisture monitoring in southern Italy test sites, Journal of Hydrology, Pages 316 – 329 (doi: 10.1016/j.jhydrol.2015.07.023), 2015. [pdf]

A physically based approach for the estimation of root-zone soil moisture from surface measurements

In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. It was derived from a simplified soil water balance equation for semiarid environments that provides a closed form of the relationship between the root zone and the surface soil moisture with a limited number of physically consistent parameters. The method sheds lights on the mentioned relationship with possible applications in the use of satellite remote sensing retrievals of soil moisture. The proposed approach was used on soil moisture measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) and the Soil Climate Analysis Network (SCAN) databases. The AMMA network was designed with the aim to monitor three so-called mesoscale sites (super sites) located in Benin, Mali, and Niger using point measurements at different locations. Thereafter the new formulation was tested on three additional stations of SCAN in the state of New Mexico (US). Both databases are ideal for the application of such method, because they provide a good description of the soil moisture dynamics at the surface and the root zone using probes installed at different depths. The model was first applied with parameters assigned based on the physical characteristics of several sites. These results highlighted the potential of the methodology, providing a good description of the root-zone soil moisture. In the second part of the paper, the model performances were compared with those of the
well-known exponential filter. Results show that this new approach provides good performances after calibration with a set of parameters consistent with the physical characteristics of the investigated areas. The limited number of parameters and their physical interpretation makes the procedure appealing for further applications to other regions.

How to cite: Manfreda, S., L. Brocca, T. Moramarco, F. Melone, and J. Sheffield, A physically based approach for the estimation of root-zone soil moisture from surface measurementsHydrology and Earth System Sciences,  18, 1199-1212, (doi:10.5194/hess-18-1199-2014), 2014. [pdf

A physically based approach for the estimation of root-zone soil moisture from surface measurements

In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. The method sheds lights on the relationship between surface and root zone soil moisture and has applications in the use of satellite remote sensing retrievals of soil moisture. It derives from a simplified form of the soil water balance equation and provides a closed form of the relationship between the root zone and the surface soil moisture with a limited number of physically consistent parameters. The approach was first used to interpret soil moisture dynamics at the point scale using soil moisture measurements taken from the African Monsoon Multidisciplinary Analysis (AMMA) database. There after it was also tested over an extended domain using modeled soil moisture data obtained from the North American Land Data Assimilation System (NLDAS). The NLDAS database provides modeled soil moisture data averaged over different depths for the conterminous US covering different climatic and physical conditions. In general, the method performed better than a traditional low pass filter and its results are found to be influenced by rainfall dynamics and also by the observed variance of soil moisture in the lower layer. The limited number of the parameters and their physical interpretation allows a direct application of the procedure to other regions.

How to cite: S. Manfreda, L. Brocca, T. Moramarco, F. Melone and J. Sheffield, A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrology and Earth System Sciences Discussions, 9, Pages 14129–14162 (doi: 10.5194/hessd-9-14129-2012), 2012. [pdf

On the use of AMSU-based products for the description of soil water content at basin scale

Characterizing the dynamics of soil moisture fields is a key issue in hydrology, offering a strategy to improve our understanding of complex climate-soil-vegetation interactions. Apart from in-situ measurements and hydrological models, soil moisture dynamics can be inferred by analyzing data acquired by sensors aboard satellite platforms. In this work, we investigated the use of the National Oceanic and Atmospheric Administration – Advanced Microwave Sounding Unit (NOAA-AMSU) radiometer for the remote characterization of soil water content. To this aim, a field measurement campaign, lasted about three months, was carried out using a portable time-domain reflectometer (TDR) to get soil water content measures over five different locations within an experimental basin of 32.5 km2, located in the South of Italy. In detail, soil moisture measurements have been carried out systematically at the times of satellite overpasses, over two square areas of 400 m2, a triangular area of 200 m2 and two transects of 60 and 170 m, respectively. Each monitored site is characterized by different land covers and soil textures, to account for spatial heterogeneity of land surface. Afterwards, a more extensive comparison (i.e. analyzing a 5-yr data time series) has been made using soil moisture simulated by a hydrological model. Achieved measured and modeled soil moisture data were compared with two AMSU-based indices: the Surface Wetness Index (SWI) and the Soil Wetness Variation Index (SWVI). Both indices have been filtered to account for soil depth by means of an exponential filter. This allowed to understand the ability of each satellite-based index to account for soil moisture dynamics and to understand its performances under different conditions. As a general remark, the comparison shows a higher ability of the filtered SWI to describe the state of the soil, while the SWVI can capture soil moisture variations with a precision that increases at the higher values of SWVI and it may represent a useful and reliable tool to frequently monitor the soil moisture state for flood forecasting purposes.

How to cite: Manfreda, S., T. Lacava, B. Onorati, N. Pergola, M. Di Leo, M. R. Margiotta, and V. Tramutoli, On the use of AMSU-based products for the description of soil water content at basin scaleHydrology and Earth System Sciences, 15, 2839-2852, (doi:10.5194/hess-15-2839-2011), 2011. [pdf

Risultati delle indagini sperimentali condotte sul bacino Fiumarella di Corleto

Nella presente memoria vengono riportati alcuni dei risultati ottenuti dall’analisi dei dati idrologici monitorati all’interno del bacino sperimentale della Fiumarella di Corleto. Tale bacino è dotato di una strumentazione di campo che garantisce il monitoraggio in continuo di tutte le principali grandezze idrologiche: forzanti atmosferiche, deflussi ed umidità del suolo. I dati di umidità del suolo, rilevati su di un transetto monitorato mediante 22 sonde TDR a diversa profondità, insieme ai dati di portata rappresentano un prezioso supporto per lo studio dei meccanismi di formazione del deflusso superficiale. Inoltre, il bacino è dotato di misuratori di portata in due sezioni, una relativa all’intero bacino della Fiumarella di Corleto (32.5 km2) e l’altra riferita ad un sottobacino individuato sul versante destro della stessa Fiumarella (0.65 km2). La possibilità di misurazione alle due scale di riferimento (bacino e sottobacino) ha consentito di ottenere interessanti evidenze sperimentali sui meccanismi di produzione del ruscellamento superficiale, mettendo in risalto il ruolo giocato dalla vegetazione e dalle dimensioni caratteristiche dei bacini.

How to cite: Fiorentino, M., S. Manfreda, M.R. Margiotta, B. Onorati, Risultati delle indagini sperimentali condotte sul bacino Fiumarella di Corleto, Tecniche per la Difesa dall’Inquinamento, Editoriale Bios, pp. 167-178, 2010.