scholarly journals Relationship between soil moisture of near surface and multiple depths of the root zone under heterogeneous land uses and varying hydroclimatic conditions

2007 ◽  
Vol 21 (25) ◽  
pp. 3449-3462 ◽  
Author(s):  
Rezaul Mahmood ◽  
Kenneth G. Hubbard
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Land Uses ◽  
Near Surface

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions

2007 ◽  
Vol 8 (4) ◽  
pp. 910-921 ◽  
Author(s):  
Nicola Montaldo ◽  
John D. Albertson ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Root Zone ◽  
Sensitivity Analyses ◽  
Surface Model ◽  
Early Model ◽  
Model Errors ◽  
Model Bias ◽  
Near Surface

Abstract In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses.

scholarly journals The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System

2012 ◽  
Vol 13 (3) ◽  
pp. 1107-1118 ◽  
Author(s):  
Viviana Maggioni ◽  
Rolf H. Reichle ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Root Zone ◽  
Error Model ◽  
Near Surface ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.

scholarly journals From Near-Surface to Root-Zone Soil Moisture Using Different Assimilation Techniques

2007 ◽  
Vol 8 (2) ◽  
pp. 194-206 ◽  
Author(s):  
Joaquín Muñoz Sabater ◽  
Lionel Jarlan ◽  
Jean-Christophe Calvet ◽  
François Bouyssel ◽  
Patricia De Rosnay
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Diffusion Processes ◽  
Root Zone ◽  
Computational Cost ◽  
Climatic Conditions ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil moisture of the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) over a 4-yr period (2001–04). This time period includes contrasting climatic conditions. Among the different algorithms, the ensemble Kalman filter (EnKF) and a simplified one-dimensional variational data assimilation (1DVAR) show the best performances. The lower computational cost of the 1DVAR is an advantage for operational root-zone soil moisture analysis based on remotely sensed surface soil moisture observations at a global scale.

scholarly journals Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8371
Author(s):  
Irina Ontel ◽  
Anisoara Irimescu ◽  
George Boldeanu ◽  
Denis Mihailescu ◽  
Claudiu-Valeriu Angearu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Standardized Precipitation Index ◽  
In Situ Measurements ◽  
Near Surface ◽  
Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

scholarly journals Developing a drought-monitoring index for the contiguous US using SMAP

2018 ◽  
Vol 22 (12) ◽  
pp. 6611-6626 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Near Surface ◽  
Index Maps

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

Subseasonal Meteorological Drought Development over the Central United States during Spring

2021 ◽  
pp. 1-64
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Meteorological Drought ◽  
Individual Case ◽  
Reanalysis Data ◽  
Late Spring ◽  
Evaporative Demand ◽  
Near Surface ◽  
Divergent Flow ◽  
Central United States

Abstract Diagnosis of rapidly developing springtime droughts in the central U.S. has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P-ET) deficits, revealed in early (April 1-May 15) and late (May 16-June 30) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958-2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1-2 weeks, and generally has a greater impact on the moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases ∼40 percentiles within ∼10 days. Anomalous near-surface warmth develops only after P-ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P-ET deficits, it still appears to capture much of the springtime development of so-called “flash droughts” as well.

Hydro-meteorological thresholds based on synthetic dataset for improved prediction of rainfall-induced shallow landslides.

2020 ◽  
Author(s):  
Pasquale Marino ◽  
Roberto Greco ◽  
David James Peres ◽  
Thom A. Bogaard
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Root Zone ◽  
Water Pressure ◽  
Shallow Landslides ◽  
Root Penetration ◽  
Landslide Prediction ◽  
Antecedent Soil Moisture ◽  
Near Surface ◽  
Physically Based

<p>Prediction of rainfall-induced landslides is often entrusted to the definition of empirical thresholds (usually expressed in terms of rainfall intensity and duration), linking the precipitation to the triggering of landslides. However, rainfall intensity-duration thresholds do not exploit the knowledge of the hydrological processes developing in the slope, so they tend to generate false and missed alarms. Rainfall-induced shallow landslides usually occur in initially unsaturated soil covers following an increase of pore water pressure, due to the increase of soil moisture, caused by large and persistent rainfall. Clearly, it should be possible to use soil moisture for landslide prediction. Recently, Bogaard & Greco (2018) proposed the cause-trigger conceptual framework to develop hydro-meteorological thresholds that combine the antecedent causal factors and the actual trigger connected with landslide initiation. In fact, in some regions where rainfall-induced shallow landslides are particularly dangerous and pose a serious risk to people and infrastructures, the antecedent saturation is the predisposing factor, while the actual landslide triggering is associated with the hydrologic response to the recent and incoming precipitation. In fact, numerous studies already tried to introduce, directly or with models, the effects of antecedent soil moisture content in the empirical thresholds for improving landslide forecasting. Soil moisture can be measured locally, by a range of on-site measurement techniques, or remotely, from satellites or airborne. On-site measurements have proved promising in improving the performance of thresholds for landslide early warning. On-site data are accurate but sparse, so there is an increasing interest on the possible use of remotely sensed data. And in fact, recent research has shown that they can provide useful information for landslide prediction at regional scale, despite their coarse resolution and inherent uncertainty.</p><p>However, while remote sensing techniques provide near-surface (5cm depth) soil moisture estimate, the depth involved in shallow landslide is typically 1-2m below the surface. This depth, overlapping with the root penetration zone, is influenced by antecedent precipitation, soil texture, vegetation and, so, it is very difficult to find a clear relationship with near-surface soil moisture. Many studies have been conducted to provide root-zone soil moisture through physically-based approaches and data driven methods, data assimilation schemes, and satellite information.</p><p>In this study, the question if soil moisture information derived from current or future satellite products can improve landslide hazard prediction, and to what extent, is investigated. Hereto, real-world landslide and hydrology information, from two sites of Southern Italy characterized by frequent shallow landslides (Peloritani mountains, in Sicily, and Partenio mountains, in Campania), is analyzed. To get datasets long enough to carry out statistical analyses, synthetic time series of rainfall and soil cover response have been generated, with the application of a stochastic rainfall model and a physically based infiltration model, for both the sites. Near-surface and root-zone soil moisture have been tested, accounting also for effects of uncertainty and of coarse spatial and temporal resolution of measurements. The obtained results show that, in all cases, soil moisture information allows building hydro-meteorological thresholds for landslide prediction, significantly outperforming the currently adopted purely meteorological thresholds.</p><p> </p><p> </p>

scholarly journals A SMAP-Based Drought Monitoring Index for the United States

2018 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Warm Season ◽  
The United States ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe between the latitude bands of 85.044° N/S in 2–3 days depending on location. SMAP Level 3 passive radiometer product (SPL3SMP) measures the amount of water in the top 5 cm of soil except for regions of heavy vegetation (vegetation water content >4.5 kg/m2) and frozen or snow covered locations. SPL3SMP retrievals are spatially and temporally discontinuous, so the 33 months offers a short SMAP record length and poses a statistical challenge for meaningful assessment of its indices. The SMAP SPL4SMAU data product provides global surface and root zone soil moisture at 9-km resolution based on assimilating the SPL3SMP product into the NASA Catchment land surface model. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. We describe here SMAP-based indices over the continental United States (CONUS) based on both near-surface and root zone soil moisture percentiles. The percentiles are based on fitting a Beta distribution to the retrieved moisture values. To assess the data adequacy, a statistical comparison is made between fitting the distribution to VIC soil moisture values for the days when SPL3SMP are available, versus fitting to a 1979–2017 VIC data record. For the cold season (November–April), 57 % of grids were deemed to be consistent between the periods, and 68 % in the warm season (May–October), based on a Kolmogorov–Smirnov statistical test. It is assumed that if grids passed the consistency test using VIC data, then the grid had sufficient SMAP data. Our near-surface and root zone drought index on maps are shown to be similar to those produced by the U.S. Drought Monitor (from D0-D4) and GRACE. In a similar manner, we extend the index to include pluvial conditions using indices W0-W4. This study is a step forward towards building a national and international soil moisture monitoring system, without which, quantitative measures of drought and pluvial conditions will remain difficult to judge.

scholarly journals From Near-Surface to Root-Zone Soil Moisture Using Year-Round Data

2000 ◽  
Vol 1 (5) ◽  
pp. 393-411 ◽  
Author(s):  
Jean-Christophe Calvet ◽  
Joël Noilhan
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Near Surface
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