Spatio‐temporal distribution of near‐surface and root zone soil moisture at the catchment scale

2008 ◽  
Vol 22 (14) ◽  
pp. 2699-2714 ◽  
Author(s):  
C. Martinez ◽  
G. R. Hancock ◽  
J. D. Kalma ◽  
T. Wells
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Temporal Distribution ◽  
Catchment Scale ◽  
Near Surface ◽  
Spatio Temporal

Catchment-scale connection between vegetation accessible storage and satellite-derived Soil Water Index

2020 ◽  
Author(s):  
Laurène Bouaziz ◽  
Susan Steele-Dunne ◽  
Jaap Schellekens ◽  
Albrecht Weerts ◽  
Jasper Stam ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Water Storage ◽  
Surface Soil Moisture ◽  
Catchment Scale ◽  
Near Surface ◽  
Water Index ◽  
Optimal Values

<p>Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. Remotely-sensed soil moisture products are available globally. However, they are representative of the upper-most few centimeters of the soil. For reliable runoff predictions, we are interested in root-zone soil moisture estimates as they regulate the partitioning of precipitation to drainage and evaporation. The Soil Water Index approximates root-zone soil moisture from near-surface soil moisture and requires a single parameter representing the characteristic time length T of temporal soil moisture variability. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established and often a standard value of 20 days is assumed. In this study, we hypothesize that optimal T values are linked to the accumulated difference between precipitation (water supply) and evaporation (atmospheric water demand) during dry periods with return periods of 20 years, and, thus, to catchment-scale vegetation-accessible water storage capacities. We identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T vary between 1 and 98 days with a median of 17 days across the studied catchments and soil moisture products. We furthermore show that T, which was previously known to increase with increasing depth of the soil layer, is positively and strongly related with catchment-scale root-zone water storage capacity, estimated based on long-term water balance data.  This is useful to generate estimates of root-zone soil moisture from satellite-based surface soil moisture, as they are a key control of the response of hydrological systems.</p>

scholarly journals Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

2021 ◽  
Vol 13 (5) ◽  
pp. 892
Author(s):  
Xiaomei Li ◽  
Pinhua Xie ◽  
Ang Li ◽  
Jin Xu ◽  
Zhaokun Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Temporal Distribution ◽  
Correlation Coefficients ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Mixing Ratios ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Gas Volume ◽  
Transport Flux

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

scholarly journals Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5211
Author(s):  
Maedeh Farokhi ◽  
Farid Faridani ◽  
Rosa Lasaponara ◽  
Hossein Ansari ◽  
Alireza Faridhosseini
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Root Zone ◽  
Analytical Relationship ◽  
Moisture Profile ◽  
Essential Variable ◽  
Potential Benefits ◽  
Spatio Temporal ◽  
Hydrological Prediction

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

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.

Spatio-temporal variations of soil moisture in the root zone of Haloxylon ammodendron at different life stages

2017 ◽  
Vol 37 (3) ◽  
Author(s):  
朱海 ZHU Hai ◽  
胡顺军 HU Shunjun ◽  
刘翔 LIU Xiang ◽  
李浩 LI Hao ◽  
李宜科 LI Yike
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Temporal Variations ◽  
Life Stages ◽  
Haloxylon Ammodendron ◽  
Spatio Temporal

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

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

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

Modeling Nitrate Export at the Catchment Scale using StorAge Selection Functions

2020 ◽  
Author(s):  
Tam Nguyen ◽  
Rohini Kumar ◽  
Stefanie R. Lutz ◽  
Andreas Musolff ◽  
Jan H. Fleckenstein
Keyword(s):  
Time Distribution ◽  
Root Zone ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Nitrate Transport ◽  
Catchment Scale ◽  
Near Surface ◽  
Soil Zone ◽  
Different Ages ◽  
Nitrate Export

<p>Catchments store and release water of different ages. The time of a water parcel remaining in contact with the catchment subsurface affects the solute dynamics in the catchment and ultimately in the stream. Catchment storage can be conceptualized as a collection of different water parcels with different ages, the so-called residence time distribution (RTD). Similarly, the distribution of water ages in streamflow at the catchment outlet, which is sampled from the RTD, is called the travel time distribution (TTD). The selection preferences for discharge can be characterized by StorAge selection (SAS) functions. In recent years, numerical experiments have shown that SAS functions are time-variant and can be approximated, for example, by the beta distribution function. SAS functions have been emerging as a promising tool for modeling catchment-scale solute export.</p><p>In this study, we aim to integrate the SAS-based description of nitrate transport with the mHM-Nitrate model (Yang et al., 2018) to simulate solute transport and turnover above and below the soil zone including legacy effects. The mHM-Nitrate is a grid based distributed model with the hydrological concept taken from the mesoscale Hydrologic Model (mHM) and the water quality concept taken from the HYdrological Predictions for the Environment (HYPE) model. Here, we replaced the description of nitrate transport in groundwater from the original mHM-Nitrate with time-variant SAS-based modeling, while we kept the detailed description of turnover of organic and inorganic nitrogen in the near-surface (root zone) from mHM-Nitrate. First-order decay was used to represent biogeochemical (denitrification) processes below the root zone and in the stream. The proposed model was tested in a mixed agricultural-forested headwater catchment in the Harz Mountains, Germany. Results show that the proposed SAS augmented nitrate model (with the time-variant beta function) is able to represent streamflow and catchment nitrate export with satisfactory results (NSE for streamflow = 0.83 and for nitrate = 0.5 at the daily time step). Overall, our combined model provides a new approach for a spatially distributed simulation of nitrogen reaction processes in the soil zone and a spatially implicit simulation of transport pathways of nitrate and denitrification in the entire catchment.</p><p><span>Yang, X.</span>, <span>Jomaa, S.</span>, <span>Zink, M.</span>, <span>Fleckenstein, J. H.</span>, <span>Borchardt, D.</span>, & <span>Rode, M.</span> ( <span>2018</span>). <span>A new fully distributed model of nitrate transport and removal at catchment scale</span>. <em>Water Resources Research</em>, <span>54</span>, <span>5856</span>– <span>5877</span>.</p>

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.

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