scholarly journals Assimilating shallow soil moisture observations into land models with a water budget constraint

2020 ◽  
Vol 24 (11) ◽  
pp. 5187-5201
Author(s):  
Bo Dan ◽  
Xiaogu Zheng ◽  
Guocan Wu ◽  
Tao Li
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Balance ◽  
Water Budget ◽  
Soil Moisture Content ◽  
Forecast Error ◽  
Deep Layers ◽  
Assimilation Process ◽  
Analysis Error ◽  
Vertical Localization

Abstract. Assimilating observations of shallow soil moisture content into land models is an important step in estimating soil moisture content. In this study, several modifications of an ensemble Kalman filter (EnKF) are proposed for improving this assimilation. It was found that a forecast error inflation-based approach improves the soil moisture content in shallow layers, but it can increase the analysis error in deep layers. To mitigate the problem in deep layers while maintaining the improvement in shallow layers, a vertical localization-based approach was introduced in this study. During the data assimilation process, although updating the forecast state using observations can reduce the analysis error, the water balance based on the physics in the model could be destroyed. To alleviate the imbalance in the water budget, a weak water balance constrain filter is adopted. The proposed weakly constrained EnKF that includes forecast error inflation and vertical localization was applied to a synthetic experiment. An additional bias-aware assimilation for reducing the analysis bias is also investigated. The results of the assimilation process suggest that the inflation approach effectively reduces the analysis error from 6.70 % to 2.00 % in shallow layers but increases from 6.38 % to 12.49 % in deep layers. The vertical localization approach leads to 6.59 % of the analysis error in deep layers, and the bias-aware assimilation scheme further reduces this to 6.05 %. The spatial average of the water balance residual is 0.0487 mm of weakly constrained EnKF scheme, and 0.0737 mm of a weakly constrained EnKF scheme with inflation and localization, which are much smaller than the 0.1389 mm of the EnKF scheme.

scholarly journals Assimilating Shallow Soil Moisture Observations into Land Models with a Water Budget Constraint

2020 ◽  
Author(s):  
Bo Dan ◽  
Xiaogu Zheng ◽  
Guocan Wu ◽  
Tao Li
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Balance ◽  
Water Budget ◽  
Soil Moisture Content ◽  
Forecast Error ◽  
Deep Layers ◽  
Assimilation Process ◽  
Analysis Error ◽  
Vertical Localization

Abstract. Incorporating observations of shallow soil moisture content into land models is an important step in assimilating satellite observations of soil moisture content. In this study, several modifications of an ensemble Kalman filter (EnKF) are proposed for improving this assimilation. It was found that a forecast error inflation-based approach improves the soil moisture content in shallow layers, but it can increase the analysis error in deep layers. To mitigate the problem in deep layers while maintaining the improvement in shallow layers, a vertical localization-based approach was introduced in this study. During the data assimilation process, although updating the forecast state using observations can reduce the analysis error, the water balance based on the physics in the model could be destroyed. To alleviate the imbalance in the water budget, a weak water balance constrain filter is adopted. The proposed weakly constrained EnKF that includes forecast error inflation and vertical localization was applied to a synthetic experiment and two real data experiments. The results of the assimilation process suggest that the inflation approach effectively reduce both the short-lived analysis error and the analysis bias in shallow layers, while the vertical localization approach avoids increase in analysis error in deep layers. The weak constraint on the water balance reduces the degree of the water budget imbalance at the price of a small increase in the analysis error.

The Canadian Forest Fire Weather Index System as a predictor of total soil moisture content as estimated by the Thornthwaite water balance

1985 ◽  
Vol 15 (6) ◽  
pp. 1194-1195
Author(s):  
Robert S. McAlpine ◽  
Thomas G. Eiber
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Balance ◽  
Forest Fire ◽  
Index System ◽  
Soil Moisture Content ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Total Soil

Weather data from Upsala and Atikokan, Ontario, were used to determine the Canadian Forest Fire Weather Index System values and to calculate the soil moisture for two soil types using the Thornthwaite water balance. The Duff Moisture Code and the Drought Code were found to give excellent correlations with the total soil moisture content under most weather patterns.

scholarly journals Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guocan Wu ◽  
Bo Dan ◽  
Xiaogu Zheng
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Water Budget ◽  
Forecast Error ◽  
Estimation Accuracy ◽  
Surface Model ◽  
Common Land Model ◽  
Ensemble Transform Kalman Filter ◽  
Analysis Error ◽  
Ensemble Transform

Assimilating observations to a land surface model can further improve soil moisture estimation accuracy. However, assimilation results largely rely on forecast error and generally cannot maintain a water budget balance. In this study, shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. A proposed forecast error inflation and water balance constraint are adopted in the Ensemble Transform Kalman Filter to reduce the analysis error and water budget residuals. The assimilation results indicate that the analysis error is reduced and the water imbalance is mitigated with this approach.

Optimizing Irrigation Rates for Cotton Production in an Extremely Arid Area Using RZWQM2-Simulated Water Stress

2017 ◽  
Vol 60 (6) ◽  
pp. 2041-2052 ◽  
Author(s):  
Che Liu ◽  
Zhiming Qi ◽  
Zhe Gu ◽  
Dongwei Gui ◽  
Fanjiang Zeng
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Moisture Content ◽  
Water Balance ◽  
Soil Moisture Content ◽  
Irrigation Scheduling ◽  
Arid Area ◽  
Cotton Production ◽  
Crop Water ◽  
Crop Water Stress

Abstract. Quantifying crop water demand and optimizing irrigation management practices are essential to water resource management in arid desert oases. Agricultural systems modeling can serve to develop a better understanding of the hydrologic cycle under various irrigation and climate conditions. RZWQM2-simulated water stress can be used as an indicator for irrigation scheduling but has not been applied to extremely arid zones. The objectives of this study were to (1) evaluate the performance of RZWQM2 in simulating soil moisture content and crop production in an extremely arid area and (2) develop an optimal irrigation strategy using model-simulated crop water stress. In this study, RZWQM2 hybridized with DSSAT was calibrated and validated against soil moisture, cottield, and development stage data collected from 2006 to 2013 in a flood-irrigated cotton field located in an extremely dry oasis in Cele, situated in Xinjiang, China (mean annual precipitation 37 mm). The simulated water balance was analyzed to determine the actual crop water consumption, crop water requirements, and seepage loss. Subsequently, an optimal irrigation scheme was developed using RZWQM2 by averting crop water stress from planting to 90% open boll. In comparison to similar studies, the accuracy of soil moisture content simulations was deemed acceptable based on percent bias (PBIAS < ±15%), coefficient of determination (0.378 = R2 = 0.636), Nash-Sutcliffe model efficiency (0.130 = ME = 0.557), and root mean squared error (0.022 m3 m-3 = RMSE = 0.031 m3 m-3). The model performed well in simulating cotton yield (R2 = 0.79, ME = 0.75, RMSE = 417.0 kg ha-1, and relative RMSE (rRMSE) = 12.5%). Model-simulated plant emergence dates were generally six days late because of the model’s lack of a component for mulching after seeding. Other phenological dates were closely matched, with a mean difference of ±4 days. On average, over eight years, the simulated growing season (planting to 90% open boll) water balance showed that the cotton crop consumed 532 mm year-1 of water under current irrigation practices, while 109 mm of water was lost through deep seepage. However, based on simulated PET, the crop water requirement was 641 mm year-1, suggesting water stress under current irrigation practices. Under these conditions, water stress occurred mainly during the late stages of cotton growth. The model-simulated actual evapotranspiration (ET) is comparable to the calculated ET using the water balance method, with percent error of -1.3%, indicating the rationality of applying model-simulated results in a water stress-based irrigation scheduling method. On average, the water stress-minimizing RZWQM2 irrigation schedule resulted in an apparent irrigation water savings of 32 mm year-1 (4.9%) and an annual yield increase of 527 kg ha-1 (16.3%). RZWQM2 was shown to be suitable for simulating soil hydrology and crop development in an agricultural system implemented in an extremely dry climate. Rescheduling of irrigation using a water stress-based method can be used to optimize irrigation water use and cotton production. Keywords: Cotton production, Optimum irrigation, RZWQM2, Soil water content, Water stress, WS-based regime.

Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Yellow River ◽  
The Yellow River

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

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