A temperature prediction-correction method for estimating surface soil heat flux from soil temperature and moisture data

Surface soil heat flux is a component of surface energy budget and its estimation is needed in land-atmosphere interaction studies. This paper develops a new simple method to estimate soil heat flux from soil temperature and moisture observations. It gives soil temperature profile with the thermal diffusion equation and, then, adjusts the temperature profile with differences between observed and computed soil temperatures. The soil flux is obtained through integrating the soil temperature profile. Compared with previous methods, the new method does not require accurate thermal conductivity. Case studies based on observations, synthetic data, and sensitivity analyses show that the new method is preferable and the results obtained with it are not sensitive to the availability of temperature data in the topsoil. In addition, we pointed out that the soil heat flux measured with a heat-plate can be quite erroneous in magnitude though its phase is accurate.     

Soil organic carbon storage and soil CO_2 flux in the alpine meadow ecosystem

High-resolution sampling,measurements of organic carbon contents and 14C signatures of selected four soil profiles in the Haibei Station situated on the northeast Tibetan Plateau,and application of 14C tracing technology were conducted in an attempt to investigate the turnover times of soil organic car-bon and the soil-CO2 flux in the alpine meadow ecosystem. The results show that the organic carbon stored in the soils varies from 22.12×104 kg C hm-2 to 30.75×104 kg C hm-2 in the alpine meadow eco-systems,with an average of 26.86×104 kg C hm-2. Turnover times of organic carbon pools increase with depth from 45 a to 73 a in the surface soil horizon to hundreds of years or millennia or even longer at the deep soil horizons in the alpine meadow ecosystems. The soil-CO2 flux ranges from 103.24 g C m-2 a-1 to 254.93 gC m-2 a-1,with an average of 191.23 g C m-2 a-1. The CO2 efflux produced from microbial decomposition of organic matter varies from 73.3 g C m-2 a-1 to 181 g C m-2 a-1. More than 30% of total soil organic carbon resides in the active carbon pool and 72.8%―81.23% of total CO2 emitted from or-ganic matter decomposition results from the topsoil horizon (from 0 cm to 10 cm) for the Kobresia meadow. Responding to global warming,the storage,volume of flow and fate of the soil organic carbon in the alpine meadow ecosystem of the Tibetan Plateau will be changed,which needs further research.     

Soil organic carbon and nitrogen content of density fractions and effect of meadow degradation to soil carbon and nitrogen of fractions in alpine Kobresia meadow

This research was conducted on the non-disturbed native alpine Kobresia meadow(YF) and the severely degraded meadow(SDL) of Dari County of Qinghai Province.By a density fractionation approach,each soil sample was divided into two fractions:light fraction(LF) and heavy fraction(HF).The obtained fractions were analyzed for organic carbon(OC) and nitrogen(N) concentrations.The results showed:(1) the OC concentration in HF and LF was 3.84% and 28.63% respectively while the nitrogen concentration in HF and LF wa...     

Use of soil moisture data and curve number method for estimating runoff in the Loess Plateau of China

The Soil Conservation Service curve number (CN) method commonly uses three discrete levels of soil antecedent moisture condition (AMC), defined by the 5‐day antecedent rainfall depth, to describe soil moisture prior to a runoff event. However, this way may not adequately represent soil water conditions of fields and watersheds in the Loess Plateau of China. The objectives of this study were: (1) to determine the effective soil moisture depth to which the CN is most related; (2) to evaluate a discrete and a linear relationship between AMC and soil moisture; and (3) to develop an equation between CN and soil moisture to predict runoff better for the climatic and soil conditions of the Loess Plateau of China. The dataset consisted of 10 years of rainfall, runoff and soil moisture measurements from four experimental plots cropped with millet, pasture and potatoes. Results indicate that the standard CN method underestimated runoff depths for 85 of the 98 observed plot‐runoff events, with a model efficiency E of only 0·243. For our experimental conditions, the discrete and linear approaches improved runoff estimation, but still underestimated most runoff events, with E values of 0·428 and 0·445 respectively. Based on the measured CN values and soil moisture values in the top 15 cm of the soil, a non‐linear equation was developed that predicted runoff better with an E value of 0·779. This modified CN equation was the most appropriate for runoff prediction in the study area, but may need adjustments for local conditions in the Loess Plateau of China. Copyright © 2006 John Wiley & Sons, Ltd.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Year‐round estimation of soil moisture content using temporally variable soil hydraulic parameters

Soil moisture plays a key role in the hydrological cycle as it controls the flux of water between soil, vegetation, and atmosphere. This study is focused on a year‐round estimation of soil moisture in a forested mountain area using the bucket model approach. For this purpose, three different soil moisture models are utilised. The procedure is based on splitting the whole year into two complement periods (dormant and vegetation). Model parameters are allowed to vary between the two periods and also from year to year in the calibration procedure. Consequently, two sets of average model parameters corresponding to dormant and vegetation seasons are proposed. The process of splitting is strongly supported by the experimental data, and it enables us to variate saturated hydraulic conductivity and pore‐size characterisation. The use of the two different parameter sets significantly enhances the simulation of two (Teuling and Troch model and soil water balance model‐green–ampt [SWBM‐GA]) out of three models in the 6‐year period from 2009 to 2014. For these two models, the overall Nash‐Sutcliffe coefficient increased from 0.64 to 0.79 and from 0.55 to 0.80. The third model (the Laio approach) proved to be insensitive to parameter changes due to its insufficient drainage prediction. The variability of the warm and cold parameter sets between particular years is more pronounced in the warm periods. The cold periods exhibited approximately similar character during all 6 years.     

Evolutionary assimilation of streamflow in distributed hydrologic modeling using in-situ soil moisture data

This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

Simulation of moisture content in alpine rockwalls during freeze–thaw events

Rock moisture during freeze–thaw events is a key factor for frost weathering. Data on moisture levels of natural rockwalls are scarce and difficult to obtain. To close this gap, we can benefit from the extensive knowledge of moisture‐related phenomena in building materials, which is incorporated into simulation software, for example the WUFI® package of the Fraunhofer Institute of Building Physics. In this paper we applied and adapted this type of simulation to natural rockwalls to gain new insights on which moisture‐related weathering mechanisms may be important under which conditions. We collected the required input data on physical rock properties and local climate for two study areas in the eastern European Alps with different elevation [Sonnblick, 3106 m above sea level (a.s.l.) and Johnsbach, 700 m a.s.l.] and different lithologies (gneiss and dolomite, respectively). From this data, moisture profiles with depth and fluctuations in the course of a typical year were calculated. The results were cross‐checked with different thermal conditions for frost weathering reported in the literature (volumetric expansion and ice segregation theories). The analyses show that in both study areas the thresholds for frost cracking by volumetric expansion of ice (90% pore saturation, temperature < ?1 °C) are hardly ever reached (in one year only 0.07% of the time in Johnsbach and 0.4% at Sonnblick, mostly in north‐exposed walls). The preconditions for weathering by ice segregation (?3 to ?8 °C, > 60% saturation) prevail over much longer periods; the time spent within this ‘frost cracking window‘ is also higher for north‐facing sites. The influence of current climate warming will reduce effective frost events towards 2100; however the increase of liquid precipitation and rock moisture will promote weathering processes like ice segregation at least at the Sonnblick site. Copyright © 2016 John Wiley & Sons, Ltd.     

A multi-objective calibration approach using in-situ soil moisture data for improved hydrological simulation of the Prairies

ABSTRACT

Traditionally, hydrological models are only calibrated to reproduce streamflow regime without considering other hydrological state variables, such as soil moisture and evapotranspiration. Limited studies have been performed on constraining the model parameters, despite the fact that the presence of a large number of parameters may provide large degree of freedom, resulting in equifinality and poor model performance. In this study, a multi-objective optimization approach is adopted, and both streamflow and soil moisture data are calibrated simultaneously for an experimental study basin in the Saskatchewan Prairies in western Canada. The results of this study show that the multi-objective calibration improves model fidelity compared to the single objective calibration. Moreover, the study demonstrates that single objective calibration performed against only streamflow can fairly mimic the streamflow hydrograph but does not yield realistic estimation of other fluxes such as evapotranspiration and soil moisture (especially in deeper soil layers).     

A particle batch smoother for soil moisture estimation using soil temperature observations

In this study, we present a particle batch smoother (PBS) to determine soil moisture profiles by assimilating soil temperatures at two depths (4 and 8 cm). The PBS can be considered as an extension of the standard particle filter (PF) in which soil moisture is updated within a window of fixed length using all observed soil temperatures in that window. This approach was developed with a view to assimilating temperature observations from distributed temperature sensing (DTS) observations, a technique which can provide temperature observations every meter or less along cables up to kilometers in length. Here, the PBS approach is tested using soil moisture and temperature, and meteorological data from an experimental site in Citra, Florida. Results demonstrate that the PBS provides a statistically significant improvement in estimated soil moisture compared to the PF, with only a marginal increase in computational expense ( < 3% of CPU time). This confirms that assimilating a sequence of temperature observations yields a better soil moisture estimate compared to sequential assimilation of individual temperature observations. The impact of observation interval was investigated for both PF and PBS, and the optimal window length was determined for the PBS. While increasing the observation interval is essential to maintain the spread of particle values in the PF, the PBS performance is best when all available observations are assimilated.     

A shot noise model for the generation of soil moisture data

An attempt is made to estimate the expected contribution of rainfall to soil moisture during the irrigation season. Effective rainfall and evapotranspiration are the parameters considered in the water balance carried out in the root zone. Rainfall occurrence is simulated by a Poisson process whereas evapotranspiration is described by a simple deterministic function of potential evapotranspiration and soil moisture in the root zone. Using the theory of shot noise models a closed form solution is derived from the expected soil moisture in the root zone at the end of the time interval (0,t]. For illustration purposes the proposed model is applied to a series of data from Mikra meteorological station in Greece.List of symbols x change in water storage in the root zone during the time interval t - X infiltrated rainfall of thei th storm event - ET actual evapotranspiration during thej th day - Poisson rate - number of storm events in (0,t] - t _r duration of rainfall - t _b interarrival time - h _i rainfall depth of thei th storm event - i _m mean rainfall intensity - i(t) instantaneous rainfall intensity - x(0),x(t) available soil moisture in the root zone at time 0 andt, respectively - PET potential evapotranspiration rate - x _F available soil moisture in the root zone at field capacity - soil moisture depletion rate (=PET/x _F ) - w impulse shape of filtered Poisson processes - E[·] mean value - S _i time of thei th rainfall event - N(t) time of storm events in (0,t] - estimated standard deviation The following symbols were used in this paper     

Validation of a soil water balance model using soil water content and pressure head data

The validation of soil water balance models and the evaluation of the quality of the model predictions at field‐scale require time‐series of in situ measured model outputs. In our study, we have validated such a model using a 6‐year period with time‐series of automatically recorded, daily volumetric soil water contents measured with the time‐domain reflectometry with intelligent microelements (TRIME) method and daily pressure heads measured with tensiometers. The comparisons of simulated with measured soil water contents and pressure heads were analysed using the modelling efficiency index (IA) and the square root of the mean square error (RMSE) in order to evaluate the prediction quality of the model. In our study, IA and RMSE, obtained either from the comparison of simulated with measured soil water contents or the comparison of calculated with observed pressure heads, in some cases lead to different results regarding the evaluation of the simulation quality of the soil water balance model. For example, a good fit between simulated and observed soil water contents does not necessarily result in a comparably good fit between the corresponding calculated and measured pressure heads. Therefore, a combined use of both measurement techniques, which takes into account their respective advantages and disadvantages, gives a more complete overview on the simulation quality of the soil water balance model than the single use of one of those techniques. Copyright © 2004 John Wiley & Sons, Ltd.     

Synergistic effect of vegetation and air temperature changes on soil water content in alpine frost meadow soil in the permafrost region of Qinghai‐Tibet

Seasonal changes over 2 years (2004–2006) in soil moisture content (θ_v) of frozen alpine frost meadow soils of the Qinghai‐Tibet plateau permafrost region under three different levels of vegetation cover were investigated. Vegetation cover and air temperature changes had significant effects (synergistic effect) on θ_v and its distribution in the soil profile. During periods of soil freezing or thawing, the less the vegetation cover, the quicker the temperature drop or rise of soil water, and the shorter the duration of the soil water freeze–thaw response in the active soil layer. Under 30% and 65% vegetation cover the amplitude of variation in θ_v during the freezing period was 20–26% greater than that under 93% cover, while during the thawing period, it was 1·5‐ to 40·5‐fold greater. The freezing temperature of the surface soil layer, ^fT_s, was 1·6 °C lower under 30% vegetation cover than under 93% vegetation cover. Changes in vegetation cover of the alpine frost meadow affected θ_v and its distribution, as well as the relationship between θ_v and soil temperature (T_s). As vegetation cover decreased, soil water circulation in the active layer increased, and the response to temperature of the water distribution across the soil profile was heightened. The quantity of transitional soil phase water at different depths significantly increased as vegetation cover decreased. The influence of vegetation cover and soil temperature distribution led to a relatively dry soil layer in the middle of the profile (0·70–0·80 m) under high vegetation cover. Alpine meadow θ_v and its pattern of distribution in the permafrost region were the result of the synergistic effect of air temperature and vegetation cover. Copyright © 2007 John Wiley & Sons, Ltd.     

Soil organic carbon storage and soil CO<Subscript>2</Subscript> flux in the alpine meadow ecosystem

High-resolution sampling, measurements of organic carbon contents and ¹⁴C signatures of selected four soil profiles in the Haibei Station situated on the northeast Tibetan Plateau, and application of ¹⁴C tracing technology were conducted in an attempt to investigate the turnover times of soil organic carbon and the soil-CO₂ flux in the alpine meadow ecosystem. The results show that the organic carbon stored in the soils varies from 22.12×10⁴ kg C hm⁻² to 30.75×10⁴ kg C hm⁻² in the alpine meadow ecosystems, with an average of 26.86×10⁴ kg C hm⁻². Turnover times of organic carbon pools increase with depth from 45 a to 73 a in the surface soil horizon to hundreds of years or millennia or even longer at the deep soil horizons in the alpine meadow ecosystems. The soil-CO₂ flux ranges from 103.24 g C m⁻² a⁻¹ to 254.93 gC m⁻² a⁻¹, with an average of 191.23 g C m⁻² a⁻¹. The CO₂ efflux produced from microbial decomposition of organic matter varies from 73.3 g C m⁻² a⁻¹ to 181 g C m⁻² a⁻¹. More than 30% of total soil organic carbon resides in the active carbon pool and 72.8%281.23% of total CO₂ emitted from organic matter decomposition results from the topsoil horizon (from 0 cm to 10 cm) for the Kobresia meadow. Responding to global warming, the storage, volume of flow and fate of the soil organic carbon in the alpine meadow ecosystem of the Tibetan Plateau will be changed, which needs further research. Supported by the National Natural Science Foundation of China (Grant Nos. 40231015, 40471120 and 40473002) and the Guangdong Provincial Natural Science Foundation of China (Grant No. 06300102)     

Determining hydrological regimes in an agriculturally used tropical inland valley wetland in Central Uganda using soil moisture,groundwater, and digital elevation data

Inadequate knowledge exists on the distribution of soil moisture and shallow groundwater in intensively cultivated inland valley wetlands in tropical environments, which are required for determining the hydrological regime. This study investigated the spatial and temporal variability of soil moisture along 4 hydrological positions segmented as riparian zone, valley bottom, fringe, and valley slope in an agriculturally used inland valley wetland in Central Uganda. The determined hydrological regimes of the defined hydrological positions are based on soil moisture deficit calculated from the depth to the groundwater table. For that, the accuracy and reliability of satellite‐derived surface models, SRTM‐30m and TanDEM‐X‐12m, for mapping microscale topography and hydrological regimes are evaluated against a 5‐m digital elevation model (DEM) derived from field measurements. Soil moisture and depth to groundwater table were measured using frequency domain reflectometry sensors and piezometers installed along the hydrological positions, respectively. Results showed that spatial and temporal variability in soil moisture increased significantly (p < .05) towards the riparian zone; however, no significant difference was observed between the valley bottom and riparian zone. The distribution of soil hydrological regimes, saturated, near‐saturated, and nonsaturated regimes does not correlate with the hydrological positions. This is due to high spatial and temporal variability in depth to groundwater and soil moisture content across the valley. Precipitation strongly controlled the temporal variability, whereas microscale topography, soil properties, distance from the stream, anthropogenic factors, and land use controlled the spatial variability in the inland valley. TanDEM‐X DEM reasonably mapped the microscale topography and thus soil hydrological regimes relative to the Shuttle Radar Topography Mission DEM. The findings of the study contribute to improved understanding of the distribution of hydrological regimes in an inland valley wetland, which is required for a better agricultural water management planning.