GNSS-R测量地表土壤湿度的地基实验

应用地基GNSS-R测量土壤湿度,相比空基而言,反射信号接收天线安装位置低,反射区面积小,反射区域内土壤构成成分一致,可以克服空基实验带来的反射区面积大、反射区内土壤地貌复杂的因素,有利于提高反演的精度.本文介绍了地基GNSS-R反演土壤湿度的原理和方法.首先通过归一化处理消除电离层和中性大气对信号强度的影响,然后利用光滑地表散射模型和土壤介电常数模型反演土壤湿度.为验证GNSS-R反演结果的精度, 利用配置右旋天线和左旋天线的GNSS-R接收机在武汉华中农业大学试验田开展地基GNSS-R测量土壤湿度实验,用土壤湿度计(TDR)与GNSS-R一起进行了联合观测, 对实测数据进行分析和统计,在低洼区和平整区观测的对比结果表明,利用多颗高仰角卫星进行联合反演,减小了单颗星反演的误差.实验证明地基实验对于GNSS-R土壤湿度的定量反演研究具有重要作用,其也为利用GNSS-R技术构建大范围的土壤湿度监测网提供了可能性.     

A stochastic model describing the impact of daily rainfall depth distribution on the soil water balance

Simplified, vertically-averaged soil moisture models have been widely used to describe and study eco-hydrological processes in water-limited ecosystems. The principal aim of these models is to understand how the main physical and biological processes linking soil, vegetation, and climate impact on the statistical properties of soil moisture. A key component of these models is the stochastic nature of daily rainfall, which is mathematically described as a compound Poisson process with daily rainfall amounts drawn from an exponential distribution. Since measurements show that the exponential distribution is often not the best candidate to fit daily rainfall, we compare the soil moisture probability density functions obtained from a soil water balance model with daily rainfall depths assumed to be distributed as exponential, mixed-exponential, and gamma. This model with different daily rainfall distributions is applied to a catchment in New South Wales, Australia, in order to show that the estimation of the seasonal statistics of soil moisture might be improved when using the distribution that better fits daily rainfall data. This study also shows that the choice of the daily rainfall distributions might considerably affect the estimation of vegetation water-stress, leakage and runoff occurrence, and the whole water balance.     

Influence of rainfall and soil properties spatial aggregation on extreme flash flood response modelling: An evaluation based on the Sesia river basin,North Western Italy

High resolution radar rainfall fields and a distributed hydrologic model are used to evaluate the sensitivity of flood and flash flood simulations to spatial aggregation of rainfall and soil properties at catchment scales ranging from 75 to 983 km². Hydrologic modeling is based on a Hortonian infiltration model and a network-based representation of hillslope and channel flow. The investigation focuses on three extreme flood and flash flood events occurred on the Sesia river basin, North Western Italy, which are analysed by using four aggregation lengths ranging from 1 to 16 km. The influence of rainfall spatial aggregation is examined by using the flow distance as a spatial coordinate, hence emphasising the role of river network in the averaging of space–time rainfall. The effects of reduced and distorted rainfall spatial variability on peak discharge have been found particularly severe for the flash flood events, with peak errors up to 35% for rainfall aggregation of 16 km and at 983 km² catchment size. Effects are particularly remarkable when significant structured rainfall variability combines with relatively important infiltration volumes due to dry initial conditions, as this emphasises the non-linear character of the rainfall–runoff relationship. In general, these results confirm that the correct estimate of rainfall volume is not enough for the accurate reproduction of flash flood events characterised by large and structured rainfall spatial variability, even at catchment scales around 250 km². However, accurate rainfall volume estimation may suffice for less spatially variable flood events. Increasing the soil properties aggregation length exerts similar effects on peak discharge errors as increasing the rainfall aggregation length, for the cases considered here and after rescaling to preserve the rainfall volume. Moreover, peak discharge errors are roughly proportional to runoff volume errors, which indicates that the shape of the flood wave is influenced in a limited way by modifying the detail of the soil property spatial representation. Conversely, rainfall aggregation may exert a pronounced influence on the discharge peak by reshaping the spatial organisation of the runoff volumes and without a comparable impact on the runoff volumes.     

Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states

We assess the potential of updating soil moisture states of a distributed hydrologic model by assimilating streamflow and in situ soil moisture data for high-resolution analysis and prediction of streamflow and soil moisture. The model used is the gridded Sacramento (SAC) and kinematic-wave routing models of the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM) operating at an hourly time step. The data assimilation (DA) technique used is variational assimilation (VAR). Assimilating streamflow and soil moisture data into distributed hydrologic models is new and particularly challenging due to the large degrees of freedom associated with the inverse problem. This paper reports findings from the first phase of the research in which we assume, among others, perfectly known hydrometeorological forcing. The motivation for the simplification is to reduce the complexity of the problem in favour of improved understanding and easier interpretation even if it may compromise the goodness of the results. To assess the potential, two types of experiments, synthetic and real-world, were carried out for Eldon (ELDO2), a 795-km² headwater catchment located near the Oklahoma (OK) and Arkansas (AR) border in the U.S. The synthetic experiment assesses the upper bound of the performance of the assimilation procedure under the idealized conditions of no structural or parametric errors in the models, a full dynamic range and no microscale variability in the in situ observations of soil moisture, and perfectly known univariate statistics of the observational errors. The results show that assimilating in situ soil moisture data in addition to streamflow data significantly improves analysis and prediction of soil moisture and streamflow, and that assimilating streamflow observations at interior locations in addition to those at the outlet improves analysis and prediction of soil moisture within the drainage areas of the interior stream gauges and of streamflow at downstream cells along the channel network. To assess performance under more realistic conditions, but still under the assumption of perfectly known hydrometeorological forcing to allow comparisons with the synthetic experiment, an exploratory real-world experiment was carried out in which all other assumptions were lifted. The results show that, expectedly, assimilating interior flows in addition to outlet flow improves analysis as well as prediction of streamflow at stream gauge locations, but that assimilating in situ soil moisture data in addition to streamflow data provides little improvement in streamflow analysis and prediction though it reduces systematic biases in soil moisture simulation.     

Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.     

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.     

Internal soil moisture and piezometric responses to rainfall-induced shallow slope failures

Better knowledge regarding internal soil moisture and piezometric responses in the process of rainfall-induced shallow slope failures is the key to an effective prediction of the landslide and/or debris flow initiation. To this end, internal soil moisture and piezometric response of 0.7-m-deep, 1.5-m-wide, 1.7-m-high, and 3.94-m-long semi-infinite sandy slopes rested on a bi-linear impermeable bedrock were explored using a chute test facility with artificial rainfall applications. The internal response time defined by the inflection point of the soil moisture and piezometric response curves obtained along the soil–bedrock interface were closely related to some critical failure states, such as the slope toe failure and extensive slope failures. It was also found that the response times obtained at the point of abrupt bedrock slope decrease can be used as indicators for the initiation of rainfall-induced shallow slope failures. An investigation of spatial distributions of soil water content, ω (or degrees of saturation, S_r), in the slope at critical failure states shows that the 0.2 m – below – surface zone remains unsaturated with S_r 40–60%, regardless of their distances from the toe and the rainfall intensity. Non-uniform distributions of ω (or S_r) along the soil–bedrock interface at critical failure states were always associated with near-saturation states (S_r 80–100%) around the point of bedrock slope change or around the transient ‘toe’ upstream of the slumped mass induced by the retrogressive failure of the slope. These observations suggest the important role of the interflow along the soil–bedrock interface and the high soil water content (or high porewater pressure) around the point of bedrock slope deflection in the rainfall-induced failure of sandy slopes consisting of shallow impermeable bedrocks. The present study proposes an ‘internal response time’ criterion to substantiate the prediction of rainfall-induced shallow slope failures. It is believed that the ‘internal response time’ reflects the overall characteristics of a slope under rainfall infiltration and can be as useful as the conventional meteorology-based threshold times. The ‘internal response time’ theory can be generalized via numerical modeling of slope hydrology, slope geology and slope stability in the future.     

The effects of initial soil moisture conditions on swale flow hydrographs

The effects of soil water content (SWC) on the formation of run‐off in grass swales draining into a storm sewer system were studied in two 30‐m test swales with trapezoidal cross sections. Swale 1 was built in a loamy fine‐sand soil, on a slope of 1.5%, and Swale 2 was built in a sandy loam soil, on a slope of 0.7%. In experimental runs, the swales were irrigated with 2 flow rates reproducing run‐off from block rainfalls with intensities approximately corresponding to 2‐month and 3‐year events. Run‐off experiments were conducted for initial SWC (SWC_ini) ranging from 0.18 to 0.43 m³/m³. For low SWC_ini, the run‐off volume was greatly reduced by up to 82%, but at high SWC_ini, the volume reduction was as low as 15%. The relative swale flow volume reductions decreased with increasing SWC_ini and, for the conditions studied, indicated a transition of the dominating swale functions from run‐off dissipation to conveyance. Run‐off flow peaks were reduced proportionally to the flow volume reductions, in the range from 4% to 55%. The swale outflow hydrograph lag times varied from 5 to 15 min, with the high values corresponding to low SWC_ini. Analysis of swale inflow/outflow hydrographs for high SWC_ini allowed estimations of the saturated hydraulic conductivities as 3.27 and 4.84 cm/hr in Swales 1 and 2, respectively. Such estimates differed from averages (N = 9) of double‐ring infiltrometer measurements (9.41 and 1.78 cm/hr). Irregularities in swale bottom slopes created bottom surface depression storage of 0.35 and 0.61 m³ for Swales 1 and 2, respectively, and functioned similarly as check berms contributing to run‐off attenuation. The experimental findings offer implications for drainage swale planning and design: (a) SWC_ini strongly affect swale functioning in run‐off dissipation and conveyance during the early phase of run‐off, which is particularly important for design storms and their antecedent moisture conditions, and (b) concerning the longevity of swale operation, Swale 1 remains fully functional even after almost 60 years of operation, as judged from its attractive appearance, good infiltration rates (3.27 cm/hr), and high flow capacity.     

Stability of banded vegetation patterns under seasonal rainfall and limited soil moisture storage capacity

The delicate equilibrium of soil moisture and biomass may become unstable under water scarcity conditions causing banded vegetation patterns to form on hillsides of semi-arid catchments. Soil related processes that induce instability (namely: soil moisture advection and diffusion), have been evaluated numerically for different rainfall regimes. This study addresses the combined influence of some relevant soil characteristics, and the effect of seasonal precipitation on vegetation patterns, advancing the comprehension of those mechanisms that cause shifts toward banded vegetation patterns or bare states.     

Sensitivity of soil moisture field evolution to rainfall forcing

In this paper the temporal behaviour of soil moisture is modelled and statistically characterized by use of the zero‐dimensional model for soil moisture dynamics and the rectangular pulses Poisson process model for rainfall forcing. The mean, covariance and spectral density function of soil moisture (both instantaneous and locally averaged cases) are analytically derived to evaluate its sensitivity to the model parameters. Finally, the probability density function of soil moisture is derived to evaluate the effect of rainfall forcing. All the model parameters used have been tuned to the Monsoon '90 data. Results can be summarized as follows. (1) Only the soil moisture model parameters (η and nZ_r) are found to affect the autocorrelation function in a distinguishable manner. On the other hand, both the rainfall model parameter (θ) and the effective soil depth (nZ_r) are found to be of impact to the soil moisture spectrum. However, as the smoothing (or damping) effect of soil is so dominant, about ±20% variation of one parameter seems not to affect significantly the second‐order statistics of soil moisture. (2) More difference can be found by applying a longer averaging time, which is found to obviously decrease the variance but increase the correlation even though no overlapping between neighbouring soil moisture data was allowed. (3) Among rainfall model parameters, the arrival rate (λ) was found to be most important for the soil moisture evolution. When increasing the arrival rate of rainfall, the histogram of soil moisture shifts its peak to a certain value as well as becomes more concentrated around the peak. However, by decreasing the arrival rate of rainfall, a much smaller (almost to zero) mean value of soil moisture was estimated, even though the total volume of rainfall remained constant. This indicates that desertification may take place without decreasing the total volume of rainfall. Copyright © 2005 John Wiley & Sons, Ltd.     

The effect of biological soil crusts on soil moisture dynamics under different rainfall conditions in the Tengger Desert,China

Rainfall is considered as the dominant water replenishment in desert ecosystems, and the conversion of rainfall into soil water availability plays a central role in sustaining the ecosystem function. In this study, the role of biological soil crusts (BSCs), typically formed in the revegetated desert ecosystem in the Tengger Desert of China, in converting rainfall into soil water, especially for the underlying soil moisture dynamics, was clarified by taking into account the synthetic effects of BSCs, rainfall characteristics, and antecedent soil water content on natural rainfall conditions at point scale. Our results showed that BSCs retard the infiltration process due to its higher water holding capacity during the initial stage of infiltration, such negative effect could be offset by the initial wet condition of BSCs. The influence of BSCs on infiltration amount was dependent on rainfall regime and soil depth. BSCs promoted a higher infiltration through the way of prolonged water containing duration in the ground surface and exhibited a lower infiltration at deep soil layer, which were much more obvious under small and medium rainfall events for the BSCs area compared with the sand area. Generally, the higher infiltration at top soil layer only increased soil moisture at 0.03 m depth; in consequence, there was no water recharge for the deep soil, and thus, BSCs had a negative effect on soil water effectiveness, which may be a potential challenge for the sustainability of the local deep‐rooted vegetation under the site specific rainfall conditions in northwestern China.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Time-dependency of runoff velocity and erosion the effect of the initial soil moisture profile

The paper reports on experiments carried out to evaluate the effect of the initial soil moisture profile on temporal variations in runoff erosion rate. The moisture profile was varied by applying infrared heating to the soil sample surface over various time periods, while runoff erosivity was varied by varying the slope of the flume. The experiment confirms that dry loamy soils are very erodible: on a slope length of only 4.3 m long sediment concentrations are near transporting capacity in case of a dry soil sample. It appears that temporal variations in sediment concentrations can be well simulated using a simple relationship between runoff erosion resistance and initial soil moisture content, thereby implicitly assuming that the effect of initial moisture content is persistent over the whole duration of the experiment. The implications of these findings with respect to the modelling of sediment output from larger catchments and the design of experiments on rill erodibility are discussed. The experiments also show that, under the present circumstances, mean velocities in the rills appear to be independent of slope. This finding may be of importance with respect to overland flow routing and deterministic erosion modelling.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

Spatial variation in solutional denudation and soil moisture over a hillslope hollow

A spatial pattern of relative solutional denudation is described for a hillslope hollow and adjacent spurs at Bicknoller Combe, Somerset. The pattern was obtained from a network of micro-weight loss rock tablets emplaced in the soil. The results show that the hollow is the main locus of solutional denudation. The soil moisture distribution over the hollow indicates that it is a transmission zone for acid soil water percolating from the adjacent spurs to the saturated wedge at the base of the hollow. The wetter acid soils in the hollow are responsible for the relatively higher solutional denudation taking place in the hollow.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

Effects of rainfall intensity and slope gradient on the application of vetiver grass mulch in soil and water conservation

The objective of this study is to investigate the effect of rainfall intensity and slope gradient on the performance ofvetiver grass mulch (VGM) in soil and water conservation.The study involved field ...     

Spatial and temporal dynamics of soil moisture after rainfall events along a slope in Regosols of southwest China

High frequency seasonal drought in purple soils (Regosols in FAO taxonomy) of the hilly upland areas of Sichuan basin, China, is one of the key restrictive factors for crop production. In order to manage irrigation and fertilizer application in these soils effectively, the soil water content in a sloped plot with 60 cm soil depth was measured by neutron probe devices to investigate the soil moisture regime during the 1998 rainy season after various amounts of rainfall events. The results showed that variation of soil moisture along the slope positions was highest in the top soil layer during the period of sporadic rainfall that did not induce any runoff. The coefficients of variation of soil moisture at various slope positions (upper, middle, and lower) are 17·36%, 8·95%, 10·25%, 8·58%, 8·05% and 9·21% at the 10 cm, 20 cm, 30 cm, 40 cm, 50 cm and 60 cm soil depths respectively. When surface runoff occurred, the soil moisture dynamics at various positions on the plot were then very different. Soil water content decreased more rapidly on the upper slope than on the middle and lower slope positions. When both surface runoff and throughflow occurred, the soil moisture dynamics in the various layers showed a stable period (soil water content is near constant as time elapses) that lasted about 1 week. Also, the pattern of moisture dynamics is ‘decreasing–stabilization–decreasing’. Thus, irrigation and fertilization management according to the spatial and temporal features of soil moisture dynamics on sloped land can increase the water and fertilizer utilization efficacy by reducing their losses during the stable period. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessment of drought vulnerability based on the soil moisture PDF

This paper studies the statistics of the soil moisture condition and its monthly variation for the purpose of evaluating drought vulnerability. A zero-dimensional soil moisture dynamics model with the rainfall forcing by the rectangular pulses Poisson process model are used to simulate the soil moisture time series for three sites in Korea: Seoul, Daegu, and Jeonju. These sites are located in the central, south-eastern, and south-western parts of the Korean Peninsular, respectively. The model parameters are estimated on a monthly basis using hourly rainfall data and monthly potential evaporation rates obtained by the Penmann method. The resulting soil moisture simulations are summarized on a monthly basis. In brief, the conclusions of our study are as follows. (1) Strong seasonality is observed in the simulations of soil moisture. The soil moisture mean is less than 0.5 during the dry spring season (March, April, and June), but other months exceed the 0.5 value. (2) The spring season is characterized by a low mean value, a high standard deviation and a positive skewness of the soil moisture content. On the other hand, the wet season is characterized by a high mean value, low standard deviation, and negative skewness of the soil moisture content. Thus, in the spring season, much drier soil moisture conditions are apparent due to the higher variability and positive skewness of the soil moisture probability density function (PDF), which also indicates more vulnerability to severe drought occurrence. (3) Seoul, Daegue, and Jeonju show very similar overall trends of soil moisture variation; however, Daegue shows the least soil moisture contents all through the year, which implies that the south-eastern part of the Korean Peninsula is most vulnerable to drought. On the other hand, the central part and the south-western part of the Korean peninsula are found to be less vulnerable to the risk of drought. The conclusions of the study are in agreement with the climatology of the Korean Peninsula.     

Capturing critical behaviour in soil moisture spatio-temporal dynamics

A physical system is subject to a phase transition process when it shows a discontinuous change of a macroscopic feature of the system under a continuous change of a system’s state variable.