SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.     

Curve‐Number/Green–Ampt mixed procedure for streamflow predictions in ungauged basins: Parameter sensitivity analysis

A procedure combining the Soil Conservation Service‐Curve Number (SCS‐CN) method and the Green–Ampt (GA) infiltration equation was recently developed to overcome some of the drawbacks of the classic SCS‐CN approach when estimating the volume of surface runoff at a sub‐daily time resolution. The rationale of this mixed procedure, named Curve Number for Green–Ampt (CN4GA), is to use the GA infiltration model to distribute the total volume of the net hyetograph (rainfall excess) provided by the SCS‐CN method over time. The initial abstraction and the total volume of rainfall given by the SCS‐CN method are used to identify the ponding time and to quantify the hydraulic conductivity parameter of the GA equation. In this paper, a sensitivity analysis of the mixed CN4GA parameters is presented with the aim to identify conditions where the mixed procedure can be effectively used within the Prediction in Ungauged Basin perspective. The effects exerted by changes in selected input parameters on the outputs are evaluated using rectangular and triangular synthetic hyetographs as well as 100 maximum annual storms selected from synthetic rainfall time series. When applied to extreme precipitation events, which are characterized by predominant peaks of rainfall, the CN4GA appears to be rather insensitive to the input hydraulic parameters of the soil, which is an interesting feature of the CN4GA approach and makes it an ideal candidate for the rainfall excess estimation at sub‐daily temporal resolution at ungauged sites. Copyright © 2013 John Wiley & Sons, Ltd.     

Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins

Approaches to modeling the continuous hydrologic response of ungauged basins use observable physical characteristics of watersheds to either directly infer values for the parameters of hydrologic models, or to establish regression relationships between watershed structure and model parameters. Both these approaches still have widely discussed limitations, including impacts of model structural uncertainty. In this paper we introduce an alternative, model independent, approach to streamflow prediction in ungauged basins based on empirical evidence of relationships between watershed structure, climate and watershed response behavior. Instead of directly estimating values for model parameters, different hydrologic response behaviors of the watershed, quantified through model independent streamflow indices, are estimated and subsequently regionalized in an uncertainty framework. This results in expected ranges of streamflow indices in ungauged watersheds. A pilot study using 30 UK watersheds shows how this regionalized information can be used to constrain ensemble predictions of any model at ungauged sites. Dominant controlling characteristics were found to be climate (wetness index), watershed topography (slope), and hydrogeology. Main streamflow indices were high pulse count, runoff ratio, and the slope of the flow duration curve. This new approach provided sharp and reliable predictions of continuous streamflow at the ungauged sites tested.     

Cotton canopy reflectance under variable solar zenith angles: Implications of use in evapotranspiration models

Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ET_c) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices.     

Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data

Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. Derived from several field and laboratory tests, various simplified procedures such as stress-based, strain-based, Chinese criteria, etc. have been developed by utilizing case studies and undisturbed soil specimens. In order to address the collective knowledge built up in conventional liquefaction engineering, an alternative general regression neural network model is proposed in this paper.To meet this objective, a total of 620 sets of data including 12 soil and seismic parameters are introduced into the model. The data includes the results of field tests from the two major earthquakes that took place in Turkey and Taiwan in 1999 and some of the desired input parameters are obtained from correlations existing in the literature.The proposed GRNN model was developed in four phases, mainly: identification phase, collection phase, implementation phase, and verification phase. An iterative procedure was followed to maximize the accuracy of the proposed model. The case records were divided randomly into testing, training, and validation datasets.Generating a model that takes into account of 12 soil and seismic parameters is not feasible by using simplified techniques; however, the proposed GRNN model effectively explored the complex relationship between the introduced soil and seismic input parameters and validated the liquefaction decision obtained by simplified methods. The proposed GRNN model predicted well the occurrence/nonoccurrence of soil liquefaction in these sites. The model provides a viable tool to geotechnical engineers in assessing seismic condition in sites susceptible to liquefaction.     

Rock-physics-based carbonate pore type characterization and reservoir permeability heterogeneity evaluation, Upper San Andres reservoir, Permian Basin, west Texas

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (σ′_v0), soil type, shear wave velocity (V_s) and earthquake parameters such as peak horizontal acceleration (a_max) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are σ′_v0, soil type, V_s, a_max and M. In the other model based on shear wave velocity alone uses V_s, a_max and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model.     

Use of terrain variables for mapping gully erosion susceptibility in Lebanon

This paper predicts the geographic distribution and size of gullies across central Lebanon using a geographic information system (GIS) and terrain analysis. Eleven primary (elevation; upslope contributing area; aspect; slope; plan, profile and tangential curvature; flow direction; flow width; flow path length; rate of change of specific catchment area along the direction of flow) and three secondary (steady‐state; quasi‐dynamic topographic wetness; sediment transport capacity) topographic variables were generated and used along with digital data collected from other sources (soil, geology) to statistically explain gully erosion field measurements. Three tree‐based regression models were developed using (1) all variables, (2) primary topographic variables only and (3) different pairs of variables. The best regression tree model combined the steady‐state topographic wetness and sediment transport capacity indices and explained 80% of the variability in field gully measurements. This model proved to be simple, quick, realistic and practical, and it can be applied to other areas of the Mediterranean region with similar environmental conditions, thereby providing a tool to help with the implementation of plans for soil conservation and sustainable management. Copyright © 2007 John Wiley & Sons, Ltd.     

Regression method for estimating rainfall recharge at unconfined sandy aquifers with an equatorial climate

A reliable estimate of rainfall recharge is essential for groundwater system managements. This study develops a method based on regression equations for estimating rainfall recharge at unconfined sandy aquifers with an equatorial climate. The developed method (GR-I method) is generally efficient for estimating long-term regional recharge, as the computational procedures could be formulated and executed easily using Microsoft's Excel spreadsheet. More importantly, its application could be extended to sand textures different from the sand texture used in developing the regression equations. To evaluate its reliability, the method was applied to estimate monthly gross recharge percentages at the Changi reclaimed land. When ignoring the effect of rainfall clusters, the GR-I method was found to underestimate the monthly gross recharge percentages for those months with high monthly rainfall depths. By integrating the effect of rainfall clusters, the GR-I method yields reliable estimates of monthly gross recharge percentages. By including daily potential evaporation as an additional input variable, the Extended GR-I method was found to be not superior to the GR-I method, implying that soil moisture availability is the major governing factor for actual soil evaporation in the highly porous sand medium, instead of atmospheric demand represented by the potential evaporation rate. Using the GR-I method, the mean annual net recharge percentage of the study site was found to fall between 56·9 and 69·9%, which corresponds to a net recharge depth of 1073·8–1745·8 mm. Although the developed method provides a good alternative to other widely used methods, its recharge estimates still needs to be collaborated with estimates from other methods, as multiple techniques are highly recommended in any groundwater recharge estimations. Copyright © 2007 John Wiley & Sons, Ltd.     

Incorporating variable source area hydrology into a curve-number-based watershed model

Many water quality models use some form of the curve number (CN) equation developed by the Soil Conservation Service (SCS; U.S. Depart of Agriculture) to predict storm runoff from watersheds based on an infiltration-excess response to rainfall. However, in humid, well-vegetated areas with shallow soils, such as in the northeastern USA, the predominant runoff generating mechanism is saturation-excess on variable source areas (VSAs). We reconceptualized the SCS–CN equation for VSAs, and incorporated it into the General Watershed Loading Function (GWLF) model. The new version of GWLF, named the Variable Source Loading Function (VSLF) model, simulates the watershed runoff response to rainfall using the standard SCS–CN equation, but spatially distributes the runoff response according to a soil wetness index. We spatially validated VSLF runoff predictions and compared VSLF to GWLF for a subwatershed of the New York City Water Supply System. The spatial distribution of runoff from VSLF is more physically realistic than the estimates from GWLF. This has important consequences for water quality modeling, and for the use of models to evaluate and guide watershed management, because correctly predicting the coincidence of runoff generation and pollutant sources is critical to simulating non-point source (NPS) pollution transported by runoff. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatial prediction and modeling of soil salinity using simple cokriging,artificial neural networks,and support vector machines in El Outaya plain,Biskra, southeastern Algeria

Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation, especially in the arid and semi-arid regions.An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and decision-makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production.El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irrigation. This study aims to compare the performance of simple kriging, cokriging(SCOK), multilayer perceptron neural networks(MLP-NN), and support vector machines(SVM)in the prediction of topsoil and subsoil salinity. The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environmental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK, MLP-NN, and SVM. The optimal input combination was determined using multiple linear stepwise regression(MLSR). The results revealed that the SCOK using field covariates including water electrical conductivity(ECw) and sand percentage(sand %), and environmental covariates including land surface temperature(LST), topographic wetness index(TWI), and elevation could significantly increase the accuracy of soil salinity spatial prediction. The comparison of the prediction accuracy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST, TWI, and elevation as inputs were more accurate in predicting the topsoil salinity [ECs(TS)] with a mean absolute error(MAE) of 0.43, root mean square error(RMSE) of 0.6 and correlation coefficient of 0.946. MLP-NN using ECw and sand % as inputs were more accurate in predicting the subsoil salinity [ECs(SS)] with MAE of 0.38, RMSE of0.6, and R of 0.968.     

On the estimation of antecedent wetness conditions in rainfall–runoff modelling

The analysis of the physical processes involved in a conceptual model of soil water content balance is addressed with the objective of its application as a component of rainfall–runoff modelling. The model uses routinely measured meteorological variables (rainfall and air temperature) and incorporates a limited number of significant parameters. Its performance in estimating the soil moisture temporal pattern was tested through local measurements of volumetric water content carried out continuously on an experimental plot located in central Italy. The analysis was carried out for different periods in order to test both the representation of infiltration at the short time‐scale and drainage and evapotranspiration processes at the long time‐scale. A robust conceptual model was identified that incorporated the Green–Ampt approach for infiltration and a gravity‐driven approximation for drainage. A sensitivity analysis was performed for the selected model to assess the model robustness and to identify the more significant parameters involved in the principal processes that control the soil moisture temporal pattern. The usefulness of the selected model was tested for the estimation of the initial wetness conditions for rainfall–runoff modelling at the catchment scale. Specifically, the runoff characteristics (runoff depth and peak discharge) were found to be dependent on the pre‐event surface soil moisture. Both observed values and those estimated by the model gave good results. On the contrary, with the antecedent wetness conditions furnished by two versions of the antecedent precipitation index (API), large errors were obtained. Copyright © 2007 John Wiley & Sons, Ltd.     

A model to simulate recharge processes of karst massifs

The recharge processes have been evaluated for two karst massifs of southern Italy, the Mt Terminio and Mt Cervialto, characterized by wide endorheic areas. The annual mean recharge has been estimated by Geographic Information System (GIS) tools, from regression of annual mean values of different ground‐elevated rain gauges and thermometers. The recharge has been distinguished for endorheic areas and the other areas of spring catchment, and the ratio between the output spring and input rainfall has been also estimated (recharge coefficient). The annual recharge has been used to calibrate a daily scale model, which allows to estimate the amount of effective rainfall, which is retained as soil moisture; the amount reaching the water table (recharge s.s.); and the amount of rainfall, which develops the runoff and leaves the catchment. All these amounts vary through the hydrological year, in function of soil moisture deficit and daily rainfall intensity. The model allows estimating the recharge conditions through the hydrological year, and it is a useful tool for water management. Copyright © 2014 John Wiley & Sons, Ltd.     

Improvement of a simplified process‐based model for estimating transpiration under water‐limited conditions

Plant transpiration depends on environmental conditions, and soil water availability is its primary control under water deficit conditions. In this study, we improve a simplified process‐based model (hereafter “BTA”) by including soil water potential (ψ_soil) to explicitly represent the dependence of plant transpiration on root‐zone moisture conditions. The improved model is denoted as the BTA‐ψ model. We assessed the performance of the BTA and BTA‐ψ models in a subtropical monsoon climate and a Mediterranean climate with different levels of water stress. The BTA model performed reasonably in estimating daily and hourly transpiration under sufficient water conditions, but it failed during dry periods. Overall, the BTA‐ψ model provided a significant improvement for estimating transpiration under a wide range of soil moisture conditions. Although both models could estimate transpiration (sap flow) at night, BTA‐ψ was superior to BTA in this regard. Species differences in the calibrated parameters of both models were consistent with leaf‐level photosynthetic measurements on each species, as expected given the physiological basis of these parameters. With a simplified representation of physiological regulation and reasonable performance across a range of soil moisture conditions, the BTA‐ψ model provides a useful alternative to purely empirical models for modelling transpiration.     

卫星遥感数据评估黄土高原陆面干湿程度研究

卫星遥感数据具有估算时空尺度上地表参量的优势,在陆地环境状况评估和监测等方面有很大的应用潜力.本文利用美国地球观测系统卫星搭载中等分辨率成像光谱仪（EOS/MODIS）在黄土高原2002-2010年期间获取的每16天归一化植被指数（NDVI）和每日地表温度（LST）数据,分析了黄土高原地区LST-NDVI空间的基本特征.结果发现：当研究区域足够大且遥感数据时间序列足够长时,LST-NDVI空间中（NDVI,LST）散点并非呈三角形或梯形分布.为了能够利用EOS/MODIS的NDVI和LST数据正确地评估陆面的干湿状况,本文给出了利用数据集合法确定LST-NDVI空间中干边和湿边的数值,即在LST-NDVI空间中,利用NDVI等值区间内LST最大值和最小值的集合代表干边和湿边的数值,并进一步证明了在LST-NDVI空间中干边和湿边数值并非呈线性关系.在分析LST-NDVI空间特征的基础上,通过构建地表温度-植被干旱指数（TVDI）,探讨其在评估黄土高原地区陆面的干湿状况的应用潜力.结果表明：由TVDI距平表征的陆面的干湿程度与局地降水距平有很好的关联性,二者在时空分布上有较好的对应关系.在我国陇东黄土高原塬区,TDVI数值与地面观测的表层土壤湿度有很好的相关性,相关系数在0.67以上,并通过显著性为1%的检验.由此说明：如果合理选取干边和湿边的数值,TDVI可应用于区域陆面干湿程度的客观评估.     

某长江大桥深度超过15米的砂土层液化势的综合评价

不同抗震设计规范的砂土液化判别方法或国内外其他有代表性的液化判别方法所采用的地震动参数和土性指标及其埋藏条件是不同的,因而采用这些方法对同一工程场地进行液化势预测时其评价结果通常有一些差异,甚至会得到相反的结论。为了给重大工程建设提供较为合理、可信的地基液化势预测结果,采用多种液化判别方法进行场地液化势的综合评价是比较客观的,也是必要的。本文结合某长江大桥桥基工程,采用建筑抗震设计规范的砂土液化判别方法、国内外有代表性的液化判别方法、有限元数值分析法等多种方法逐一对该工程场地砂性土层进行液化判别,并结合室内动三轴液化试验结果,对主桥墩不考虑冲刷条件和考虑一般冲刷深度5m条件时的砂性土层进行了液化势的综合评价,并将各土层的液化势分为液化、可能液化和不液化3个等级,得到了较为合理可靠的判别结果。     

Evaluating Earthquake-Triggered Landslide Hazard at the Basin Scale Through Gis in the Upper Sele River Valley

To evaluate techniques for assessing earthquake-triggeredlandslide hazard in the Southern Apennines (Italy), a GIS-based analysis was used to modelseismically induced slope deformations. Geological, geotechnical, geomorphological and seismologicaldata were integrated into a standard earthquake slope stability model. The model assessed thelandslide potential that existed during the 1980 Irpinian earthquake in the Upper Sele river Valley.The standard Newmark displacement analysis, widely used for predicting the location of shallowunstable slopes, does not take into account errors and/or uncertainties in the input parameters.Therefore, a probabilistic Newmark displacement analysis technique has been used. Probabilistictechniques allow, e.g., an estimation of the probability that a slope will exceed a certain criticalvalue of Newmark displacement. In our probabilistic method, a Monte-Carlo based simulation modelis used in conjunction with a GIS. The random variability of geotechnical data is modelled by probabilitydensity functions (pdfs), while for the seismic input three different regression laws wereconsidered. Input probability distributions are sampled and the resulting values input into empiricalrelations for estimating Newmark displacement. The outcome is a map in which to each siteis related a spatial probability distribution for the expected displacement in response to seismic loading.Results of the experiments show a high grade of uncertainty in the application of the Newmarkanalysis both for the deterministic and probabilistic approach in a complex geological setting suchas the high Sele valley, quite common in the Southern Apennines. They show a strong dependence onthe reliability of the spatial data used in input, so that, when the model is used at basin scale,results are strongly influenced by local environmental condition (e.g., topography, lithology, groundwatercondition) and decrease the model performance.     

Stochastic 1D site response analysis at a site in central Italy

Site response analysis is strongly influenced by the uncertainty associated to the definition of soil properties and model parameters. Deterministic, or even parametric analyses are unable to systematically assess such uncertainty, since the site characterisation can hardly be sufficiently accurate for a deterministic prediction of site response and alternative approaches are hence needed. A fully stochastic procedure for estimating the site amplification of ground motion is proposed and applied to a case study in central Italy. The methodology allows to take into account the record-to-record variability in an input ground motion and the uncertainty in dynamic soil properties and in the definition of the soil model. In particular, their effect on response spectra at the ground surface is evaluated.     

Spatial prediction of temporal soil moisture dynamics using HYDRUS‐1D

This article investigates the soil moisture dynamics within two catchments (Stanley and Krui) in the Goulburn River in NSW during a 3‐year period (2005–2007) using the HYDRUS‐1D soil water model. Sensitivity analyses indicated that soil type, and leaf area index were the key parameters affecting model performance. The model was satisfactorily calibrated on the Stanley microcatchment sites with a single point rainfall record from this microcatchment for both surface 30 cm and full‐profile soil moisture measurements. Good correlations were obtained between observed and simulated soil water storage when calibrations for one site were applied to the other sites. We extended the predictions of soil moisture to a larger spatial scale using the calibrated soil and vegetation parameters to the sites in the Krui catchment where soil moisture measurement sites were up to 30 km distant from Stanley. Similarly good results show that it is possible to use a calibrated soil moisture model with measurements at a single site to extrapolate the soil moisture to other sites for a catchment with an area of up to 1000 km² given similar soils and vegetation and local rainfall data. Site predictions were effectively improved by our simple data assimilation method using only a few sample data collected from the site. This article demonstrates the potential usefulness of continuous time, point‐scale soil moisture data (typical of that measured by permanently installed TDR probes) and simulations for predicting the soil wetness status over a catchment of significant size (up to 1000 km²). Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of land use changes on catchment soil erosion and sediment yield in the northeastern China: A panel data model application

Land use is an important factor influencing soil erosion and sediment yield (SESY). Regressing soil erosion intensity (SEI) and sediment yield (SY) to land use characteristics can provide necessary information for controlling soil loss. However, current simple regression methods emphasize cross sectional parameters, with less emphasis on temporal variability of relevant land use parameters so that the derived effects of land use change on SESY can be biased. Here, a panel data method was applied to quantify the impact of land use change on SESY in 1954, 1975, and 2015, based on the WaTEM/SEDEM model and seven landscape metrics for 25 reservoir catchments in northeastern China. The results indicate that SEI and area-specific SY (SSY) continuously decreased from 1954 to 2015, which were significantly correlated with landscape metrics such as area-edge metrics of mean patch area (AREA_MN), shape index of the mean related circumscribing circle (CIRCLE_MN), aggregation index of effective mesh size (MESH), patch cohesion index (COHESION), and diversity metrics such as Shannon's diversity index (SHDI), patch richness density (PRD), and modified Simpson's evenness index (MSIEI). The results suggested that catchment SESY can be reduced through decreasing mean patch area, patch mesh size, and physical connectivity of patches, enriching landscape types, and elongating land use patches. These findings are helpful to effectively implement soil conservation measures in northeastern China and similar regions worldwide. The current study also implies that the panel data approach will have beneficial potential applications in earth-science research fields.