Application of cubic spline in soil erosion modeling from Narmada Watersheds,India

Soil erosion by water is ubiquitous, exhibits spatio-temporal variability, and is fundamental to determining sediment yield which is key to proper watershed management. In this study, we propose a relationship between the curve number and sediment yield index (SYI) using cubic splines. Using field data from four watersheds, the relation between observed and computed SYI is found to have a coefficient of determination (R²) value from 0.63 to 0.88 suggesting that such a relation can be used to determine SYI from the available CN value. It is found that cubic splines perform satisfactorily with Nash-Sutcliff efficiency ranging from 60.18 to 64.01%, absolute prediction error from 1.35 to 5.56%, integral square error from 1.21 to 5.82%, coefficient of correlation from 79.32 to 93.78%, and degree of agreement from 0.87 to 0.99%.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

Prediction of longitudinal dispersion coefficient using multivariate adaptive regression splines

In this paper, multivariate adaptive regression splines (MARS) was developed as a novel soft-computing technique for predicting longitudinal dispersion coefficient (D_L) in rivers. As mentioned in the literature, experimental dataset related to D_L was collected and used for preparing MARS model. Results of MARS model were compared with multi-layer neural network model and empirical formulas. To define the most effective parameters on D_L, the Gamma test was used. Performance of MARS model was assessed by calculation of standard error indices. Error indices showed that MARS model has suitable performance and is more accurate compared to multi-layer neural network model and empirical formulas. Results of the Gamma test and MARS model showed that flow depth (H) and ratio of the mean velocity to shear velocity (u/u^?) were the most effective parameters on the D_L.     

SWAT manual calibration and parameters sensitivity analysis in a semi-arid watershed in North-western Morocco

Being a laborious approach, manual calibration of hydrologic model in a semi-arid context requires in-depth knowledge of the watershed and as much as possible field input data to obtain reliable simulations. In this study, manual calibration and relative sensitivity analysis approaches of the SWAT model (Soil and Water Assessment Tool) were applied for water balance in a 1993 km² watershed (on the R’dom river) located in North-western Morocco. The watershed is located in a semi-arid area dominated by agro-forestry activities. The objectives of this study were (i) to perform a local sensitivity analysis of the SWAT model taking into consideration the watershed characteristics and (ii) to implement a detailed methodology of manual calibration and validation of the model in a semi-arid context. Sensitivity analysis has been carried out on 12 different SWAT input parameters, and has revealed that 4 input parameters only were the most influential ones on flow components of the R’dom watershed. Model manual calibration was conducted along 2006 and 2007 by comparing measured and predicted monthly and daily discharges and taking Nash-Sutcliffe coefficient (NSE), determination coefficient (R ²), and percent bias (PBIAS) as goodness-of-fit indicators. Validation has been performed by the same approach through 2008 and 2009 period. All final NSE values were above 0.5, R ² values exceeded 0.7, and PBIAS lower than 25% demonstrating satisfactory model performances over the study watershed conditions. The SWAT model set-up with measured input data, manually calibrated and validated, reflects well the real hydrologic processes occurring in the R’dom watershed and can be used to assess current and future conditions and to evaluate alternative management practices.     

Evaluating the impact of management scenarios and land use changes on annual surface runoff and sediment yield using the GeoWEPP: a case study from the Lighvanchai watershed,Iran

This study was undertaken to evaluate land use change impact and management scenarios on annual average surface runoff (SR) and sediment yield (SY) using the GeoWEPP tool in the Lighvanchai watershed (located in northwestern Iran). Following a sensitivity analysis, the WEPP model was calibrated (2005–2007) and validated (2008–2010) against monthly observed SY and SR. The coefficient of determination (R ²), Nash–Sutcliffe efficiency (NSE), mean bias error (MBE), and root-mean-square error (RMSE) were applied to quantitatively evaluate the WEPP model. The results indicate a satisfactory model performance with R ² > 0.80 and NSE > 0.60. Therefore, the model for current land use (scenario 1) was run for a 30-year time period (1982–2011). The annual average of SR and sediment load were predicted as 93,584 m³/year and 4340 ton/year, respectively. To reduce the annual average surface runoff and sediment yield at the watershed scale, the second scenario (alfalfa cultivation with suitable tillage) and the third scenario (grassland development) as two management scenarios of land use changes were defined by identifying the critical hillslopes. The rate of SR and sediment load in the second scenario were 42,096 m³/year and 429 ton/year, respectively. For the third scenario, the model predictions were 30,239 m³/year and 226 ton/year, respectively. Compared to the first scenario, the reduction rates in annual average of sediment load were about 90 and 94%, respectively. Moreover, for the second and third management scenarios, the reduction rates in annual average of SR were about 55 and 67%, respectively.     

Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha^?1R^?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r ²= 0.561) followed by slope (r ²= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r ²) and RMSE were computed to be 0.76 and 0.07 t ha^?1R^?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.     

The morphological characteristics of gully systems and watersheds in Dry-Hot Valley,SW China

Gully systems and watersheds are geomorphic units with clear boundaries that are relatively independent of basin landscapes and play an important role in natural geography. In order to explore the morphological characteristics of gully systems and watersheds in the Dry-Hot Valley [South West (SW) China], gullies are interpreted from online Google images with high resolution and watersheds are extracted from digital elevation model at a scale of 1:50,000. The results show that: (1) There are 17,382 gullies (with a total area of 1141.66 km²) and 42 watersheds in the study area. (2) The average gully density of the study area (D) is 4.29 km/km², gully frequency (F) is 14.39 gullies/km², the branching ratio (B) is 5.13, the length ratio (L) is 3.12, and the coefficient of the main and tributary gullies (M) is 0.06. The degree of gully erosion is strong to extremely strong, the main development intensity of gully erosion ranges from intense to moderate, and the type of gully system is tributary. (3) The watershed areas (A) are between 0.39 and 96.43 km², the relief ratio (R) is from 0.10 to 0.19, the circularity ratio (C) is from 0.30 to 0.83, the texture ratio (T) is from 0.82 to 39.35, and the dominant geomorphological texture type is fine. (4) There is a quantitative relationship between F and D:F?=?0.624D² (R?=0.84) and T is closely related to D, F, M (R²?>?0.7). A, R and C are related to M (R²?>?0.5). The development of gully systems is the result of coupling effects between multiple factors. In this area, the degree of erosion and the condition of the main and tributary gullies can be controlled by the degree of topographic breakage in the watershed, which provides some theoretical basis for the evaluation of gully erosion by the latter. In addition, the scale, relief, and shape have a significant impact on the locations of the main and tributary gullies. For tributary gullies, attention should be paid to the interception and control of runoff and sediment in the small confluence branches in order to prevent gully expansion and head advance. These features can inform the development of targeted measures for the control of soil erosion.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

Quantifying land use influences on event-based flow frequency,timing, magnitude,and rate of change in an urbanizing watershed of the central USA

There is a need for research that advances understanding of flow alterations in contemporary watersheds where natural and anthropogenic interactions can confound mitigation efforts. Event-based flow frequency, timing, magnitude, and rate of change were quantified at five-site nested gauging sites in a representative mixed-land-use watershed of the central USA. Statistically independent storms were paired by site (n = 111 × 5 sites) to test for significant differences in event-based rainfall and flow response variables (n = 17) between gauging sites. Increased frequency of small peak flow events (i.e., 64 more events less than 4.0 m³ s^?1) was observed at the rural–urban interface of the watershed. Differences in flow response were apparent during drier periods when small rainfall events resulted in increased flow response at urban sites in the lower reaches. Relationships between rainfall and peak flow were stronger with decreased pasture/crop land use and increased urban land use by approximately 20%. Event-based total rainfall explained 40–68% of the variance in peak flow (p < 0.001). Coefficients of determination (r²) were negatively correlated with pasture/crop land use (r² = 0.92; p = 0.007; n = 5) and positively correlated with urban land use (r² = 0.90; p = 0.008; n = 5). Significant differences in flow metrics were observed between rural and urban sites (p < 0.05; n = 111) that were not explained by differences in rainfall variables and drainage area. An urban influence on flow timing was observed using median time lag to peak centroid and time of maximum precipitation to peak flow. Results highlight the need to establish manageable flow targets in rapidly urbanizing mixed-land-use watersheds.     

Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R ²), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS–ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS–ICA model with R ² of 0.942 can forecast PPV better than the USBM with R ² of 0.634, Ambraseys–Hendron with R ² of 0.638, Langefors–Kihlstrom with R ² of 0.637 and Indian Standard with R ² of 0.519.     

Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems

Circular failure is generally observed in the slope of soil, highly jointed rock mass, mine dump and weak rock. Accurate estimation of the safety factor (SF) of slopes and their performance is not an easy task. In this research, based on rock engineering systems (RES), a new approach for the estimation of the SF is presented. The introduced model involves six effective parameters on SF [unit weight (γ), pore pressure ratio (r _u), height (H), angle of internal friction (φ), cohesion (C) and slope angle (\(\beta\))], while retaining simplicity as well. In the case of SF prediction, all the datasets were divided randomly to training and testing datasets for proposing the RES model. For comparison purposes, nonlinear multiple regression models were also employed for estimating SF. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R ²) and mean square error. The obtained results of this study indicated that the RES is a reliable method to predict SF with a higher degree of accuracy in comparison with nonlinear multiple regression models.     

A comparative study of shallow groundwater level simulation with WA–ANN and ITS model in a coastal island of south China

Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R ²), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.     

The formation and development of debris flows in large watersheds after the 2008 Wenchuan Earthquake

The Wenchuan earthquake has caused abundance of loose materials supplies for debris flows. Many debris flows have occurred in watersheds in area beyond 20 km², presenting characteristics differing from those in small watersheds. The debris flows yearly frequency decreases exponentially, and the average debris flow magnitude increases linearly with watershed size. The rainfall thresholds for debris flows in large watersheds were expressed as I?=?14.7 D ^?0.79 (2 h?<?D?<?56 h), which is considerably higher than those in small watersheds as I?=?4.4 D ^?0.70 (2 h?<?D?<?37 h). A case study is conducted in Ergou, 39.4 km² in area, to illustrate the formation and development processes of debris flows in large watersheds. A debris flow develops in a large watershed only when the rainfall was high enough to trigger the wide-spread failures and erosions on slope and realize the confluence in the watershed. The debris flow was supplied by the widely distributed failures dominated by rill erosions (14 in 22 sources in this case). The intermittent supplying increased the size and duration of debris flow. While the landslide dam failures provided most amounts for debris flows (57 % of the total amount), and amplified the discharge suddenly. During these processes, the debris flow velocity and density increased as well. The similar processes were observed in other large watersheds, indicating this case is representative.     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Linear regression models for estimating true subsurface resistivity from apparent resistivity data

Simple linear regression (SLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time and computer memory required to carry out inversion with conventional algorithms. The arrays considered are Wenner, Wenner–Schlumberger and dipole–dipole. The parameters investigated are apparent resistivity (\(\rho _a \)) and true resistivity (\(\rho _t\)) as independent and dependent variables, respectively. For the fact that subsurface resistivity is nonlinear, the datasets were first transformed into logarithmic scale to satisfy the basic regression assumptions. Three models, one each for the three array types, are thus developed based on simple linear relationships between the dependent and independent variables. The generated SLR coefficients were used to estimate \(\rho _t\) for different \(\rho _a\) datasets for validation. Accuracy of the models was assessed using coefficient of determination (\(R^{2})\), F-test, standard error (SE) and weighted mean absolute percentage error (wMAPE). The model calibration \(R^{2}\) and F-value are obtained as 0.75 and 2286, 0.63 and 1097, and 0.47 and 446 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. The SE for calibration and validation are obtained as 0.12 and 0.13, 0.16 and 0.25, and 0.21 and 0.24 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. Similarly, the wMAPE for calibration and validation are estimated as 3.27 and 3.49%, 3.88 and 5.72%, and 5.35 and 6.07% for the three array models, respectively. When compared with standard constraint least-squares (SCLS) inversion and Incomplete Gauss–Newton (IGN) algorithms, the SLR models were found to reduce about 80–96.5% of the processing time and memory space required to carry out the inversion with the SCLS algorithm. It is concluded that the SLR models can rapidly estimate \(\rho _t\) for the various arrays accurately.     

Using Stable Isotopes to Assess the Contribution of Terrestrial and Riverine Organic Matter to Diets of Nearshore Marine Consumers in a Glacially Influenced Estuary

Terrestrial and marine ecosystems in Southeast Alaska are linked by the flow of freshwater from precipitation and glacial runoff, which transports nutrients and organic matter (OM) downstream to estuaries. We examined the contribution of terrestrial-riverine and marine OM to diets of fishes (N = 257, four species) and invertebrates (N = 90, six species) collected from glacially influenced estuaries in Southeast Alaska using multiple stable isotopes (δ¹³C, δ¹⁵N, and δ³⁴S). Multivariate analysis of similarity (ANOSIM) was used to quantify variation in stable isotope composition of consumers across 6 months and three sites with watersheds that differed in their glacier and forest composition. Fishes showed weak differences (ANOSIM R = 0.141) in stable isotope composition among sampling months, moderate differences (ANOSIM R = 0.375) among sites, and strong differences (ANOSIM R = 0.583) among species. Invertebrates showed moderate differences (ANOSIM R = 0.352) in stable isotope composition among sampling months and strong differences among sites (ANOSIM R = 0.710) and species (ANOSIM R = 0.858). We found the greatest differences in stable isotope composition between the two estuary sites with watersheds containing the highest and lowest glacial coverage, indicating that the contribution of allochthonous OM to consumer diets varies across watershed types. Invertebrates collected from the site with the lowest glacial coverage in the watershed were more depleted in δ¹³C and δ³⁴S, indicating higher use of terrestrial-riverine OM, than those at sites with higher watershed glacial coverage. High variation in stable isotope composition among species, months, and sites underscores the complexity of estuary food web responses to future glacier loss.     

A modified normalized model for predicting effective soil thermal conductivity

Effective soil thermal conductivity (λ _eff) describes the ability of a multiphase soil to transmit heat by conduction under unit temperature gradient. It is a critical parameter for environmental science, earth and planetary science, and engineering applications. Numerous models are available in the literature, but their applicability is generally restricted to certain soil types or water contents (θ). The objective of this study was to develop a new model in the similar form of the Johansen 1975 model to simulate the λ _eff(θ) relationship of soils of various soil textures and water contents. An exponential type model with two parameters is developed and a new function for calculating dry soil thermal conductivity is presented. Performance of the new model and six other normalized models were evaluated with published datasets. The results show that the new model is able to well mimic λ _eff(θ) relationship of soils from sand to silt loam and from oven dry to full saturation. In addition, it has the best performance among the seven models under test (with root-mean-square error of 0.059 W m^?1 °C^?1, average deviations of 0.0009 W m^?1 °C^?1, and Nash–Sutcliffe efficiency of 0.994). The new model has potential to improve the reliability of soil thermal conductivity estimation and be incorporated into numerical modeling for environmental, earth and engineering studies.     

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district,Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\)), differences of circular vertical and horizontal \(\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})\) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (\({\hbox {RMS}}_{\mathrm{height}}\)). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., \(\sigma ^{\mathrm{o}}\). Near surface SM measurements were related to \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\) derived using 5.35 GHz (C-band) image of RISAT-1 and \({\hbox {RMS}}_{\mathrm{height}}\). The roughness component derived in terms of \({\hbox {RMS}}_{\mathrm{height}}\) showed a good positive correlation with \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)\). By considering all the major influencing factors (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\)), an SEM was developed where SM (volumetric) predicted values depend on \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\). This SEM showed \(R^{2}\) of 0.87 and adjusted \(R^{2}\) of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (\({\hbox {SM}}_{\mathrm{Observed}}\)) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (\({\approx } 1\)), index of agreement (d) = 1, coefficient of determination \((R^{2}) = 0.87\), mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences \(({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004\). The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on \(\sigma ^{\mathrm{o}}\). By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.     

Estimation of Pullout Capacity of Vertical Plate Anchors in Cohesionless Soil Using MARS

Vertical plate anchors provide an economical solution to safely resist the large horizontal forces experienced by the foundation of different structures such as bulkheads, sheet piles, retaining walls and so forth. This paper develops a multivariate adaptive regression spline (MARS) model-based approach for the determination of horizontal pullout capacity (P _u ) of vertical plate anchors buried in cohesionless soil by utilizing experimental results reported by different researchers. Based on the collection of forty different pullout experimental test results reported in the literature for anchors buried in loose to dense cohesionless soil with an embedment ratio ranges from 1 to 5, a predictive approach for P _u of vertical plate anchors has been developed in terms of non-dimensional pullout coefficient (M _γq ). The capability of the proposed MARS model for estimating the values of M _γq is examined by comparing the results obtained in the present study with those methods available in the literature. Using different statistical error measure criteria, this study indicates that the present approach is efficient in estimating the horizontal pullout capacity of vertical plate anchors as compared to other methods. The sensitivity analysis indicates that the embedment ratio (H/h, where H = embedment depth of anchor, and h = height of anchor) and internal friction angle (?) of soil mass are the two most important parameters for the evaluation of non-dimensional pullout coefficient (M _γq ) using the proposed MARS model.     

Land-use-mediated <Emphasis Type="Italic">Escherichia coli</Emphasis> concentrations in a contemporary Appalachian watershed

A high-spatial resolution study design was used to investigate the relationship between land use practices, stream physicochemistry, hydroclimate, and stream Escherichia (E) coli concentrations in a mixed-land-use watershed in the Appalachian region. Stream samples were collected daily from six monitoring sites and analyzed for total E. coli counts using an enzyme metabolism indicator method. Statistical comparison of E. coli concentration time series showed significant (p?<?0.05) differences between study sites. Although highest average E. coli concentrations were observed at two agricultural sites (534 and 582 colony-forming counts (CFU) per 100 mL, respectively), highest total loadings were observed within the receiving stream, with values increasing downstream (2?×?10¹² and 4.2?×?10¹² study total CFU for bracketed upstream and downstream sites, respectively). No single physical variable displayed a significant correlation (p?<?0.05) with observed E. coli concentration at every site. However, sites displayed different patterns of significant correlations (p?<?0.05) between E. coli concentration and both physicochemical (e.g. pH, dissolved oxygen saturation) and hydroclimate variables (e.g. streamflow and precipitation). Percent agricultural land cover was the only land use category that showed significant (p?<?0.04) correlation with study average E. coli concentrations, thereby emphasizing the importance of land use practices to stream pathogen regimes. Results validate the analytical method and provide high-resolution, detailed, quantitative characterizations of stream E. coli regimes, thereby supplying land and water resource managers with science-based information to advance management decisions and improve public health.