Bayesian Analysis of the Impact of Rainfall Data Product on Simulated Slope Failure for North Carolina Locations

In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the factor of safety, Fs) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated Fs values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantles properties (hydraulic, geotechnical, and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference truth Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and null periods using multivariate frequency distribution-based metrics such as the false discovery and false omission rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicates their inability to accurately characterize hillslope stability

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes

Evaluating the Use of Environmental Tracers to Reduce Conceptual Model Uncertainty of Hydrogeologic Models

Environmental tracer concentrations for CFC12, SF6, and tritium are used in groundwater simulations to assess the ability of these tracers to reduce conceptual model uncertainty due to uncertainty of a site’s geologic and recharge characterization. The resulting groundwater simulations are characterized by site-specific hydrologic and geologic data, and with coordination from a field team with years of knowledge about the site. First-order (conceptual) uncertainty is directly addressed by using a stochastic modeling approach for spatial variability of the proposed subsurface configurations. Simulations of environmental tracer concentrations and water levels are used to assess six alternate conceptual models that are based on three alternate geologic interpretations and two levels of spatial complexity in groundwater recharge. Our results show that water levels and tracers both provide unique information, but tracers enhance our ability to distinguish between models throughout multiple analyses. Tracers CFC12 and tritium show how simulating environmental tracer transport in groundwater is better than using water levels at testing alternate hydrogeologic conceptual models and reducing conceptual uncertainty between them

Effect of the Soil Spatial Variability on the Static and Dynamic Stability Analysis of a Lebanese Slope

The accidental topography and heterogeneous Lebanese geology in addition to the active seismicity have initiated the static and dynamic stability analysis of Lebanese slopes. In this paper, the stability of a sandy Lebanese slope situated at Mansourieh near Beirut is investigated using deterministic and probabilistic approaches. The characterization of the variability of the slope soil properties is done based on geological investigation, as well as geophysical (Resistivity and Ambient noise) and geotechnical tests performed on this slope. Three dimensional 3D static deterministic analyses is performed to determine the overall safety factor of the slope and to find the location of the critical failure surface. The deterministic model is based on numerical simulations using the finite difference code FLAC3D. Then, two-dimensional probabilistic analysis is carried out on the critical section obtained from the 3D model. In the probabilistic analysis, the soil properties are modeled using the random field theory. An efficient uncertainty propagation methodology based on the expansion optimal linear estimation EOLE method is used to discretize the random field. Concerning the dynamic analysis, it is implemented in order to determine the amplification at the top of slope, where the looseness of the soil there may amplify the earthquake acceleration. The results have shown a small safety factor as well as high amplification. The importance of using the probabilistic approach versus the deterministic one is also presented and discussed

Vulnerability assessments of pesticide leaching to groundwater

Pesticides may have adverse environmental effects if they are transported to groundwater and surface waters. The vulnerability of water resources to contamination of pesticides must therefore be evaluated. Different stakeholders, with different objectives and requirements, are interested in such vulnerability assessments. Various assessment methods have been developed in the past. For example, the vulnerability of groundwater to pesticide leaching may be evaluated by indices and overlay-based methods, by statistical analyses of monitoring data, or by using process-based models of pesticide fate. No single tool or methodology is likely to be appropriate for all end-users and stakeholders, since their suitability depends on the available data and the specific goals of the assessment. The overall purpose of this thesis was to develop tools, based on different process-based models of pesticide leaching that may be used in groundwater vulnerability assessments. Four different tools have been developed for end-users with varying goals and interests: (i) a tool based on the attenuation factor implemented in a GIS, where vulnerability maps are generated for the islands of Hawaii (U.S.A.), (ii) a simulation tool based on the MACRO model developed to support decision-makers at local authorities to assess potential risks of leaching of pesticides to groundwater following normal usage in drinking water abstraction districts, (iii) linked models of the soil root zone and groundwater to investigate leaching of the pesticide mecoprop to shallow and deep groundwater in fractured till, and (iv) a meta-model of the pesticide fate model MACRO developed for 'worst-case' groundwater vulnerability assessments in southern Sweden. The strengths and weaknesses of the different approaches are discussed

Probabilistic Three-Dimensional Model of an Offshore Monopile Foundation: Reliability Based Approach

When wind turbines are to be installed offshore, expensive geotechnical in-situ tests are carried out at the location of each turbine and only a quantile value (typically the 5% quantile) of the measured strength parameters is used as design parameter, e.g., the 5% quantile value of the undrained shear strength of the soil. Typically, measurement, statistical and model uncertainties are not taken into account in code-based, deterministic design. Hence, current methodology based design may be expensive, but the reliability of the foundation is unknown. Instead, a reliability-based design process based on stochastic analysis of the soil parameters is proposed to obtain an efficient design with known reliability and smaller costs for tests and construction. In this study a monopile foundation in undrained, over-consolidated clay is considered as an example. A three-dimensional (3D) finite-element model is established and a stochastic model for the undrained shear strength of the soil is proposed using random field theory. The Mohr–Coulomb constitutive model is used to model the soil behavior. Reliability indices of the monopile are obtained through an advanced reliability method and a probabilistic procedure is proposed regarding the 3D design of monopile foundations

Use of site specific farming systems and computer simulation models for agricultural productivity and environmental quality

Site specific farming systems have the potential to increase farmers\u27 net income by reducing the use of agro-chemicals and applying variable rate technology for areas showing stable yield patterns. This study was designed to: (1) study yield patterns in given fields using variography; (2) seek correlation among soil attributes and yield data using GIS; and (3) simulate the effects of N-fertilizer and swine manure application rates on NO 3-N losses with subsurface drainage water and crop yields. The results of this study showed that the spatial correlation lengths were found to vary from 40 m for corn to about 90 m for soybean. The lack of temporal stability in either the large-scale deterministic structure or small-scale stochastic structure revealed that yield variability was not only controlled by intrinsic soil properties but also by other extrinsic factors including climate and management. Map overlay analysis showed that areas of lower yield in the vicinity of Ottosen and Okoboji soils for corn in a central Iowa field were consistent from year to year whereas areas of higher yield were variable. Results from both GIS and statistical analysis showed that interactions between soil type and topography has a more pronounced effect on yield variability patterns for this field;The simulation component of the study showed that the Root Zone Water Quality Model (RZWQM, V. 3.25) predicted subsurface drain flow, NO3-N concentrations in subsurface drain water, NO3-N losses with subsurface drain water, and grain yields satisfactorily by showing an average difference of --10.9%, --7.2%, --5.6%, and 0.9% respectively, between predicted and observed values for all the N-fertilizer treatments for the years 1996 and 1998 for a central Iowa field. Model simulations for 1996 and 1998 showed that by doubling the N application, the grain yield increased on the average by 46% and NO3-N losses increased by 42%. By increasing the N applications four times, the grain yields increased by 55% and NO 3-N loss increased by 60%. These results showed that the increase in corn yield was not linearly proportional to the N applications. The calibration and evaluation of the RZWQM for the northeast Iowa fields indicate that RZWQM has the potential to successfully simulate the effect of N and manure management systems on corn yields and NO3-N concentrations in the subsurface drainage water

Hydrogeological modeling to improve remediation strategies for a drinking water aquifer contaminated by an aqueous phase liquid

In many communities, groundwater is an important source of drinking water. Groundwater aquifers are, however, vulnerable to the widespread and increasing problem of contamination from anthropogenic sources. Once in the groundwater, contaminants are likely to remain there for a long time as the attenuation rate is slow. In this thesis, different tools for modeling subsurface transport were adapted and evaluated in order to improve remediation strategies for a contaminated esker aquifer. The work focuses on the entire transport process at a regional scale from the source at the soil surface, through the vadose zone, and in groundwater. Few comparable studies exist, especially for aquifer systems in glaciofluvial sediments. The studied aquifer supplies drinking water to the municipality of Umeå, which is a medium-sized city in northern Sweden. The aquifer is contaminated by the commonly found pesticide degradation product 2,6-dichlorobenzoamide (BAM). Hydrogeological and chemical field data were collected, and the contaminant migration analyzed by a stationary non-distributed model and a transient distributed model. To remediate the aquifer so that it meets the drinking water standard, it was necessary to combine extraction at two up-gradient wells, with an increased rate of artificial recharge via two infiltration ponds. Using only one of the techniques would either affect the water balance negatively, or would increase the risk of clogging the infiltrating surface. However, in order to reinstate the two up-gradient wells as producers of drinking water as soon as possible, it was necessary to establish the remediation wells in close proximity to the contaminant source. When the data quality is insufficient the simple mass-balance model was found to be most useful, since it reflects the uncertainty of the result. However, if it is essential for the contaminant transport to be calculated more accurately, a distributed model is required. To strengthen the credibility of such a model, it should be validated with independent data from various sources: in this study it was stable isotope oxygen-18 data, data on the BAM contamination, and time-variant hydraulic head data. The overall findings are expected to be relevant to many other sites in similar settings