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

Comment on 'Shang S. 2012. Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step. Hydrological Processes 26: 3338-3343. DOI: 10.1002/hyp.8405'

A previous study analyzed errors in the numerical calculation of actual crop evapotranspiration (ET(sub a)) under soil water stress. Assuming no irrigation or precipitation, it constructed equations for ET(sub a) over limited soil-water ranges in a root zone drying out due to evapotranspiration. It then used a single crop-soil composite to provide recommendations about the appropriate usage of numerical methods under different values of the time step and the maximum crop evapotranspiration (ET(sub c)). This comment reformulates those ET(sub a) equations for applicability over the full range of soil water values, revealing a dependence of the relative error in numerical ET(sub a) on the initial soil water that was not seen in the previous study. It is shown that the recommendations based on a single crop-soil composite can be invalid for other crop-soil composites. Finally, a consideration of the numerical error in the time-cumulative value of ET(sub a) is discussed besides the existing consideration of that error over individual time steps as done in the previous study. This cumulative ET(sub a) is more relevant to the final crop yield

Quantifying Parameter Sensitivity, Interaction and Transferability in Hydrologically Enhanced Versions of Noah-LSM over Transition Zones

We use sensitivity analysis to identify the parameters that are most responsible for shaping land surface model (LSM) simulations and to understand the complex interactions in three versions of the Noah LSM: the standard version (STD), a version enhanced with a simple groundwater module (GW), and version augmented by a dynamic phenology module (DV). We use warm season, high-frequency, near-surface states and turbulent fluxes collected over nine sites in the US Southern Great Plains. We quantify changes in the pattern of sensitive parameters, the amount and nature of the interaction between parameters, and the covariance structure of the distribution of behavioral parameter sets. Using Sobol s total and first-order sensitivity indexes, we show that very few parameters directly control the variance of the model output. Significant parameter interaction occurs so that not only the optimal parameter values differ between models, but the relationships between parameters change. GW decreases parameter interaction and appears to improve model realism, especially at wetter sites. DV increases parameter interaction and decreases identifiability, implying it is overparameterized and/or underconstrained. A case study at a wet site shows GW has two functional modes: one that mimics STD and a second in which GW improves model function by decoupling direct evaporation and baseflow. Unsupervised classification of the posterior distributions of behavioral parameter sets cannot group similar sites based solely on soil or vegetation type, helping to explain why transferability between sites and models is not straightforward. This evidence suggests a priori assignment of parameters should also consider climatic differences

Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include dynamic simulation of vegetation and carbon to forty-three primary user-specified parameters. This is done using thirty-two years' worth of data from ten international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue

Flash Flood Forecasting for the Semi-Arid Southwestern United States

Flash flooding in the semi-arid United States poses a significant danger to life and property. One effective way to mitigate flood risk is by implementing a rainfall-runoff model in a real-time forecast and warning system. This study investigated the feasibility of using the mechanistic, distributed semi-arid rainfall-runoff model KINEROS2 driven by high resolution radar rainfall input estimates obtained from the NEXRAD WSR-88D DHR reflectivity measurements in such a system. The original procedural paradigm-based KINEROS2 Fortran 77 code with space-time looping was recoded into an object-oriented Fortran 90 code with time-space looping for this purpose. The recoded form is now applicable to large basins, is easily future-extensible, and individual modules can be incorporated into other models.Sources of operational uncertainty in the above system were investigated for their influence over several events within a sub-basin of the USDA-ARS Walnut Gulch Experimental Watershed. Uncertainties considered were in the rainfall estimates, the model parameters, and the initial conditions. The variance-based Sobol' method of global sensitivity analysis conditioned on the observed streamflow showed that the uncertainty in the modeled response was heavily dominated by the operational variability of biases in the radar rainfall depth estimates. Sensitivities to KINEROS2 parameters indicates the need for improved representation of semi-arid hillslope hydrology in small basins, while pointing to specific influential, but poorly identified model parameters towards which field investigations should be directed. The significant influence of initial hillslope soil moisture showed the requirement of a sophisticated inter-storm model component for a continuous forecasting model.A synthetic study data was used to further explore the phenomena seen in the above real data study, of behavioral modifier set inconsistency across all events and of irreducibility in the spatial modifier ranges. The former was found to be attributable to wide uncertainty ranges in the sources of uncertainty, and the latter to the high distributed model non-linearity with associated interactions. These contribute towards a high predictive uncertainty in operational forecasting.Overall, the GLUE-based predictive uncertainty method with behavioral classification and accommodation of wide operational source uncertainty ranges is recommended as a simple and effective setup for operational flash flood forecasting

Calibration of a distributed land surface model for a semi-arid basin using remotely sensed data.

A meso-scale medium-resolution land surface model using the NCEP's NOAH code has been setup over the San Pedro basin in Arizona. The model is driven using the 50-year hydrologically balanced land surface data set developed at the University of Washington (UW), precipitation data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system, and solar radiation influx from the University of Maryland's SRB (Surface Radiation Budget). The remotely sensed forcings are introduced in stages to explore the sensitivity on the simulation results to the remote sensing information. The model was calibrated, using the available remote sensing forcings, to ground observations of the turbulent heat fluxes and ground temperatures using multi-criteria calibration techniques. The simulation results for the distributed model using the default (uncalibrated) and the calibrated parameters are intercompared against each other and against the UW outputs. Results show the PERSIANN data substitution to be a major improving factor in the results in capturing the surface runoffs and the soil moisture diurnal variations because of its fmer sub-hourly temporal resolution as against the UW precipitation forcing which is basically at a daily temporal resolution. Separately, the default seasonal vegetation variation in the NOAH, i.e., constant greenness leaf area index (LAI) and variable greenness fraction (GF) was changed to the opposite case of constant GF and varying LAI using LAI data from the MODIS sensor. Visual inspection of the simulation results show the incompatibility between the NOAH and the MODIS LAI as per the MODIS LAI substitution method used in this study.hydrology collectio

Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step.

A previous study analyzed errors in the numerical calculation of actual crop evapotranspiration (ETa) under soil water stress. Assuming no irrigation or precipitation, it constructed equations for ETa over limited soil-water ranges in a root zone drying out due to evapotranspiration. It then used a single crop-soil composite to provide recommendations about the appropriate usage of numerical methods under different values of the time step and the maximum crop evapotranspiration (ETc). This comment reformulates those ETa equations for applicability over the full range of soil water values, revealing a dependence of the relative error in numerical ETa on the initial soil water that was not seen in the previous study. It is shown that the recommendations based on a single crop-soil composite can be invalid for other crop-soil composites. Finally, a consideration of the numerical error in the time-cumulative value of ETa is discussed besides the existing consideration of that error over individual time steps as done in the previous study. This cumulative ETa is more relevant to the final crop yield. Published 2014. This article is a U.S. Government work and is in the public domain in the USA

The Efficiency of Data Assimilation

Data assimilation is the application of Bayes' theorem to condition the states of a dynamical systems model on observations. Any real-world application of Bayes' theorem is approximate, and therefore we cannot expect that data assimilation will preserve all of the information available from models and observations. We outline a framework for measuring information in models, observations, and evaluation data in a way that allows us to quantify information loss during (necessarily imperfect) data assimilation. This facilitates quantitative analysis of tradeoffs between improving (usually expensive) remote sensing observing systems vs. improving data assimilation design and implementation. We demonstrate this methodology on a previously published application of the Ensemble Kalman Filter used to assimilate remote sensing soil moisture retrievals from AMSR-E into the Noah land surface model