Special Session on:
Linking Observation and Prediction: Frameworks for Data Assimilation, Uncertainty Analysis, and Valuing Information


Patrick Reed, Penn State University
Ming Pan, Princeton University


This session is focused on innovative computational frameworks for improving monitoring and forecasting of the environmental systems under uncertainty. In hydrological and environmental contests, there is a strong and present need to improve observation - prediction feedbacks. These feedbacks will be important for nonlinear systems where critical thresholds could lead to substantial and sustained changes in observable dynamics. This session encourages submissions that advance (1) new data assimilation frameworks, (2) Bayesian or other estimation frameworks characterizing both observation and model uncertainties, (3) decision support frameworks for model-based design and adaptation observation systems, and (4) approaches for reducing uncertainty and improving information quality through data synthesis. Applications in surface hydrology, groundwater, water quality, and meteorology are encouraged.

Papers and Abstracts:

Estimating Hydraulic Conductivity Geostatistical Parameters using An Iterative Ensemble Smoother Scheme
Ryan Bailey, Domenico Bau

Statistical reconstruction of subsurface hydro-meteorological and crack aperture time series based on residual auto-regressive processes and other techniques
David Bailly, Jean-Michael Matray, Rachid Ababou

Data Assimilation of Land Subsidence Measurements for the Estimation of Reservoir Geomechanical Parameters
Domenico Bau, Massimiliano Ferronato, Giuseppe Gambolati, Pietro Teatini

Assessment of local hydraulic parameters by EnKF data assimilation in real aquifers: a case study in downtown Padova (Italy)
Matteo Camporese, Elena Crestani, Paolo Salandin

Hybrid Uncertainty Quantification Techniques for Reactive Transport Applications
Xiao Chen, Brenda Ng, Yunwei Sun, Charles Tong

Machine Learning Algorithms of Soil Drying
Evan Coopersmith, Barbara Spang Minsker, Craig Wenzel, Brian Gilmore

Inverse Modeling for Estimating Parameters of Groundwater Models with Uncertain Forcing Data
Yonas Demissie, Albert Valocchi, Ximing Cai, Nicholas Brozović

Some inverse problems for groundwater
Nicholas Dudley Ward, Tiangang Cui

A Dual Strategy for Ensemble Kalman Data Assimilation with a Coupled Subsurface Contaminant Transport Model
Mohamad El Gharamti, Ibrahim Hoteit, Johan Valstar

Model calibration with external error models
Daniel Erdal, Insa Neuweiler, Johan Huisman

Identifiability of the soil hydraulic parameters from drainage experiments
Noura Fajraoui, Didier Calogine, François Lehmann, Theirry Alex Mara, Anis Younès

Bayesian characterization of the uncertainty associated with geomorphic, habitat and water quality data in Vermont streams
Nikolaos Fytilis, Donna Rizzo

Sensitivity Analysis Of Parameter And State Estimation Of Groundwater Flow And Transport Models
Graciela del Socorro Herrera-Zamarrón, Jessica Briseño-Ruiz

Enhanced transparency and refutability in modeling environmental systems
Mary Hill, Dmitri Kavetski, Martyn Clark, Ming Ye, Mazdak Arabi

A geostatistical approach to estimating river bathymetry in near real-time
Sanjeev Jha, Barbara Bailey, Barbara Minsker, Jim Best

Integrated Groundwater Quality Monitoring Network Design Case Study: Eocene Aquifer, Palestine
Abdelhaleem Khader, Mac McKee, David Rosenberg

Sequential Monte Carlo Methods for the Calibration of Stochastic Rainfall-Runoff Models
Franz Konecny, Hans-Peter Nachtnebel

Improving Groundwater Modeling by Coupled HydroGeophysical Data Assimilation
Gabriele Manoli, Damiano Pasetto, Matteo Rossi, Pietro Teatini, Rita Deiana

Using Time-Lapse Electrical Resistivity Tomography to Visualize Conduit-Matrix Exchange a Sink-Rise System of a Semi-Confined Karst Aquifer
Steven B Meyerhoff, Reed M Maxwell, Andre Revil, Marios Karaoulis, Wendy D Graham

Bayesian estimation of multiscale structures in a binary medium from sparse observations
Jaideep Ray, S. Lefantzi, S. McKenna, Bart van Bloemen Waanders

Save Now, Pay Later? Multi-Period Many Objective Groundwater Monitoring Design Given Systematic Model Errors and Uncertainty
Patrick Reed, Joshua Kollat

Modeling Groundwater Flow though Dikes for Real Time Stability Assessment
John van Esch, M.A.T. Visschedijk

Model analysis and decision support (MADS) for complex physics models
Velimir Vesselinov, Dylan Harp

Linearized Functional Minimization for Inverse Modeling
Brendt Wohlberg, Daniel M. Tartakovsky, and Marco Dentz

Multimodel Bayesian Analysis of Data-Worth: Theory and Applications
Ming Ye, Dan Lu, Liang Xue, Shlomo Neuman

Data worth and optimal sampling analyses to reduce model prediction uncertainty for groundwater transport
Hongkyu Yoon, Yonas Demissie, Sean McKenna, Albert Valocchi, Charles Werth