Probabilistic estimation and prediction of groundwater recharge in a semi-arid environment

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-161).Quantifying and characterizing groundwater recharge are critical for water resources management. Unfortunately, low recharge rates are difficult to resolve in dry environments, where groundwater is often most important. Motivated by such concerns, this thesis presents a new probabilistic approach for analyzing diffuse recharge in semiarid environments and demonstrates it for the Southern High Plains (SHP) in Texas. Diffuse recharge in semi-arid and arid regions is likely to be episodic, which could have important implications for groundwater. Our approach makes it possible to assess how episodic recharge can occur and to investigate the control mechanisms behind it. Of the common recharge analysis methods, numerical modeling is best suited for considering control mechanisms and is the only option for predicting future recharge. However, it is overly sensitive to model errors in dry environments. Natural chloride tracer measurements provide more robust indicators of low flux rates, yet traditional chloride-based estimation methods only produce recharge at coarse time scales that mask most control mechanisms. We present a data assimilation approach based on importance sampling that combines modeling and data-based estimation methods in a consistent probabilistic manner. Our estimates of historical recharge time series indicate that at the SHP data sites, deep percolation (potential recharge) is indeed highly episodic and shows significant interannual variability. Conditions that allow major percolation events are high intensity rains, moist antecedent soil conditions, and below-maximum root density. El Niño events can contribute to interannual variability of percolation by bringing wetter winters, which produce modest percolation events and provide wet antecedent conditions that trigger spring episodic recharge.(cont.) Our data assimilation approach also generates conditional parameter distributions, which are used to examine sensitivity of recharge to potential climate changes. A range of global circulation model predictions are considered, including wetter and drier futures. Relative changes in recharge are generally more pronounced than relative changes in rainfall, demonstrating high susceptibility to climate change impacts. The temporal distribution of rainfall changes is critical for recharge. Our results suggest that increased total precipitation or higher rain intensity during key months could make strong percolation peaks more common.by Gene-Hua Crystal Ng.Ph.D

The role of model dynamics in ensemble Kalman filter performance for chaotic systems

The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or ‘diverging’, when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter’s update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as nonlinearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics.National Science Foundation (U.S.) (CMF Program Grant 0530851)National Science Foundation (U.S.) (DDAS Program Grant 0540259)National Science Foundation (U.S.) (ITR/AP Program Grant 0121182

Probabilistic analysis of the effects of climate change on groundwater recharge

[1] Groundwater recharge is likely to be affected by climate change. In semiarid regions where groundwater resources are often critical, annual recharge rates are typically small and most recharge occurs episodically. Such episodic recharge is uncertain and difficult to predict. This paper analyzes the impacts of different climate predictions on diffuse episodic recharge at a low-relief semiarid rain-fed agricultural area. The analysis relies on a probabilistic approach that explicitly accounts for uncertainties in meteorological forcing and in soil and vegetation properties. An ensemble of recharge forecasts is generated from Monte Carlo simulations of a study site in the southern High Plains, United States. Soil and vegetation parameter realizations are conditioned on soil moisture and soil water chloride observations (Ng et al., 2009). A stochastic weather generator provides realizations of meteorological time series for climate alternatives from different general circulation models. For most climate alternatives, predicted changes in average recharge (spanning −75% to +35%) are larger than the corresponding changes in average precipitation (spanning −25% to +20%). This suggests that amplification of climate change impacts may occur in groundwater systems. Predictions also include varying changes in the frequency and magnitude of recharge events. The temporal distribution of precipitation change (over seasons and rain events) explains most of the variability in predictions of recharge totals and episodic occurrence. The ensemble recharge analysis presented in this study offers a systematic approach to investigating interactions between uncertainty and nonlinearities in episodic recharge.National Science Foundation (U.S.) (award 0121182)National Science Foundation (U.S.) (awards 0530851)National Science Foundation (U.S.) (award 0540259

Using data assimilation to identify diffuse recharge mechanisms from chemical and physical data in the unsaturated zone

1] It is difficult to estimate groundwater recharge in semiarid environments, where precipitation and evapotranspiration nearly balance. In such environments, groundwater supplies are sensitive to small changes in the processes that control recharge. Numerical modeling provides the temporal resolution needed to analyze these processes but is highly sensitive to model errors. Natural chloride tracer measurements in the unsaturated zone provide more robust indicators of low recharge rates but yield estimates at coarse time scales that mask most control mechanisms. This study presents a new probabilistic approach for analyzing diffuse recharge in semiarid environments, with an application to study sites in the U.S. southern High Plains. The approach uses data assimilation to combine model predictions and chloride-based recharge estimates. It has the advantage of providing probability distributions rather than point values for uncertain soil and vegetation properties. These can then be used to quantify recharge uncertainty. Estimates of moisture flux time series indicate that percolation (or potential recharge) at the data sites is episodic and exhibits interannual variability. Most percolation occurs during intense rains when crop roots are not fully developed and there is ample antecedent soil moisture. El Niño events can contribute to interannual variability of recharge if they bring rainy winters that provide wet antecedent conditions for spring precipitation. Data assimilation methods that combine modeling and chloride observations provide the high temporal resolution information needed to identify mechanisms controlling diffuse recharge and offer a way to examine the effects of land use change and climatic variability on groundwater resources.National Science Foundation (U.S.) (grant 0003361)National Science Foundation (U.S.) (grant 0121182)National Science Foundation (U.S.) (grant 0530851)National Institutes of Health (U.S.) (grant 0540259