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1. Establishment of agencies for local groundwater governance under California’s Sustainable Groundwater Management Act

   Milman, Anita; Galindo, Luisa; Blomquist, William; Conrad, Esther (January 2018, Water alternatives)

   With the passage of its 'Sustainable Groundwater Management Act' (SGMA), California devolved both authority and responsibility for achieving sustainable groundwater management to the local level, with state-level oversight. The passage of SGMA created a new political situation within each groundwater basin covered by the law, as public agencies were tasked with self-organizing to establish local Groundwater Sustainability Agencies (GSAs). This research examines GSA formation decisions to determine where GSAs formed, whether they were formed by a single agency or a partnership, and whether agencies chose to pursue sustainable groundwater management by way of a single basin-wide organization or by coordinating across multiple organizational structures. The research then tests hypotheses regarding the relative influence of control over the resource, control over decision making, transaction costs, heterogeneity and institutional bricolage on GSA formation decisions. Results indicate mixed preferences for GSA structure, though a majority of public water agencies preferred to independently form a GSA rather than to partner in forming a GSA. Results also suggest GSA formation decisions are the result of overlapping and interacting concerns about control, heterogeneity, and transaction costs. Future research should examine how GSA formation choices serve to influence achievement of groundwater sustainability at the basin scale. 

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2. Data-driven Global Sensitivity Analysis of Three- Phase Distribution System with PVs

   https://doi.org/10.1109/PESGM46819.2021.9638171  

   Ye, Ketian; Zhao, Junbo; Huang, Can; Duan, Nan; Ding, Fei; Yang, Rui (July 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency. 

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3. An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

   https://doi.org/10.1111/tgis.12837  

   Kang, Jeon‐Young; Michels, Alexander; Crooks, Andrew; Aldstadt, Jared; Wang, Shaowen (September 2021, Transactions in GIS)

   Abstract Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. 

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4. Iterative Global Sensitivity Analysis Algorithm with Neural Network Surrogate Modeling

   https://doi.org/10.1007/978-3-030-77970-2_23  

   Liu, Y.C.; Nagawkar, J.; Leifsson, L.; Koziel, S.; Pietrenko-Dabrowska, A. (June 2021, Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science)

   Paszynski, M.; Kranzlmüller, D.; Krzhizhanovskaya, V.V.; Dongarra, J.J.; Sloot, P.M. (Ed.)

   Global sensitivity analysis (GSA) is a method to quantify the effect of the input parameters on outputs of physics-based systems. Performing GSA can be challenging due to the combined effect of the high computational cost of each individual physics-based model, a large number of input parameters, and the need to perform repetitive model evaluations. To reduce this cost, neural networks (NNs) are used to replace the expensive physics-based model in this work. This introduces the additional challenge of finding the minimum number of training data samples required to train the NNs accurately. In this work, a new method is introduced to accurately quantify the GSA values by iterating over both the number of samples required to train the NNs, terminated using an outer-loop sensitivity convergence criteria, and the number of model responses required to calculate the GSA, terminated with an inner-loop sensitivity convergence criteria. The iterative surrogate-based GSA guarantees converged values for the Sobol’ indices and, at the same time, alleviates the specification of arbitrary accuracy metrics for the surrogate model. The proposed method is demonstrated in two cases, namely, an eight-variable borehole function and a three-variable nondestructive testing (NDT) case. For the borehole function, both the first- and total-order Sobol’ indices required 200 and 105 data points to terminate on the outer- and inner-loop sensitivity convergence criteria, respectively. For the NDT case, these values were 100 for both first- and total-order indices for the outer-loop sensitivity convergence, and 106 and 103 for the inner-loop sensitivity convergence, respectively, for the first- and total-order indices, on the inner-loop sensitivity convergence. The differences of the proposed method with GSA on the true functions are less than 3% in the analytical case and less than 10% in the physics-based case (where the large error comes from small Sobol’ indices). 

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5. Arrival of New Great Salinity Anomaly Weakens Convection in the Irminger Sea

   https://doi.org/10.1029/2022GL098857  

   Biló, T. C.; Straneo, F.; Holte, J.; Le Bras, I. A. ‐A. (June 2022, Geophysical Research Letters)

   Abstract The Subpolar North Atlantic is prone to recurrent extreme freshening events called Great Salinity Anomalies (GSAs). Here, we combine hydrographic ocean analyses and moored observations to document the arrival, spreading, and impacts of the most recent GSA in the Irminger Sea. This GSA is associated with a rapid freshening of the upper Irminger Sea between 2015 and 2020, culminating in annually averaged salinities as low as the freshest years of the 1990s and possibly since 1960. Upon the GSA propagation into the Irminger Sea over the Reykjanes Ridge, the boundary currents rapidly advected its signal around the basin within months while fresher waters slowly spread and accumulated into the interior. The anomalies in the interior freshened waters produced by deep convection during the 2017–2018 winter and actively contributed to the suppression of deep convection in the following two winters. 

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