Jonathan D. Herman

Bio: Jonathan D. Herman is an academic researcher from University of California, Davis. The author has contributed to research in topics: Water supply & Water resources. The author has an hindex of 27, co-authored 58 publications receiving 2983 citations. Previous affiliations of Jonathan D. Herman include North Shore University Hospital & Albert Einstein College of Medicine.

SALib: An open-source Python library for Sensitivity Analysis

Abstract: SALib contains Python implementations of commonly used global sensitivity analysis methods, including Sobol (Sobol’ 2001, Andrea Saltelli (2002), Andrea Saltelli et al. (2010)), Morris (Morris 1991 ...

Evolutionary multiobjective optimization in water resources: The past, present, and future

Abstract: This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall–runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with four or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are given for the new algorithms that should serve as the benchmarks for innovations in the water resources literature. The future of MOEAs in water resources needs to emphasize self-adaptive search, new technologies for visualizing tradeoffs, and the next generation of computing technologies.

How should robustness be defined for water systems planning under change

Abstract: Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Robustness analyses have shifted from expected utility to exploratory bottom-up approaches which identify vulnerable scenarios prior to assigning likelihoods. Examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches based on their methods of (1) alternative generation, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) sensitivity analysis to identify important uncertainties. Building from the proposed taxonomy, we use a regional urban water supply case study in the Research Triangle region of North Carolina to illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the ...

Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty

Abstract: While optimality is a foundational mathematical concept in water resources planning and management, “optimal” solutions may be vulnerable to failure if deeply uncertain future conditions deviate from those assumed during optimization. These vulnerabilities may produce severely asymmetric impacts across a region, making it vital to evaluate the robustness of management strategies as well as their impacts for regional stakeholders. In this study, we contribute a multistakeholder many-objective robust decision making (MORDM) framework that blends many-objective search and uncertainty analysis tools to discover key tradeoffs between water supply alternatives and their robustness to deep uncertainties (e.g., population pressures, climate change, and financial risks). The proposed framework is demonstrated for four interconnected water utilities representing major stakeholders in the “Research Triangle” region of North Carolina, U.S. The utilities supply well over one million customers and have the ability to collectively manage drought via transfer agreements and shared infrastructure. We show that water portfolios for this region that compose optimal tradeoffs (i.e., Pareto-approximate solutions) under expected future conditions may suffer significantly degraded performance with only modest changes in deeply uncertain hydrologic and economic factors. We then use the Patient Rule Induction Method (PRIM) to identify which uncertain factors drive the individual and collective vulnerabilities for the four cooperating utilities. Our framework identifies key stakeholder dependencies and robustness tradeoffs associated with cooperative regional planning, which are critical to understanding the tensions between individual versus regional water supply goals. Cooperative demand management was found to be the key factor controlling the robustness of regional water supply planning, dominating other hydroclimatic and economic uncertainties through the 2025 planning horizon. Results suggest that a modest reduction in the projected rate of demand growth (from approximately 3% per year to 2.4%) will substantially improve the utilities' robustness to future uncertainty and reduce the potential for regional tensions. The proposed multistakeholder MORDM framework offers critical insights into the risks and challenges posed by rising water demands and hydrological uncertainties, providing a planning template for regions now forced to confront rapidly evolving water scarcity risks.

Sensitivity analysis of environmental models

Abstract: Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.We provide a systematic review of existing approaches, which can support users in the choice of an SA method.We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.We give best practice examples from the literature and highlight trends and gaps for future research.

Natural fractures in shale: A review and new observations

Abstract: Natural fractures have long been suspected as a factor in production from shale reservoirs because gas and oil production commonly exceeds the rates expected from low-porosity and low-permeability shale host rock. Many shale outcrops, cores, and image logs contain fractures or fracture traces, and microseismic event patterns associated with hydraulic-fracture stimulation have been ascribed to natural fracture reactivation. Here we review previous work, and present new core and outcrop data from 18 shale plays that reveal common types of shale fractures and their mineralization, orientation, and size patterns. A wide range of shales have a common suite of types and configurations of fractures: those at high angle to bedding, faults, bed-parallel fractures, early compacted fractures, and fractures associated with concretions. These fractures differ markedly in their prevalence and arrangement within each shale play, however, constituting different fracture stratigraphies—differences that depend on interface and mechanical properties governed by depositional, diagenetic, and structural setting. Several mechanisms may act independently or in combination to cause fracture growth, including differential compaction, local and regional stress changes associated with tectonic events, strain accommodation around large structures, catagenesis, and uplift. Fracture systems in shales are heterogeneous; they can enhance or detract from producibility, augment or reduce rock strength and the propensity to interact with hydraulic-fracture stimulation. Burial history and fracture diagenesis influence fracture attributes and may provide more information for fracture prediction than is commonly appreciated. The role of microfractures in production from shale is currently poorly understood yet potentially critical; we identify a need for further work in this field and on the role of natural fractures generally.