Collaborative Research: A Spatiotemporal Approach to Sensitivity Analysis in Human-Environment Systems Models

这个地理和空间科学（GSS）项目将开发新的方法来理解和分类人类和环境系统相互作用的复杂空间模型中的不确定性。越来越多的科学家正在利用这些模型来了解各种过程的动态，包括城市扩张，气候变化，森林砍伐和水体的非点源污染。这些过程的内在复杂性，导致模型的不确定性，是在空间和时间上的可变性。例如，底特律的城市扩张动力与克利夫兰不同，今天的城市扩张动力与十年前不同。这类系统的模型通常需要大量的解释性变量来描述整个系统的经济、社会、政治和环境组成部分。为了解决模型的不确定性，科学家们使用敏感性分析：一种用于了解解释变量的不同值如何影响感兴趣结果的技术。例如，增加对农民的付款如何影响他们为养护目的改变农业用地的意愿？该项目将开发一个新的理论框架，用于将敏感性分析应用于复杂的人类-环境系统模型，这些模型在空间和时间上表现出很大的变化性。具体而言，该框架将有助于：（1）确定导致模型简化的模型结果中最大可变性的解释变量;（2）探索模型结果在空间和时间上的可变性;（3）模拟系统动态，以了解各种政策情景的影响。开发的框架将在一个模型上进行测试，该模型旨在了解农业土地保护决策及其对湖泊，当地经济和人民的影响。该项目将大大有助于科学家，政策制定者和公民了解人类环境系统模型中各种驱动因素对模型结果的影响程度的能力。此外，该项目将促进开发具有更强解释力的更简单的系统模型。因此，开发的方法将提高这些模型的透明度，可能允许更多的社区使用，理解和参与这些建模练习。该框架还将提供优先数据采集的方法，提高复杂人类环境模型的鲁棒性，并提高此类建模工作对解决重大社会问题的政策和决策的有用性。该项目将通过研究生指导和建模认证研究生课程培训学生，以采用开发的新方法来描述和解决复杂的人类环境系统问题。我们的研究结果，包括时空敏感性分析工具箱（ST SA），将以电子方式在线传播，同行评审的手稿，并在专业会议。

This Geography and Spatial Sciences (GSS) project will develop novel methods for understanding and categorizing uncertainty in complex spatial models of human and environmental systems interactions. Increasingly scientists are utilizing such models to understand the dynamics of various processes including urban expansion, climate change, deforestation, and nonpoint pollution of water bodies. An inherent complexity in these processes, leading to model uncertainty, is the variability over both space and time. For example, the dynamics of urban expansion in Detroit are different than those in Cleveland, and their dynamics today are different than those ten years ago. Models of such systems typically require a large number of explanatory variables that describe the economic, social, political, and environmental components of the overall system. To address model uncertainty, scientists use sensitivity analysis: a technique employed to understand how different values of an explanatory variable affect the outcome of interest. For example, how do increasing payments to farmers affect their willingness to convert agricultural lands for conservation purposes? This project will develop a new theoretical framework for applying sensitivity analysis to complex human-environment systems models exhibiting great variability over space and time. Specifically, the framework will facilitate 1) the identification of explanatory variables that account for the greatest variability in model outcomes leading to model simplification; 2) the exploration of the model outcome variability over space and time; and 3) the simulation of system dynamics to understand the implications of various policy scenarios. The developed framework will be tested on a model designed to understand agricultural land conservation decisions and their effects on lakes, local economies, and people.This project will contribute greatly to scientists', policy-makers', and citizens' abilities to understand the extent to which various drivers in human-environment systems models affect model outcomes. Moreover, the project will facilitate the development of simpler systems models with greater explanatory power. As such, the methods developed will improve the transparency of such models potentially allowing greater community use, understanding, and participation in such modeling exercises. The framework will also provide methods for prioritizing data acquisition, improving the robustness of complex human-environment models, and enhancing the usefulness of such modeling endeavors for policy and decision making to address major societal problems. The project will train students, through graduate student mentoring and a modeling certification graduate program, to employ the new methods developed in order to describe and solve complex human-environment systems problems. The results of our research, including a spatiotemporal sensitivity analysis toolbox (ST-SA), will be disseminated electronically online, in peer reviewed manuscripts, and at professional conferences.