Can meta‐analysis be used as a decision‐making tool for developing scenarios and causal chains in eco‐hydrological systems? Case study in Florida

Abstract

To date, there are a high volume of studies concerning climate change impact assessments in ecosystems. Meta‐analysis, scenario development, and causal chains/loops have been used as tools in these assessments as well as in decision making either individually or combined in pairs. There exists a need to develop decision support tools that improve the linkage between climate‐impacts research and planning, management, adaptation, and mitigation decisions by providing quantitative and timely information to stakeholders and managers. The overall goal is to address this need. A specific objective was to develop a decision support tool in eco‐hydrological applications that combine three components: meta‐analysis, scenario development, and causal chains/loop. The developed tool is novel, warranted, and timely. The use of the tool is demonstrated for Florida. The meta‐analysis of 32 studies revealed precipitation changes ranged between +30% and −40%, and temperature changes ranged from +6°C to −3°C for Florida. Seven incremental scenarios were developed at 10% increments in the precipitation change range and nine scenarios with 1°C increments in the temperature change range (driving forces). The causal chains/loops were developed using Driver‐Pressure‐State‐Impact‐Response framework for selected ecosystems and environment (e.g., agroecosystem, mangroves, water resources, and sea turtles) in Florida. The driving force puts pressure on the ecosystem or environment impacting their state, which in turn had a response (e.g., mitigation and adaptation strategies). The framework used indicators selected from studies on climate impact assessments (meta‐analysis and others) for the selected ecosystems as well as author expertise on the topic to develop the chains/loops. The decision tool is applicable to stakeholders and any ecosystem within and outside of Florida.

Sustainable Development Tool Using Meta‐Analysis and DPSIR Framework — Application to Savannah River Basin, U.S.

“…The aim of Stages 1 and 2 is to identify references and synthesize the studies for each component of the DPSIR framework using meta‐analysis. A meta‐analysis can best be described as the quantitative synthesis, analysis, and summary of a collection of studies (Mondelaers et al 2009; Anandhi et al 2018b).…”

“…The developed tool uses a drivers–pressures–state–impacts–responses (DPSIR) framework (Lewison et al 2016; Patrício et al 2016; Elliott et al 2017) and meta‐analysis (Anandhi and Bentley 2018; Anandhi et al 2018b) to better understand the complex and context‐dependent human benefits and impacts of river basin‐related challenges and measures of actions in the SRB. However, the most innovative part of this research is that the four‐stage novel sustainable development tool is developed in the form of a conceptual framework that can organize appropriate scientific research for improved understanding about SRB and the management of basin sustainability challenges for various stakeholders and decision makers.…”

DPSIR-ESA Vulnerability Assessment (DEVA) Framework: Synthesis, Foundational Overview, and Expert Case Studies

“…This case study is based on three studies in several states in the southeastern U.S. (Anandhi and Bentley, 2018;Anandhi et al, 2018aAnandhi et al, , 2018b. Together, these studies used a broad definition of the land-resource system (multiple elements including land use, water resources, other natural resources, climate, biological systems) and stressors (multiple elements including precipitation and temperature change, land-use change, changes in other natural resources).…”

Development of a decision support system at the stages of pre‐design studies and design of irrigation systems based on IDEFo functional modelling methodology*

“…In this aspect, agriculture is no exception (Borozenets and Tsysar, 2013; Udias et al, 2018). Thus, information and communication technologies have found application, in particular, in the field of agriculture (Chimonyo et al 2016; Graetskaya et al 2018), ecology (Pulsani, 2017; Anandhi et al 2018; Kazak and Świaçder, 2018), forestry management (Khanina et al 2009; Sakellariou et al, 2017; Kašpar et al, 2018; Kazak et al 2018) and water resources (Pulido‐Velazquez et al, 2016; Cavazza et al, 2018; Neilsen et al, 2018). For example, the Agrophysical Research Institute has been conducting studies on development of the theoretical and methodological foundations for constructing a ‘single computerized technological space in the field of agronomy’ for many years, and there is experience in the creation and operation of various agrotechnological solutions with help of DSS (Yakushev, 2007).…”