Operationalizing multi-benefit robust spatial planning under deep uncertainties

Abstract:

Decreasing surface water availability and depleting groundwater aquifers are posing unprecedented challenges to land management, especially in intensively irrigated agricultural landscapes. To shape landscapes in a way that achieves multiple socio-economic and environmental outcomes while minimizing trade-offs from land uses that may only be partially compatible (e.g., agricultural production, habitat restoration), stakeholders must take proactive and analytically informed action. However, the evolution of such landscapes is subject to deep uncertainties in climate and social-environmental systems. The spatial interdependence and multi-scale determinants of benefits flowing from landscapes further complicate multi-benefit spatial planning. In this study, we demonstrate how to integrate deep uncertainties from multiple sources to assess the robustness of multi-benefit conservation planning solutions through a case study in California’s San Joaquin Valley, which is expected to undergo significant land use change (including permanent and temporary fallowing) under the dual pressures of climate change and Sustainable Groundwater Management Act (SGMA). Drawing on an existing but under-explored workflow linking SGMA and climate change to agricultural land use change to conservation outcomes, we significantly extend the approach to assess robustness of conservation solutions and explore trade-offs between conservation and agricultural outcomes under a wide range of uncertainties. These include assumptions about future water availability, land retirement needed to achieve SGMA objectives, characteristics of high-quality habitat, and perceived costs to the agricultural community associated with restoring land. We also explore each of these conditional on different assumptions about drivers of agricultural suitability, which affect the spatial patterns of the resulting landscapes. We identify the most important drivers of these changes and demonstrate metrics for assessing robustness of conservation portfolios. The modular structure of our decision-analytic workflow allows for the integration of other climatic and non-climatic uncertainties across a wide range of spatial scales, including constraints from other objectives such as high priority solar and wind projects.