USGS ScienceSearch

USGS · 70228376

Integrating urban planning and water management through green infrastructure in the United States-Mexico border

Abstract

Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 31.23159167205059° to 31.423975737976697° latitude; -111.0772705078125° to -110.8685302734375° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Francisco Lara-Valencia, Margaret Garcia, Laura M. Norman, Alma Anides Morales, Edgar E. Castellanos-Rubio. 2022-02-01. Integrating urban planning and water management through green infrastructure in the United States-Mexico border. https://doi.org/10.3389/frwa.2022.782922

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Facilitating water resilience in wildfire affected communities: Lessons learned from rapid response research

Wildland–urban interface fires (WUI fires) can pose a significant threat to water resources, including drinking water supplies, water treatment infrastructure, ecosystem function, and agricultural irrigation. Wildfires, especially WUI fires, are expected to increase in frequency and severity. Despite the need for effective mitigation and response strategies for wildfires, rapid research co-production to support decision-making for water incident response and water management is generally limited. This manuscript draws on five U.S. wildfire case studies to highlight how research co-production between scientists, water agencies, and managers supports more effective decision-making for water resilience and recovery. The case studies demonstrate the importance of rapid response activities, coordinating collaborative response, pre-wildfire preparation, and knowledge co-production among agencies, researchers, and managers in addressing the impacts of wildfires on water supply and quality. The lessons learned emphasize opportunities to pivot wildfire-water research and operations from reactive to proactive, focusing on mutually beneficial activities such as understanding watershed health, fostering collaboration, embracing new discoveries and tools, and enabling pre-wildfire research through table-top activities, workshops, pre-fire data collection and analysis, and appointing a central water response lead. These outcomes inform the development of a research-to-operations and operations-to-research (R2O2R) co-production framework and future opportunities to guide proactive response and management efforts before, during, and after wildfire.

California, Hawaii, New Mexico

Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations

Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.

conterminous United States

In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands

Introduction: Rangeland ponds are vital to the livelihoods of pastoral and agropastoral communities in Africa, providing an important source of water for livestock. However, sparse instrumentation across much of Africa makes it extremely challenging to monitor surface water availability in these areas. Model estimates of surface water, for example, as used by the Famine Early Warning Systems Network (FEWS NET) Water Point Viewer, are one of the few operational tools available to monitor surface water stress across pastoral areas of the Sahel and East Africa. Methods: Water availability data from these models are difficult to validate. New methods using satellite data to classify surface water provide an opportunity to assess the performance of these tools. This study compares water availability estimates derived from Landsat and Sentinel 1 satellite imagery to in situ observations and model simulations of water availability in 22 ephemeral ponds located in the Ferlo region of Senegal. Results and discussion: The Active-Passive Water Classification (APWC) algorithm detected surface water at each location. Over 2022 and 2023, water was detected in pond locations annually at a frequency of 68.2% for all ponds and at a frequency of 43.8% for ponds with a surface area <10,000 square meters (m 2 ). The APWC results outperform global and continental surface water datasets in the Ferlo region. Seasonal water availability was captured in 12 ponds over the 2022 and 2023 seasons. The 12 locations can function as sentinel ponds to monitor local water availability. Study results demonstrate the viability of satellite methods to assess water availability in the region, as well as the challenges to using satellite-based methods to estimate water availability in small ponds.

Ferlo Region