Geology topics

Saira M. Haider

Publications and source records attributed to Saira M. Haider.

14 recordsLinked to original sources

USGS2026

A tool to monitor hydrologic conditions on tree islands in the Everglades

Tree islands are patchy upland forested habitats in Florida's Everglades that face degradation and disappearance due to altered hydrologic patterns. The U.S. Geological Survey coordinated with the Miccosukee Tribe of Indians of Florida and the Seminole Tribe of Florida to co-develop a decision-support tool based on tree-island hydrologic conditions. Everglades managers can use this tool to help with restoration planning and water operations decisions that affect tree-island conditions. After a series of organized workshops and meetings, a list of hydrologic metrics was selected as indicators of tree-island health, including hydroperiod, number of days since last dry, and maximum water depth at the head of the island. As a result, a web application tool, called ETree, has been developed and is publicly available online. This web application provides data on daily metrics for the current Everglades water year and annual summaries for past years, beginning in 2000.

Florida↗

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

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USGS2023

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region

The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.

PLoS ONE↗

USGS2023

The Everglades vulnerability analysis: Linking ecological models to support ecosystem restoration

Understanding of the Everglades’ ecological vulnerabilities and restoration needs has advanced over the past decade but has not been applied in an integrated manner. To address this need, we developed the Everglades Vulnerability Analysis (EVA), a decision support tool that uses modular Bayesian networks to predict the ecological outcomes of a subset of the ecosystem’s health indicators. This tool takes advantage of the extensive modeling work already done in the Everglades and synthesizes information across indicators of ecosystem health to forecast long-term, landscape-scale changes. In addition, the tool can predict indicator vulnerability through comparison to user-defined ideal system states that can vary in the level of certainty of outcomes. An integrated understanding of the Everglades system is essential for evaluation of trade-offs at local, regional, and system-wide scales. Through EVA, Everglades restoration decision makers can provide effective guidance during restoration planning and implementation processes to mitigate unintended consequences that could result in further damage to the Everglades system.

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USGS2023

Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration

dictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest-site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest-site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.

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USGS2023

Sea level rise may pose conservation challenges for the endangered Cape Sable seaside sparrow

Biodiversity conservation under a changing climate is a challenging endeavor. Landscapes are shifting as a result of climate change and sea level rise but plant communities in particular may not keep up with the pace of change. Predictive ecological models can help decision makers understand how species are likely to respond to change and then adjust management actions to align with desired future conditions. Florida’s Everglades is a wetland ecosystem that is host to many species, including a large number of endangered and endemic species. Everglades ecosystem restoration has been ongoing for decades, but consideration of sea level rise impacts in restoration planning is more recent. Incorporating potential impacts from sea level rise into restoration planning should benefit species and their coastal habitats, most notably at the southern Florida peninsula. The endangered Cape Sable seaside sparrow ( Ammospiza maritima mirabilis ) occurs in marl prairie habitat at the southern end of the Everglades. The locations of three of its six subpopulations are proximate to the coast. We used a spatially explicit predictive model, EverSparrow, to estimate probability of sparrow presence considering both hydrologic change from restoration and sea level rise. We found that the probability of sparrow presence decreased with increasing sea level rise. Within approximately 50 years, probability of presence significantly decreased for all three coastal subpopulation areas, with areas above 40% probability increasingly limited. Given the exceptionally low dispersal ability of this species and the geographic restrictions for habitat expansion, our results highlight the importance of freshwater flow into the southern Everglades marl prairie for habitat conservation.

Florida↗

USGS2022

Managing multiple species with conflicting needs in the Greater Everglades

Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.

Florida↗

USGS2021

The Everglades vulnerability analysis—Integrating ecological models and addressing uncertainty

The Everglades vulnerability analysis (EVA) is a project led by the U.S. Geological Survey in cooperation with the National Park Service and U.S. Army Corps of Engineers to accomplish one of the science goals of Restoration Coordination & Verification (RECOVER), a multiagency group responsible for providing scientific and technical evaluations and assessments for improving the ability of the Comprehensive Everglades Restoration Plan to restore, preserve, and protect the south Florida ecosystem while providing for the region’s other water-related needs. In 2016, RECOVER acknowledged the need for a tool that could synthesize the decades of Everglades ecosystem science and identify areas vulnerable to changing conditions on the landscape. The EVA tool answers this need through a landscape-scale modeling framework that provides annual responses and relative vulnerability for a suite of indicators of Everglades ecosystem health.

Florida↗

USGS2021

EverForecast—A near-term forecasting application for ecological decision support

The Everglades Forecasting application (EverForecast) provides decision makers with a support tool to examine optimal allocations of water across the managed landscape while explicitly quantifying the conflicting needs of multiple species. Covering the Greater Everglades (a vast, subtropical wetland ecosystem in South Florida), EverForecast provides 6-month forecasts of daily projected water stage across the region. It then runs these forecasts through a suite of species models and illustrates potential tradeoffs. All output is summarized by subregion and hydrologic category. Decision makers can use these near-term forecasts to manage the transition from current conditions to future alternatives according to their management priorities.

Florida↗

USGS2021

Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach

The Florida Everglades is a vast and iconic wetland ecosystem in the southern United States that has undergone dramatic changes from habitat degradation, development encroachment, and water impoundment. Starting in the past few decades, large restoration projects have been undertaken to restore the landscape, including improving conditions for threatened and imperiled taxa. One focus of restoration has been the marl prairie ecosystem, where the federally endangered Cape Sable Seaside Sparrow ( Ammospiza maritima mirabilis ; CSSS) resides. The CSSS is endemic to the Everglades where populations have been steadily declining, signaling the importance of decision support tools for natural resource managers for evaluating water management and restoration scenarios. Here we developed an ensemble logistic regression, combining a frequentist and Bayesian approach, to model CSSS presence and measure how environmental factors such as hydrometrics, fire occurrence, and vegetation structure impact CSSS habitat suitability. This is the first analysis to quantitatively assess the interdependent relationships between a broad range of environmental factors and CSSS presence across the landscape. Our results show that the probability of CSSS presence was highest in areas with dry conditions, hydroperiods between 80 and 120 days, percentages of canopy cover and woody vegetation less than 10%, and more than six years post-fire where 75% or more of the area was burned. Because the frequentist and Bayesian models had nearly identical spatial outputs with the Bayesian model having slightly higher validation metrics, we used the Bayesian approach as our final model (EverSparrow). The results from our analysis can provide a valuable decision support tool as natural resource managers work to restore the Everglades landscape.

Florida↗

USGS2021

Knowledge synthesis of Cape Sable Seaside Sparrow science

This report represents a literature review of science conducted on the Cape Sable Seaside Sparrow ( Ammospiza maritima mirabilis , hereafter “CSSS” or “spar-row”). This information can be used as a foundation for the upcoming Species Status Assessment and for updating the CSSS Recovery Plan. This report focuses on areas of interest relative to CSSS management such as habitat, hydrology, fire, and population estimates. We include peer-reviewed scientific literature, synthesis reports, and some field reports. Many field reports to funding agencies and other documents exist beyond what is reviewed here; however, we include the most relevant documents relative to the focus of CSSS management. All documents reviewed are listed in the References section.

Florida↗

USGS2020

The Everglades Depth Estimation Network (EDEN) surface-water interpolation model, version 3

The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that estimate daily water-level data at ungaged locations, and applications that generate derived hydrologic data across the freshwater part of the Greater Everglades landscape. Version 3 (V3) of the EDEN interpolation surface-water model is the most recent update, replacing the version 2 (V2) model released in 2011. The primary revision for the V3 model is the switch to the R programming language to create a more efficient and portable EDEN code relative to V2, without reliance on proprietary software. Using R, the interpolation script runs over 10 times faster and is more easily updated, for example, to accommodate changes in the gage network or to incorporate R software updates. Additional revisions made for the V3 model include updates to the interpolation model, the gage network, and groundwater-level estimations. The EDEN model domain in the Greater Everglades and Big Cypress National Preserve is divided into subdomains that are based on hydrologic boundaries. In the V3 model, the number of subdomains was increased from five to eight, which allows hydrologic boundaries, such as levees and canals, to be better represented in the interpolation scheme. Five pseudogages were added to constrain the water-level surface at subdomain boundaries. Changes made to the water-level gage network between the implementation of the V2 and V3 models are incorporated, and groundwater-level estimations are added, which are important information for hydrologic and ecological studies. Summary model performance statistics indicate similar accuracy in water-level surfaces generated by the V3 and V2 models, with a root mean square error of 4.78 centimeters for both interpolation models against independent water-level measurements. Providing stability and continuity for the EDEN user community, the V3 model closely replicates the V2 model, with a root mean square difference of 3.87 centimeters for interpolated surfaces from April 1, 2014, to March 31, 2018. The additional groundwater levels provide a realistic estimate of the saturated groundwater surface continuous with the surface-water surface for Water Conservation Areas 2A and 2B from 2000 to 2011. This continuous surface is a more accurate estimation of the spatial distribution of water in the hydrologic system than before, providing needed information for ecological studies in areas where depth to water table affects habitats. Development of the EDEN V3 model advances the tools available to scientists and resource managers for guiding large-scale field operations, describing hydrologic changes, and supporting biological and ecological assessments.

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USGS2020

Potential impacts of future urbanization and sea level rise on Florida’s natural resources

As urban development continues to encroach into natural systems, these ecosystems experience increasing degradation to their form and function. Losses in biodiversity and ecosystem function are further compounded by changing climatic conditions. The State of Florida is known for its biodiversity but has experienced declines in species populations and habitats because of urbanization and sea level rise. These declines are particularly challenging in an economy that benefits from a multibillion-dollar income from natural resources tourism. In this study, we assessed the potential future impacts of urbanization and sea level rise on a suite of conservation targets that have been set for the State. We developed six scenarios of all combinations of intermediate and high sea level rise paired with two types of urbanization (sprawling and compact) in both 2040 and 2070 to examine the potential future threats to conservation targets in High Pine and Scrub, Coastal Uplands, and Freshwater Aquatics ecosystems. Our results show projected decreases in extent and area of these priority ecosystems into the future. Florida's current trend in urbanization practices are projected to have a greater impact on conservation targets than if sprawl reduction practices are implemented. Coastal Uplands are projected to experience the greatest loss in area, at up to 47%. Conservation-focused urban planning and climate adaptation strategies can help protect Florida's natural resources with benefits to Florida's tourism economy as well as critical ecosystem functions and services such as coastal flood protection and storm surge risk reduction.

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USGS2018

Managing conflicts in the River of Grass

Chances are, you would not pack up and move to a new home without first researching the neighborhood, reviewing your finances, and maybe investigating schools nearby. Similarly, you would not buy the first car you find on a magazine cover without first reviewing the technical specifications, exploring your options, and perhaps taking a test drive. Even when making simple purchases online, you probably shop around, check out customer reviews, and scan seller feedback ratings. Rarely do we act on impulse alone when faced with decisions. Rather, we typically start with a vision of what we want before gathering the necessary information, weighing our options, prioritizing our values, and evaluating possible tradeoffs. In making choices that range from lighthearted to life-changing, we use this commonsense strategy every day of our lives. Environmental decision-making is no different. For decades, natural resource managers faced with complicated problems have made decisions by breaking them into smaller components that are easier to approach. And like our day-to day decisions, the process is ideally guided by a shared vision of the future. However, it can become complicated when multiple, seemingly disparate views on what the future should hold emerge. As Lewis Carroll once wrote, “If you don’t know where you are going, any road will get you there.” Divining a vision of the future for the Florida Everglades and making supporting decisions is a complex undertaking. But unlike Alice thrust through the looking glass, we have tools and techniques to help us make sense of it all. The application of tools to help decision-making can move us closer to the successful restoration of this imperiled landscape.

Florida↗