State-level multidimensional agricultural drought susceptibility and risk assessment for agriculturally prominent areas

Islam, S. M. Samiul; Yeşilköy, Serhan; Baydaroğlu, Özlem; Yıldırım, Enes; Demir, Ibrahim

doi:10.1080/15715124.2024.2304546

Cited by 5 publications

(1 citation statement)

References 89 publications

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“…There were various efforts to study HAB events, observed in the state of Iowa, which heavily relies on agriculture as its primary economic sector (Islam et al, 2024). In their study, Lee et al (2020) conducted a collection of 65 water samples from various lake beaches in Iowa to examine the potential relationship between the concentrations of microcystin and the abundances of genes responsible for toxin production.…”

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confidence: 99%

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Baydaroğlu,

Yeşilköy,

Dave

et al. 2024

Preprint

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Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public health, recreational services, ecological balance, wildlife, fisheries, microbiota, water quality, and economics. HABs are caused by many sources, such as water pollution based on agricultural activities, wastewater treatment plant discharges, leakages from sewer systems, natural factors like pH and light levels, and climate change impacts. While many causes of HABs are recognized, it is unknown how toxin-producing algae develop as well as the key processes and components that contribute to their weight due to the distinct algal dynamics of each lake and the variety and unpredictability of the conditions influencing these dynamics. Modeling HABs in a changing climate is essential for achieving sustainable development goals regarding clean water and sanitation. However, the lack of consistent and adequate data on HABs is a significant challenge for all these studies. In this study, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines sparse regression and machine learning methods to reconstruct the analytical representation of a dynamical system. Moreover, a model-driven and web-based interactive tool was created to disseminate and develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Users can easily share images of HABs in lakes on an interactive and user-friendly platform, allowing others to view the status of the lakes.

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mentioning

confidence: 99%

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Baydaroğlu,

Yeşilköy,

Dave

et al. 2024

Preprint

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Crop yield prediction based on reanalysis and crop phenology data in the agroclimatic zones

Yeşilköy,

Demir

2024

Theor Appl Climatol

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A contemporary systematic review of cyberinfrastructure systems and applications for flood and drought data analytics and communication

Yeşilköy,

Baydaroğlu,

Singh

et al. 2024

Environ. Res. Commun.

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Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facilitating proactive responses. Today's information systems offer a vast and intricate mesh of data, encompassing satellite imagery, meteorological metrics, and predictive modeling. Easily accessible to the general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids to decision-making processes. This review collates key literature on water-related disaster information systems, underscoring the transformative impact of emerging information and Internet technologies. These advancements promise enhanced flood and drought warning timeliness and greater preparedness through improved management, analysis, visualization, and data sharing. Moreover, these systems aid in hydrometeorological predictions, foster the development of web-based educational platforms, and support decision-making frameworks, digital twins, and metaverse applications in disaster contexts. They further bolster scientific research and development, enrich climate change vulnerability frameworks, and strengthen associated cyberinfrastructures. This article delves into prospective developments in the realm of natural disasters, pinpointing primary challenges and gaps in current water-related disaster information systems, and highlighting the potential intersections with future artificial intelligence solutions.

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State-level multidimensional agricultural drought susceptibility and risk assessment for agriculturally prominent areas

Cited by 5 publications

References 89 publications

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Crop yield prediction based on reanalysis and crop phenology data in the agroclimatic zones

A contemporary systematic review of cyberinfrastructure systems and applications for flood and drought data analytics and communication

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