About

Steven Quiring is a professor in the Department of Geography at The Ohio State University, where he also serves as the Promotion and Tenure Chair. He holds a Ph.D. in Climatology from the University of Delaware, an M.A. in Geography from the University of Manitoba, and a B.A. in Geography from the University of Winnipeg. His research interests include climatology, hydroclimatology, synoptic climatology, and climate data analytics. Quiring's current research focuses on understanding land-atmosphere interactions to improve drought and seasonal climate predictability, as well as modeling the impact of weather events on power infrastructure, including hurricanes, thunderstorms, and winter storms. He employs a variety of data mining, data analytics approaches, and models such as weather/climate, hydrologic, land surface, and crop yield models. He teaches courses related to weather, climate, and global warming, microclimatology, and applied climatology.

Research topics

• Geography
• Meteorology
• Environmental science
• Geology
• Climatology
• Ecology
• Physical geography

Selected publications

• Modelling Urban Pluvial Flooding in Cincinnati, Ohio, Using Machine Learning

  ISPRS International Journal of Geo-Information · 2026-04-14

  articleOpen accessSenior author

  Urban pluvial flooding presents growing challenges for disaster risk management, yet most susceptibility studies rely on watershed-based frameworks that inadequately capture the localized dynamics of urban systems. This study proposes a city-scale flood susceptibility modeling framework for Cincinnati, Ohio. Cincinnati was chosen because it is a city with a documented history of severe urban flooding, including a once-in-a-century storm in 2016. Multi-source historical flood data were compiled from NOAA storm event records and crowdsourced reports to enhance spatial coverage. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and Logistic Regression) were implemented to identify the most effective approach for urban pluvial flood prediction. Random Forest (RF) and Support Vector Machine (SVM) achieved the highest accuracy (0.84) and demonstrated strong discriminatory power. RF was selected as the optimal model because it had a higher AUC (90%) and the lowest RMSE (0.35). To assess generalizability, the RF model was validated on updated land use data and flood records from a 2020 storm event. It demonstrated robust performance (accuracy = 0.89, RMSE = 0.36, precision = 0.75, recall = 1, and AUC = 0.95), despite urban development changes. This study’s novelty lies in combining multi-source flood records with a grid-based machine learning framework and rigorously validating model robustness under evolving urban conditions. The results advance urban pluvial flood susceptibility modeling and offer actionable guidance for evidence-based flood risk management worldwide.

  PublisherOA PDFDOI

• Evaluating the Financial Impact of Soil Moisture Measurements for Livestock Producers in the Upper Missouri River Basin

  Bulletin of the American Meteorological Society · 2026-04-01

  articleSenior author

  Abstract Droughts pose a significant financial risk to the agricultural sector in the United States. Livestock producers are particularly affected, as drought reduces forage availability and increases costs. To mitigate these impacts, the U.S. Department of Agriculture (USDA) Livestock Forage Disaster Program (LFP) provides direct payments to qualifying producers based on drought severity. In situ observations play a key role in assessing drought severity by providing localized information on conditions. Station density in the Upper Missouri River basin (UMRB) is being expanded from 78 to 540 stations through a project led by the U.S. Army Corps of Engineers. This study evaluates the economic impact of this expansion in the UMRB by calculating how improvements in the accuracy of drought monitoring influence LFP payments. Our results show that the 2017 drought LFP payments would have been ∼$480 million if they were solely based on the 78-station network, while payments based on the expanded 540-station network would have been ∼$635 million. Expanding the station network decreased drought classification error from 29.6% to 6.9%. This study demonstrates that expanding observing infrastructure leads to improvements in drought monitoring and allows government agencies to optimize resource allocation and drought response strategies.

  PublisherDOI

• Evaluating the Utility of Drought Indices and Indicators for Monitoring Environmental Drought in Pollinators

  Journal of Applied Meteorology and Climatology · 2026-03-31

  articleSenior author

  Abstract Drought is a climatic hazard that is both costly and destructive. The impacts of drought affect not only hydrologic and agricultural systems but also cascade through the natural environment. Pollinators play a crucial role in maintaining biodiversity as well as the health of ecosystems. Here, we evaluate 26 drought indices and indicators to determine which are most appropriate for quantifying ecological drought impacts to pollinators in the mid-Atlantic United States. This study evaluates what indices and indicators are most strongly correlated with pollinator abundance, and how these relationships vary across different genera, as well as spatially and temporally. This analysis demonstrates that the relationships between drought and pollinator abundance are complex. We found that groundwater storage, root-zone soil moisture, and surface soil moisture are the most appropriate indices for quantifying how environmental drought influences pollinator abundance. The Bombus genus was the most responsive to drought conditions ( r = 0.15–0.52), while other genera, including Lasioglossum , Andrena , and Halictus, did not have statistically significant relationships with drought conditions. There is substantial spatial variability in the relationships between pollinator population and drought conditions. The relationship was stronger in the ridge and valley ecoregion (mean r = 0.26) and weaker in the Blue Ridge ecoregion (mean r = 0.09). There is also temporal variability in these relationships. Drought conditions in June have the greatest impact on pollinator abundance ( r = −0.57 to −0.62).

  PublisherDOI

• Hydrology, Floods, and Droughts | Drought

  Elsevier eBooks · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Comparison of Different Machine Learning Techniques for Downscaling Smap and Nldas Soil Moisture Over Conus

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• 1 Harnessing Deep Learning to Enhance Spatial Resolution of Smap And Nldas Soil Moisture Products Over Conus

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Identifying Important Features for Downscaling Soil Moisture to 1-km in the Contiguous United States

  2025-04-07 · 5 citations

  preprintOpen accessSenior author

  Abstract. Soil moisture is a fundamental state variable in climatology, meteorology, and hydrology. Many of the available soil moisture products have a coarse spatial resolution that is not useful for agricultural applications. This study used Random Forest to identify which features are most helpful for accurately downscaling soil moisture to 1-km resolution. Fourteen features were considered: precipitation, antecedent precipitation index, maximum daily air temperature, minimum daily air temperature, mean daily air temperature, diurnal temperature range, dew point temperature, elevation, slope, aspect, normalized difference vegetation index, leaf area index (LAI), soil texture, and land use/land cover. The analysis of variable importance was repeated using two different sources of soil moisture data (e.g., satellite-derived soil moisture from NASA’s Soil Moisture Active Passive (SMAP) and model-derived soil moisture from the North American Land Assimilation System (NLDAS)) and two different ways of representing soil saturation (e.g., volumetric water content (VWC) and percentiles). We found that dew point temperature is the most important variable for downscaling SMAP percentiles (0.18), NLDAS VWC (0.27), and NLDAS percentiles (0.17) over CONUS, while elevation is the most important variable for downscaling SMAP VWC (0.28). Dew point temperature is crucial for downscaling in most regions of the United States, except in the South and WestNorthCentral, where elevation is the most important feature. The accuracy of the downscaling varies by region. In the South, SMAP VWC and NLDAS VWC downscaling are relatively accurate, both have mean absolute errors of ~0.07. The MAE values in the South region are 0.196 for SMAP percentiles and 0.175 for NLDAS percentiles.

  PublisherOA PDFDOI

• Comparison of Different Machine Learning Techniques for Downscaling Smap and Nldas Soil Moisture Over Conus

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Hybrid Machine Learning Model for Hurricane Power Outage Estimation from Satellite Night Light Data

  Remote Sensing · 2025-07-09

  articleOpen accessSenior author

  Hurricanes can cause massive power outages and pose significant disruptions to society. Accurately monitoring hurricane power outages will improve predictive models and guide disaster emergency management. However, many challenges exist in obtaining high-quality data on hurricane power outages. We systematically evaluated machine learning (ML) approaches to reconstruct historical hurricane power outages based on high-resolution (1 km) satellite night light observations from the Defense Meteorological Satellite Program (DMSP) and other ancillary information. We found that the two-step hybrid model significantly improved model prediction performance by capturing a substantial portion of the uncertainty in the zero-inflated data. In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. For example, the xgb+xgb model has 14% less RMSE than the log+cnn model, and the R-squared value is 25 times larger. The Interpretable ML (SHAP value) identified geographic locations, population, and stable and hurricane night light values as important variables in the XGBoost power outage model. These variables also exhibit meaningful physical relationships with power outages. Our study lays the groundwork for monitoring power outages caused by natural disasters using satellite data and machine learning (ML) approaches. Future work should aim to improve the accuracy of power outage estimations and incorporate more hurricanes from the recently available Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data.

  PublisherOA PDFDOI

• Estimation of impacts-based drought thresholds for the U.S. Corn Belt

  Agricultural and Forest Meteorology · 2025-07-01 · 4 citations

  articleOpen access

  • Impacts-based SPI and SPEI thresholds were developed using corn yield data. • SPEI-3 showed stronger correlation with corn yield than SPI-3 across four states. • Fixed thresholds underestimated drought severity in the U.S. Corn Belt. • Yield-informed thresholds improved detection of extreme drought years (1988, 2012). • The method enhances drought monitoring and crop insurance decision-making. Drought remains a major challenge for agricultural sustainability, particularly in the U.S. Corn Belt, where a significant proportion of corn production is rainfed. Traditional drought monitoring systems rely on fixed drought thresholds, which may not accurately reflect the localized impacts on crop yields. This study develops impacts-based agricultural drought thresholds for the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) using county-level corn yield data from Illinois, Indiana, Iowa, and Ohio as agricultural drought indicators. The findings show that the 3-month SPEI exhibits a stronger correlation with detrended corn yield than the 3-month SPI highlighting the significance of incorporating temperature-driven evapotranspiration effects. Moreover, the impacts-based thresholds consistently classify agricultural drought conditions more accurately than fixed thresholds, revealing that fixed thresholds tend to underestimate agricultural drought severity. For example, for D3 (Extreme) and D4 (Exceptional) drought categories, the impacts-based thresholds of SPEI-3 were -1.3 and -1.7, respectively (versus -1.6 and -2 for the fixed thresholds). Our findings suggest that implementing impacts-based thresholds can enhance drought classification for agricultural decision-making, thereby improving risk management strategies and crop insurance assessments.

  PublisherDOI

Recent grants

• CAREER: Drought Predictability and the Role of Land-Atmosphere Interactions in the U.S. Great Plains

  NSF · $571k · 2011–2016

• Doctoral Dissertation Research: Assessing Factors that Influence Streamflow

  NSF · $17k · 2020–2021

• Doctoral Dissertation Research: Mesoscale Impacts of Soil Moisture on Convective Precipitation

  NSF · $12k · 2014–2016

• CAREER: Drought Predictability and the Role of Land-Atmosphere Interactions in the U.S. Great Plains

  NSF · $30k · 2016–2018

Frequent coauthors

• Trent W. Ford

  54 shared

• Seth D. Guikema

  University of Michigan–Ann Arbor

  37 shared

• Shanshui Yuan

  21 shared

• Roshanak Nateghi

  Purdue University West Lafayette

  19 shared

• Laiyin Zhu

  Western Michigan University

  16 shared

• Anita D. Rapp

  Texas A&M University

  12 shared

• Oliver W. Frauenfeld

  11 shared

• Zachary T. Leasor

  10 shared