Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application
Climate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. Traditional drought indicators typically focus on recent conditions rather than future projections, and conventional forecasting methods often struggle to capt...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1564670/full |
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| author | Ingrid Cintura Antonio Arenas |
| author_facet | Ingrid Cintura Antonio Arenas |
| author_sort | Ingrid Cintura |
| collection | DOAJ |
| description | Climate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. Traditional drought indicators typically focus on recent conditions rather than future projections, and conventional forecasting methods often struggle to capture the complex, non-linear relationships between long-term climate variables and droughts. This project aims to fill this gap by developing a machine-learning model to project drought conditions in Iowa, specifically focusing on the U.S. Drought Monitor categories. The developed model, a Long Short-Term Memory neural network, was validated to assess its reliability and accuracy. With a Root Mean Squared Error of 0.19 and an R2 of 91%, the model achieved a high level of accuracy, making it effective in guiding conservation practices and enabling timely interventions. The model was trained on historical data from 2012 to 2019 and thoroughly evaluated using out-of-sample data from 2002 to 2011. It exhibited strong performance in the projection of drought conditions across Iowa’s Hydrologic Unit Code 08 watersheds. Drought conditions for the period 2030–2050 were projected using three general circulation models (GCMs): MPI-ESM1-2-HR, BCC-CSM2-MR, and CNRM-ESM2-1. These projections were conducted under two contrasting Shared Socioeconomic Pathways (SSPs): SSP1-2.6, representing a low-emissions sustainability scenario, and SSP5-8.5, reflecting a high-emissions, fossil–fuel–intensive trajectory. Results indicate that droughts in the coming decades will become more intense, prolonged, and frequent, with projections suggesting intensities up to twice as severe and durations and frequencies in northwestern regions up to nine times higher than historical records. Moreover, this research developed an interactive application for visualizing future drought conditions in Iowa. This tool aids users in making informed water management decisions by providing stakeholders with detailed visualizations and technical information. |
| format | Article |
| id | doaj-art-2869037008e94e76839bc7cd42651cb6 |
| institution | Kabale University |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-2869037008e94e76839bc7cd42651cb62025-08-20T03:39:18ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-08-011310.3389/fenvs.2025.15646701564670Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive applicationIngrid CinturaAntonio ArenasClimate change has intensified the frequency and severity of droughts, significantly impacting water resources, agriculture, and ecosystems. Traditional drought indicators typically focus on recent conditions rather than future projections, and conventional forecasting methods often struggle to capture the complex, non-linear relationships between long-term climate variables and droughts. This project aims to fill this gap by developing a machine-learning model to project drought conditions in Iowa, specifically focusing on the U.S. Drought Monitor categories. The developed model, a Long Short-Term Memory neural network, was validated to assess its reliability and accuracy. With a Root Mean Squared Error of 0.19 and an R2 of 91%, the model achieved a high level of accuracy, making it effective in guiding conservation practices and enabling timely interventions. The model was trained on historical data from 2012 to 2019 and thoroughly evaluated using out-of-sample data from 2002 to 2011. It exhibited strong performance in the projection of drought conditions across Iowa’s Hydrologic Unit Code 08 watersheds. Drought conditions for the period 2030–2050 were projected using three general circulation models (GCMs): MPI-ESM1-2-HR, BCC-CSM2-MR, and CNRM-ESM2-1. These projections were conducted under two contrasting Shared Socioeconomic Pathways (SSPs): SSP1-2.6, representing a low-emissions sustainability scenario, and SSP5-8.5, reflecting a high-emissions, fossil–fuel–intensive trajectory. Results indicate that droughts in the coming decades will become more intense, prolonged, and frequent, with projections suggesting intensities up to twice as severe and durations and frequencies in northwestern regions up to nine times higher than historical records. Moreover, this research developed an interactive application for visualizing future drought conditions in Iowa. This tool aids users in making informed water management decisions by providing stakeholders with detailed visualizations and technical information.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1564670/fulldrought projectionslong short-term memoryclimate data analysisdrought intensitydrought durationdrought frequency |
| spellingShingle | Ingrid Cintura Antonio Arenas Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application Frontiers in Environmental Science drought projections long short-term memory climate data analysis drought intensity drought duration drought frequency |
| title | Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application |
| title_full | Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application |
| title_fullStr | Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application |
| title_full_unstemmed | Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application |
| title_short | Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application |
| title_sort | projection and assessment of future droughts in iowa developing a machine learning model and an interactive application |
| topic | drought projections long short-term memory climate data analysis drought intensity drought duration drought frequency |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1564670/full |
| work_keys_str_mv | AT ingridcintura projectionandassessmentoffuturedroughtsiniowadevelopingamachinelearningmodelandaninteractiveapplication AT antonioarenas projectionandassessmentoffuturedroughtsiniowadevelopingamachinelearningmodelandaninteractiveapplication |