GC099
Sub-Seasonal to Seasonal Forecasting for Hydrological, Agricultural, and Ecological Applications
Session ID#: 251721
Session Description:
Sub-seasonal to seasonal (S2S) forecasts hold significant potential for supporting informed decision-making in agriculture, water resource management, and related fields. They can guide reservoir operations, water rights leasing, agricultural input planning, irrigation scheduling, and strategies to mitigate the impacts of extreme events like droughts, floods, wildfires, and heatwaves. Despite their potential, applications of S2S forecasts remain limited.This session aims to highlight recent advances in S2S forecasting and explore their practical applications across hydrological, agricultural, and ecological domains. We welcome contributions on a wide range of topics, including: (A) innovative forecasting tools, climate services, and applications, (B) advances in artificial intelligence and machine learning for S2S forecasting, with an emphasis on explainability and uncertainty quantification, and (C) methods for evaluating the economic value of forecasts in specific decision-making contexts. We encourage case studies that showcase applications—from field to watershed scales—highlighting collaboration among researchers, farmers, and natural resource managers.
Co-Sponsor(s):
Index Terms:
1842 Irrigation [HYDROLOGY]
1880 Water management [HYDROLOGY]
6309 Decision making under uncertainty [POLICY SCIENCES & PUBLIC ISSUES]
6334 Regional planning [POLICY SCIENCES & PUBLIC ISSUES]
Primary Convener: Nivedita Dubey, Washington State University, Pullman, WA, United States
Conveners: Kirti Rajagopalan, Washington State University, Biological Systems Engineering, Pullman, WA, United States, Samarth Swarup, University of Virginia, Charlottesville, United States and Subimal Ghosh, Indian Institute of Technology Bombay, Mumbai, India
Student/Early Career Convener: Tejasvi Chauhan, Max Planck Institute for Biogeochemistry, Jena, Germany
Improving Sub-seasonal Monsoon Forecasts Over India with Machine Learning for Hydrologic Applications (Invited) (1954843)
Andrew William Robertson1, Emile Esmaili2, Muhammad Azhar Ehsan3, A. Dey4, Susmitha Joseph4, Shiqi Fang5, Chandramauli Awasthi6, Jeongwoo Hwang7 and Sankar Arumugam5, (1)Center for Climate Systems Research @NASA GISS, New York, United States, (2)Columbia University in the City of New York, Columbia Climate School, New York, United States, (3)Center for Climate Systems Research, New York, United States, (4)Indian Institute of Tropical Meteorology, Pune, India, (5)North Carolina State University, Civil, Construction, and Environmental Engineering, Raleigh, United States, (6)North Carolina A & T State University, Greensboro, United States, (7)NC State University, Department of Civil, Construction, and Environmental Engineering, Raleigh, United States
ForeSWE: An Attention Model for Forecasting SWE with Uncertainty Quantification (1977599)
Krishu Thapu1, Supriya Savalkar2, Bhupinderjeet Singh2, Kirti Rajagopalan3, Nghia Hoang1 and Anantharaman Kalyanaraman1, (1)Washington State University, EECS, Pullman, United States, (2)Washington State University, Department of Biological Systems Engineering, Pullman, United States, (3)Washington State University, Biological Systems Engineering, Pullman, WA, United States
Streamflow Forecasting via Vision Transformer over Clustered Water Basins (1985862)
Mohammed Amine Gharsallaoui1, Long Minh Bui2, Bhupinderjeet Singh3, Supriya Savalkar3, Nghia Hoang2, Kirti Rajagopalan4 and Janardhan Rao Doppa2, (1)Washington State University, EECS, Pullman, WA, United States, (2)Washington State University, EECS, Pullman, United States, (3)Washington State University, Department of Biological Systems Engineering, Pullman, United States, (4)Washington State University, Biological Systems Engineering, Pullman, WA, United States
Practitioner Perspectives on the Potential Utility of Seasonal Forecasts to Support Agricultural Decisions. (1991494)
Kirti Rajagopalan, Washington State University, Biological Systems Engineering, Pullman, WA, United States, Sonia Hall, Washington State University, Center for Sustaining Agriculture and Natural Resources, Wenatchee, United States, Georgine Yorgey, Washington State University, Center for Sustaining Agriculture and Natural Resources, Pullman, United States, Michael Brady, Washington State University, School of Economic Sciences, Pullman, United States, Supriya Savalkar, Washington State University, Department of Biological Systems Engineering, Pullman, United States and Karie Boone, Washington State University, Pullman, United States
Bayesian Neural Networks for Uncertainty-Aware Fog Detection and Forecasting (2005142)
Prasad Deshpande, Kansas State University, Agronomy, Manhattan, KS, United States, Shivam Tripathi, Indian Institute of Technology Kanpur, Department of Civil Engineering, Kanpur, India and Arnab Bhattacharya, Indian Institute of Technology Kanpur, Computer Science and Engineering, Kanpur, India
