2025
1.
Bhupinderjeet Singh; Mingliang Liu; John T. Abatzoglou; Jennifer C. Adam; Kirti Rajagopalan
In: Journal of Hydrology, vol. 662, pp. 133833, 2025, ISSN: 0022-1694.
Abstract | Links | BibTeX | Tags: AI, Water
@article{singh_incorporating_2025,
title = {Incorporating relative humidity in precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US},
author = {Bhupinderjeet Singh and Mingliang Liu and John T. Abatzoglou and Jennifer C. Adam and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0022169425011710},
doi = {10.1016/j.jhydrol.2025.133833},
issn = {0022-1694},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
journal = {Journal of Hydrology},
volume = {662},
pages = {133833},
abstract = {While the importance of bivariate precipitation phase partitioning\textemdashthat incorporates both surface air temperature and relative humidity\textemdashhas been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative-humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop-hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50% reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30% to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75\textendash88% of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics\textemdashboth of which cannot be resolved by changing the precipitation partitioning\textemdashbecome important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias\textemdashat least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
While the importance of bivariate precipitation phase partitioning—that incorporates both surface air temperature and relative humidity—has been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative-humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop-hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50% reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30% to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75–88% of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics—both of which cannot be resolved by changing the precipitation partitioning—become important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias—at least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.
2.
Oishee Bintey Hoque; Abhijin Adiga; Aniruddha Adiga; Siddharth Chaudhary; Madhav V. Marathe; S. S. Ravi; Kirti Rajagopalan; Amanda Wilson; Samarth Swarup
IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation Proceedings Article
In: pp. 9683–9691, 2025, ISSN: 1045-0823.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{hoque_igrass_2025,
title = {IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation},
author = {Oishee Bintey Hoque and Abhijin Adiga and Aniruddha Adiga and Siddharth Chaudhary and Madhav V. Marathe and S. S. Ravi and Kirti Rajagopalan and Amanda Wilson and Samarth Swarup},
url = {https://www.ijcai.org/proceedings/2025/1076},
doi = {10.24963/ijcai.2025/1076},
issn = {1045-0823},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-01},
volume = {11},
pages = {9683\textendash9691},
abstract = {Electronic proceedings of IJCAI 2025},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Electronic proceedings of IJCAI 2025
3.
Oishee Bintey Hoque; Nibir Chandra Mandal; Abhijin Adiga; Samarth Swarup; Sayjro Kossi Nouwakpo; Amanda Wilson; Madhav Marathe
Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing Proceedings Article
In: pp. 9692–9700, 2025, ISSN: 1045-0823.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{hoque_knowledge-informed_2025,
title = {Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing},
author = {Oishee Bintey Hoque and Nibir Chandra Mandal and Abhijin Adiga and Samarth Swarup and Sayjro Kossi Nouwakpo and Amanda Wilson and Madhav Marathe},
url = {https://www.ijcai.org/proceedings/2025/1077},
doi = {10.24963/ijcai.2025/1077},
issn = {1045-0823},
year = {2025},
date = {2025-09-01},
urldate = {2025-09-01},
volume = {11},
pages = {9692\textendash9700},
abstract = {Electronic proceedings of IJCAI 2025},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Electronic proceedings of IJCAI 2025
4.
Nibir Chandra Mandal; Oishee Bintey Hoque; Abhijin Adiga; Samarth Swarup; Mandy L. Wilson; Lu Feng; Yangfeng Ji; Miaomiao Zhang; Geoffrey Fox; Madhav Marathe
IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping Proceedings Article
In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, pp. 5698–5709, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 979-8-4007-1454-2.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{mandal_irrmap_2025,
title = {IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping},
author = {Nibir Chandra Mandal and Oishee Bintey Hoque and Abhijin Adiga and Samarth Swarup and Mandy L. Wilson and Lu Feng and Yangfeng Ji and Miaomiao Zhang and Geoffrey Fox and Madhav Marathe},
url = {https://dl.acm.org/doi/10.1145/3711896.3737380},
doi = {10.1145/3711896.3737380},
isbn = {979-8-4007-1454-2},
year = {2025},
date = {2025-08-01},
urldate = {2025-08-01},
booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
pages = {5698\textendash5709},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {KDD \'25},
abstract = {We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,668,899 farms and 11,443,492 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224×224 GeoTIFF patches, the multiple input modalities, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training and benchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository: https://github.com/Nibir088/IrrMap and Data repository: https://huggingface.co/Nibir/IrrMap, providing comprehensive documentation and implementation details.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,668,899 farms and 11,443,492 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224×224 GeoTIFF patches, the multiple input modalities, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training and benchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository: https://github.com/Nibir088/IrrMap and Data repository: https://huggingface.co/Nibir/IrrMap, providing comprehensive documentation and implementation details.
5.
John T. Abatzoglou; Charles A. Young; Vishal K. Mehta; Joshua H. Viers; Katherine C. Hegewisch
Predicting California Water-Year Types Using Seasonal Climate Forecasts Journal Article
In: 2025, (Section: Journal of Applied Meteorology and Climatology).
Abstract | Links | BibTeX | Tags: AI, Water
@article{abatzoglou_predicting_2025,
title = {Predicting California Water-Year Types Using Seasonal Climate Forecasts},
author = {John T. Abatzoglou and Charles A. Young and Vishal K. Mehta and Joshua H. Viers and Katherine C. Hegewisch},
url = {https://journals.ametsoc.org/view/journals/apme/64/8/JAMC-D-24-0244.1.xml},
doi = {10.1175/JAMC-D-24-0244.1},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
abstract = {California’s water management is confounded by large interannual variability in water availability inherent in Mediterranean climates and competing water demands in the state. Hydrologic outlooks during late winter and spring are an important consideration that water managers use in water allocation decisions. While hydrologic outlooks are informed by initial conditions, particularly in snowmelt-dominated systems such as California’s Sierra Nevada, there is potential for seasonal climate forecasts to improve such outlooks. California uses a standard water-year typology based on indices of runoff for the Sacramento and San Joaquin Rivers which form the basis of state surface water allocation decisions. We present a simple intuitive model that uses November\textendashMarch watershed average precipitation and temperature in each river basin that explains approximately 90% of the variance in water-year type indices. We then evaluate the utility of these models to forecast water-year types by appending observational temperature and precipitation data with downscaled seasonal climate forecasts from the North American Multimodel Ensemble (NMME). We demonstrate that seasonal climate forecasts augment forecasts based exclusively on initial conditions (e.g., Sierra snowpack). For example, NMME-informed forecasts in January for critical or dry water-year types had an accuracy of 50%\textemdashcomparable to the skill of forecasts based solely on initial conditions 1\textendash2 months later. While California’s hydroclimate is renowned for its unpredictability, results suggest that NMME-informed forecasts may provide additional lead time for water managers to proactively implement water management strategies in anticipation of dry conditions. Significance Statement Water resources in California are wildly variable from year to year resulting in challenges for water managers and users. Improved hydroclimate forecasts can enable proactive measures to secure water for multiple uses. Seasonal forecasts of winter precipitation are notoriously poor in California’s Sierra Nevada, which provides much of the state’s water resources. However, here we show that incorporating seasonal temperature and precipitation forecasts alongside observations adds skill to water-year type outlooks in the Sacramento and San Joaquin River basins. Such forecasts can better inform surface water allocation decisions to minimize the economic and ecological impacts of the state’s variable hydroclimate.},
note = {Section: Journal of Applied Meteorology and Climatology},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
California’s water management is confounded by large interannual variability in water availability inherent in Mediterranean climates and competing water demands in the state. Hydrologic outlooks during late winter and spring are an important consideration that water managers use in water allocation decisions. While hydrologic outlooks are informed by initial conditions, particularly in snowmelt-dominated systems such as California’s Sierra Nevada, there is potential for seasonal climate forecasts to improve such outlooks. California uses a standard water-year typology based on indices of runoff for the Sacramento and San Joaquin Rivers which form the basis of state surface water allocation decisions. We present a simple intuitive model that uses November–March watershed average precipitation and temperature in each river basin that explains approximately 90% of the variance in water-year type indices. We then evaluate the utility of these models to forecast water-year types by appending observational temperature and precipitation data with downscaled seasonal climate forecasts from the North American Multimodel Ensemble (NMME). We demonstrate that seasonal climate forecasts augment forecasts based exclusively on initial conditions (e.g., Sierra snowpack). For example, NMME-informed forecasts in January for critical or dry water-year types had an accuracy of 50%—comparable to the skill of forecasts based solely on initial conditions 1–2 months later. While California’s hydroclimate is renowned for its unpredictability, results suggest that NMME-informed forecasts may provide additional lead time for water managers to proactively implement water management strategies in anticipation of dry conditions. Significance Statement Water resources in California are wildly variable from year to year resulting in challenges for water managers and users. Improved hydroclimate forecasts can enable proactive measures to secure water for multiple uses. Seasonal forecasts of winter precipitation are notoriously poor in California’s Sierra Nevada, which provides much of the state’s water resources. However, here we show that incorporating seasonal temperature and precipitation forecasts alongside observations adds skill to water-year type outlooks in the Sacramento and San Joaquin River basins. Such forecasts can better inform surface water allocation decisions to minimize the economic and ecological impacts of the state’s variable hydroclimate.
6.
Yuanjie Shi; Hooman Shahrokhi; Xuesong Jia; Xiongzhi Chen; Janardhan Rao Doppa; Yan Yan
Direct prediction set minimization via bilevel conformal classifier training Proceedings Article
In: Proceedings of the 42nd International Conference on Machine Learning, pp. 55016–55045, JMLR.org, Vancouver, Canada, 2025.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{shi_direct_2025,
title = {Direct prediction set minimization via bilevel conformal classifier training},
author = {Yuanjie Shi and Hooman Shahrokhi and Xuesong Jia and Xiongzhi Chen and Janardhan Rao Doppa and Yan Yan},
url = {https://proceedings.mlr.press/v267/shi25i.html},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
volume = {267},
pages = {55016\textendash55045},
publisher = {JMLR.org},
address = {Vancouver, Canada},
series = {ICML\'25},
abstract = {Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of candidate classes) with provable guarantees. However, standard calibration methods for CP tend to produce large prediction sets which makes them less useful in practice. This paper considers the problem of integrating conformal principles into the training process of deep classifiers to directly minimize the size of prediction sets. We formulate conformal training as a bilevel optimization problem and propose the Direct Prediction Set Minimization (DPSM) algorithm to solve it. The key insight behind DPSM is to minimize a measure of the prediction set size (upper level) that is conditioned on the learned quantile of conformity scores (lower level). We analyze that DPSM has a learning bound of O(1/√n) (with n training samples), while prior conformal training methods based on stochastic approximation for the quantile has a bound of Ω(1/s) (with batch size s and typically s ≪ √n). Experiments on various benchmark datasets and deep models show that DPSM significantly outperforms the best prior conformal training baseline with 20.46% ↓ in the prediction set size and validates our theory.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of candidate classes) with provable guarantees. However, standard calibration methods for CP tend to produce large prediction sets which makes them less useful in practice. This paper considers the problem of integrating conformal principles into the training process of deep classifiers to directly minimize the size of prediction sets. We formulate conformal training as a bilevel optimization problem and propose the Direct Prediction Set Minimization (DPSM) algorithm to solve it. The key insight behind DPSM is to minimize a measure of the prediction set size (upper level) that is conditioned on the learned quantile of conformity scores (lower level). We analyze that DPSM has a learning bound of O(1/√n) (with n training samples), while prior conformal training methods based on stochastic approximation for the quantile has a bound of Ω(1/s) (with batch size s and typically s ≪ √n). Experiments on various benchmark datasets and deep models show that DPSM significantly outperforms the best prior conformal training baseline with 20.46% ↓ in the prediction set size and validates our theory.
7.
Alexis Fox; Samarth Swarup; Abhijin Adiga
A Unifying Information-theoretic Perspective on Evaluating Generative Models Proceedings Article
In: pp. 16630–16638, 2025, ISSN: 2374-3468.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{fox_unifying_2025,
title = {A Unifying Information-theoretic Perspective on Evaluating Generative Models},
author = {Alexis Fox and Samarth Swarup and Abhijin Adiga},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/33827},
doi = {10.1609/aaai.v39i16.33827},
issn = {2374-3468},
year = {2025},
date = {2025-04-01},
urldate = {2025-04-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {39},
number = {16},
pages = {16630\textendash16638},
abstract = {Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluation metrics. Several recent approaches utilize "precision" and "recall," borrowed from the classification domain, to individually quantify the output fidelity (realism) and output diversity (representation of the real data variation), respectively. With the increase in metric proposals, there is a need for a unifying perspective, allowing for easier comparison and clearer explanation of their benefits and drawbacks. To this end, we unify a class of kth-nearest neighbors (kNN)-based metrics under an information-theoretic lens using approaches from kNN density estimation. Additionally, we propose a tri-dimensional metric composed of Precision Cross-Entropy (PCE), Recall Cross-Entropy (RCE), and Recall Entropy (RE), which separately measure fidelity and two distinct aspects of diversity, inter- and intra-class. Our domain-agnostic metric, derived from the information-theoretic concepts of entropy and cross-entropy, can be dissected for both sample- and mode-level analysis. Our detailed experimental results demonstrate the sensitivity of our metric components to their respective qualities and reveal undesirable behaviors of other metrics.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluation metrics. Several recent approaches utilize "precision" and "recall," borrowed from the classification domain, to individually quantify the output fidelity (realism) and output diversity (representation of the real data variation), respectively. With the increase in metric proposals, there is a need for a unifying perspective, allowing for easier comparison and clearer explanation of their benefits and drawbacks. To this end, we unify a class of kth-nearest neighbors (kNN)-based metrics under an information-theoretic lens using approaches from kNN density estimation. Additionally, we propose a tri-dimensional metric composed of Precision Cross-Entropy (PCE), Recall Cross-Entropy (RCE), and Recall Entropy (RE), which separately measure fidelity and two distinct aspects of diversity, inter- and intra-class. Our domain-agnostic metric, derived from the information-theoretic concepts of entropy and cross-entropy, can be dissected for both sample- and mode-level analysis. Our detailed experimental results demonstrate the sensitivity of our metric components to their respective qualities and reveal undesirable behaviors of other metrics.
8.
John T Abatzoglou; Lauren E Parker; Joshua H Viers; Josue Medellín-Azuara; Alvar Escriva-Bou; Justin L Huntington; Emily L Williams; Kitri Rajagopalan
Shorter growing seasons may moderate climate change effects on crop water demands Journal Article
In: Environmental Research Letters, vol. 20, no. 3, pp. 034017, 2025, ISSN: 1748-9326, (Publisher: IOP Publishing).
Abstract | Links | BibTeX | Tags: AI, Water
@article{abatzoglou_shorter_2025,
title = {Shorter growing seasons may moderate climate change effects on crop water demands},
author = {John T Abatzoglou and Lauren E Parker and Joshua H Viers and Josue Medell\'{i}n-Azuara and Alvar Escriva-Bou and Justin L Huntington and Emily L Williams and Kitri Rajagopalan},
url = {https://dx.doi.org/10.1088/1748-9326/adb1f5},
doi = {10.1088/1748-9326/adb1f5},
issn = {1748-9326},
year = {2025},
date = {2025-02-01},
urldate = {2025-02-01},
journal = {Environmental Research Letters},
volume = {20},
number = {3},
pages = {034017},
abstract = {Rising evaporative demand (ETo) with a warming climate contributes to diminished water availability in water-stressed agricultural regions globally. While increased ETo typically necessitates increased irrigation, we explore how crop phenological response can moderate this challenge. Focusing on five key agricultural crops in California’s San Joaquin Valley (SJV), we employ coupled water balance and phenology models to project crop water demands as a function of increased ETo and changing phenology. All crops exhibited accelerated growth from a shortened growing season with warming. The shortened crop maturation period partially to fully offset increased crop water demands due to rising ETo, with the largest phenological influence for annual crops such as tomato and corn. By contrast, models that do not account for phenological changes showed increased irrigation demands of approximately 3.5%\textendash4.5% per °C of global warming primarily due to increased ETo. Integration with dynamic phenological models for the five key crops across the extent of agricultural land in the SJV showed a 1.6% decrease in irrigation needs under a 2 °C warming scenario. While phenological change alongside plant physiological responses to increased atmospheric CO2 may help buffer the impact of climate change on crop irrigation demand, decreased crop yields with a shorter growing season and continued reliance of groundwater reserves for agricultural water use and reduced spring snowpack will threaten coupled agricultural and water security in the region.},
note = {Publisher: IOP Publishing},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
Rising evaporative demand (ETo) with a warming climate contributes to diminished water availability in water-stressed agricultural regions globally. While increased ETo typically necessitates increased irrigation, we explore how crop phenological response can moderate this challenge. Focusing on five key agricultural crops in California’s San Joaquin Valley (SJV), we employ coupled water balance and phenology models to project crop water demands as a function of increased ETo and changing phenology. All crops exhibited accelerated growth from a shortened growing season with warming. The shortened crop maturation period partially to fully offset increased crop water demands due to rising ETo, with the largest phenological influence for annual crops such as tomato and corn. By contrast, models that do not account for phenological changes showed increased irrigation demands of approximately 3.5%–4.5% per °C of global warming primarily due to increased ETo. Integration with dynamic phenological models for the five key crops across the extent of agricultural land in the SJV showed a 1.6% decrease in irrigation needs under a 2 °C warming scenario. While phenological change alongside plant physiological responses to increased atmospheric CO2 may help buffer the impact of climate change on crop irrigation demand, decreased crop yields with a shorter growing season and continued reliance of groundwater reserves for agricultural water use and reduced spring snowpack will threaten coupled agricultural and water security in the region.
2024
9.
Bhupinderjeet Singh; Tanvir Ferdousi; John T. Abatzoglou; Samarth Swarup; Jennifer C. Adam; Kirti Rajagopalan
Sensitivity of snow magnitude and duration to hydrology model parameters Journal Article
In: Journal of Hydrology, vol. 645, pp. 132193, 2024, ISSN: 0022-1694.
Abstract | Links | BibTeX | Tags: AI, Water
@article{singh_sensitivity_2024,
title = {Sensitivity of snow magnitude and duration to hydrology model parameters},
author = {Bhupinderjeet Singh and Tanvir Ferdousi and John T. Abatzoglou and Samarth Swarup and Jennifer C. Adam and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0022169424015890},
doi = {10.1016/j.jhydrol.2024.132193},
issn = {0022-1694},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Journal of Hydrology},
volume = {645},
pages = {132193},
abstract = {Process-based hydrology models are critical for understanding streamflow and water supply under global change. However, these models require parameterization which introduces additional uncertainty into the models. The role that these parameters play in driving uncertainty is under-studied, especially for intermediary processes outside of streamflow. An important example in snowmelt dominant regions are intermediary processes related to snowpack accumulation and ablation. We examine the sensitivity of snow magnitude and duration to eleven parameters relevant to snow processes in the coupled crop-hydrology model VIC-CropSyst using a hybrid global\textendashlocal Distributed Evaluation of Local Sensitivity Analysis approach. With the Pacific Northwest US as a case study, our specific research questions are: (a) What is the sensitivity response of peak snow water equivalent (SWE) and snow duration and how does it vary in the parameter space? (b) What are the key drivers of the sensitivity response? and (c) Which of the most sensitive parameters can we immediately improve by leveraging existing data products? Both target variables were sensitive to less than four of the eleven parameters. We found that peak SWE was most sensitive to either the precipitation partitioning temperature threshold or the albedo of new snow, depending on the geography and associated interplay between hydro-meteorological factors. In contrast, snow duration was primarily sensitive to the albedo of new snow and the albedo decay coefficient during snowmelt. Machine learning explainability workflows applied on the sensitivity response explained the model behavior and determined key geographic and hydro-meteorological drivers of the sensitivity response. Regions where the significant precipitation co-occurred with near-freezing temperature exhibited higher sensitivity of peak SWE to precipitation partitioning. In contrast, much colder high-elevation regions that have a delayed snowmelt-driven runoff when downward shortwave radiation is higher, displayed more sensitivity to albedo parameters. We also noted differences in the list of key parameters and in the level of sensitivity between our work and the limited comparable existing work. This highlights the need for comprehensive sensitivity analysis of snow metrics to become a routine component of hydrology model application studies in addition to streamflow. This is critical for us to better understand model behavior, identify key model parameters that can benefit from more dynamic representation in the models, and strategically improve models to best support decision-makers.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
Process-based hydrology models are critical for understanding streamflow and water supply under global change. However, these models require parameterization which introduces additional uncertainty into the models. The role that these parameters play in driving uncertainty is under-studied, especially for intermediary processes outside of streamflow. An important example in snowmelt dominant regions are intermediary processes related to snowpack accumulation and ablation. We examine the sensitivity of snow magnitude and duration to eleven parameters relevant to snow processes in the coupled crop-hydrology model VIC-CropSyst using a hybrid global–local Distributed Evaluation of Local Sensitivity Analysis approach. With the Pacific Northwest US as a case study, our specific research questions are: (a) What is the sensitivity response of peak snow water equivalent (SWE) and snow duration and how does it vary in the parameter space? (b) What are the key drivers of the sensitivity response? and (c) Which of the most sensitive parameters can we immediately improve by leveraging existing data products? Both target variables were sensitive to less than four of the eleven parameters. We found that peak SWE was most sensitive to either the precipitation partitioning temperature threshold or the albedo of new snow, depending on the geography and associated interplay between hydro-meteorological factors. In contrast, snow duration was primarily sensitive to the albedo of new snow and the albedo decay coefficient during snowmelt. Machine learning explainability workflows applied on the sensitivity response explained the model behavior and determined key geographic and hydro-meteorological drivers of the sensitivity response. Regions where the significant precipitation co-occurred with near-freezing temperature exhibited higher sensitivity of peak SWE to precipitation partitioning. In contrast, much colder high-elevation regions that have a delayed snowmelt-driven runoff when downward shortwave radiation is higher, displayed more sensitivity to albedo parameters. We also noted differences in the list of key parameters and in the level of sensitivity between our work and the limited comparable existing work. This highlights the need for comprehensive sensitivity analysis of snow metrics to become a routine component of hydrology model application studies in addition to streamflow. This is critical for us to better understand model behavior, identify key model parameters that can benefit from more dynamic representation in the models, and strategically improve models to best support decision-makers.
10.
Krishu K. Thapa; Bhupinderjeet Singh; Supriya Savalkar; Alan Fern; Kirti Rajagopalan; Ananth Kalyanaraman
Attention-based Models for Snow-Water Equivalent Prediction Proceedings Article
In: Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24), 2024, (arXiv:2311.03388 [physics]).
Abstract | Links | BibTeX | Tags: AI, Snow Water Equivalent, Water
@inproceedings{thapa_attention-based_2023,
title = {Attention-based Models for Snow-Water Equivalent Prediction},
author = {Krishu K. Thapa and Bhupinderjeet Singh and Supriya Savalkar and Alan Fern and Kirti Rajagopalan and Ananth Kalyanaraman},
url = {http://arxiv.org/abs/2311.03388},
doi = {10.48550/arXiv.2311.03388},
year = {2024},
date = {2024-02-20},
urldate = {2024-02-20},
publisher = {Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24)},
abstract = {Snow Water-Equivalent (SWE) \textendash the amount of water available if snowpack is melted \textendash is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally \textendash affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.},
note = {arXiv:2311.03388 [physics]},
keywords = {AI, Snow Water Equivalent, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Snow Water-Equivalent (SWE) – the amount of water available if snowpack is melted – is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally – affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.
