About

Professor Soroosh Sorooshian is a Distinguished Professor in the Department of Civil & Environmental Engineering at UC Irvine, holding the Henry Samueli Endowed Chair in Engineering. His academic background includes a Ph.D. from UCLA in 1978, with prior degrees in systems engineering, operations research, and mechanical engineering. His research primarily focuses on surface hydrology, especially rainfall-runoff modeling, model identification and calibration, remote sensing applications for hydrologic parameters, and the implications of climate variability and change on water resources. He has developed special estimation criteria to address uncertainties in calibration data and has contributed significantly to hydrologic modeling and water resources management. Professor Sorooshian has been recognized with numerous awards and honors, including election to the U.S. National Academy of Engineering, the American Geophysical Union Robert Horton Medal, and the Prince Sultan Bin Abdulaziz International Prize for Water Resources Management. He has served on various national and international advisory committees, including the NOAA Science Advisory Board, NRC committees, and UNESCO panels. His leadership roles include past-president of the AGU Hydrology Section, former editor of Water Resources Research, and chair of the GEWEX Scientific Steering Group. He has been an invited and keynote speaker at over 240 events and has actively contributed to the scientific community through organizing conferences, serving on review panels, and providing expert testimonies to U.S. Senate and House Committees. His work integrates remote sensing, hydrologic modeling, and climate change impacts, making him a prominent figure in hydrologic sciences and water resources management worldwide.

Research topics

• Geography
• Meteorology
• Geology
• Environmental science
• Climatology
• Machine Learning
• Computer Science
• Artificial Intelligence
• Data Mining
• Mathematics
• Engineering
• Aerospace engineering
• Remote sensing

Selected publications

• Comment on Beven (2025) “the Seven Ages of Hydrology”: Recognizing the Remote Sensing Era of Hydrology (1990s ‐)

  Hydrological Processes · 2026-05-01

  article

  ABSTRACT This commentary offers a constructive extension to Beven (2025), arguing that the Remote Sensing Era has already transformed hydrology's observational and inferential foundation, enabling globally consistent, process‐relevant analysis of the water cycle.

  PublisherDOI

• Warming climate and water withdrawals threaten river flow connectivity in China

  Proceedings of the National Academy of Sciences · 2025-08-18 · 9 citations

  articleOpen access

  River flow connectivity, the continuity of fluvial discharge in space and time, provides a crucial lifeline for most biotic communities on Earth. Yet there is still limited understanding of the impacts of climate change and human water withdrawal on river connectivity. Here, we assess the river flow connectivity of 217,001 river reaches in mainland China from 1961 to 2020 and the impact of different climate warming trends and water withdrawals for different sectors. We estimate that naturally intermittent rivers represent about 13% of all river reaches, with a large contrast between northern and southern China (12% vs. 1%, respectively). Although river intermittency decreased slightly during this period (i.e., river connectivity lengthened due to increasing precipitation), warming temperatures offset this decrease by reducing surface water persistence, causing the decrease (-476 vs. -233 km/y) to double when removing the long-term temperature trend. Critically, the length of intermittent rivers increased remarkably from 13 to 50% when considering human water withdrawal by agricultural, domestic, and industrial sectors, in addition to environmental flow requirements. Our findings highlight the urgent need to maintain sustainable water resources in a warming climate in which unregulated water abstractions increasingly threaten river flow connectivity, particularly in drying regions.

  PublisherDOI

• Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record Version 2.0 (PERSIANN-CCS-CDR V2.0)

  Open MIND · 2025-12-09 · 1 citations

  datasetSenior author

  PERSIANN-CCS-CDR Version 2.0, a near-global 37+ year high-resolution precipitation dataset with both high spatial and temporal resolutions. Developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). PERSIANN-CCS-CDR V2.0 consists of 2 sub-products PERSIANN-CCS-CDR B1 from 1983 to present and PERSIANN-CCS-CDR CPC from March 2000 to present, both provide precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions over the global domain of 60°S to 60°N. PERSIANN-CCS-CDR Version 2.0 is regularly updated; therefore, this page serves as the DOI landing page for the dataset. A few sample files are provided here for reference. To access and download the full dataset, please visit the CHRS Data Portal. (https://chrsdata.eng.uci.edu/)

  DOI

• Improving Rainfall-runoff Modeling Using an Attention-based Model

  2025-05-10 · 1 citations

  preprintSenior author

  Accurate and reliable rainfall-runoff modeling is essential for flood forecasting, drought monitoring, and reservoir management. Currently, the benchmark practice in data-driven modeling is to train Long Short-Term Memory (LSTM) models across multiple catchments to learn diverse hydrological responses. However, the recurrent structure and inductive bias of LSTMs tend to emphasize recent inputs, making them less effective at capturing very long-range dependencies. Their sequential, thus not parallel structure also limits scalability for very large-scale datasets. With the rapid growth of Earth observations, a key challenge still remains whether a model can effectively train data from multi-sources, learn long-range temporal dependencies, and scale performance with increasing data availability? In this study, we introduce an Attention-based model that outperforms LSTM with a median Nash-Sutcliffe Efficiency (NSE) of 0.781 and Kling-Gupta Efficiency (KGE) of 0.808 compared to LSTM’s NSE of 0.765 and KGE of 0.760, respectively, without relying on ensemble runs or multiple precipitation datasets. The model also demonstrates improved skills in high-flow events and shows continued performance with additional training data. In addition, the Attention-based model offers enhanced interpretability, by assigning dynamic importance to historical meteorological forcings and captures diverse rainfall–runoff responses. Furthermore, by embedding catchment-specific attributes, the model adapts to different hydrological conditions and allows extraction of generalized knowledge through activation-based analysis. This study highlights the potential of Attention-based models in hydrological modeling, particularly for scalable, data-driven flood prediction at continental to global scales.

  PublisherDOI

• Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application

  Environmental Modelling & Software · 2025-01-27 · 8 citations

  articleOpen accessSenior author

  Pre-trained models like FourCastNet, Pangu and GraphCast have gained popularity in the meteorological field. In hydrology, data-driven rainfall-runoff models based on long short-term memory (LSTM) networks have been successfully applied for various purposes. As large-sample hydrological datasets (e.g., Caravan) continue to grow, it is foreseeable that pre-trained models tailored for hydrology will emerge. These pre-trained models have the potential to bypass the need for training data-driven models from scratch, enabling us to focus more swiftly on customized applications. Additionally, they offer opportunities explore model performance in changing environment, which is also a key consideration when using data-driven models in unseen scenarios. However, the hydrological field has seen limited attempts to employ, transfer, and fine-tune pre-trained models. This study aims to explore the possibility of using fine-tuning techniques to achieve a smooth transition of LSTM-based rainfall-runoff models from pre-training to post-application scenarios. By utilizing ERA5-Land reanalysis precipitation data within the Caravan dataset, we calibrated a pre-trained LSTM model for runoff simulation. Subsequently, we transitioned the model to use near-real-time satellite precipitation estimates as the input, targeting satellite-driven predictions. Our results show that fine-tuning parameters lead to improvements in various metrics, including the Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and hydrological signature metrics such as high and low flows, compared to outcomes without parameter fine-tuning. Specifically, fine-tuning using locally calibrated models enhanced performance in 73.5% of the basins. In contrast, the results of fine-tuning regional models were mixed; while it benefited 55.1% of the basins, it also led to a deterioration in model performance in 44.9% of cases. This study is a pioneering exploration of the adaptability of LSTM models from pre-training to post-application. It also lays the groundwork for future investigations aimed at enhancing the adaptability of data-driven models to the impacts of changing environment. • We explored the feasibility of fine-tuning pre-trained LSTM-based rainfall-runoff models for hydrological applications. • Local fine-tuning enhanced 73.5% of basins, whereas regional fine-tuning benefited only 55.1%. • The quality of precipitation data significantly affects the performance of both pre-trained and fine-tuned models.

  PublisherDOI

• Bias Correction of Satellite Precipitation Estimation Using Deep Neural Networks and Topographic Information Over the Western U.S.

  Journal of Geophysical Research Atmospheres · 2025-02-24 · 9 citations

  articleOpen accessSenior author

  Abstract Satellite‐based precipitation products (SPPs) have gained popularity among researchers due to their utility in hydrologic studies. Several gridded satellite‐based precipitation products with global coverage, such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family of products, are available worldwide. However, the accuracy of these products may vary due to retrieval algorithms or geographic location. Numerous correction techniques have been implemented, and machine learning techniques, especially Deep Neural Networks, have proven to be the most effective in improving precipitation estimation. This study aims to investigate the performance of the PERSIANN‐Dynamic Infrared Rain Rate near real‐time product (PDIR‐Now) in the Western U.S. and assess the effectiveness of three deep learning models including U‐Net, Efficient‐UNet, and a conditional Generative Adversarial Network (cGAN) in correcting biases present in the product. The developed models are expected to be more accurate than traditional methods, as they include digital elevation information and can resolve complex orographic enhancements in precipitation processes. This incorporation will mitigate the bias associated with SPPs, enabling further potential applications in water resource management. The findings revealed that the corrected results, utilizing the Efficient‐UNet and cGAN models, surpassed the original PDIR‐Now product and U‐Net model across various statistical and categorical metrics at different temporal scales. This bias‐correction scheme will enhance the assessment and understanding of precipitation patterns and can be used to improve the quality of precipitation estimates in other regions.

  PublisherOA PDFDOI

• Improve streamflow simulations by combining machine learning pre-processing and post-processing

  Journal of Hydrology · 2025-02-21 · 10 citations

  articleSenior author
  PublisherDOI

• Beyond Expectations: Investigating Anomalous 2022–23 Winter Weather Conditions and Water Resources Impacts in California

  Bulletin of the American Meteorological Society · 2025-04-16 · 2 citations

  articleOpen accessSenior author

  Abstract The 2022–23 winter in the western United States, particularly in Southern California, experienced unusually wet and cold conditions, prompting vigilant water management. This study chronicles the water year, highlighting the challenges state water managers faced as California shifted from extreme drought to elevated flood risks due to an unprecedented “weather whiplash” and a subsequent record-setting snowpack. By analyzing precipitation and temperature data from 2002 to 2023, the research highlights the anomalous nature of these variables in California during this period. It focuses on the impacts of atmospheric rivers (ARs) due to their proven influence on seasonal precipitation patterns and intensities, examining their hydrologic impacts—specifically, snow water equivalent (SWE) in Sierra Nevada and reservoir storage—compared to other high precipitation years in California to gauge the effects of this atypical weather on water resources. The study reveals that Southern California’s wintertime precipitation in 2022–23 was the highest in over two decades. Precipitation was closely linked to the occurrence of 11 moderate to strong ARs, which alleviated the state’s drought conditions—94% of California was drought free by the end of the water year. Additionally, the mean maximum temperature was below the long-term average during spring and summer, decelerating snowpack melt and mitigating flood potential. Notably, 2022–23 saw the most significant increases in SWE and reservoir storage among the years analyzed. This research delves into the complex interplay between AR-driven precipitation, temperature, and snowpack, providing valuable insights into the precarious dynamics of California’s regional hydrology with a real-world example. Significance Statement The purpose of this study is to analyze the anomalous weather observed during the 2022–23 winter and its effects on California’s water resources. Specifically, we study precipitation, temperature, atmospheric rivers (ARs), drought, snowpack, and reservoir storage chronologically. We found that the high precipitation and low temperatures during the 2022–23 winter allowed California to reach a record-high snowpack and mitigated drought conditions. The 2022–23 wintertime precipitation was the highest since 2002 in Southern California. Fortunately, the low temperatures over the spring and summer helped slow the rate of snowpack melting and prevented significant flooding. This research contributes to a better understanding of the interplay between precipitation patterns, temperature changes, and AR events and offers insights into the dynamics of the hydrological cycle.

  PublisherOA PDFDOI

• A Comparative Evaluation of Target Datasets for U-Net-Based Precipitation Estimation

  Journal of Hydrometeorology · 2025-10-01

  articleSenior author

  Abstract Precipitation is a critical component of the hydrologic cycle, affecting water availability and flood risk. Its estimation can help protect lives and mitigate property damage. Machine learning (ML) models have become popular for estimating precipitation; however, when choosing a target dataset, certain characteristics must be considered. In situ data are ideal to train an ML model, but alternatives must be explored when such data are sparse. Thus, a U-Net was trained three times using the Climate Prediction Center (CPC) 4-km infrared data as input and different precipitation target datasets. These included the National Severe Storms Laboratory (NSSL) Multi-Radar Multi-Sensor (MRMS) system, NASA’s Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERG Final), and CPC Combined Passive Microwave Precipitation (MWCOMB). The results were evaluated against Stage IV. The MRMS model performed best with a correlation coefficient (CC) of 0.50 and a probability of detection (POD) of 0.90 at an hourly scale. The IMERG Final model followed with a CC of 0.47 and a POD of 0.85, making it a viable alternative given its temporal resolution and data availability. The MWCOMB model overestimated precipitation and had a CC and POD of 0.45 and 0.82. Across daily and monthly scales, MRMS consistently outperformed the other models, with IMERG Final ranking second. Due to MWCOMB’s persistent overestimation, different data preparation may be required for effective use. In conclusion, MRMS is the most suitable dataset for ML-based precipitation estimation due to its in situ nature and robust performance; however, IMERG Final is a viable alternative. Significance Statement Precipitation estimation is extremely important for hydrologic applications. However, limited access to in situ precipitation data is a challenge for machine learning (ML) models, as they require large amounts for training. This study explores how the selection of precipitation target datasets affects the performance of a U-Net model for precipitation estimation. Using infrared data as input, the results not only show superior performance of the National Severe Storm Laboratory (NSSL) Multi-Radar Multi-Sensor (MRMS) but also demonstrate that NASA’s IMERG Final offers a viable alternative when in situ data are unavailable. The findings of this study provide insight for researchers seeking to improve ML-based precipitation models, specifically in regions with sparse in situ data.

  PublisherDOI

• Comparative Analysis of Satellite-Based Precipitation Products During Extreme Rainfall from Super Typhoon Yagi in Hanoi, Vietnam (September 2024)

  Remote Sensing · 2025-04-30 · 2 citations

  articleOpen accessSenior author

  This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events.

  PublisherOA PDFDOI

Recent grants

• CyberSEES:Type 2: Precipitation Estimation from Multi-Source Information using Advanced Machine Learning

  NSF · $1.1M · 2013–2018

Frequent coauthors

• Kuolin Hsu

  University of California, Irvine

  505 shared

• Phu Nguyen

  University of California, Irvine

  166 shared

• Xiaogang Gao

  152 shared

• Hoshin V. Gupta

  University of Arizona

  122 shared

• B. Imam

  91 shared

• Dan Braithwaite

  Samueli Institute

  89 shared

• Hamed Ashouri

  University of California, Irvine

  81 shared

• Amir AghaKouchak

  University of California, Irvine

  75 shared

Education

• PhD, civil engineering

  ucla

  1978

Awards & honors

• U.S. National Academy of Engineering (NAE) (2003)
• Robert Horton Medal from AGU (recipient of AGU Robert Horton…
• Prince Sultan Bin Abdulaziz International Prize for Water Re…
• UNESCO’s 2007 Great Man-Made River International Water Prize…
• American Geophysical Union (AGU) Fellow