Publications

Project 1.1

  1. Milner, Phillip J., et al. "A Diaminopropane-Appended Metal–Organic Framework Enabling Efficient CO2 Capture from Coal Flue Gas via a Mixed Adsorption Mechanism." Journal of the American Chemical Society 139.38 (2017): 13541-13553.

Project 1.2

  1. Andreades, Charalampos, and Per Peterson. "Nuclear Air Brayton Combined Cycle and Mark 1 Pebble Bed Fluoride-Salt-Cooled High-Temperature Reactor economic performance in a regulated electricity market." Nuclear Engineering and Design (2016).
  2. Andreades, C., Greenop, A., Gallagher, S., Choi, J. K., & Peterson, P. Coiled Tube Air Heater Test Loop Design. In 2017 25th International Conference on Nuclear Engineering (pp. V009T15A053-V009T15A053). American Society of Mechanical Engineers, (2017, July).

Project 1.3

  1. Chen, Q., Rosner, F., Rao, A., Samuelsen, S., Jayaraman, A., & Alptekin, G. (2019). Simulation of elevated temperature solid sorbent CO2 capture for pre-combustion applications using computational fluid dynamics. Applied energy, 237, 314-325
  2. Rosner, F., Chen, Q., Rao, A., Samuelsen, S., Jayaraman, A., & Alptekin, G. (2019). Thermo-economic analyses of IGCC power plants employing warm gas CO2 separation technology. Energy, 185, 541-553.
  3. Rosner, F., Chen, Q., Rao, A., Samuelsen, S., Jayaraman, A., & Alptekin, G. (2019). Process and economic data for the thermo-economic analyses of IGCC power plants employing warm gas CO2 separation technology. Data in brief27104716.
  4. Rosner, F., Chen, Q., Rao, A., & Samuelsen, S. (2019). Thermo-economic analyses of concepts for increasing carbon capture in high-methane syngas integrated gasification combined cycle power plants. Energy Conversion and Management,199, 112020.
  5. Chen, Q., Rosner, F., Rao, A., Samuelsen, S., Bonnema, M., Jayaraman, A., & Alptekin, G. (2020). Simulation of elevated temperature combined water gas shift and solid sorbent CO2 capture for pre-combustion applications using computational fluid dynamics. Applied Energy, 267, 114878.
  6. Rosner, F., Rao, A., & Samuelsen, S. (2020). Water gas shift reactor modelling and new dimensionless number for thermal management/design of isothermal reactors. Applied Thermal Engineering, 115033
  7. Rosner, F., Chen, Q., Rao, A., & Samuelsen, S. (2020). Thermo-economic analyses of isothermal water gas shift reactor integrations into IGCC power plant. Applied Energy, 277, 115500.
  8. Wang, B., Rosner, F., Rao, A., Zhao, L., & Samuelsen, S. (2021). Method for Determining the Change in Gas Turbine Firing Temperature. Journal of Engineering for Gas Turbines and Power, 143(9), 094501.

Project 1.6

  1. Mizerak, Jordan P., and Van P. Carey. "Modeling of Transport During Droplet Deposition and Spreading on Smooth and Microstructured Superhydrophilic Surfaces." ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016.
  2. Kunkle, C.M. and Carey, V.P., Metrics For Quantifying Surface Wetting Effects On Boiling And Evaporation At Nanostructured Hydrophilic Surfaces, paper PRTEC-15163, Proceedings of the First Pacific Rim Thermal Engineering Conference, PRTEC, March 13-17, 2016, Hawaii's Big Island, USA. 
  3. Kunkle, Claire M., and Van P. Carey. "Metrics for Quantifying Surface Wetting Effects on Vaporization Processes at Nanostructured Hydrophilic Surfaces." ASME 2016 Heat Transfer Summer Conference collocated with the ASME 2016 Fluids Engineering Division Summer Meeting and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016.
  4. LaBrie, Russell J., Jorge Padilla Jr, and Van P. Carey. "Experimental Study of Aqueous Binary Mixture Droplet Vaporization on Nanostructured Surfaces." Heat Transfer Engineering 38.14-15 (2017): 1260-1273.
  5. Kunkle, Claire M., Jordan P. Mizerak, and Van P. Carey. "The Effects of Wettability and Surface Morphology on Heat Transfer for Zinc Oxide Nanostructured Aluminum Surfaces." ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017.
  6. Wemp, C.K. and Carey, V.P., Tuning Superhydrophilic Nanostructured Surfaces To Maximize Water Droplet Evaporation Heat Transfer Performance, paper IMECE2017-72679, presented and included in the Proceedings of the ASME 2017 International Mechanical Engineering Congress and Exposition, November 3-9, 2017, Tampa, Florida, USA.
  7. Wemp, Claire K., and Van P. Carey. "Water Wicking and Droplet Spreading on Randomly Structured Thin Nanoporous Layers." Langmuir 33.50 (2017): 14513-14525.
  8. Wemp, Claire K., and Van P. Carey. "Tuning Superhydrophilic Nanostructured Surfaces to Maximize Water Droplet Evaporation Heat Transfer Performance." Journal of Heat Transfer140.10 (2018): 102401.
  9. Carey, V. P., Wemp, C. K., McClure, E. R., & Cabrera, S. (2018, November). Mechanism Interaction During Droplet Evaporation on Nanostructured Hydrophilic Surfaces. In ASME 2018 International Mechanical Engineering Congress and Exposition (pp. V08AT10A029-V08AT10A029). American Society of Mechanical Engineers.
  10. Wemp, C. K., & Carey, V. P. (2019). Heat transport for evaporating droplets on superhydrophilic, thin, nanoporous layers. International Journal of Heat and Mass Transfer, 132, 34-51.

Project 2.1

  1. Shao-Wei Tsai,Arkadeep Kumar, Bavisha Kalyan, Chia-Hung Hou, Pen-Chi Chiang, and Ashok J. Gadgil (2019). Additive Manufacturing of Electrodes for Desalination.. Procedia Manufacturing. Vol. 34, pp. 252-259.DOI:10.1016/j.promfg.2019.06.147
  2. Subban, Chinmayee, V., and Ashok Gadgil, (2019) Electrically Regenerated Ion-Exchange Technology for Desalination of Low-Salinity Water Sources. Desalination. Volume 465, 1 September 2019, Pages 38-43. DOI:10.1016/j.desal.2019.04.019
  3. Xu, Ke, Yanhui Liu, Zihan An, Guorong Xu, Ashok Gadgil, and Guolin Ruan (2020). The polymeric conformational effect on capacitive deionization performance of graphene oxide/polypyrrole composite electrode.<Desalination.Volume 486, March 2020, 114407.DOI:10.1016/j.desal.2020.114407

Project 2.2

  1. Wei, Y.; Wang, J.; Li, H.; Zhao, M.; Zhang, H.; Guan, Y.; Huang, H.; Mi, B.; Zhang, Y., “Partially reduced graphene oxide and chitosan nanohybrid membranes for selective retention of divalent cations,” RSC Advances 2018, 8, 13656-13663.
  2. Kang, Y., Zheng, S., Finnerty, C., Lee, M. J., & Mi, B.. "Regenerable Polyelectrolyte Membrane for Ultimate Fouling Control in Forward Osmosis." Environmental Science & Technology, (51(6), 2017) 3242-3249.
  3. Zheng, S., Tu, Q., Urban, J., Li, S., and Mi, B. (2017). "Swelling of graphene oxide membranes in aqueous solution: Characterization of interlayer spacing and insight into water transport mechanisms." ACS Nano, 11(6), pp. 6440-6450, DOI: 10.1021/acsnano.7b02999.
  4. Wang, Z., and Mi, B. (2017). "Environmental applications of 2D molybdenum disulfide (MoS2) nanosheets."Environmental Science and Technology, accpted, DOI: 10.1021/acsnano.7b02999.
  5. Oh, Y., Armstrong, D. L., Finnerty, C., Zheng, S., Hu, M., Torrents, A., & Mi, B. (2017). "Understanding the pH-responsive behavior of graphene oxide membrane in removing ions and organic micropollulants." Journal of Membrane Science, (2017) 541, 235-243.
  6. Jin, L., Wang, Z., Zheng, S., & Mi, B. "Polyamide-crosslinked graphene oxide membrane for forward osmosis." Journal of Membrane Science (2018), 545, 11-18.
  7. Finnerty, C., Zhang, L., Sedlak, D. L., Nelson, K. L., & Mi, B. "Synthetic Graphene Oxide Leaf for Solar Desalination with Zero Liquid Discharge." Environmental science & technology, (2017) 51(20), 11701-11709.
  8. Liu, Y., Zheng, S., Gu, P., Ng, A. J., Wang, M., Wei, Y., ... & Mi, B. (2020). Graphene-polyelectrolyte multilayer membranes with tunable structure and internal charge.Carbon 160, 219-227.
  9. Wei, Y., Gao, X., Wang, J., Chen, J., Mi, B., Tian, X., ... & Zhang, Y. (2021). Facile and extensible preparation of multi-layered graphene oxide membranes with enhanced long-term desalting performance.Journal of Membrane Science, 119695.

Project 2.4

  1. Scholes, R. C., Stiegler, A. N., Anderson, C. M., & Sedlak, D. L. (2021). Enabling Water Reuse by Treatment of Reverse Osmosis Concentrate: The Promise of Constructed Wetlands. ACS Environmental Au.
  2. Scholes, R. C., Prasse, C., & Sedlak, D. L. (2019). The role of reactive nitrogen species in sensitized photolysis of wastewater-derived trace organic contaminants. Environmental science & technology, 53(11), 6483-6491.
  3. Scholes R.C., Vega M.A., Sharp J.O. and Sedlak D.L. (2021)  Nitrate removal from reverse osmosis concentrate in pilot-scale open-water unit process wetlands.  Env Sci. Water Res. & Technol. 7:650-661.  doi: 10.1039/d0ew00911c
  4. Scholes, RC; King, JF; Mitch, WA; Sedlak, DL (2020) Transformation of Trace Organic Contaminants from Reverse Osmosis Concentrate by Open-Water Unit-Process Wetlands with and without Ozone Pretreatment. Environ Sci Technol 54: 16176-16185.

Project 2.6

  1. Stringfellow, William T., et al. "Identifying chemicals of concern in hydraulic fracturing fluids used for oil production." Environmental Pollution 220 (2017): 413-420.
  2. Camarillo, M. K., J. K. Domen, W. T. Stringfellow. 2016. "Physical-chemical evaluation of hydraulic fracturing chemicals in the context of produced water treatment." Journal of Environ. Management 183: 164 - 174.
  3. Camarillo, M. K., W. T. Stringfellow. 2018. Biological treatment of oil and gas produced water: a review and meta-analysis. Clean Technologies and Environmental Policy, 20: 1127-1146.

Project 2.7

  1. Rao, E., McVerry, B., Borenstein, A., Anderson, M., Jordan, R. S., & Kaner, R. B. (2018). Roll-to-Roll Functionalization of Polyolefin Separators for High-Performance Lithium-Ion Batteries. ACS Applied Energy Materials, 1(7), 3292-3300.
  2. Lin, C. W., Aguilar, S., Rao, E., Mak, W. H., Huang, X., He, N., ... & Kaner, R. B. (2019). Direct grafting of tetraaniline via perfluorophenylazide photochemistry to create antifouling, low bio-adhesion surfaces. Chemical science, 10(16), 4445-4457
  3. Kweon, H., Lin, C. W., Faruque Hasan, M. M., Kaner, R., & Sant, G. N. (2019). Highly permeable polyaniline–graphene oxide nanocomposite membranes for CO2 separations. ACS Applied Polymer Materials, 1(12), 3233-3241
  4. McVerry, B. T., Wong, M. C., Marsh, K. L., Temple, J. A., Marambio‐Jones, C., Hoek, E. M., & Kaner, R. B. (2014). Scalable antifouling reverse osmosis membranes utilizing perfluorophenyl azide photochemistry. Macromolecular rapid communications,35(17), 1528-1533.
  5. McVerry, B., Anderson, M., He, N., Kweon, H., Ji, C., Xue, S., ... & Jun, D. (2019). Next-Generation Asymmetric Membranes Using Thin-Film Liftoff. Nano letters, 19(8), 5036-5043.
  6. Xue, S., Ji, C., Kowal, M. D., Molas, J. C., Lin, C. W., McVerry, B. T., ... & Hoek, E. M. (2020). Nanostructured graphene oxide composite membranes with ultrapermeability and mechanical robustness. Nano Letters, 20(4), 2209-2218.
  7. Ji, C., Xue, S., Lin, C. W., Mak, W. H., McVerry, B. T., Turner, C. L., ... & Kaner, R. B. (2020). Ultra-Permeable Organic Solvent Nanofiltration Membranes with Precisely Tailored Support Layers Fabricated Using Thin-Film Lift-Off. ACS Applied Materials & Interfaces.
  8. Cheng-Wei Lin, Shuangmei Xue, Chenhao Ji, Shu-Chuan Huang, Vincent Tung, and Richard B. Kaner. (2021) Conducting Polyaniline for Antifouling Ultrafiltration Membranes: Solutions and Challenges Nano Letters
  9. Khor, C. M., Wang, J., Li, M., Oettel, B. A., Kaner, R. B., Jassby, D., & Hoek, E. M. V. (2020). “Performance, Energy and Cost of Produced Water Treatment by Chemical and Electrochemical Coagulation.” Water 12, 3426
  10. Wang, J., Mo, Y., McVerry, B. T., Mahendra, S., Kaner, R. B., & Hoek, E. M. (2020). How permeable could a reverse osmosis membrane be if it was specifically developed for uncharged organic solute rejection?. AWWA Water Science, 2(5), e1189.
  11. Hoek, E. M., Jassby, D., Kaner, R. B., Wu, J., Wang, J., Liu, Y., & Rao, U. (2021). Sustainable Desalination and Water Reuse. Synthesis Lectures on Sustainable Development, 2 (2), 1-204.
  12. Wu, J., Wang, J., Liu, Y., Rao, U. (2021). Sustainable Desalination and Water Reuse.

Project 2.9

  1. Bandaru, S. R., van Genuchten, C. M., Kumar, A., Glade, S., Hernandez, D., Nahata, M., & Gadgil, A. (2020). Rapid and efficient arsenic removal by iron electrocoagulation enabled with in situ generation of hydrogen peroxide. Environmental science & technology, 54(10), 6094-6103.
  2. Glade, S., Bandaru, S. R., Nahata, M., Majmudar, J., & Gadgil, A. (2021). Adapting a drinking water treatment technology for arsenic removal to the context of a small, low-income California community. Water Research, 117595.

Project 3.1

  1. Viers, Joshua H., and Daniel M. Nover. "Too Big To Fail: Limiting Public Risk in Hydropower Licensing." Hastings Envt'l LJ 24 (2018): 143.
  2. Viers, Joshua H. "Meeting ecosystem needs while satisfying human demands." Environmental Research Letters 12.6 (2017): 061001.
  3. Hao, Z., Rallings, A.M.; Espinoza, V., Luo, P, Duan, W., Peng, Q., and Viers, J.H. (2021). Flowing from East to West: Bibliometric analysis of recent advances in environmental flow science in China. Ecological Indicators, vol 125.
  4. Dogan, M. S., Lund, J. R., & Medellin-Azuara, J. (2021). Hybrid Linear and Nonlinear Programming Model for Hydropower Reservoir Optimization.Journal of Water Resources Planning and Management, 147(3), 06021001.
  5. Bellido-Leiva, F. J., Lusardi, R. A., & Lund, J. R. (2021). Modeling the effect of habitat availability and quality on endangered winter-run Chinook salmon (Oncorhynchus tshawytscha) production in the Sacramento Valley. Ecological Modelling, 447, 109511.
     

Project 3.2

  1. Yang, T., Asanjan, A.A., Faridzad, M., Hayatbini, N., Gao, X. and Sorooshian, S."An Enhanced Artificial Neural Network with A Shuffled Complex Evolutionary Global Optimization with Principal Component Analysis. Information Sciences." Information Sciences, 418, (2017)  302-316.
  2. Yang, T., Xiaogang, G., Sorooshian, S. (2016). "Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme." Water Resources Research. 52 (3). pp. 1626 - 1651. DOI 10.1002/2015WR017394
  3. Yang, T., Gao, X., Sellars, S. L., & Sorooshian, S. "Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex." Environmental Modelling & Software, 69, (2015) 262-279.
  4. Liu, X., Luo, Y., Yang, T., Liang, K., Zhang, M., & Liu, C. "Investigation of the probability of concurrent drought events between the water source and destination regions of China's water diversion project." Geophysical Research Letters, (2015). 42(20), 8424-8431.
  5. Thorstensen, A., Nguyen, P., Hsu, K., & Sorooshian, S. "Using densely distributed soil moisture observations for calibration of a hydrologic model." Journal of Hydrometeorology, (2016). 17(2), 571-590.
  6. Naeini, M. R., Yang, T., Sadegh, M., AghaKouchak, A., Hsu, K. L., Sorooshian, S., ... & Lei, X. (2018). Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework. Environmental Modelling & Software,  215-235.
  7. Chen, K., Guo, S., Wang, J., Qin, P., He, S., Sun, S., & Naeini, M. R. (2019). Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River. Water, 11(12), 2539.
  8. Rahnamay Naeini, M., Yang, T., Tavakoly, A., Analui, B., AghaKouchak, A., Hsu, K. L., & Sorooshian, S. (2020). A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing. Water, 12(9), 2373.

Project 3.3

  1. Yang, Tiantian, et al. "Multi-criterion model ensemble of CMIP5 surface air temperature over China." Theoretical and Applied Climatology (2017): 1-16.
  2. Yang, T., Asanjan, A. A., Welles, E., Gao, X., Sorooshian, S., & Liu, X. "Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information." Water Resources Research, (2017) 53(4), 2786-2812.
  3. Tao, Y., Gao, X., Hsu, K., Sorooshian, S., & Ihler, A. "A deep neural network modeling framework to reduce bias in satellite precipitation products." Journal of Hydrometeorology, 17(3), (2016) 931-945.
  4. Sarachi, Sepideh, Kuo-lin Hsu, and Soroosh Sorooshian. "A statistical model for the uncertainty analysis of satellite precipitation products." Journal of Hydrometeorology 16.5 (2015): 2101-2117.
  5. Nasrollahi, N., AghaKouchak, A., Cheng, L., Damberg, L., Phillips, T. J., Miao, C., ... & Sorooshian, S. "How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts?." Water Resources Research,  (2015) 51(4), 2847-2864.
  6. Yang, Z., Hsu, K., Sorooshian, S., Xu, X., Braithwaite, D., & Verbist, K. M. "Bias adjustment of satellite‐based precipitation estimation using gauge observations: A case study in Chile." Journal of Geophysical Research: Atmospheres, 121(8) (2016), 3790-3806.
  7. Ata Akbari Akbari Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., & Peng, Q. (2018). Short‐Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks. Journal of Geophysical Research: Atmospheres, 123(22), 12-543.
  8. Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water, 11(5), 977.
  9. Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P., Hsu, K., Sorooshian, S., & Braithwaite, D. (2019). PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Convolutional Neural Networks. Journal of Hydrometeorology, (2019).
  10. Sadeghi, M., Akbari Asanjan, A., Faridzad, M., Afzali Gorooh, V., Nguyen, P., Hsu, K., ... & Braithwaite, D. (2019). Evaluation of PERSIANN-CDR Constructed Using GPCP V2. 2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale. Remote Sensing, 11(23), 2755.
  11. Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving monsoon precipitation prediction using combined convolutional and long short term memory neural network. Water, 11(5), 977.
  12. Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P., Hsu, K., Sorooshian, S., & Braithwaite, D. (2019). Persiann-cnn: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks. Journal of Hydrometeorology, 20(12), 2273-2289.
  13. Sadeghi, M., Akbari Asanjan, A., Faridzad, M., Afzali Gorooh, V., Nguyen, P., Hsu, K., ... & Braithwaite, D. (2019). Evaluation of persiann-cdr constructed using gpcp v2. 2 and v2. 3 and a comparison with trmm 3b42 v7 and cpc unified gauge-based analysis in global scale. Remote Sensing, 11(23), 2755.
  14. Pan, B., Hsu, K., AghaKouchak, A., & Sorooshian, S. (2019). Improving precipitation estimation using convolutional neural network. Water Resources Research, 55(3), 2301-2321.
  15. Sadeghi, M., Nguyen, P., Hsu, K., & Sorooshian, S. (2020). Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information. Environmental Modelling & Software, 134, 104856.
  16. Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2021). Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling. Remote Sensing, 13(16), 3061.
  17. Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2021). New insights into error decomposition for precipitation products. Geophysical Research Letters, 48(17), e2021GL094092.
  18. Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2022). QRF4P‐NRT Probabilistic Post‐processing of Near‐real‐time Satellite Precipitation Estimates using Quantile Regression Forests. Water Resources Research, e2022WR032117.

Project 3.4

  1. Hardin, E., AghaKouchak, A., Qomi, M. J. A., Madani, K., Tarroja, B., Zhou, Y., ... & Samuelsen, S. California drought increases CO 2 footprint of energy. Sustainable cities and society, 28 (2017): 450-452.
  2. Tarroja, Brian, Amir AghaKouchak, and Scott Samuelsen. "Quantifying climate change impacts on hydropower generation and implications on electric grid greenhouse gas emissions and operation." Energy 111 (2016): 295-305.
  3. Forrest, K., Tarroja, B., Chiang, F., AghaKouchak, A., and Samuelsen, S., “Assessing Climate Change Impacts on California Hydropower Generation and Ancillary Services Provision”, Climatic Change, 2018. 151: p.395-412.

Project 3.5

  1. Ding, Z., Wen, X., Tan, Q., Yang, T., Fang, G., Lei, X., ... & Wang, H. (2021). A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system. Applied Energy , 291, 116820.
  2. Yang, T., Zhang, L., Kim, T., Hong, Y., Zhang, D., & Peng, Q. (2021). A Large-scale Comparison of Artificial Intelligence and Data Mining (AI&DM) Techniques in Simulating Reservoir Releases Over the Upper Colorado Region. Journal of Hydrology, 126723.
  3. Kim, T., Yang, T., Gao, S., Zhang, L., Ding, Z., Wen, X., ... & Hong, Y. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology, 598, 126423.

 

Project 4.3

  1. Li, J., Hsu, K., AghaKouchak, A., Sorooshian, S. "Object-Based Assessment of Satellite Precipitation Products." Remote Sens. (2016). 8, 547.
  2. Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., ... & Kuligowski, B. (2019). The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Scientific data, 6, 180296.
  3. Hayatbini, N., Hsu, K. L., Sorooshian, S., Zhang, Y., & Zhang, F. (2019). Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS. Journal of Hydrometeorology, (2019).
  4. Hayatbini, N., Kong, B., Hsu, K. L., Nguyen, P., Sorooshian, S., Stephens, G., ... & Ganguly, S. (2019). Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN. Remote Sensing, 11(19), 2193.
  5. Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., ... & Ralph, M.(2019). PERSIANN Dynamic Infrared-Rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation. Bulletin of the American Meteorological Society, (2019).
  6. Afzali Gorooh, V., Kalia, S., Nguyen, P., Hsu, K. L., Sorooshian, S., Ganguly, S., & Nemani, R. R. (2020). Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS. Remote Sensing, 12(2), 316.
  7. Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., ... & Ralph, M. (2020). PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation. Bulletin of the American Meteorological Society, 101(3), E286-E302.
  8. Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2021). Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling. Remote Sensing, 13(16), 3061.
  9. Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2021). New insights into error decomposition for precipitation products. Geophysical Research Letters, 48(17), e2021GL094092.

Project 4.4

  1. Tao, Y., Yang, T., Faridzad, M., Jiang, L., He, X. and Zhang, X. (2017), "Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model." Int. J. Climatol. doi:10.1002/joc.5188
  2. Liu, X., Yang, T., Hsu, K., Liu, C., & Sorooshian, S. "Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau." Hydrology and Earth System Sciences, (2017) 21(1), 169.
  3. Katiraie-Boroujerdy, P. S., Ashouri, H., Hsu, K. L., & Sorooshian, S. "Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR." Theoretical and Applied Climatology, (2017) 130(1-2), 249-260.
  4. Nguyen P, Sorooshian S, Thorstensen A, Tran H, Huynh P, Pham T, Braithwaite D, Hsu K, AghaKouchak A, Ashouri H. Exploring trends through “RainSphere”: Research data transformed into public knowledge. Bulletin of the American Meteorological Society. 2016 Nov 2(2016).
  5. Ashouri, H., Nguyen, P., Thorstensen, A., Hsu, K. L., Sorooshian, S., & Braithwaite, D. (2016). Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow. Journal of Hydrometeorology, 17(7), 2061-2076.
  6. Faridzad, Mohammad, et al. "Rainfall Frequency Analysis for Ungauged Regions using Remotely Sensed Precipitation Information." Journal of Hydrology(2018).
  7. Ombadi, Mohammed, et al. "Developing Intensity‐Duration‐Frequency (IDF) Curves From Satellite‐Based Precipitation: Methodology and Evaluation." Water Resources Research(2018).
  8. Chen, K., Guo, S., Wang, J., Qin, P., He, S., Sun, S., & Naeini, M. R. (2019). Evaluation of GloFAS-Seasonal Forecasts for cascade reservoir impoundment operation in the upper Yangtze River. Water, 11(12), 2539.
  9. Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. L. (2020). Evaluation of methods for causal discovery in hydrometeorological systems. Water Resources Research, 56(7), e2020WR027251.
  10. Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. L. (2021). Complexity of Hydrologic Basins: A Chaotic Dynamics Perspective. Journal of Hydrology, 126222.
  11. Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. L. (2021). How much information on precipitation is contained in satellite infrared imagery?. Atmospheric Research, 105578.
  12. Shearer, Eric J., Phu Nguyen, Scott L. Sellars, Bita Analui, Brian Kawzenuk, Kuo‐lin Hsu, and Soroosh Sorooshian. "Examination of Global Midlatitude Atmospheric River Lifecycles Using an Object‐Oriented Methodology" Journal of Geophysical Research: Atmospheres 125, no. 22 (2020): e2020JD033425.

Project 4.5

  1. Guo, Z., Fogg, G. E., & Henri, C. V. (2019). Upscaling of regional scale transport under transient conditions: evaluation of the Multi‐rate Mass Transfer model. Water Resources Research.
  2. Pauloo, Richard A., Graham E. Fogg, Zhilin Guo, and Christopher V. Henri. 2021. “Mean Flow Direction Modulates Non-Fickian Transport in a Heterogeneous Alluvial Aquifer-Aquitard System.” Water Resources Research 57 (3): e2020WR028655. https://doi.org/10.1029/2020WR028655.
  3. Pauloo, Richard A., Graham E. Fogg, Zhilin Guo, and Thomas Harter. 2020. “Anthropogenic Basin Closure and Groundwater Salinization (ABCSAL).”Journal of Hydrology, December, 125787

Project 4.6

  1. Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The value of accurate high-resolution and spatially continuous snow information to streamflow forecasts. Journal of Hydrometeorology, (2019).
  2. Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., & Andreadis, K. (2019). The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent. Geophysical Research Letters, 46(10), 5331-5340.
  3. Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The role of rain‐on‐snow in flooding over the conterminous United States. Water Resources Research.

Project 5.3

  1. Li, Xi, et al. "Energy for water utilization in China and policy implications for integrated planning." International Journal of Water Resources Development 32.3 (2016): 477-494.

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