HydroIn

2026

1. Liu R, Dai Q, Xiao Y, et al. Designing Flood‐Resilient Cities: How Urban Morphology Shapes Population Exposure Risk[J]. Risk Analysis, 2026, 46(4): e70218.
https://doi.org/10.1111/risa.70218

2025

1. Xiao Y, Dai Q, Liu R, et al. Modeling dynamic flood population exposure in coupled human‐water systems: The role of reservoir regulation and population movement[J]. Water Resources Research, 2025, 61(10): e2025WR041234.
https://doi.org/10.1029/2025WR041234
2. Ji D, Dai Q, Sun C, et al. Reshaping global rainfall erosivity rates: A study on precipitation phase correction from 2015 to 2022[J]. Journal of Hydrology, 2025, 659: 133271.
https://doi.org/10.1016/j.jhydrol.2025.133271
3. Zhu J, Dai Q, Xiao Y, et al. Radar remote sensing retrieval of vertical profile of rainfall kinetic energy in the UK[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025.
10.1109/TGRS.2025.3542493
4. Yang F, Zhang X, Yang J, et al. Enhancing groundwater predictions by incorporating response lag effects in machine learning models[J]. Journal of Hydroinformatics, 2025, 27(2): 338-356.
https://doi.org/10.2166/hydro.2025.295
5. Xiao Y, Zhang J, Zhu J, et al. Exploration of aerosol-precipitation relationships under different climate regimes in China[J]. GIScience & Remote Sensing, 2025, 62(1): 2457992.
https://doi.org/10.1080/15481603.2025.2457992
6. Fan X, Zhang J, Song Y, et al. Assessment of Potential Complementarity of Pumped Hydropower Storage to Solar and Wind Energy[J]. Transactions in GIS, 2025, 29(1): e70001.
https://doi.org/10.1111/tgis.70001

2024

2023

1. Rachel A. Spinti, Laura E. Condon, Jun Zhang. The evolution of dam induced river fragmentation in the United States, Nature Communications, 2023 (14), 3820.
https://www.nature.com/articles/s41467-023-39194-x
2. Dai, Q., Zhu, J., Lv, G., Kalin, L., Yao, Y., Zhang, J., & Han, D., 2023, Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale, Science Advances, 9: eadg5551.
https://www.science.org/doi/full/10.1126/sciadv.adg5551
3. Zhu J, Dai Q, Xiao Y, et al. Microphysics-based rainfall energy estimation using remote sensing and reanalysis data[J]. Journal of Hydrology, 2023, 627: 130314.
https://doi.org/10.1016/j.jhydrol.2023.130314
4. Chen Y, Chen E, Zhang J, et al. Investigation of Model Uncertainty in Rainfall-Induced Landslide Prediction under Changing Climate Conditions[J]. Land, 2023, 12(9): 1732.
https://doi.org/10.3390/land12091732
5. 戴强，刘超楠，张亚茹，朱净萱，张林. 2023. 基于多模式雷达遥感的陆表降雨反演研究进展[J]. 遥感学报， 27(7)：1574-1589.
https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20231768/
6. 朱净萱，戴强，肖媛媛，刘超楠，李雁鹏. 2023. GPM 双频雷达观测的长三角区域雨滴微物理特征分析[J]. 遥感学报，27(7)：1615-1627.
https://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20221839/
7. 孙晨玥，戴强，纪端阳，顾于，刘超楠，李雁鹏. 2023. 基于多相态 KE-I 关系的降水动能计算方法[J]. 山地学报，41(05)：771-784.
https://www.geores.com.cn/sdxb/CN/10.16089/j.cnki.1008-2786.000786

2022

1. Zhao, B., Dai, Q., Zhuo, L., Mao, J., Zhu, S., & Han, D., 2022, Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting, Geomorphology, 398: 108051.
https://www.sciencedirect.com/science/article/pii/S0169555X21004591
2. Dai, Q., Zhu, J., Lv, G., Kalin, L., Yao, Y., Zhang, J., & Han, D., 2023, Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale, Science Advances, 9: eadg5551.
https://www.sciencedirect.com/science/article/pii/S0022169422001810
3. Zhu J, Dai Q, Xiao Y, et al. Microphysics-based rainfall energy estimation using remote sensing and reanalysis data[J]. Journal of Hydrology, 2023, 627: 130314.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JD035934
4. Yang, Q., Dai, Q., Zhang, S., Zhu, K., & Zhang, L., 2022, Raindrop size distribution retrieval model for Xband dual-polarization radar in China incorporating various climatic and geographical elements, IEEE Transactions on Geoscience and Remote Sensing, 60: 5112417.
https://ieeexplore.ieee.org/abstract/document/9759454
5. 赵彬如，陈恩泽，戴强，朱少楠，张君. 2022. 基于水文-气象阈值的区域降雨型滑坡预测研究[J] .测绘学报， 51(10)：2216-2225.
http://xb.chinasmp.com/CN/10.11947/j.AGCS.2022.20220293

2021

1. Zhao, B., Dai, Q., Zhuo, L., Zhu, S., Shen, Q., & Han, D., 2021, Assessing the potential of different satellite soil moisture products in landslide hazard assessment, Remote Sensing of Environment, 264: 112583.
https://www.sciencedirect.com/science/article/pii/S0034425721003035
2. Zhuang, H., Wang, Y., Liu, H., Wang, S., Zhang, W., Zhang, S., & Dai, Q, 2021, Large-scale Soil Erosion Estimation Considering Vegetation Growth Cycle, Land, 10(5), 473.
https://www.mdpi.com/2073-445X/10/5/473
3. Zhang, J., Condon, L. E., Tran, H., & Maxwell, R. M. (2021). A national topographic dataset for hydrological modeling over the contiguous United States. Earth System Science Data, 13(7), 3263-3279.
https://essd.copernicus.org/articles/13/3263/2021/
4. Zhu, J., Zhang, S., Yang, Q., Shen, Q., Zhuo, L. & Dai, Q., Comparison of rainfall microphysics characteristics derived by numerical weather prediction modeling and dual-frequency precipitation radar. Meteorological Applications, 2021, 28(3): e2000.
https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.2000
5. 朱净萱, 戴强, 蔡俊逸, 朱少楠, 张书亮. 基于多智能体的城市洪涝灾害动态脆弱性计算模型构建[J]. 地球信息科学学报, 2021, 23(10):1787-1797.
http://www.dqxxkx.cn/CN/10.12082/dqxxkx.2021.210053
6. 李文慧,许剑辉,孙彩歌.基于灰色关联度的深圳市生态环境与城市扩展时空演化研究[J].生态环境学报,2021,30(04):880-888.
http://www.jeesci.com/CN/10.16258/j.cnki.1674-5906.2021.04.026

2020

1. Dai, Q., Zhu, J., Zhang, S., Zhu, S., Han, D., & Lv, G., 2020, Estimation of rainfall erosivity based on WRF-derived raindrop size distributions, Hydrology and Earth System Sciences, in press.
https://hess.copernicus.org/articles/24/5407/2020/hess-24-5407-2020.html
2. Yang, Q., Dai, Q., Han, D., Zhu, Z., & Zhang, S., 2020, Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks, Journal of Hydrology, in press.
https://www.sciencedirect.com/science/article/pii/S0022169420309422
3. Zhuo, L., Dai, Q., Zhao, B., & Han, D., 2020, Soil Moisture Sensor Network Design for Hydrological Applications, Hydrology and Earth System Sciences, 24: 2577–2591.
https://hess.copernicus.org/articles/24/2577/2020/hess-24-2577-2020.html
4. Zou, X., Dai, Q., Wu, K., Yang, Q., & Zhang, S., 2020, An empirical ensemble rainfall nowcasting model using multi-scaled analogues, Natural Hazards, 103(1): 165-188.
https://link.springer.com/content/pdf/10.1007/s11069-020-03964-3.pdf
5. Dai, Q., Zhu, X., Zhuo, L., Han, D., Liu, Z., & Zhang, S., 2020. A hazard-human coupled model (HazardCM) to assess city dynamic exposure to rainfall-triggered natural hazards. Environmental Modelling & Software, 127: 104684.
https://www.sciencedirect.com/science/article/pii/S1364815219304293
6. Zhao, B., Dai, Q., Han, D., Zhang, J., Zhuo, L., & Berti, M, 2020, Application of hydrological model simulations in Landslide Predictions, Landslide, 17(4): 877-891.
https://link.springer.com/content/pdf/10.1007/s10346-019-01296-3.pdf
7. Cai, J., Zhu, J., Dai, Q., Yang, Q., & Zhang, S., 2020, Sensitivity of a weather research and forecasting model to downscaling schemes in ensemble rainfall estimation, Meteorological Applications, 27(1): e1806.
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/met.1806

2019

1. Dai, Q., Yang, Q., Han, D., Rico-Ramirez, M.A., & Zhang, S., 2019. Adjustment of radar‐gauge rainfall discrepancy due to raindrop drift and evaporation using the Weather Research and Forecasting model and dual-polarization radar. Water Resources Research, 55: 9211–9233.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019WR025517
2. Zhao, B., Dai, Q., Han, D., Dai, H., Mao, J, Zhuo, L. & Rong, G., 2019, Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions, Landslide, 16: 2381-2393.
https://link.springer.com/article/10.1007/s10346-019-01255-y
3. Zhuo, L., Dai, Q., Han, D., Chen, N., & Zhao, B., 2019, Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM Land surface schemes for landslide hazard application, Hydrology and Earth System Sciences, 23: 4199–4218.
https://hess.copernicus.org/articles/23/4199/2019/
4. Zhu, X., Dai, Q., Han, D., Zhuo, L., Zhu, S., & Zhang, S. 2019, Modeling the high-resolution dynamic exposure to flooding in a city region, Hydrology and Earth System Sciences, 23, 3353–3372.
https://hess.copernicus.org/articles/23/3353/2019/
5. Zhao, B., Dai, Q., Han, D., Dai, H., Mao, J & Zhuo, L., 2019, Probabilistic thresholds for landslides warning by integrating soil moisture conditions with rainfall thresholds, Journal of Hydrology, 574: 276-287.
https://www.sciencedirect.com/science/article/pii/S0022169419304020
6. Yang, Q., Dai, Q., Han, D., Chen, Y., & Zhang, S., 2019, Sensitivity analysis of raindrop size distribution parameterizations in weather research and forecasting rainfall simulation, Atmospheric Research, 228:1-13.
https://www.sciencedirect.com/science/article/pii/S0169809518316211
7. Liu, Z., Dai, Q., & Zhuo, L., 2019, Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models, Atmosphere, 10(8): 458.
https://www.mdpi.com/2073-4433/10/8/458
8. Zhuo, L., Dai, Q., Han, D., Zhao, B., Chen, N., & Berit, M., 2019, Evaluation of remotely sensed soil moisture for landslide hazard assessment, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,12(1): 162-173.
http://eprints.whiterose.ac.uk/153254/
9. Dai, Q., Zhu, J., Yang, Q., & Han, D., 2019, Adjustment of Radar–gauge Rainfall Discrepancy Due to Raindrop Microphysical Processes, AGU Fall Meeting, San Francisco, USA.
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019WR025517

2018

1. Dai, Q., Yang, Q., Zhang, J., & Zhang, S. 2018, Impact of gauge representative error on a radar rainfall uncertainty model, Journal of Applied Meteorology and Climatology, 57: 2769–2787.
https://www.jstor.org/stable/26677206?seq=1
2. Yang, Q., Dai, Q., Han, D., Zhu, X., & Zhang, S., 2018, Impact of the Storm Sewer Network Complexity on Flood Simulations According to the Stroke Scaling Method, Water, 10(5): 645.
https://www.mdpi.com/2073-4441/10/5/645
3. Zhu, J., Dai, Q., Deng, Y., Zhang, A., Zhang, Y., & Zhang, S., 2018, Indirect Damage of Urban Flooding: Investigation of Flood-Induced Traffic Congestion Using Dynamic Modeling, Water, 10(5): 622.
https://www.mdpi.com/2073-4441/10/5/622
4. Zhang, J., Han, D., Song, Y., & Dai, Q., 2018, Study on the effect of rainfall spatial variability on runoff modelling, Journal of Hydroinformatics, 10.2166/hydro.2018.129.
https://iwaponline.com/jh/article/20/3/577/37919/Study-on-the-effect-of-rainfall-spatial
5. Yang, Q., Dai, Q., Han, D., Zhu, X., & Zhang, S., 2018, An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves, Atmosphere, 9(4): 151.
https://www.mdpi.com/2073-4433/9/4/151
6. Dai, Q., Zhu, X., & Cai, J., 2018, A human-hazard integrated city model for dynamic vulnerability assessment, AGU Fall Meeting, Washington DC, USA.
https://ui.adsabs.harvard.edu/abs/2018AGUFMNH43D1081D/abstract
7. Dai, Q., Han, D., Chen N. & Jacomo, A.L., 2018, Modelling the interaction between natural hazards and socioeconomic systems in city-regions, EGU General Assembly, Vienna, Austria.
https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.3389D/abstract
8. Dai, Q., Han, D., Chen N. & Jacomo, A.L., 2018, Probabilistic critical rainfall thresholds for landslide occurrence using the WRF model in China, EGU General Assembly, Vienna, Austria.
https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.3404D/abstract

2017

1. Dai, Q., Bray, M., Zhuo, L., Islam, T., & Han, D. 2017, A scheme for raingauge network design based on remotely-sensed rainfall measurements, Journal of Hydrometeorology, 18: 363–379.
https://journals.ametsoc.org/view/journals/hydr/18/2/jhm-d-16-0136_1.xml
2. Dai, Q., & Zhang, S., 2017, Impact of scale discrepancy between radar and gauge on radar ensemble rainfall generator, International Symposium on Weather Radar and Hydrology, Seoul, Korea.
3. Dai Q, Han D, Srivastava P K. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble Generator[M]//Sensitivity Analysis in Earth Observation Modelling. Elsevier, 2017: 91-102.
https://doi.org/10.1016/B978-0-12-803011-0.00005-7

2016

1. Dai, Q., Han, D., Zhuo, L., Zhang J., Islam, T., & Srivastava, P.K. 2016, Seasonal generation of ensemble radar rainfall estimates using copula and autoregressive model, Stochastic Environmental Research and Risk Assessment, 30(1): 27-38.
https://link.springer.com/content/pdf/10.1007/s00477-014-1017-x.pdf
2. Zhuo, L., Han, D., & Dai, Q., 2016, Soil moisture deficit estimation using satellite multi-angle brightness temperature, Journal of Hydrology, 539: 392-405.
https://www.sciencedirect.com/science/article/pii/S0022169416303274
3. Zhuo, L., Dai, Q., Islam, T., & Han, D., 2016, Error distribution modelling of satellite soil moisture measurements for hydrological applications, Hydrological Processes, 30: 2223-2236.
https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.10789
4. Islam, T., Srivastava, P.K., Kumar, D., Petropoulos, G.P., Dai, Q., & Zhuo, L. 2016, Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts. Natural Hazards, 82(2): 845-855.
https://link.springer.com/article/10.1007/s11069-016-2221-4
5. Srivastava, P.K., Han, D., Islam, T., Petropoulos, G., Gupta, M. & Dai, Q. 2016, Seasonal evaluation of Evapotranspiration fluxes from MODIS Satellite and Mesoscale Model Downscaled Global Reanalysis Datasets. Theoretical and Applied Climatology, 124: 461-473.
https://link.springer.com/content/pdf/10.1007/s00704-015-1430-1.pdf
6. Srivastava, P.K., Islam, T., Singh, S.K., Petropoulos, G., Gupta, M. & Dai, Q. 2016, Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data. Meteorological Applications, 23(4): 633-639.
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/met.1585
7. Islam, T., Srivastava, P.K., & Dai, Q. 2016, High resolution WRF simulation of cloud properties over the super typhoon Haiyan: Physics parameterizations and comparison against MODIS. Theoretical and Applied Climatology, 126: 427–435.
https://link.springer.com/article/10.1007%2Fs00704-015-1575-y
8. Dai, Q., Han, D., Rico-Ramirez, M.A. & Srivastava, P.K., 2016, Geospatial Technology for Water Resources Development: Spatio-temporal Uncertainty Model for Radar Rainfall, CRC Press

2015

1. Dai, Q., Han, D., Rico-Ramirez, M.A., Zhuo, L., Nanding, N. & Islam, T., 2015, Radar rainfall uncertainty modelling influenced by wind, Hydrological Processes, 29: 1704-1716.
https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.10292
2. Dai, Q., Rico-Ramirez, M.A., Han, D., Islam, T. & Liguori S. 2015, Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models, Hydrological Processes, 29: 66-79.
https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.10133
3. Dai, Q., Han, D., Zhuo, L., Huang J., Islam, T., & Srivastava, P.K. 2015, Impact of complexity of radar rainfall uncertainty model on flow simulation, Atmospheric Research, 161-162: 93-101.
https://www.sciencedirect.com/science/article/pii/S0169809515001027
4. Dai, Q., Han, D., Zhuo, L., Huang J., Islam, T. & Zhang, S. 2015, Adjustment of wind-drift effect for real-time deviation correction in radar rainfall data, Physics and Chemistry of the Earth, 83-84: 178-186.
https://www.researchgate.net/publication/281325624_Adjustment_of_wind-drift_effect_for_real-time_systematic_error_correction_in_radar_rainfall_data
5. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., O’Neill, P., Islam, T., Gupta, M. & Dai, Q. 2015, Performance evaluation of WRF-Noah Land surface model estimated soil moisture for hydrological application: Synergistic evaluation using SMOS retrieved soil moisture, Journal of Hydrology,511: 17-27
https://www.sciencedirect.com/science/article/pii/S0022169415005478
6. Zhuo, L., Dai, Q. & Han, D., 2015, Meta-analysis of flow modeling performances—to build a matching system between catchment complexity and model types, Hydrological Processes, 29: 2463–2477.
https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.10371
7. Zhuo, L., Han, D., Dai, Q., Islam, T., & Srivastava, P.K., 2015, Appraisal of NLDAS-2 multi-model simulated soil moistures for hydrological modelling, Water Resources Management, 29: 3503-3517.
https://link.springer.com/article/10.1007/s11269-015-1011-1
8. Islam, T., Srivastava, P.K., Dai, Q., Gupta, M. & Zhuo, L. 2015, An introduction to factor analysis for radio frequency interference (RFI) detection on satellite observations. Meteorological Applications, 22: 436-443.
https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/met.1473
9. Srivastava, P.K., M.A., Islam, T., Gupta, M., Petropoulos, G., & Dai, Q. 2015, WRF dynamical downscaling and bias correction schemes for NCEP estimated Hydro-meteorological variables. Water Resources Management, 29: 2267-2284.
https://link.springer.com/article/10.1007/s11269-015-0940-z
10. Islam, T., Srivastava, P.K., Rico-Ramirez, M.A., Dai, Q., Gupta, M. & Singh, S.K. 2015, Tracking a tropical cyclone through WRF ARW simulation and sensitivity of model physics. Natural Hazards, 76: 1473-1495.
https://link.springer.com/article/10.1007/s11069-014-1494-8
11. Zhuo, L., Dai, Q., & Han, D., 2015, Evaluation of SMOS soil moisture retrievals over the central United States for hydro-meteorological application, Physics and Chemistry of the Earth, 83-84: 146-155.
https://www.sciencedirect.com/science/article/pii/S1474706515000728
12. Islam, T., Rico-Ramirez, M.A., Srivastava, P.K., Dai, Q., Han, D & Zhuo, L. 2015, Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided By Random Forest, RReliefF and Multivariate Adaptive Regression Splines (RAMARS). IEEE Sensors Journal, 15: 2186-2193.
https://ieeexplore.ieee.org/document/7031473
13. Islam, T., Srivastava, P.K., Dai, Q. & Wan Jaafar, W.Z. 2015, Stratiform/convective rain delineation for TRMM microwave imager. Journal of Atmospheric and Solar-Terrestrial Physics, 133: 25-35.
https://www.sciencedirect.com/science/article/pii/S1364682615300158
14. Dai, Q., Zhuo L. & Han, D., 2015, Decision making for urban drainage systems under uncertainty caused by weather radar rainfall measurement, EGU General Assembly, Vienna, Austria.
https://ui.adsabs.harvard.edu/abs/2015EGUGA..17.3340D/abstract

2014

1. Dai, Q., Han, D., Rico-Ramirez, M.A. & Islam, T. 2014, Modeling radar-rainfall estimation uncertainties using elliptical and Archimedean copulas with different marginal distributions, Hydrological Sciences Journal, 59: 1992-2008.
https://www.sciencedirect.com/science/article/pii/S0309170808001449
2. Dai, Q. & Han, D., 2014, Exploration of discrepancy between radar and gauge rainfall surfaces driven by the downscaled wind field, Water Resources Research, 50: 8571-8588.
https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014WR015794
3. Dai, Q., Han, D., Rico-Ramirez, M.A. & Srivastava, P.K. 2014, Multivariate Distributed Ensemble Generator: A new scheme for ensemble radar precipitation estimation over temperate maritime climate, Journal of Hydrology, 511: 17-27.
https://www.sciencedirect.com/science/article/pii/S0022169414000225
4. Islam, T., Srivastava, P.K., Rico-Ramirez, M.A., Dai, Q., Han, D. & Gupta, M. 2014, An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR. Atmospheric Research, 145: 57-68.
https://www.sciencedirect.com/science/article/pii/S0169809514001434
5. Islam, T., Srivastava, P.K., Dai, Q. & Gupta, M. 2014, Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar. Remote Sensing Letters, 5: 1012-1021.
https://www.tandfonline.com/doi/full/10.1080/2150704X.2014.990643
6. Huang, J., Wang, H., Dai, Q. & Han, D. 2014, Analysis of NDVI Data for Crop Identification and Yield Estimation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE-J-STARS), 99: 1-11.
https://ieeexplore.ieee.org/document/6871305
7. Islam, T., Rico-Ramirez, M.A., Srivastava, P.K., Dai, Q., Han, D., Gupta, M. & Zhuo, L. 2014, CLOUDET: A cloud detection and estimation algorithm for passive microwave imagers and sounders aided by Naive Bayes classifier and multilayer perceptron, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IEEE-J-STARS), 99: 1-7.
https://ieeexplore.ieee.org/document/6835215
8. Islam, T., Rico-Ramirez, M.A., Srivastava, P.K. & Dai, Q. 2014, Non-parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19-85 GHz channels with random foreasts algorithm, International Journal of Remote Sensing, 35: 3254–3267.
https://www.tandfonline.com/doi/full/10.1080/01431161.2014.903444
9. Dai, Q., Han, D. & Rico-Ramirez, M.A., 2014, Analysis of combined effects of input uncertainty and parameter uncertainty in hydrological modelling, 11th International Conference on Hydroinformatics (HIC), New York, USA.
https://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1016&context=cc_conf_hic

2013

1. Dai, Q., Han, D., Rico-Ramirez, M.A. & Islam, T. 2013, The impact of raindrop drift in a three-dimensional wind field on a radar-gauge rainfall comparison, International Journal of Remote Sensing, 34: 7739-7760.
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2013.826838
2. Dai, Q., Han, D. & Rico-Ramirez, M.A., 2013, Empirically modeling radar quantitative precipitation estimation uncertainty for an C-band radar in the United Kingdom, in: Proceeding of the 35th International Association for Hydro-Environment Engineering and Research (IAHR), Chengdu, China.
https://www.researchgate.net/publication/229917744_Empirically_Based_Modeling_of_Radar-Rainfall_Uncertainties_for_a_C-Band_Radar_at_Different_Time-Scales