Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1147-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin
Mohammed Abdallah
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
The Hydraulics Research Station, P.O. Box 318, Wad Madani, Republic of the Sudan
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Lijun Chao
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Abubaker Omer
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Khalid Hassaballah
IGAD Climate Prediction and Applications Center (ICPAC), Nairobi, Kenya
Kidane Welde Reda
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Tigray Agricultural Research Institute, Mekele, Ethiopia
Linxin Liu
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Tolossa Lemma Tola
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Omar M. Nour
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
The Hydraulics Research Station, P.O. Box 318, Wad Madani, Republic of the Sudan
Related authors
No articles found.
Nan Wu, Ke Zhang, Amir Naghibi, Hossein Hashemi, Zhongrui Ning, Qinuo Zhang, Xuejun Yi, Haijun Wang, Wei Liu, Wei Gao, and Jerker Jarsjö
Hydrol. Earth Syst. Sci., 29, 3703–3725, https://doi.org/10.5194/hess-29-3703-2025, https://doi.org/10.5194/hess-29-3703-2025, 2025
Short summary
Short summary
This study enhanced a popular water flow model by adding two components: one for snow melting and another for frozen ground cycles. Tested with satellite data and streamflow, the updated model improved accuracy, especially in winter. Frozen ground delays soil drainage, boosting spring runoff by 39 %–77 % and cutting evaporation by 85 %. These findings reveal that frozen ground drives seasonal water patterns.
Jin Feng, Ke Zhang, Lijun Chao, Huijie Zhan, and Yunping Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-137, https://doi.org/10.5194/essd-2025-137, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Understanding how soil moisture affects evapotranspiration (ET) is essential for improving ET estimates. However, many global ET datasets overlook soil moisture constraints, causing large uncertainties. In this study, we developed an improved model that better captures the influence of soil moisture on vegetation and soil evaporation. Our model significantly improves ET estimation accuracy and provides a new long-term global ET dataset to support water cycle and climate research.
Nan Wu, Ke Zhang, Amir Naghibi, Hossein Hashemi, Zhongrui Ning, and Jerker Jarsjö
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-401, https://doi.org/10.5194/hess-2024-401, 2025
Revised manuscript under review for HESS
Short summary
Short summary
This study explores how snow dynamics and hydropower reservoirs shape monthly runoff in the Yalong River basin, China. Using 15 years of data and a extended Budyko framework, we found that snow accumulation and melt dominate runoff in high-altitude areas, while reservoirs increasingly influence lower elevations. These factors reduce runoff seasonality at the basin outlet, emphasizing how climate change and human activity alter water availability in cold, mountainous regions.
Jiefan Niu, Ke Zhang, Xi Li, and Hongjun Bao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-304, https://doi.org/10.5194/hess-2024-304, 2024
Preprint under review for HESS
Short summary
Short summary
This study developed a new method for classifying catchments, combining machine learning techniques with climate and landscape data. By analyzing catchments across China, we identified six climate regions and 35 unique catchment types, each with distinct streamflow patterns. This classification method improves hydrological predictions, especially in areas lacking direct data.
Ileen N. Streefkerk, Jeroen C. J. H. Aerts, Jens de Bruijn, Khalid Hassaballah, Rhoda Odongo, Teun Schrieks, Oliver Wasonga, and Anne F. Van Loon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2382, https://doi.org/10.5194/egusphere-2024-2382, 2024
Short summary
Short summary
In East Africa are conflict over water and vegetation prominent. On top of that, water abstraction of commercial farms are increasing the competition of water. Therefore, this study has developed a model which can investigate what the influence is of these farming activities on the water balance of the region and people's livelihood activities in times of dry periods. We do that by ‘replacing’ the farms in the model, and see what the effect would be if there were communities or forests instead.
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
Short summary
Short summary
Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jin Feng, Ke Zhang, Huijie Zhan, and Lijun Chao
Hydrol. Earth Syst. Sci., 27, 363–383, https://doi.org/10.5194/hess-27-363-2023, https://doi.org/10.5194/hess-27-363-2023, 2023
Short summary
Short summary
Here we improved a satellite-driven evaporation algorithm by introducing the modified versions of the two constraint schemes. The two moisture constraint schemes largely improved the evaporation estimation on two barren-dominated basins of the Tibetan Plateau. Investigation of moisture constraint uncertainty showed that high-quality soil moisture can optimally represent moisture, and more accessible precipitation data generally help improve the estimation of barren evaporation.
Cited articles
Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance, 44, 182–198, https://doi.org/10.1016/j.insmatheco.2007.02.001, 2009.
Abdalla, E. M. H., Pons, V., Stovin, V., De-Ville, S., Fassman-Beck, E., Alfredsen, K., and Muthanna, T. M.: Evaluating different machine learning methods to simulate runoff from extensive green roofs, Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, 2021.
Abdallah, M.: A D-vine copula-based quantile regression towards merging satellite precipitation products over a rugged topography: A case study at the upper Tekeze Atbara Basin of the Nile Basin, HydroShare [data set], http://www.hydroshare.org/resource/d0d9140845144d73ac578d865411a10a (last access: 25 February 2024), 2024.
Abdallah, M., Mohammadi, B., Zaroug, M. A. H., Omer, A., Cheraghalizadeh, M., Eldow, M. E. E., and Duan, Z.: Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models, J. Hydrol.: Reg. Stud., 44, 101259, https://doi.org/10.1016/j.ejrh.2022.101259, 2022.
Abebe, S. A., Qin, T. L., Yan, D. H., Gelaw, E. B., Workneh, H. T., Kun, W., Liu, S. S., and Dong, B. O.: Spatial and Temporal Evaluation of the Latest High-Resolution Precipitation Products over the Upper Blue Nile River Basin, Ethiopia, Water, 12, 20, https://doi.org/10.3390/w12113072, 2020.
Alfieri, L., Pappenberger, F., Wetterhall, F., Haiden, T., Richardson, D., and Salamon, P.: Evaluation of ensemble streamflow predictions in Europe, J. Hydrol., 517, 913–922, https://doi.org/10.1016/j.jhydrol.2014.06.035, 2014.
Amjad, M., Yilmaz, M. T., Yucel, I., and Yilmaz, K. K.: Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography, J. Hydrol., 584, 124707, https://doi.org/10.1016/j.jhydrol.2020.124707, 2020.
Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Beck, H. E., McNamara, I., Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J. D., and Thinh, N. X.: RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements, Remote Sens. Environ., 239, 111606, https://doi.org/10.1016/j.rse.2019.111606, 2020.
Barrett, E. C. and Martin, D. W.: Use of satellite data in rainfall monitoring, Academic Press, ISBN 0120796805, https://cir.nii.ac.jp/crid/1130000793777022720 (last access: 25 February 2024), 1981.
Belete, M., Deng, J. S., Wang, K., Zhou, M. M., Zhu, E. Y., Shifaw, E., and Bayissa, Y.: Evaluation of satellite rainfall products for modeling water yield over the source region of Blue Nile Basin, Sci. Total Environ., 708, 134834, https://doi.org/10.1016/j.scitotenv.2019.134834, 2020.
Bhuiyan, M. A. E., Nikolopoulos, E. I., Anagnostou, E. N., Quintana-Seguí, P., and Barella-Ortiz, A.: A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula, Hydrol. Earth Syst. Sci., 22, 1371–1389, https://doi.org/10.5194/hess-22-1371-2018, 2018.
Bhuiyan, M. A. E., Nikolopoulos, E. I., and Anagnostou, E. N.: Machine learning–based blending of satellite and reanalysis precipitation datasets: A multiregional tropical complex terrain evaluation, J. Hydrometeorol., 20, 2147–2161, https://doi.org/10.1175/JHM-D-19-0073.1, 2019.
Blocken, B., Poesen, J., and Carmeliet, J.: Impact of wind on the spatial distribution of rain over micro-scale topography: numerical modelling and experimental verification, Hydrol. Process., 20, 345–368, https://doi.org/10.1002/hyp.5865, 2006.
Bouyé, E. and Salmon, M.: Dynamic copula quantile regressions and tail area dynamic dependence in Forex markets, Eur. J. Finance, 15, 721–750, https://doi.org/10.1080/13518470902853491, 2009.
Chao, L. J., Zhang, K., Li, Z. J., Zhu, Y. L., Wang, J. F., and Yu, Z. B.: Geographically weighted regression based methods for merging satellite and gauge precipitation, J. Hydrol., 558, 275–289, https://doi.org/10.1016/j.jhydrol.2018.01.042, 2018.
Chao, L. J., Zhang, K., Wang, J. F., Feng, J., and Zhang, M. J.: A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm, Remote Sens., 13, 2414, https://doi.org/10.3390/rs13122414, 2021.
Chen, L. and Guo, S.: Copulas and its application in hydrology and water resources, Springer, ISBN 978-981-13-0573-3, https://doi.org/10.1007/978-981-13-0574-0, 2019.
Chen, Y., Sharma, S., Zhou, X., Yang, K., Li, X., Niu, X., Hu, X., and Khadka, N.: Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya, Atmos. Res., 250, 105365, https://doi.org/10.1016/j.atmosres.2020.105365, 2021.
Chen, Y. Y., Huang, J. F., Sheng, S. X., Mansaray, L. R., Liu, Z. X., Wu, H. Y., and Wang, X. Z.: A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data, Remote Sens. Environ., 214, 154–172, https://doi.org/10.1016/j.rse.2018.05.021, 2018.
CHRS: PERSIANN, https://chrsdata.eng.uci.edu/ (last access: 25 February 2024), 2024.
Diem, J. E., Hartter, J., Ryan, S. J., and Palace, M. W.: Validation of satellite rainfall products for western Uganda, Journal of Hydrometeorology, 15, 2030–2038, https://doi.org/10.1175/JHM-D-13-0193.1, 2014.
Din, S. U., Al-Dousari, A., Ramdan, A., and Al Ghadban, A.: Site-specific precipitation estimate from TRMM data using bilinear weighted interpolation technique: An example from Kuwait, J. Arid Environ., 72, 1320–1328, https://doi.org/10.1016/j.jaridenv.2007.12.013, 2008.
Dinku, T., Ceccato, P., Grover-Kopec, E., Lemma, M., Connor, S. J., and Ropelewski, C. F.: Validation of satellite rainfall products over East Africa's complex topography, Int. J. Remote Sens., 28, 1503–1526, https://doi.org/10.1080/01431160600954688, 2007.
Dinku, T., Chidzambwa, S., Ceccato, P., Connor, S. J., and Ropelewski, C. F.: Validation of high-resolution satellite rainfall products over complex terrain, Int. J. Remote Sens., 29, 4097–4110, https://doi.org/10.1080/01431160701772526, 2008.
Dinku, T., Ceccato, P., and Connor, S. J.: Challenges of satellite rainfall estimation over mountainous and arid parts of east Africa, Int. J. Remote Sens., 32, 5965–5979, https://doi.org/10.1080/01431161.2010.499381, 2011.
Duan, Q. and Phillips, T. J.: Bayesian estimation of local signal and noise in multimodel simulations of climate change, J. Geophys. Res.-Atmos., 115, D18123, https://doi.org/10.1029/2009JD013654, 2010.
Duan, Q. Y., Ajami, N. K., Gao, X. G., and Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371–1386, https://doi.org/10.1016/j.advwatres.2006.11.014, 2007.
Fenta, A. A., Yasuda, H., Shimizu, K., Ibaraki, Y., Haregeweyn, N., Kawai, T., Belay, A. S., Sultan, D., and Ebabu, K.: Evaluation of satellite rainfall estimates over the Lake Tana basin at the source region of the Blue Nile River, Atmos. Res., 212, 43–53, https://doi.org/10.1016/j.atmosres.2018.05.009, 2018.
Gebremedhin, M. A., Lubczynski, M. W., Maathuis, B. P., and Teka, D.: Novel approach to integrate daily satellite rainfall with in-situ rainfall, Upper Tekeze Basin, Ethiopia, Atmos. Res., 248, 105135, https://doi.org/10.1016/j.atmosres.2020.105135, 2021.
Gebremicael, T. G., Mohamed, Y. A., van der Zaag, P., Gebremedhin, A., Gebremeskel, G., Yazew, E., and Kifle, M.: Evaluation of multiple satellite rainfall products over the rugged topography of the Tekeze-Atbara basin in Ethiopia, Int. J. Remote Sens., 40, 4326–4345, https://doi.org/10.1080/01431161.2018.1562585, 2019.
Gebremicael, T. G., Deitch, M. J., Gancel, H. N., Croteau, A. C., Haile, G. G., Beyene, A. N., and Kumar, L.: Satellite-based rainfall estimates evaluation using a parsimonious hydrological model in the complex climate and topography of the Nile River Catchments, Atmos. Res., 266, 105939, https://doi.org/10.1016/j.atmosres.2021.105939, 2022.
Genest, C. and MacKay, R.J.,: Fonctions de repartition an dimensions et leurs marges, Can. J. Stat., 8, 229–231, https://doi.org/10.2307/3314660, 1959.
GLEAM: Method Global Land Evaporation Amsterdam Model, https://www.gleam.eu/ (last access: 25 February 2024), 2024.
Haile, A. T., Rientjes, T., Gieske, A., and Gebremichael, M.: Rainfall Variability over Mountainous and Adjacent Lake Areas: The Case of Lake Tana Basin at the Source of the Blue Nile River, J. Appl. Meteorol. Clim., 48, 1696–1717, https://doi.org/10.1175/2009jamc2092.1, 2009.
Haile, A. T., Habib, E., and Rientjes, T.: Evaluation of the climate prediction center (CPC) morphing technique (CMORPH) rainfall product on hourly time scales over the source of the Blue Nile River, Hydrol. Process., 27, 1829–1839, https://doi.org/10.1002/hyp.9330, 2013.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The Global Precipitation Measurement Mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/bams-d-13-00164.1, 2014.
Hsu, K. L., Gao, X. G., Sorooshian, S., and Gupta, H. V.: Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 1176–1190, https://doi.org/10.1175/1520-0450(1997)036<1176:Pefrsi>2.0.Co;2, 1997.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P. and Yoo, S. H.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), p. 30, https://gpm.nasa.gov/sites/default/files/2020-05/IMERG_ATBD_V06.3.pdf (last access: 24 February 2024), 2015.
Jennifer, A., David, M., Adrian, E. R., and Chris, V.: Bayesian model averaging: a tutorial, Stat. Sci., 14, 382–417, 1999.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P. P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:Camtpg>2.0.Co;2, 2004.
Kidd, C. and Huffman, G.: Global precipitation measurement, Meteorol. Appl., 18, 334–353, https://doi.org/10.1002/met.284, 2011.
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., and Kirschbaum, D. B.: So, how much of the Earth's surface is covered by rain gauges?, B. Am. Meteorol. Soc., 98, 69–78, https://doi.org/10.1175/BAMS-D-14-00283.1, 2017.
Kimani, M. W., Hoedjes, J. C. B., and Su, Z. B.: An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa, Remote Sens., 9, 21, https://doi.org/10.3390/rs9050430, 2017.
Koenker, R. and Bassett, G.: Regression Quantiles, Econometrica, 46, 33–50, https://doi.org/10.2307/1913643, 1978.
Koenker, R. and Ng, P.: Inequality Constrained Quantile Regression. Sankhyā, 67, 418–440, 2005.
Koenker, R., Portnoy, S., Ng, P. T., Zeileis, A., Grosjean, P., and Ripley, B. D.: Package `quantreg', CRAN [code], https://cran.r-project.org/web/packages/quantreg/ (last access: 25 February 2024), 2018.
Kolluru, V., Kolluru, S., Wagle, N., and Acharya, T. D.: Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India, Remote Sens., 12, 23, https://doi.org/10.3390/rs12183013, 2020.
Kraus, D. and Czado, C.: D-vine copula based quantile regression, Comput. Stat. Data Anal., 110, 1–18, https://doi.org/10.1016/j.csda.2016.12.009, 2017.
Kumar, A., Ramsankaran, R., Brocca, L., and Munoz-Arriola, F.: A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture, Remote Sens., 11, 20, https://doi.org/10.3390/rs11192221, 2019.
Kumar, A., Ramsankaran, R., Brocca, L., and Muñoz-Arriola, F.: A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchment, J. Hydrol., 595, 126046, https://doi.org/10.1016/j.jhydrol.2021.126046, 2021.
Kurowicka, D. and Cooke, R. M.: Distribution-free continuous Bayesian belief, in: Modern statistical and mathematical methods in reliability, 10, World Scientific, p. 309. https://doi.org/10.1142/9789812703378_0022, 2005.
Li, Z., Yang, D. W., Gao, B., Jiao, Y., Hong, Y., and Xu, T.: Multiscale Hydrologic Applications of the Latest Satellite Precipitation Products in the Yangtze River Basin using a Distributed Hydrologic Model, J. Hydrometeorol., 16, 407–426, https://doi.org/10.1175/jhm-d-14-0105.1, 2015.
Lu, X., Tang, G., Wang, X., Liu, Y., Jia, L., Xie, G., Li, S., and Zhang, Y.: Correcting GPM IMERG precipitation data over the Tianshan Mountains in China, J. Hydrol., 575, 1239–1252, https://doi.org/10.1016/j.jhydrol.2019.06.019, 2019.
Ma, Y. Z., Zhang, Y. S., Yang, D. Q., and Bin Farhan, S.: Precipitation bias variability versus various gauges under different climatic conditions over the Third Pole Environment (TPE) region, Int. J. Climatol., 35, 1201–1211, https://doi.org/10.1002/joc.4045, 2015.
Ma, Y. Z., Yang, Y., Han, Z. Y., Tang, G. Q., Maguire, L., Chu, Z. G., and Hong, Y.: Comprehensive evaluation of Ensemble Multi-Satellite Precipitation Dataset using the Dynamic Bayesian Model Averaging scheme over the Tibetan plateau, J. Hydrol., 556, 634–644, https://doi.org/10.1016/j.jhydrol.2017.11.050, 2018.
Maidment, R. I., Grimes, D., Allan, R. P., Tarnavsky, E., Stringer, M., Hewison, T., Roebeling, R., and Black, E.: The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set, J. Geophys. Res.-Atmos., 119, 10619–10644, https://doi.org/10.1002/2014jd021927, 2014.
Maidment, R. I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex, H., Allan, R. P., Stein, T., Nkonde, E., Senkunda, S., and Alcantara, E. M. U.: Data Descriptor: A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa, Sci. Data, 4, 17, https://doi.org/10.1038/sdata.2017.63, 2017.
Manz, B., Buytaert, W., Zulkafli, Z., Lavado, W., Willems, B., Robles, L. A., and Rodríguez-Sánchez, J. P.: High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes, J. Geophys. Res.-Atmos., 121, 1190–1207, https://doi.org/10.1002/2015JD023788, 2016.
Mastrantonas, N., Bhattacharya, B., Shibuo, Y., Rasmy, M., Espinoza-Davalos, G., and Solomatine, D.: Evaluating the Benefits of Merging Near-Real-Time Satellite Precipitation Products: A Case Study in the Kinu Basin Region, Japan, J. Hydrometeorol., 20, 1213–1233, https://doi.org/10.1175/jhm-d-18-0190.1, 2019.
Moazami, S., Golian, S., Kavianpour, M. R., and Hong, Y.: Uncertainty analysis of bias from satellite rainfall estimates using copula method, Atmos. Res., 137, 145–166, https://doi.org/10.1016/j.atmosres.2013.08.016, 2014.
Mohammadi, B. and Aghashariatmadari, Z.: Estimation of solar radiation using neighboring stations through hybrid support vector regression boosted by Krill Herd algorithm, Arab. J. Geosci., 13, 16, https://doi.org/10.1007/s12517-020-05355-1, 2020.
Muhammad, W., Yang, H. B., Lei, H. M., Muhammad, A., and Yang, D. W.: Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm, Remote Sens., 10, 19, https://doi.org/10.3390/rs10040577, 2018.
Nagler, T.: vinereg: D-Vine Quantile Regression, R package version 0.10.0, https://tnagler.github.io/vinereg/, GitHub [code], (last access: 25 February 2024), 2024.
NASA: GES DISC, https://disc.gsfc.nasa.gov/ (last access: 25 February 2024), 2024.
NASA Shuttle Radar Topography Mission – SRTM: Shuttle Radar Topography Mission (SRTM) Global, OpenTopography [data set], https://doi.org/10.5069/G9445JDF, 2013.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nelsen, R. B.: An Introduction to Copulas, in: Springer Series in Statistics, Springer, ISBN 13:978-0387-28659-4, https://doi.org/10.1007/0-387-28678-0, 2005.
Nguyen, G. V., Le, X. H., Van, L. N., Jung, S., Yeon, M., and Lee, G.: Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea, Remote Sens., 13, 17, https://doi.org/10.3390/rs13204033, 2021.
Nguyen, H. H., Cho, S., Jeong, J., and Choi, M.: A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains, Remote Sens. Environ., 255, 112283, https://doi.org/10.1016/j.rse.2021.112283, 2021.
Niemierko, R., Toppel, J., and Trankler, T.: A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data, Appl. Energy, 233, 691–708, https://doi.org/10.1016/j.apenergy.2018.10.025, 2019.
NOAA: Index of /data/cmorph-high-resolution-global-precipitation-estimates, NOAA [data set], https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/ (last access: 25 February 2024), 2024.
Parzen, E.: On estimation of a probability density function and mode, Ann. Math. Stat., 33, 1065–1076, 1962.
Pham, M. T., Vernieuwe, H., Baets, B. D., Willems, P., and Verhoest, N. E. C.: Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas, Stoch. Environ. Res. Risk A., 30, 2197–2214, https://doi.org/10.1007/s00477-015-1181-7, 2016.
Pradhan, B., Jebur, M. N., Shafri, H. Z. M., and Tehrany, M. S.: Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery, IEEE T. Geosci. Remote, 54, 1610–1622, https://doi.org/10.1109/TGRS.2015.2484325, 2015.
Qi, W., Zhang, C., Fu, G. T., Sweetapple, C., and Liu, Y. L.: Impact of robustness of hydrological model parameters on flood prediction uncertainty, J. Flood Risk Manage., 12, e12488, https://doi.org/10.1111/jfr3.12488, 2019.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155—1174, https://doi.org/10.1175/mwr2906.1, 2005a.
Raftery, A. E., Painter, I. S., and Volinsky, C. T.: BMA: an R package for Bayesian model averaging, The Newsletter of the R Project Volume, CRAN [code], https://cran.r-project.org/web/packages/BMA (last access: 25 February 2024), 2005b.
Rahman, H. L. R., Shang, S. H., Shahid, M., Wen, Y. Q., and Khan, Z.: Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan, J. Hydrometeorol., 21, 17–37, https://doi.org/10.1175/jhm-d-19-0087.1, 2020.
Rahman, K. U., Shang, S. H., Shahid, M., and Li, J.: Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations Over Pakistan, Remote Sens., 10, 23, https://doi.org/10.3390/rs10111835, 2018.
Rahman, K. U., Shang, S. H., Shahid, M., and Wen, Y. Q.: An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan, Remote Sens., 12, 30, hhttps://doi.org/10.3390/rs12010010, 2020a.
Rahman, K. U., Shang, S. H., Shahid, M., and Wen, Y. Q.: Hydrological evaluation of merged satellite precipitation datasets for streamflow simulation using SWAT: A case study of Potohar Plateau, Pakistan, J. Hydrol., 587, 125040, https://doi.org/10.1016/j.jhydrol.2020.125040, 2020b.
Rahman, K. U., Shang, S. H., Shahid, M., Wen, Y. Q., and Khan, A. J.: Development of a novel Weighted Average Least Squares-based ensemble multi-satellite precipitation dataset and its comprehensive evaluation over Pakistan, Atmos. Res., 246, 18, https://doi.org/10.1016/j.atmosres.2020.105133, 2020c.
Rahman, K. U., Shang, S. H., and Zohaib, M.: Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan, Remote Sens., 13, 37, https://doi.org/10.3390/rs13091662, 2021.
Reda, K. W., Liu, X. C., Tang, Q. H., and Gebremicael, T. G.: Evaluation of Global Gridded Precipitation and Temperature Datasets against Gauged Observations over the Upper Tekeze River Basin, Ethiopia, J. Meteorol. Res., 35, 673–689, https://doi.org/10.1007/s13351-021-0199-7, 2021.
Reda, K. W., Liu, X. C., Haile, G. G., Sun, S. A., and Tang, Q. H.: Hydrological evaluation of satellite and reanalysis-based rainfall estimates over the Upper Tekeze Basin, Ethiopia, Hydrol. Res., 53, 584–604, https://doi.org/10.2166/nh.2022.131, 2022.
Sen Gupta, A. and Tarboton, D. G.: A tool for downscaling weather data from large-grid reanalysis products to finer spatial scales for distributed hydrological applications, Environ. Model. Softw., 84, 50–69, https://doi.org/10.1016/j.envsoft.2016.06.014, 2016.
Sharifi, E., Saghafian, B., and Steinacker, R.: Copula-based stochastic uncertainty analysis of satellite precipitation products, J. Hydrol., 570, 739–754, https://doi.org/10.1016/j.jhydrol.2019.01.035, 2019.
Shen, Y., Xiong, A. Y., Hong, Y., Yu, J. J., Pan, Y., Chen, Z. Q., and Saharia, M.: Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau, Int. J. Remote Sens., 35, 6843–6858, https://doi.org/10.1080/01431161.2014.960612, 2014.
Shi, Y., Chen, C., Chen, J., Mohammadi, B., Cheraghalizadeh, M., Abdallah, M., Mert Katipoğlu, O., Li, H., and Duan, Z.: Evaluation of the RF-MEP Method for Merging Multiple Gridded Precipitation Products in the Chongqing City, China, Remote Sens., 15, 4230, https://doi.org/10.3390/rs15174230, 2023.
Sun, R. C., Yuan, H. L., Liu, X. L., and Jiang, X. M.: Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River basin, J. Hydrol., 536, 302–319, https://doi.org/10.1016/j.jhydrol.2016.02.054, 2016.
Sun, R. C., Yuan, H. L., and Yang, Y. Z.: Using multiple satellite-gauge merged precipitation products ensemble for hydrologic uncertainty analysis over the Huaihe River basin, J. Hydrol., 566, 406–420, https://doi.org/10.1016/j.jhydrol.2018.09.024, 2018.
Tan, J., Huffman, G. J., Bolvin, D. T., and Nelkin, E. J.: IMERG V06: Changes to the Morphing Algorithm, J. Atmos. Ocean. Tech., 36, 2471–2482, https://doi.org/10.1175/jtech-d-19-0114.1, 2019.
The National Meteorological Agency: Meteorological Station Information, http://www.ethiomet.gov.et/ (last access: 24 February 2024), 2016.
Ulloa, J., Ballari, D., Campozano, L., and Samaniego, E.: Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America, Remote Sens., 9, 23, https://doi.org/10.3390/rs9070758, 2017.
University of Reading: TAMSAT, http://www.tamsat.org.uk/data (last access: 25 February 2024), 2024.
Viste, E. and Sorteberg, A.: Moisture transport into the Ethiopian highlands, Int. J. Climatol., 33, 249–263, https://doi.org/10.1002/joc.3409, 2013.
Wang, S., Zhang, K., Chao, L. J., Li, D. H., Tian, X., Bao, H. J., Chen, G. D., and Xia, Y.: Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards, J. Hydrol., 603, 126964, https://doi.org/10.1016/j.jhydrol.2021.126964, 2021.
WMO: Guide to hydrological practices: data aquisition and processing, analysis, forecasting and other applications, http://www.innovativehydrology.com/WMO-No.168-1994.pdf (last access: 24 February 2024), 1994.
Worqlul, A. W., Yen, H., Collick, A. S., Tilahun, S. A., Langan, S., and Steenhuis, T. S.: Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models, in data-scarce regions: The upper Blue Nile Basin, Ethiopia, Catena, 152, 242–251, https://doi.org/10.1016/j.catena.2017.01.019, 2017.
Wu, H., Zhang, X., Liang, S., Yang, H., and Zhou, G.: Estimation of clear-sky land surface longwave radiation from MODIS data products by merging multiple models, J. Geophys. Res.-Atmos., 117, D22107, https://doi.org/10.1029/2012JD017567, 2012.
Wu, H., Yang, Q., Liu, J., and Wang, G.: A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China, J. Hydrol., 584, 124664, https://doi.org/10.1016/j.jhydrol.2020.124664, 2020.
Wu, T., Bai, J., and Han, H.: Short-Term Agricultural Drought Prediction based on D-vine copula quantile regression in snow-free unfrozen surface area, China, Geocarto Int., 37, 9320–9338, https://doi.org/10.1080/10106049.2021.2017015, 2022.
Xiao, S., Xia, J., and Zou, L.: Evaluation of multi-satellite precipitation products and their ability in capturing the characteristics of extreme climate events over the Yangtze River Basin, China, Water, 12, 1179, https://doi.org/10.3390/w12041179, 2020.
Yong, B., Ren, L. L., Hong, Y., Wang, J. H., Gourley, J. J., Jiang, S. H., Chen, X., and Wang, W.: Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China, Water Resour. Res., 46, W07542, https://doi.org/10.1029/2009wr008965, 2010.
Young, M. P., Williams, C. J. R., Chiu, J. C., Maidment, R. I., and Chen, S.-H.: Investigation of discrepancies in satellite rainfall estimates over Ethiopia, J. Hydrometeorol., 15, 2347–2369, https://doi.org/10.1175/JHM-D-13-0111.1, 2014.
Yumnam, K., Guntu, R. K., Rathinasamy, M., and Agarwal, A.: Quantile-based Bayesian Model Averaging approach towards merging of precipitation products, J. Hydrol., 604, 127206, https://doi.org/10.1016/j.jhydrol.2021.127206, 2022.
Zhang, K., Xue, X. W., Hong, Y., Gourley, J. J., Lu, N., Wan, Z. M., Hong, Z., and Wooten, R.: iCRESTRIGRS: a coupled modeling system for cascading flood-landslide disaster forecasting, Hydrol. Earth Syst. Sci., 20, 5035–5048, https://doi.org/10.5194/hess-20-5035-2016, 2016.
Zubieta, R., Getirana, A., Espinoza, J. C., Lavado-Casimiro, W., and Aragon, L.: Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset, Hydrol. Earth Syst. Sci., 21, 3543–3555, https://doi.org/10.5194/hess-21-3543-2017, 2017.
Short summary
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation products. The performance of the DVQR model is compared with the simple model average and one-outlier-removed average methods. The nonlinear DVQR model outperforms the quantile-regression-based multivariate linear and Bayesian model averaging methods.
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation...