Uncertainty quantification of multi-source hydrological data products for the improvement of water budget estimations in small-scale Sakarya basin, Turkey

References

• Abatzoglou, J.T., et al., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data, 5, 1–12. doi:10.1038/sdata.2017.191
   PubMed Web of Science ®Google Scholar
• Agutu, N.O., et al., 2021. Understanding uncertainty of model-reanalysis soil moisture within Greater Horn of Africa (1982–2014). J. Hydrol, 603, 127169. doi:10.1016/J.JHYDROL.2021.127169
   Web of Science ®Google Scholar
• Ahamed, A., et al., 2022. Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage. Sci. Total Environ, 807, 150635. doi:10.1016/J.SCITOTENV.2021.150635
   PubMed Web of Science ®Google Scholar
• Aires, F., 2014. Combining datasets of satellite-retrieved products. Part I: Methodology and water budget closure. J. Hydrometeorol, 15, 1677–1691. doi:10.1175/jhm-d-13-0148.1
   Web of Science ®Google Scholar
• Allen, R.G., et al., 2011. Evapotranspiration information reporting: i. Factors governing measurement accuracy. Agric. Water Manag, 98, 899–920. doi:10.1016/j.agwat.2010.12.015
   Web of Science ®Google Scholar
• Azarderakhsh, M., et al., 2011. Diagnosing water variations within the Amazon basin using satellite data. J. Geophys. Res. Atmos, 116, 1–18. doi:10.1029/2011JD015997
   Web of Science ®Google Scholar
• Bhanja, S.N., Mukherjee, A., and Rodell, M., 2020. Groundwater storage change detection from in situ and GRACE-based estimates in major river basins across India. Hydrol. Sci. J, 65, 650–659. doi:10.1080/02626667.2020.1716238
   PubMed Web of Science ®Google Scholar
• Çeribaşı, G. and Doğan, E., 2015. Trend Analizi Yöntemi kullanılarak Batı ve Doğu Karadeniz ile Sakarya Havzası akım miktarlarının değerlendirilmesi. Uluslararası Teknol. Bilim. Derg, 7, 1–12.
   Google Scholar
• Chen, H., Zhang, W., and Jafari Shalamzari, M., 2019. Remote detection of human-induced evapotranspiration in a regional system experiencing increased anthropogenic demands and extreme climatic variability. Int. J. Remote Sens, 40, 1887–1908. doi:10.1080/01431161.2018.1523590
   Web of Science ®Google Scholar
• Chen, S., et al., 2020. Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. J. Hydrol, 589, 125156. doi:10.1016/j.jhydrol.2020.125156
   Web of Science ®Google Scholar
• Di Baldassarre, G. and Montanari, A., 2009. Uncertainty in river discharge observations: a quantitative analysis. Hydrol. Earth Syst. Sci, 13, 913–921. doi:10.5194/hess-13-913-2009
   Web of Science ®Google Scholar
• Evensen, G., 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. Ocean, 99, 10143–10162. https://doi.org/10.1029/94JC00572
   Web of Science ®Google Scholar
• Fagiolini, E., et al., 2015. Correction of inconsistencies in ECMWF’s operational analysis data during de-aliasing of GRACE gravity models. Geophys. J. Int, 202, 2150–2158. doi:10.1093/gji/ggv276
   Web of Science ®Google Scholar
• Fernández-Prieto, D., et al., 2012. Editorial “advances in Earth observation for water cycle science. Hydrol. Earth Syst. Sci, 16, 543–549. doi:10.5194/hess-16-543-2012
   Web of Science ®Google Scholar
• Galindo, F. and Palacio, J., 1999. Estimating the instabilities of N correlated clocks. 31st annu. Precise Time and Time Interval Meet, Dana Point, California, 285–296.
   Google Scholar
• Gao, H., et al., 2010. Estimating the water budget of major US river basins via remote sensing. Int. J. Remote Sens, 31, 3955–3978. doi:10.1080/01431161.2010.483488
   Web of Science ®Google Scholar
• Güner, H.T., Köse, N., and Harley, G.L., 2017. A 200-year reconstruction of Kocasu River (Sakarya River Basin, Turkey) streamflow derived from a tree-ring network. Int. J. Biometeorol, 61, 427–437. doi:10.1007/s00484-016-1223-y
   PubMed Web of Science ®Google Scholar
• Guo, R. and Liu, Y., 2016. Evaluation of satellite precipitation products with rain gauge data at different scales: implications for hydrological applications. Water (Switzerland), 8. doi:10.3390/w8070281
   Google Scholar
• He, X., et al., 2020a. Mapping regional evapotranspiration in cloudy skies via variational assimilation of all-weather land surface temperature observations. J. Hydrol, 585, 124790. doi:10.1016/J.JHYDROL.2020.124790
   Web of Science ®Google Scholar
• He, X., et al., 2020b. A Bayesian three-cornered hat (BTCH) method: improving the terrestrial evapotranspiration estimation. Remote Sens, 12, 878. doi:10.3390/rs12050878
   Google Scholar
• Huffman, G.J., et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol, 8, 38–55. doi:10.1175/JHM560.1
   Web of Science ®Google Scholar
• Huffman, G.J., et al., 2019. Algorithm theoretical basis document (ATBD) for global precipitation climatology project version 3.0 precipitation data. In: Meas. Proj. Greenbelt, MD: MEaSUREs project, NASA. pp. 1–32.
   Google Scholar
• Joyce, R.J., et al., 2004. 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.0.CO;2
   Web of Science ®Google Scholar
• Kayan, G., et al., 2017. Understanding of Cyprus total water storage under climate change, in: International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, Texas, USA. pp. 5591–5594. https://doi.org/10.1109/IGARSS.2017.8128272
   Google Scholar
• Landerer, F.W. and Swenson, S.C., 2012. Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res, 48. doi:10.1029/2011WR011453
   Web of Science ®Google Scholar
• Liang, X., et al., 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. Atmos, 99, 14415–14428. https://doi.org/10.1029/94JD00483
   Web of Science ®Google Scholar
• Liu, J., et al., 2015. Evaluation of three satellite precipitation products TRMM 3B42, CMORPH, and PERSIANN over a subtropical watershed in China. Adv. Meteorol, https://doi.org/10.1155/2015/151239.
   Google Scholar
• Liu, Y., et al., 2020. Assessment of human-induced evapotranspiration with GRACE satellites in the Ziya-daqing Basins, China. Hydrol. Sci. J, 65, 2577–2589. https://doi.org/10.1080/02626667.2020.1820507
   Web of Science ®Google Scholar
• Liu, J., et al., 2021. Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote Sens. Environ, 255, 112225. doi:10.1016/j.rse.2020.112225
   Web of Science ®Google Scholar
• Long, D., et al., 2017. Global analysis of spatiotemporal variability in merged total water storage changes using multiple GRACE products and global hydrological models. Remote Sens. Environ, 192, 198–216. doi:10.1016/j.rse.2017.02.011
   Web of Science ®Google Scholar
• Lu, X., et al., 2018. Evaluation of multi-satellite precipitation products in Xinjiang, China. Int. J. Remote Sens, 39, 7437–7462. doi:10.1080/01431161.2018.1471246
   Web of Science ®Google Scholar
• Massari, C., et al., 2015. Data assimilation of satellite soil moisture into rainfall-runoff modelling: acomplex recipe? Remote Sens, 7 (9), 11403–11433. https://doi.org/10.3390/rs70911403
   Google Scholar
• Meixia, L., et al., 2017. Water budget closure based on GRACE measurements and reconstructed evapotranspiration using GLDAS and water use data for two large densely-populated mid-latitude basins. J. Hydrol, 547, 585–599. https://doi.org/10.1016/j.jhydrol.2017.02.027
   Web of Science ®Google Scholar
• Miralles, D.G., et al., 2011. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci, 15, 453–469. https://doi.org/10.5194/hess-15-453-2011
   Web of Science ®Google Scholar
• Mo, K.C. and Lettenmaier, D.P., 2014. Objective drought classification using multiple land surface models. J. Hydrometeorol, 15, 990–1010. doi:10.1175/JHM-D-13-071.1
   Web of Science ®Google Scholar
• Moreira, A.A., et al., 2019. Assessment of terrestrial water balance using remote sensing data in South America. J. Hydrol, 575, 131–147. doi:10.1016/j.jhydrol.2019.05.021
   Web of Science ®Google Scholar
• Mu, Q., et al., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ, 111, 519–536. doi:10.1016/J.RSE.2007.04.015
   Web of Science ®Google Scholar
• Nguyen, P., et al., 2019. The CHRS data portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Sci. Data, 6, 1–11. doi:10.1038/sdata.2018.296
   PubMed Web of Science ®Google Scholar
• Ochoa, A., et al., 2014. Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific-andean region of Ecuador and Peru. Hydrol. Earth Syst. Sci, 18, 3179–3193. doi:10.5194/hess-18-3179-2014
   Web of Science ®Google Scholar
• Oliveira, P.T.S., et al., 2014. Trends in water balance components across the Brazilian Cerrado. Water Resour. Res, 50, 7100–7114. doi:10.1002/2013WR015202
   Web of Science ®Google Scholar
• Pan, M. and Wood, E.F., 2006. Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J. Hydrometeorol, 7, 534–547. doi:10.1175/JHM495.1
   Web of Science ®Google Scholar
• Pan, M., et al., 2012. Multisource estimation of long-term terrestrial water budget for major global river basins. J. Clim, 25, 3191–3206. doi:10.1175/JCLI-D-11-00300.1
   Web of Science ®Google Scholar
• Penatti, N.C., et al., 2015. Satellite-based hydrological dynamics of the world’s largest continuous wetland. Remote Sens. Environ, 170, 1–13. doi:10.1016/j.rse.2015.08.031
   Web of Science ®Google Scholar
• Peng, F., et al., 2020. Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmos. Res, 236, 104814. doi:10.1016/j.atmosres.2019.104814
   Web of Science ®Google Scholar
• Premoli, A. and Tavella, P., 1993. A revisited three-cornered hat method for estimating frequency standard instability. IEEE Trans. Instrum. Meas, 42, 7–13. doi:10.1109/19.206671
   Web of Science ®Google Scholar
• Sahoo, A.K., et al., 2011. Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens. Environ, 115, 1850–1865. doi:10.1016/j.rse.2011.03.009
   Web of Science ®Google Scholar
• Scanlon, B.R., et al., 2016. Global evaluation of new GRACE mascon products for hydrologic applications. Water Resour. Res, 52, 9412–9429.
   Web of Science ®Google Scholar
• Sheffield, J., et al., 2009. Closing the terrestrial water budget from satellite remote sensing. Geophys. Res. Lett, 36, 1–5. doi:10.1029/2009GL037338
   Web of Science ®Google Scholar
• Shrestha, S., Shrestha, M., and Babel, M.S., 2016. Modelling the potential impacts of climate change on hydrology and water resources in the Indrawati River Basin, Nepal. Environ. Earth Sci, 75, 1–13. doi:10.1007/s12665-015-5150-8
   Web of Science ®Google Scholar
• Singh, N., et al., 2020. Observations on the distribution of precipitation over northern India using joint CloudSat, CALIPSO and TRMM measurements. Remote Sens. Lett, 11, 117–126. doi:10.1080/2150704X.2019.1692388
   Web of Science ®Google Scholar
• Spellman, P., Pritt, A.B.C., and Salazar, N., 2021. Tracking changing water budgets across the Bahamian archipelago. J. Hydrol, 598, 126178. doi:10.1016/J.JHYDROL.2021.126178
   Web of Science ®Google Scholar
• Tapley, B.D., et al., 2004. GRACE measurements of mass variability in the Earth system. Science, 305 (80), 503–505. doi:10.1126/science.1099192
   PubMed Web of Science ®Google Scholar
• Teutschbein, C. and Seibert, J., 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J. Hydrol, 456–457, 12–29. doi:10.1016/J.JHYDROL.2012.05.052
   Web of Science ®Google Scholar
• Verdin, A., et al., 2015. A Bayesian kriging approach for blending satellite and ground precipitation observations. Water Resour. Res, 51, 908–921. doi:10.1002/2014WR015963
   Web of Science ®Google Scholar
• Wang, H., et al., 2014. Examination of water budget using satellite products over Australia. J. Hydrol, 511, 546–554. doi:10.1016/j.jhydrol.2014.01.076
   Web of Science ®Google Scholar
• Wongchuig-Correa, S., et al., 2020. Assimilation of future SWOT-based river elevations, surface extent observations and discharge estimations into uncertain global hydrological models. J. Hydrol, 590, 125473. doi:10.1016/j.jhydrol.2020.125473
   Web of Science ®Google Scholar
• Xu, T., et al., 2019. Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. J. Hydrol, 578, 124105. doi:10.1016/j.jhydrol.2019.124105
   Web of Science ®Google Scholar
• Yan, X., et al., 2021. GRACE and land surface models reveal severe drought in eastern China in 2019. J. Hydrol, 601, 126640. doi:10.1016/j.jhydrol.2021.126640
   Web of Science ®Google Scholar
• Yaykiran, S., Cuceloglu, G., and Ekdal, A., 2019. Estimation of water budget components of the Sakarya River basin by using the WEAP-PGM model. Water (Switzerland), 11, 1–17. doi:10.3390/w11020271
   Google Scholar
• Yumnam, K., et al., 2021. Quantile-based Bayesian model averaging approach towards merging of precipitation products. J. Hydrol, 127206. doi:10.1016/J.JHYDROL.2021.127206
   Web of Science ®Google Scholar
• Zad, S.N.M., Zulkafli, Z., and Muharram, F.M., 2018. Satellite rainfall (TRMM 3B42-V7) performance assessment and adjustment over Pahang river basin, Malaysia. Remote Sens, 10, 1–24. doi:10.3390/rs10030388
   Google Scholar
• Zhang, K., et al., 2010. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resour. Res, 46, 1–21. doi:10.1029/2009WR008800
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2018. A climate data record (CDR) for the global terrestrial water budget: 1984-2010. Hydrol. Earth Syst. Sci, 22, 241–263. doi:10.5194/hess-22-241-2018
   Web of Science ®Google Scholar
• Zhou, J., et al., 2019. Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability. Hydrol. Sci. J, 64, 1982–1996. doi:10.1080/02626667.2019.1662024
   Web of Science ®Google Scholar