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Warianty tytułu
Języki publikacji
Abstrakty
Global gridded products efficiency in closing water balance models: various modeling scenarios for behavioral assessments
Wydawca
Czasopismo
Rocznik
Tom
Strony
2401--2422
Opis fizyczny
Bibliogr. 83 poz., rys., tab.
Twórcy
autor
- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran
autor
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
autor
- Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran
autor
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
- Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N6N5, Canada
autor
- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
Bibliografia
- 1. Abatzoglou J and National Center for Atmospheric Research Staff (Eds). Last modified 25 Jan 2021. "The Climate Data Guide: TerraClimate: Global, high-resolution gridded temperature, precipitation, and other water balance variables." Retrieved from https://climatedataguide.ucar.edu/climate-data/terraclimate-global-high-resolution-gridded-temperature-precipitation-and-other-water
- 2. Adane GB, Hirpa BA, Gebru BM, Song C, Lee W-K (2021) Integrating satellite rainfall estimates with hydrological water balance model: rainfall-runoff modeling in Awash River Basin. Ethiopia Water 13:800
- 3. Ahmadi A, Nasseri M (2020) Do direct and inverse uncertainty assessment methods present the same results? J Hydroinf 22:842–855
- 4. Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J Irrig Drain Eng 133(4):380–394
- 5. Amini Y, Nasseri M (2021) Improving spatial estimation of hydrologic attributes via optimized moving search strategies. Arab J Geosci 14:723. https://doi.org/10.1007/s12517-021-06961-3
- 6. Bastiaanssen WG, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formul J Hydrol 212:198–212
- 7. Becker R, Koppa A, Schulz S, Usman M, Aus Der Beek T, Schuth C (2019) Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data. J Hydrol 577:123944
- 8. Behrangi A, Khakbaz B, Jaw TC, Aghakouchak A, Hsu K, Sorooshian S (2011) Hydrologic evaluation of satellite precipitation products over a mid-size basin. J Hydrol 39:225–237
- 9. Beven K (2016) Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrol Sci J 61(9):1652–1665. https://doi.org/10.1080/02626667.2015.1031761
- 10. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298
- 11. Che T, Li X, Gao F (2004) Estimation of snow water equivalent in the Tibetan Plateau using passive microwave remote sensing data (SSM/I). J Glaciol Geocryol 3:19–368
- 12. Chen M, Senay GB, Singh RK, Verdin JP (2016) Uncertainty analysis of the operational simplified surface energy balance (SSEBop) model at multiple flux tower sites. J Hydrol 536:384–399
- 13. Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home
- 14. Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in burkina faso, West Africa. Int J Remote Sens 37:3995–4014. https://doi.org/10.1080/01431161.2016.1207258
- 15. Dembele M, Ceperley N, Zwart SJ, Salvadore E, Mariethoz G, Schaefli B (2020) Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies. Adv Water Resour 143:103667
- 16. Dorigo W, Gruber A, de Jeu R, Wagner W, Stacke T, Loew A, Albergel C, Brocca L, Chung D, Parinussa R (2015) Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens Environ 162:380–395
- 17. Duan Z, Tuo Y, Liu J, Gao H, Song X, Zhang Z, Yang L, Mekonnen DF (2019) Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia. J Hydrol 569:612–626
- 18. Eini MR, Javadi J, Delavar M, Gassman PW, Jarihani B (2020) Development of alternative SWAT-based models for simulating water budget components and streamflow for a karstic-influenced watershed. CATENA 195:104801
- 19. Funk CC, Peterson PJ, Landsfeld MF, Pedreros DH, Verdin JP, Rowland JD, Romero BE, Husak GJ, Michaelsen JC, Verdin AP (2014) A quasi-global precipitation time series for drought monitoring: U.S.Geological Survey Data Series 832, 4 p.ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/docs/USGS-DS832.CHIRPS.pdf
- 20. Gao H, Tang Q, Ferguson CR, Wood EF, Lettenmaier DP (2010) Estimating the water budget of major US river basins via remote sensing. Int J Remote Sens 31:3955–3978
- 21. Guo S, Chen H, Zhang H, Xiong L, Liu P, Pang B, Wang G, Wang Y (2005) A semi-distributed monthly water balance model and its application in a climate change impact study in the middle and lower Yellow River basin. Water Int 30:250–260
- 22. Ha LT, Bastiaanssen WG, van Griensven A, van Dijk AI, Senay GB (2018) Calibration of spatially distributed hydrological processes and model parameters in SWAT using remote sensing data and an auto-calibration procedure: a case study in a Vietnamese river basin. Water 10:212
- 23. Herman MR, Nejadhashemi AP, Abouali M, Hernandez-Suarez JS, Daneshvar F, Zhang Z, Anderson MC, Sadeghi AM, Hain CR, Sharifi A (2018) Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability. J Hydrol 556:39–49
- 24. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55
- 25. Immerzeel W, Droogers P (2008) Calibration of a distributed hydrological model based on satellite evapotranspiration. J Hydrol 349:411–424
- 26. Jazim AA (2006) A monthly six-parameter water balance model and its application at arid and semiarid low yielding catchments. J King Saud Univ-Eng Sci 19:65–81
- 27. Jiang L, Islam S (2001) Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resour Res 37:329–340
- 28. Jiang L, Wu H, Tao J, Kimball JS, Alfieri L, Chen X (2020) Satellite-based evapotranspiration in hydrological model calibration. Remote Sens 12:428
- 29. Jin X, Xu C-Y, Zhang Q, Singh V (2010) Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J Hydrol 383:147–155
- 30. Khan MS, Liaqat UW, Baik J, Choi M (2018) Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach. Agric For Meteorol 252:256–268
- 31. Knoben WJ, Freer JE, Woods RA (2019) Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol Earth Syst Sci 23:4323–4331
- 32. Kouchi DH, Esmaili K, Faridhosseini A, Sanaeinejad SH, Khalili D, Abbaspour KC (2017) Sensitivity of calibrated parameters and water sresource estimates on different objective functions and optimization algorithms. Water 9:384
- 33. Kunnath-Poovakka A, Ryu D, Renzullo L, George B (2016) The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction. J Hydrol 535:509–524
- 34. Lancaster P, Salkauskas K (1981) Surfaces generated by moving least squares methods. Math Comput 37:141–158
- 35. Lauri H, Räsänen T, Kummu M (2014) Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. J Hydrometeorol 15:1532–1545
- 36. le Coz C, van de Giesen N (2020) Comparison of rainfall products over sub-saharan africa. J Hydrometeorol 21:553–596
- 37. Li L, Xia J, Xu C-Y, Singh V (2010) Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models. J Hydrol 390:210–221
- 38. Liu W, Wang L, Zhou J, Li Y, Sun F, Fu G, Li X, Sang Y-F (2016) A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method. J Hydrol 538:82–95
- 39. Long D, Longuevergne L, Scanlon BR (2014) Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour Res 50:1131–1151
- 40. Lopez PL, Sutanudjaja EH, Schellekens J, Sterk G, Bierkens MF (2017) Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products. Hydrol Earth Syst Sci 21:3125–3144
- 41. Mackay JD, Jackson CR, Wang L (2014) A lumped conceptual model to simulate groundwater level time-series. Environ Model Softw 61:229–245
- 42. Martens B, Miralles DG, Lievens H, van der Schalie R, de Jeu RAM, Fernández-Prieto D, Beck HE, Dorigo WA, Verhoest NEC (2017) GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903–1925. https://doi.org/10.5194/gmd-10-1903-2017
- 43. Moreira AA, Ruhoff AL, Roberti DR, de Arruda Souza V, da Rocha HR, de Paiva RCD (2019) Assessment of terrestrial water balance using remote sensing data in South America. J Hydrol 575:131–147
- 44. Moriasi DN, Arnold JG, van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
- 45. Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58:1763–1785
- 46. Moshir Panahi D, Sadeghi Tabas S, Kalantari Z, Ferreira CSS, Zahabiyoun B (2021) Spatio-temporal assessment of global gridded evapotranspiration datasets across Iran. Remote Sens 13(9):1816
- 47. Muñoz Sabater J (2019) ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb3
- 48. Muthuwatta LP, Booij MJ, Rientjes TH, Bos M, Gieske A, Ahmad M-U-D (2009) Calibration of a semi-distributed hydrological model using discharge and remote sensing data. IAHS Publ 333:52
- 49. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–290
- 50. Nasseri M, Ansari A, Zahraie B (2014) Uncertainty assessment of hydrological models with fuzzy extension principle: evaluation of a new arithmetic operator. Water Resour Res 50:1095–1111
- 51. Nasseri M, Schoups G, Taheri M (2022) A spatiotemporal framework to calibrate high-resolution global monthly precipitation products: an application to the Urmia Lake Watershed in Iran. Int J Climatol 42(4):2169–2194
- 52. Odusanya AE, Mehdi B, Schurz C, Oke AO, Awokola OS, Awomeso JA, Adejuwon JO, Schulz K (2019) Multi-site calibration and validation of SWAT with satellite-based evapotranspiration in a data-sparse catchment in southwestern Nigeria. Hydrol Earth Syst Sci 23
- 53. Odusanya AE, Schulz K, Biao EI, Degan BA, Mehdi-Schulz B (2021) Evaluating the performance of streamflow simulated by an eco-hydrological model calibrated and validated with global land surface actual evapotranspiration from remote sensing at a catchment scale in West Africa. J Hydrol Reg Stud 37:100893
- 54. Pan S, Liu L, Bai Z, Xu Y-P (2018) Integration of remote sensing evapotranspiration into multi-objective calibration of distributed hydrology–soil–vegetation model (DHSVM) in a Humid Region of China. Water 10:1841
- 55. Parajka J, Blöschl G (2008) The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models. J Hydrol 358:240–258
- 56. Pomeon T, Diekkruger B, Springer A, Kusche J, Eicker A (2018) Multi-objective validation of SWAT for sparsely-gauged West African River Basins—A remote sensing approach. Water 10:451
- 57. Qin C, Jia Y, Su Z, Zhou Z, Qiu Y, Suhui S (2008) Integrating remote sensing information into a distributed hydrological model for improving water budget predictions in large-scale basins through data assimilation. Sensors 8:4441–4465
- 58. Rabuffetti D, Ravazzani G, Corbari C, Mancini M (2008) Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system? the AMPHORE case studies in the upper Po River
- 59. Rajib A, Evenson GR, Golden HE, Lane CR (2018) Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters. J Hydrol 567:668–683
- 60. Rientjes T, Muthuwatta LP, Bos M, Booij MJ, Bhatti H (2013) Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration. J Hydrol 505:276–290
- 61. Rossetto R, de Filippis G, Triana F, Ghetta M, Borsi I, Schmid W (2019) Software tools for management of conjunctive use of surface- and ground-water in the rural environment: integration of the Farm Process and the Crop Growth Module in the FREEWAT platform. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.105717
- 62. Roy T, Gupta HV, Serrat-Capdevila A, Valdes JB (2017) Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model
- 63. Running S, Mu Q, Zhao M (2017) MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. Accessed 2021-09-21 from https://doi.org/10.5067/MODIS/MOD16A2.006
- 64. Rusli S, Weerts A, Taufiq A, Bense V (2021) Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: an example for the Upper Citarum basin. J Hydrol Reg Stud 37:100911
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- 68. Shahrban M (2017) On the Importance of Soil Moisture for Streamflow Forecasting. Monash University
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-244c5e4e-ec87-440c-b173-7856e9bcad97