Satellite-based streamflow simulation using CHIRPS satellite precipitation product in Shah Bahram Basin, Iran

Shirin, Mokhtari; Ahmad, Sharafati; Tayeb, Raziei

doi:10.1007/s11600-021-00724-0

Artykuł - szczegóły

Tytuł artykułu

Satellite-based streamflow simulation using CHIRPS satellite precipitation product in Shah Bahram Basin, Iran

Autorzy

Shirin Mokhtari , Ahmad Sharafati , Tayeb Raziei

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00724-0

Warianty tytułu

Języki publikacji

Abstrakty

Precipitation is the most important climate variable in hydrological practices, so accurate estimation of its intensity and volume is very crucial for hydrological applications. Remote sensing precipitation estimations have recently been widely employed in water resources management due to the lack of observed precipitation measurements in remote areas. However, remote sensing precipitation estimations are not free from systematic errors. This study aims to bias-correct the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) satellite precipitation estimations using the Gaussian-Copula approach and illustrates how it improves the simulated flow characteristics in the Shah Bahram basin in Kohgiluyeh and Boyer-Ahmad Province, southwestern Iran. The Nash-Sutclif Efciency (NSE) calculated between the original CHIRPS precipitation estimation and observation in the Shah Bahram basin equals −0.14; however, when bias-corrected CHIRPS data was compared to observation, the NSE increased to 0.23, suggesting about 158% improvement in the CHIRPS precipitation estimation when bias-corrected with the Gaussian-Copula approach. Next, the bias-corrected precipitation time series were utilized as the hydrologic modeling system inputs to simulate flow specifications such as discharge and peak value. Then, the simulation of the flow parameters was carried out with both original and bias-corrected CHIRPS satellite precipitation estimations and the ground-based precipitation. Though the NSE statistic of the simulation for the testing period has not changed significantly, the Pbias statistic has considerably improved. The result of the study indicates the good performance of the proposed bias correction approach in reducing the CHIRPS satellite estimations errors, concluding that it is a suitable approach for bias correction of the other satellite precipitation estimations in areas that suffer from the lack of ground-based observations necessary for food forecasting and other hydrological practices.

Słowa kluczowe

bias correction copula function streamflow simulation remote sensing satellite rainfall estimation

korekcja błędu funkcja kopuły symulacja strumienia teledetekcja

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 1

Strony

385--398

Opis fizyczny

Bibliogr. 76 poz.

Twórcy

autor

Shirin Mokhtari

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

autor

Ahmad Sharafati

asharafati@srbiau.ac.ir

Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

https://orcid.org/0000-0003-0448-2871

autor

Tayeb Raziei

Agricultural Research, Education and Extension Organization (AREEO), Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran

Bibliografia

1. Ali M, Prasad R, Xiang Y, Yaseen ZM (2020) Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.124647
2. Azmat M, Qamar MU, Ahmed S, Hussain E, Umair M (2017) Application of HEC-HMS for the event and continuous simulation in high- altitude scarcely-gauged catchment under changing climate. Eur Water 57:77–84
3. Bali TG (2003) The generalized extreme value distribution. Econ Lett 79:423–427
4. Belayneh A, Sintayehu G, Gedam K, Muluken T (2020) Evaluation of satellite precipitation products using HEC-HMS model. Model Earth Syst Environ 6:2015–2032. https://doi.org/10.1007/s40808-020-00792-z
5. Bell TL (1987) A space-time stochastic model of rainfall for satellite remote-sensing studies. J Geophys Res Atmos 92:9631–9643
6. Bhatti HA, Rientjes T, Haile AT, Habib E, Verhoef W (2016) Evaluation of bias correction method for satellite-based rainfall data. Sensors 16:884
7. Bormann N, Saarinen S, Kelly G, Thépaut J-N (2003) The spatial structure of observation errors in atmospheric motion vectors from geostationary satellite data. Mon Weather Rev 131:706–718
8. Castro CV, Maidment DR (2020) GIS preprocessing for rapid initialization of HEC-HMS hydrological basin models using web-based data services. Environ Model Softw 130:104732
9. Chakraborty S, Biswas S (2021) Simulation of flow at an ungauged river site based on HEC-HMS model for a mountainous river basin. Arab J Geosci 14:1–17
10. Chen H, Sun L, Cifelli R, Xie P (2021) Deep learning for bias correction of satellite retrievals of orographic precipitation. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2021.3105438
11. Cho D, Yoo C, Im J, Cha D (2020) Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas. Earth Space Sci 7:e2019EA000740
12. Chu X, Steinman A (2009) Event and continuous hydrologic modeling with HEC-HMS. J Irrig Drain Eng 135:119–124
13. Cronshey R (1986) Urban hydrology for small watersheds. US Department of Agriculture, Soil Conservation Service, Engineering Division
14. Dambe N, Smit J (2021) Application of low to medium resolution data for hydrological modeling in Malawi. Space Foster Afr Soc Dev Afr Cont. through Space Part 2:39–53
15. Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Softw 22:1034–1052
16. Dinku T, Funk C, Peterson P, Maidment R, Tadesse T, Gadain H, Ceccato P (2018) Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q J R Meteorol Soc 144:292–312. https://doi.org/10.1002/qj.3244
17. Eini MR, Javadi S, 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
18. Eini MR, Javadi S, Hashemy Shahdany M, Kisi O (2021) Comprehensive assessment and scenario simulation for the future of the hydrological processes in Dez river basin, Iran. Water Supply 21:1157–1176
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. US Geol Surv Data Ser 832:1–12
20. Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:1–21
21. Gebremedhin MA, Lubczynski MW, Maathuis BHP, Teka D (2021) Novel approach to integrate daily satellite rainfall with in-situ rainfall, Upper Tekeze Basin, Ethiopia. Atmos Res 248:105135
22. Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH (2017) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA ) model: case study in North Iran. Theor Appl Climatol. https://doi.org/10.1007/s00704-017-2244-0
23. Gruber A, De Lannoy G, Albergel C, Al-Yaari A, Brocca L, Calvet J-C, Colliander A, Cosh M, Crow W, Dorigo W (2020) Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sens Environ 244:111806
24. Gunathilake MB, Amaratunga YV, Perera A, Karunanayake C, Gunathilake AS, Rathnayake U (2020) Statistical evaluation and hydrologic simulation capacity of different satellite-based precipitation products (SbPPs) in the Upper Nan River Basin, Northern Thailand. J Hydrol Reg Stud 32:100743
25. Hall DK, Bayr KJ, Schöner W, Bindschadler RA, Chien JYL (2003) Consideration of the errors inherent in mapping historical glacier positions in Austria from the ground and space (1893–2001). Remote Sens Environ 86:566–577
26. Halwatura D, Najim MMM (2013) Application of the HEC-HMS model for runoff simulation in a tropical catchment. Environ Model Softw 46:155–162
27. Hosking JRM, Wallis JR, Wood EF (1985) Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics 27:251–261
28. Hurtado SI, Zaninelli PG, Agosta EA, Ricetti L (2021) Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina. Atmos Res 254:105482
29. Islam MA, Yu B, Cartwright N (2020) Assessment and comparison of five satellite precipitation products in Australia. J Hydrol 590:125474
30. Katiraie-Boroujerdy P-S, Rahnamay Naeini M, Akbari Asanjan A, Chavoshian A, Hsu K, Sorooshian S (2020) Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran. Remote Sens 12:2102
31. Khedri A, Kalantari N, Vadiati M (2020) Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer. Water Supply 20:909–921
32. Lazoglou G, Anagnostopoulou C, Skoulikaris C, Tolika K (2019) Bias correction of climate model’s precipitation using the copula method and its application in river basin simulation. Water 11:600
33. Lee JE, Heo J-H, Lee J, Kim NW (2017) Assessment of flood frequency alteration by dam construction via SWAT simulation. Water 9:264
34. Li W, Sun W, He X, Scaioni M, Yao D, Chen Y, Gao J, Li X, Cheng G (2019) Improving CHIRPS daily satellite-precipitation products using coarser ground observations. IEEE Geosci Remote Sens Lett 16:1678–1682
35. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34:1044–1055
36. Madsen H, Rosbjerg D, Damgard J, Hansen FS (2003) Data assimilation in the MIKE 11 flood forecasting system using Kalman filtering. Int Assoc Hydrol Sci Publ 281:75–81
37. Mao G, Vogl S, Laux P, Wagner S, Kunstmann H (2015) Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data. Hydrol Earth Syst Sci 19:1787–1806
38. Martins ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour Res 36:737–744
39. Massari C, Crow W, Brocca L (2017) An assessment of the performance of global rainfall estimates without ground-based observations. Hydrol Earth Syst Sci 21:4347–4361
40. McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the nash-sutcliffe efficiency index. J Hydrol Eng 11:597–602
41. Meresa H (2019) Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), artificial neural network (ANN) and hydrological model (HEC-HMS). Model Earth Syst Environ 5:257–273. https://doi.org/10.1007/s40808-018-0532-z
42. Michot V, Arvor D, Ronchail J, Corpetti T, Jegou N, Lucio PS, Dubreuil V (2019) Validation and reconstruction of rain gauge–based daily time series for the entire Amazon basin. Theor Appl Climatol 138:759–775
43. Milan SG, Roozbahani A, Azar NA, Javadi S (2021) Development of adaptive neuro fuzzy inference system—evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation. J Hydrol 598:126258
44. Milewski A, Elkadiri R, Durham M (2015) Assessment and comparison of TMPA satellite precipitation products in varying climatic and topographic regimes in Morocco. Remote Sens 7:5697–5717
45. Moazami S, Golian S, Kavianpour MR, Hong Y (2014) Uncertainty analysis of bias from satellite rainfall estimates using copula method. Atmos Res 137:145–166
46. Moazami S, Golian S, Hong Y, Sheng C, Kavianpour MR (2016) Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran. Hydrol Sci J 61:420–440. https://doi.org/10.1080/02626667.2014.987675
47. Mokhtari S, Sharafati A, Raziei T (2021) Validation of CHIRPS satellite-based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin Iran. Acta Geophys. https://doi.org/10.1007/s11600-021-00682-7
48. Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: Performance measures and evaluation criteria. Trans ASABE 58:1763–1785
49. Nelsen RB (2007) An introduction to copulas. Springer, Berlin
50. Oleyiblo JO, Li Z (2010) Application of HEC-HMS for flood forecasting in Misai and Wan’an catchments in China. Water Sci Eng 3:14–22
51. Olofintoye OO, Sule BF, Salami AW (2009) Best–fit Probability distribution model for peak daily rainfall of selected Cities in Nigeria. New York Sci J 2:1–12
52. Omondi CK, Rientjes THM, Booij MJ, Nelson AD (2021) Satellite rainfall bias assessment for crop growth simulation—a case study of maize growth in Kenya. Agric Water Manag 258:107204
53. Ouédraogo WAA, Raude JM, Gathenya JM (2018) Continuous modeling of the Mkurumudzi River catchment in Kenya using the HEC-HMS conceptual model: calibration, validation, model performance evaluation and sensitivity analysis. Hydrology. https://doi.org/10.3390/hydrology5030044
54. Patro S, Chatterjee C, Mohanty S, Singh R, Raghuwanshi NS (2009) Flood inundation modeling using MIKE FLOOD and remote sensing data. J Indian Soc Remote Sens 37:107–118
55. Prat OP, Nelson BR, Nickl E, Leeper RD (2021) Global evaluation of gridded satellite precipitation products from the noaa climate data record program. J Hydrometeorol 22:2291–2310
56. Pratama AW, Buono A, Hidayat R, Harsa H (2018) Estimating parameter of nonlinear bias correction method using NSGA-II in daily precipitation data. Telkomnika Telecommun Comput Electron Control 16:241–249. https://doi.org/10.12928/TELKOMNIKA.v16i1.6848
57. Ramly S, Tahir W (2016) Application of HEC-GeoHMS and HEC-HMS as rainfall–runoff model for flood simulation. In: ISFRAM 2015. Springer, pp 181–192
58. Ramly S, Tahir W, Abdullah J, Jani J, Ramli S, Asmat A (2020) Flood estimation for SMART control operation using integrated radar rainfall input with the HEC-HMS model. Water Resour Manag 34:3113–3127
59. Razaghian H, Shahedi K, Mohseni B (2018) Evaluation of SIMHYD rainfall-runoff model efficiency in climate change conditions. J Watershed Manag Res 9:216–225
60. Santhi C, Arnold JG, Williams JR, Hauck LM, Dugas WA (2001) Application of a watershed model to evaluate management effects on point and nonpoint source pollution. Trans ASAE 44:1559
61. Schweizer B (2007) Introduction to copulas. J Hydrol Eng 12(4):346
62. Sharafati A, Haji Seyed Asadollah SB, Motta D, Yaseen ZM (2020) Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrol Sci J 65(12):2022–2042
63. Sharma MA, Singh JB (2010) Use of probability distribution in rainfall analysis. N Y Sci J 3:40–49
64. Shen Z, Yong B, Gourley JJ, Qi W, Lu D, Liu J, Ren L, Hong Y, Zhang J (2020) Recent global performance of the climate hazards group infrared precipitation (CHIRP) with Stations (CHIRPS). J Hydrol 591:125284. https://doi.org/10.1016/j.jhydrol.2020.125284
65. Swenson S, Wahr J (2006) Post‐processing removal of correlated errors in GRACE data. Geophys Res Lett 33:L08402. https://doi.org/10.1029/2005GL025285
66. Tang G (2020) Characterization of the systematic and random errors in satellite precipitation using the multiplicative error model. IEEE Trans Geosci Remote Sens 59(7):5407–5416
67. Tassew BG, Belete MA, Miegel K (2019) Application of HEC-HMS model for flow simulation in the Lake Tana basin: the case of Gilgel Abay catchment, upper Blue Nile basin. Ethiop Hydrol 6:21
68. Tian Y, Peters‐Lidard CD, Eylander JB, Joyce RJ, Huffman GJ, Adler RF, Hsu K, Turk FJ, Garcia M, Zeng J (2009) Component analysis of errors in satellite‐based precipitation estimates. J Geophys Res Atmos 114:D24101. https://doi.org/10.1029/2009JD011949
69. Tiyasha T, Tung TM, Bhagat SK, Tan ML, Jawad AH, Mohtar WHMW, Yaseen ZM (2021) Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: development of hybrid tree-based artificial intelligence models. Mar Pollut Bull 170:112639
70. Tork H, Javadi S, Shahdany SMH (2021) A new framework of a multi-criteria decision making for agriculture water distribution system. J Clean Prod 306:127178
71. Wang N, Liu W, Sun F, Yao Z, Wang H, Liu W (2020) Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China. Atmos Res 234:104746
72. Worqlul AW, Yen H, Collick AS, Tilahun SA, Langan S, Steenhuis TS (2017) 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
73. Xiao S, Xia J, Zou L (2020) 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
74. Xiao S, Zou L, Xia J, Yang Z, Yao T (2021) Bias correction framework for satellite precipitation products using a rain/no rain discriminative model. Sci Total Environ 151679
75. Yaseen ZM, Ghareb MI, Ebtehaj I, Bonakdari H, Siddique R, Heddam S, Yusif AA, Deo R (2018) Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resour Manag 32:105–122
76. Yu D, Xie P, Dong X, Hu X, Liu J, Li Y, Peng T, Ma H, Wang K, Xu S (2018) Improvement of the SWAT model for event-based flood simulation on a sub-daily timescale. Hydrol Earth Syst Sci 22:5001–5019

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-1d73e9cf-80ab-45d5-ab5b-0443284af4bf