Regionalization of hydrological model parameters using gradient boosting machine

The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regiona...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Hydrology and earth system sciences Ročník 26; číslo 2; s. 505 - 524
Hlavní autoři: Song, Zhihong, Xia, Jun, Wang, Gangsheng, She, Dunxian, Hu, Chen, Hong, Si
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 31.01.2022
Copernicus Publications
Témata:
Analysis
Arid regions
Arid zones
Calibration
Climatic zones
Datasets
Elevation
Evapotranspiration
Evapotranspiration processes
Heterogeneity
Hydrologic models
Hydrology
Learning algorithms
Machine learning
Mathematical models
Modelling
Moisture content
Neural networks
Parameters
Performance evaluation
Properties
Regression analysis
Runoff
Saturated soils
Soil
Soil moisture
Soil moisture content
Stream discharge
Stream flow
Variables
Water content
Watersheds
China
ISSN:1607-7938, 1027-5606, 1607-7938
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
AbstractList The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25 ∘ ; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman-Monteith-Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25.sup." ; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
Audience Academic
Author Xia, Jun
Hu, Chen
Hong, Si
Song, Zhihong
Wang, Gangsheng
She, Dunxian
Author_xml – sequence: 1
  givenname: Zhihong
  surname: Song
  fullname: Song, Zhihong
– sequence: 2
  givenname: Jun
  surname: Xia
  fullname: Xia, Jun
– sequence: 3
  givenname: Gangsheng
  surname: Wang
  fullname: Wang, Gangsheng
– sequence: 4
  givenname: Dunxian
  surname: She
  fullname: She, Dunxian
– sequence: 5
  givenname: Chen
  surname: Hu
  fullname: Hu, Chen
– sequence: 6
  givenname: Si
  surname: Hong
  fullname: Hong, Si
BookMark eNp1UsFu1DAQjVCRaAt3jpE4cUixndixj1UFZaVKoAJna2KPs14l8WJ7Jdqvr3cXAYtAPng0eu_pzcy7qM6WsGBVvabkilPVvVtjSg0TDSe8YYSxZ9U5FaRvetXKsz_qF9VFShtCmJSCnVef73H0YYHJP0IuRR1cvX6wMUxh9Aameg4Wp3oLEWbMGFO9S34Z6zGC9bjkeggh5X1nBrP2C76snjuYEr76-V9W3z68_3rzsbn7dLu6ub5rTCdJbqTkPeXGmYHIgVvn2sGgQtK2yPpiX1DF6IAKLEdA7voOrRO2F2zoAalqL6vVUdcG2Oht9DPEBx3A60MjxFFDzN5MqOnAGOdqgM62nZSgDtMTalE6ZQgrWm-OWtsYvu8wZb0Ju1iWkjQTrJUdYy35jRqhiPrFhRzBzD4ZfS0UpYR1tC-oq3-gyrM4e1Nu5nzpnxDenhAKJuOPPMIuJb36cn-KJUesiSGliO7X4JTofQz0PgbFtC4x0PsYFIr4i2J8Ppy6-PLT_4lPIGO4XQ
CitedBy_id crossref_primary_10_3390_a17110502
crossref_primary_10_1016_j_jhydrol_2024_130948
crossref_primary_10_1007_s12145_024_01390_8
crossref_primary_10_1007_s00382_023_06893_6
crossref_primary_10_3390_su15043804
crossref_primary_10_1016_j_jhydrol_2024_131598
crossref_primary_10_1016_j_hydres_2025_06_001
crossref_primary_10_3389_frwa_2024_1332888
crossref_primary_10_1080_10256016_2025_2508811
crossref_primary_10_1016_j_jhydrol_2024_131472
crossref_primary_10_1016_j_jhydrol_2024_131175
crossref_primary_10_3390_w15142572
crossref_primary_10_1007_s10661_025_13870_5
crossref_primary_10_2166_nh_2025_184
crossref_primary_10_1080_02626667_2025_2496281
crossref_primary_10_3390_w17182722
crossref_primary_10_3390_rs17173122
crossref_primary_10_3389_frwa_2025_1472695
crossref_primary_10_1016_j_jhydrol_2023_129519
crossref_primary_10_1016_j_jhydrol_2022_128848
crossref_primary_10_1016_j_jhydrol_2024_131627
crossref_primary_10_1007_s11104_024_07079_7
crossref_primary_10_1016_j_jhydrol_2023_130057
crossref_primary_10_1016_j_ejrh_2024_102114
crossref_primary_10_3390_rs16203756
crossref_primary_10_1016_j_ejrh_2025_102373
crossref_primary_10_1016_j_jhydrol_2024_131061
crossref_primary_10_1016_j_jhydrol_2024_132130
crossref_primary_10_1080_02626667_2023_2248112
crossref_primary_10_2166_wcc_2023_467
crossref_primary_10_3390_su17072990
crossref_primary_10_1051_e3sconf_202455201074
crossref_primary_10_1016_j_jhydrol_2025_133181
crossref_primary_10_1080_02626667_2023_2273402
crossref_primary_10_3390_w16121729
Cites_doi 10.1016/j.jhydrol.2019.124357
10.5194/hess-11-983-2007
10.1109/IGARSS.2016.7729148
10.1029/2020WR028205
10.1016/0022-1694(70)90255-6
10.1016/0022-1694(94)90057-4
10.1175/2009JHM1061.1
10.1016/j.envsoft.2015.08.002
10.1029/2020WR028831
10.1016/j.jhydrol.2019.124390
10.5194/hess-16-3315-2012
10.1016/j.scitotenv.2018.06.233
10.1002/2015WR018247
10.1016/j.jhydrol.2009.03.003
10.5194/hess-18-67-2014
10.1504/IJHST.2013.057626
10.1016/j.jhydrol.2017.12.025
10.1007/s12205-016-0038-z
10.1029/RG012i004p00627
10.1029/2006WR005588
10.1017/CBO9780511535734
10.5194/hess-9-157-2005
10.1029/2019JD031485
10.1016/j.agrformet.2009.08.004
10.1016/j.jhydrol.2014.02.029
10.1029/91WR02985
10.1029/2007WR006562
10.1080/02626667.2013.809088
10.1002/2017JD027025
10.1029/2017WR021895
10.1029/2007WR006768
10.1023/A:1008191517801
10.5194/hess-24-2343-2020
10.1016/j.envsoft.2012.10.009
10.1016/j.jhydrol.2015.10.038
10.1029/2018WR024178
10.1214/aos/1013203451
10.1016/j.jhydrol.2010.06.025
10.1080/02626667.2013.803183
10.1029/2008WR007474
10.1002/9781119196037
10.1029/2011WR011501
10.5194/hess-23-851-2019
10.1029/2007WR006563
10.1016/S0022-1694(98)00163-2
10.1016/j.jhydrol.2020.125772
10.1002/9781119951001
10.1016/j.geoderma.2019.114061
10.1029/2012WR012220
10.1016/j.rse.2004.01.007
10.1029/2001WR000822
10.1016/j.jhydrol.2012.07.048
10.1016/j.jhydrol.2005.07.030
10.3389/fgene.2020.00207
10.1061/(ASCE)HE.1943-5584.0000338
10.1029/WR024i010p01651
10.1007/s00382-019-04874-2
10.5194/gmd-11-2429-2018
10.1029/2018WR022913
10.1016/0022-1694(95)02681-E
10.1061/(ASCE)1084-0699(1999)4:2(135)
10.1002/2017WR020401
10.1080/02626667.2018.1469756
10.1038/s41597-020-0369-y
10.1029/2010WR009797
10.1029/WR008i005p01272
10.1016/j.watres.2020.116221
10.2166/nh.2017.071
10.2136/sssaj1995.03615995005900040004x
10.1029/2000JD900719
10.1080/01431161.2017.1346400
10.1016/j.jhydrol.2009.08.003
10.1029/2018WR023254
10.1016/j.jhydrol.2018.12.037
10.1016/S0022-1694(01)00392-4
10.1029/2007WR006240
10.1214/aos/1016120463
10.1061/(ASCE)HE.1943-5584.0000690
10.1029/2019WR026304
10.1016/j.jhydrol.2018.12.071
10.1360/03yd0183
10.1029/2018WR023044
10.1088/1748-9326/ab4e55
10.1029/2019WR026236
10.1016/j.rse.2019.02.022
10.1002/2014WR015712
10.1016/j.rse.2006.07.026
10.1029/2009WR008887
10.1016/j.jhydrol.2005.07.017
10.1016/j.jhydrol.2007.11.017
10.1175/JHM-D-16-0284.1
10.1002/0470848944.hsa140
10.3389/fnbot.2013.00021
10.1017/CBO9781139235761.013
10.5194/hess-17-1783-2013
10.2747/1548-1603.45.1.1
10.5194/hess-17-3841-2013
10.1034/j.1600-0870.1996.t01-3-00009.x
10.1016/j.agrformet.2013.04.018
10.1002/hyp.10804
10.1016/j.jhydrol.2016.09.001
10.1016/j.jhydrol.2014.01.023
10.17221/26/2012-JFS
10.1029/2018WR022643
10.5194/hess-15-3539-2011
10.1016/j.jhydrol.2004.01.002
10.1029/98WR00496
10.1029/2018WR023325
10.5194/gmd-10-1903-2017
10.1029/2019WR026085
10.1016/j.advwatres.2010.04.009
10.1016/j.jhydrol.2017.04.036
10.1007/s10462-018-9625-3
10.5194/hess-22-1299-2018
10.2136/sssaj2003.8440
10.1007/s11269-014-0641-z
10.1007/s40333-016-0126-4
10.1016/j.jhydrol.2012.01.011
10.1002/(SICI)1099-1085(199606)10:6<877::AID-HYP377>3.0.CO;2-T
10.1016/j.scitotenv.2017.02.065
10.1016/j.jhydrol.2019.123981
10.1002/wat2.1487
10.1175/JHM-D-13-0170.1
ContentType Journal Article
Copyright COPYRIGHT 2022 Copernicus GmbH
2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: COPYRIGHT 2022 Copernicus GmbH
– notice: 2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
ISR
7QH
7TG
7UA
8FD
8FE
8FG
ABJCF
ABUWG
AEUYN
AFKRA
ATCPS
AZQEC
BENPR
BFMQW
BGLVJ
BHPHI
BKSAR
C1K
CCPQU
DWQXO
F1W
FR3
GNUQQ
H96
HCIFZ
KL.
KR7
L.G
L6V
M7S
PATMY
PCBAR
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
PYCSY
DOA
DOI 10.5194/hess-26-505-2022
DatabaseName CrossRef
Gale In Context: Science
Aqualine
Meteorological & Geoastrophysical Abstracts
Water Resources Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest One Sustainability (subscription)
ProQuest Central UK/Ireland
ProQuest : Agricultural & Environmental Science Collection [unlimited simultaneous users]
ProQuest Central Essentials
ProQuest Central
Continental Europe Database
Technology collection
Natural Science Collection
ProQuest Earth, Atmospheric & Aquatic Science Collection
Environmental Sciences and Pollution Management
ProQuest One Community College
ProQuest Central
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
ProQuest Central Student
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
SciTech Premium Collection
Meteorological & Geoastrophysical Abstracts - Academic
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
ProQuest Engineering Collection
Engineering Database
Environmental Science Database
Earth, Atmospheric & Aquatic Science Database
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
Engineering Collection
Environmental Science Collection
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
Aquatic Science & Fisheries Abstracts (ASFA) Professional
ProQuest Central Student
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
Water Resources Abstracts
Environmental Sciences and Pollution Management
Earth, Atmospheric & Aquatic Science Collection
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
ProQuest Engineering Collection
Meteorological & Geoastrophysical Abstracts
Natural Science Collection
ProQuest Central Korea
Agricultural & Environmental Science Collection
ProQuest Central (New)
Engineering Collection
Civil Engineering Abstracts
Engineering Database
ProQuest One Academic Eastern Edition
Earth, Atmospheric & Aquatic Science Database
ProQuest Technology Collection
Continental Europe Database
ProQuest SciTech Collection
Aqualine
Environmental Science Collection
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ProQuest One Academic UKI Edition
ASFA: Aquatic Sciences and Fisheries Abstracts
Materials Science & Engineering Collection
Environmental Science Database
Engineering Research Database
ProQuest One Academic
Meteorological & Geoastrophysical Abstracts - Academic
ProQuest One Academic (New)
DatabaseTitleList CrossRef


Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Geography
EISSN 1607-7938
EndPage 524
ExternalDocumentID oai_doaj_org_article_1b22559ba4d3488a90028801de8f9c02
A691102417
10_5194_hess_26_505_2022
GeographicLocations China
GeographicLocations_xml – name: China
GroupedDBID 29I
2WC
5GY
5VS
7XC
8CJ
8FE
8FG
8FH
8R4
8R5
AAFWJ
AAYXX
ABJCF
ABUWG
ACGFO
ACIWK
ADBBV
AENEX
AEUYN
AFFHD
AFKRA
AFPKN
AFRAH
AHGZY
AIAGR
ALMA_UNASSIGNED_HOLDINGS
ATCPS
BANNL
BCNDV
BENPR
BFMQW
BGLVJ
BHPHI
BKSAR
BPHCQ
CCPQU
CITATION
D1J
D1K
E3Z
EBS
ECGQY
EDH
EJD
GROUPED_DOAJ
GX1
H13
HCIFZ
IAO
IEA
IEP
IGS
ISR
ITC
K6-
KQ8
L6V
L8X
LK5
M7R
M7S
OK1
OVT
P2P
PATMY
PCBAR
PHGZM
PHGZT
PIMPY
PQGLB
PQQKQ
PROAC
PTHSS
PYCSY
Q2X
RKB
RNS
TR2
XSB
~02
~KM
7QH
7TG
7UA
8FD
AZQEC
C1K
DWQXO
F1W
FR3
GNUQQ
H96
KL.
KR7
L.G
PKEHL
PQEST
PQUKI
ID FETCH-LOGICAL-c480t-885715cfcb08b5dff3bce9e033e2702261921be9ad5eae5f74edf6d762b7ae193
IEDL.DBID BENPR
ISICitedReferencesCount 41
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000751424000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1607-7938
1027-5606
IngestDate Fri Oct 03 12:49:19 EDT 2025
Fri Aug 22 20:07:08 EDT 2025
Sat Nov 29 13:04:07 EST 2025
Sat Nov 29 09:57:46 EST 2025
Wed Nov 26 09:59:34 EST 2025
Tue Nov 18 21:09:30 EST 2025
Sat Nov 29 05:01:38 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Language English
License https://creativecommons.org/licenses/by/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c480t-885715cfcb08b5dff3bce9e033e2702261921be9ad5eae5f74edf6d762b7ae193
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/2623842230?pq-origsite=%requestingapplication%
PQID 2623842230
PQPubID 105724
PageCount 20
ParticipantIDs doaj_primary_oai_doaj_org_article_1b22559ba4d3488a90028801de8f9c02
proquest_journals_2623842230
gale_infotracmisc_A691102417
gale_infotracacademiconefile_A691102417
gale_incontextgauss_ISR_A691102417
crossref_primary_10_5194_hess_26_505_2022
crossref_citationtrail_10_5194_hess_26_505_2022
PublicationCentury 2000
PublicationDate 2022-01-31
PublicationDateYYYYMMDD 2022-01-31
PublicationDate_xml – month: 01
  year: 2022
  text: 2022-01-31
  day: 31
PublicationDecade 2020
PublicationPlace Katlenburg-Lindau
PublicationPlace_xml – name: Katlenburg-Lindau
PublicationTitle Hydrology and earth system sciences
PublicationYear 2022
Publisher Copernicus GmbH
Copernicus Publications
Publisher_xml – name: Copernicus GmbH
– name: Copernicus Publications
References ref57
ref56
ref59
ref58
ref53
ref52
ref55
ref54
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref100
ref101
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref39
ref38
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref13
ref12
ref15
ref128
ref14
ref129
ref97
ref126
ref96
ref127
ref11
ref99
ref124
ref10
ref98
ref125
ref17
ref16
ref19
ref18
ref93
ref92
ref95
ref131
ref94
ref130
ref91
ref90
ref89
ref86
ref85
ref88
ref87
ref82
ref81
ref84
ref83
ref80
ref79
ref108
ref78
ref109
ref106
ref107
ref75
ref104
ref74
ref105
ref77
ref102
ref76
ref103
ref2
ref1
ref71
ref111
ref70
ref112
ref73
ref72
ref110
ref68
ref119
ref67
ref117
ref69
ref118
ref64
ref115
ref63
ref116
ref66
ref113
ref65
ref114
ref60
ref122
ref123
ref62
ref120
ref61
ref121
References_xml – ident: ref62
– ident: ref114
  doi: 10.1016/j.jhydrol.2019.124357
– ident: ref17
  doi: 10.5194/hess-11-983-2007
– ident: ref100
  doi: 10.1109/IGARSS.2016.7729148
– ident: ref42
  doi: 10.1029/2020WR028205
– ident: ref67
  doi: 10.1016/0022-1694(70)90255-6
– ident: ref20
  doi: 10.1016/0022-1694(94)90057-4
– ident: ref123
  doi: 10.1175/2009JHM1061.1
– ident: ref57
  doi: 10.1016/j.envsoft.2015.08.002
– ident: ref22
  doi: 10.1029/2020WR028831
– ident: ref119
  doi: 10.1016/j.jhydrol.2019.124390
– ident: ref87
  doi: 10.5194/hess-16-3315-2012
– ident: ref3
  doi: 10.1016/j.scitotenv.2018.06.233
– ident: ref6
  doi: 10.1002/2015WR018247
– ident: ref55
  doi: 10.1016/j.jhydrol.2009.03.003
– ident: ref72
  doi: 10.5194/hess-18-67-2014
– ident: ref29
  doi: 10.1504/IJHST.2013.057626
– ident: ref4
  doi: 10.1016/j.jhydrol.2017.12.025
– ident: ref101
  doi: 10.1007/s12205-016-0038-z
– ident: ref26
  doi: 10.1029/RG012i004p00627
– ident: ref46
  doi: 10.1029/2006WR005588
– ident: ref103
  doi: 10.1017/CBO9780511535734
– ident: ref76
  doi: 10.5194/hess-9-157-2005
– ident: ref7
  doi: 10.1029/2019JD031485
– ident: ref111
  doi: 10.1016/j.agrformet.2009.08.004
– ident: ref53
  doi: 10.1016/j.jhydrol.2014.02.029
– ident: ref127
– ident: ref19
  doi: 10.1029/91WR02985
– ident: ref54
  doi: 10.1029/2007WR006562
– ident: ref65
  doi: 10.1080/02626667.2013.809088
– ident: ref124
  doi: 10.1002/2017JD027025
– ident: ref70
  doi: 10.1029/2017WR021895
– ident: ref99
  doi: 10.1029/2007WR006768
– ident: ref108
  doi: 10.1023/A:1008191517801
– ident: ref56
  doi: 10.5194/hess-24-2343-2020
– ident: ref120
  doi: 10.1016/j.envsoft.2012.10.009
– ident: ref116
  doi: 10.1016/j.jhydrol.2015.10.038
– ident: ref115
  doi: 10.1029/2018WR024178
– ident: ref28
  doi: 10.1214/aos/1013203451
– ident: ref18
  doi: 10.1016/j.jhydrol.2010.06.025
– ident: ref39
  doi: 10.1080/02626667.2013.803183
– ident: ref95
  doi: 10.1029/2008WR007474
– ident: ref38
  doi: 10.1002/9781119196037
– ident: ref89
  doi: 10.1029/2011WR011501
– ident: ref37
  doi: 10.5194/hess-23-851-2019
– ident: ref122
  doi: 10.1029/2007WR006563
– ident: ref86
  doi: 10.1016/S0022-1694(98)00163-2
– ident: ref107
  doi: 10.1016/j.jhydrol.2020.125772
– ident: ref8
  doi: 10.1002/9781119951001
– ident: ref58
  doi: 10.1016/j.geoderma.2019.114061
– ident: ref59
  doi: 10.1029/2012WR012220
– ident: ref52
  doi: 10.1016/j.rse.2004.01.007
– ident: ref66
  doi: 10.1029/2001WR000822
– ident: ref5
  doi: 10.1016/j.jhydrol.2012.07.048
– ident: ref36
  doi: 10.1016/j.jhydrol.2005.07.030
– ident: ref21
– ident: ref10
  doi: 10.3389/fgene.2020.00207
– ident: ref84
  doi: 10.1061/(ASCE)HE.1943-5584.0000338
– ident: ref118
  doi: 10.1029/WR024i010p01651
– ident: ref64
  doi: 10.1007/s00382-019-04874-2
– ident: ref92
  doi: 10.5194/gmd-11-2429-2018
– ident: ref48
  doi: 10.1029/2018WR022913
– ident: ref97
  doi: 10.1016/0022-1694(95)02681-E
– ident: ref32
  doi: 10.1061/(ASCE)1084-0699(1999)4:2(135)
– ident: ref63
  doi: 10.1002/2017WR020401
– ident: ref128
  doi: 10.1080/02626667.2018.1469756
– ident: ref34
  doi: 10.1038/s41597-020-0369-y
– ident: ref96
  doi: 10.1029/2010WR009797
– ident: ref25
  doi: 10.1029/WR008i005p01272
– ident: ref106
  doi: 10.1016/j.watres.2020.116221
– ident: ref113
  doi: 10.2166/nh.2017.071
– ident: ref40
  doi: 10.2136/sssaj1995.03615995005900040004x
– ident: ref94
  doi: 10.1029/2000JD900719
– ident: ref112
  doi: 10.1080/01431161.2017.1346400
– ident: ref33
  doi: 10.1016/j.jhydrol.2009.08.003
– ident: ref80
  doi: 10.1029/2018WR023254
– ident: ref81
  doi: 10.1016/j.jhydrol.2018.12.037
– ident: ref98
  doi: 10.1016/S0022-1694(01)00392-4
– ident: ref73
  doi: 10.1029/2007WR006240
– ident: ref27
  doi: 10.1214/aos/1016120463
– ident: ref82
  doi: 10.1061/(ASCE)HE.1943-5584.0000690
– ident: ref104
  doi: 10.1029/2019WR026304
– ident: ref75
  doi: 10.1016/j.jhydrol.2018.12.071
– ident: ref105
  doi: 10.1360/03yd0183
– ident: ref109
  doi: 10.1029/2018WR023044
– ident: ref44
  doi: 10.1088/1748-9326/ab4e55
– ident: ref126
  doi: 10.1029/2019WR026236
– ident: ref102
  doi: 10.1016/j.rse.2019.02.022
– ident: ref24
  doi: 10.1002/2014WR015712
– ident: ref23
  doi: 10.1016/j.rse.2006.07.026
– ident: ref74
  doi: 10.1029/2009WR008887
– ident: ref117
  doi: 10.1016/j.jhydrol.2005.07.017
– ident: ref45
  doi: 10.1016/j.jhydrol.2007.11.017
– ident: ref69
  doi: 10.1175/JHM-D-16-0284.1
– ident: ref9
  doi: 10.1002/0470848944.hsa140
– ident: ref68
  doi: 10.3389/fnbot.2013.00021
– ident: ref77
  doi: 10.1017/CBO9781139235761.013
– ident: ref78
  doi: 10.5194/hess-17-1783-2013
– ident: ref83
  doi: 10.2747/1548-1603.45.1.1
– ident: ref30
  doi: 10.5194/hess-17-3841-2013
– ident: ref60
  doi: 10.1034/j.1600-0870.1996.t01-3-00009.x
– ident: ref129
  doi: 10.1016/j.agrformet.2013.04.018
– ident: ref130
  doi: 10.1002/hyp.10804
– ident: ref41
  doi: 10.1016/j.jhydrol.2016.09.001
– ident: ref51
– ident: ref91
  doi: 10.1016/j.jhydrol.2014.01.023
– ident: ref2
  doi: 10.17221/26/2012-JFS
– ident: ref88
  doi: 10.1029/2018WR022643
– ident: ref35
  doi: 10.5194/hess-15-3539-2011
– ident: ref93
– ident: ref110
– ident: ref43
  doi: 10.1016/j.jhydrol.2004.01.002
– ident: ref50
  doi: 10.1029/98WR00496
– ident: ref90
– ident: ref125
  doi: 10.1029/2018WR023325
– ident: ref61
  doi: 10.5194/gmd-10-1903-2017
– ident: ref15
  doi: 10.1029/2019WR026085
– ident: ref12
  doi: 10.1016/j.advwatres.2010.04.009
– ident: ref131
  doi: 10.1016/j.jhydrol.2017.04.036
– ident: ref85
  doi: 10.1007/s10462-018-9625-3
– ident: ref16
  doi: 10.5194/hess-22-1299-2018
– ident: ref13
  doi: 10.2136/sssaj2003.8440
– ident: ref11
  doi: 10.1007/s11269-014-0641-z
– ident: ref71
  doi: 10.1007/s40333-016-0126-4
– ident: ref47
  doi: 10.1016/j.jhydrol.2012.01.011
– ident: ref79
  doi: 10.1002/(SICI)1099-1085(199606)10:6<877::AID-HYP377>3.0.CO;2-T
– ident: ref49
  doi: 10.1016/j.scitotenv.2017.02.065
– ident: ref1
  doi: 10.1016/j.jhydrol.2019.123981
– ident: ref31
  doi: 10.1002/wat2.1487
– ident: ref14
– ident: ref121
  doi: 10.1175/JHM-D-13-0170.1
SSID ssj0028862
Score 2.5388603
Snippet The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR)...
The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR)...
SourceID doaj
proquest
gale
crossref
SourceType Open Website
Aggregation Database
Enrichment Source
Index Database
StartPage 505
SubjectTerms Analysis
Arid regions
Arid zones
Calibration
Climatic zones
Datasets
Elevation
Evapotranspiration
Evapotranspiration processes
Heterogeneity
Hydrologic models
Hydrology
Learning algorithms
Machine learning
Mathematical models
Modelling
Moisture content
Neural networks
Parameters
Performance evaluation
Properties
Regression analysis
Runoff
Saturated soils
Soil
Soil moisture
Soil moisture content
Stream discharge
Stream flow
Variables
Water content
Watersheds
SummonAdditionalLinks – databaseName: Copernicus Publications
  dbid: RKB
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fS1sxFA5DhPmibjqsP0YYg-HDpb0_k_uoomwwpHQTfAtJbtIOtJW2Cv73fic3LfZB96Cv7Umb5EvO-XLvyXcY-57ZvLbaV0mtG5nAS1psqdICkBT2zlbehKolv8Xlpby-rvvPSn1RTlgrD9xOXDc1GbFeo4smx2LTNZ0S4FYbJ31tg4wkliHVLhhQDbd41JKyat9zZiJBTI8vKMFWiu4IHiTJqgShHz3KspWAFHT7X_LOIeRcbL2hs9tsM_JMftI2-cQ-uPFn9jGWPB897rD-wA3DY8B4EZNPPB89NtOFM-ShRg4nafBbSpmZcUqRH3K0pySxOQc9n1HONL8N-Zhul11dnP89-5nE8gqJLWRvnkhZCsDirelJUzbe58a62vXy3NEltXC0So0DiKXTrvSicI2vGnhPI7QD8fvC1saTsdtjPC0yU-W2boTwhUHI0-Q28cMC9MRa0WHdxRwrG7XHqQTGjcIZhFBRhIrKKgVUFKHSYcfLFnet7sYrtqeExNKOFLPDB4BGRWjU_6DpsG8EuiJNjDEl3Qz1Pf7m15-BOqkQEcBlUozjRzTyE_Tf6niHAbNAMlorlocrlti0dvXrxdpS0WnQkMCfCvC13v57jOiAbdDs0POiPD1ka_PpvTti6_Zh_m82_Rr2yxOHSxUh
  priority: 102
  providerName: Copernicus Gesellschaft
– databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NaxsxEBUhFJJLaZuUOHGLCIGSw2Lvp7RHt9Q0EIxxWshNSFrJLjR28EfB_75vtHKoD00vuXpn19LTaObN7miGsavM5rXVvkpq3cgEVtJiS5UWC5JC3tnKm9C15FaMRvL-vh7_1eqLcsLa8sAtcL3UZMR6jS6aHMqma4oSYFYbJ31t2zKSYD27YCqGWlJW7XfOTCTw6fEDJdhK0ZvBgiRZlcD1Y0RZtueQQt3-f1nn4HKGb9jryBX5oB3jW3bg5u_YUWxbPtuesPHETcOrvHiYki88n22b5c6g8dDnhlN57wdKe1lxSnOfctxPiV5rDoq9orxn_hByKt0p-zH8-v3LtyS2SEhsIfvrRMpSAFpvTV-asvE-N9bVrp_njg6ahfAoNQ4LUTrtSi8K1_iqgQU0QjuQt_fscL6YuzPG0yIzVW7rRghfGLgtTaYPDxagGNaKDuvtcFI21g-nNha_FOIIQlYRsiqrFJBVhGyHXT_d8djWznhG9jNB_yRHVa_DD9AFFXVB_U8XOuySFk5RXYs5Jc5M9QZ_c3M3UYMKVh18JMU8PkUhv8D4rY7nEIAClcLak-zuSWLj2f3LO_1QcePTlMCBCnCu_vlLzOiCHRM69M4nT7vscL3cuA_slf29_rlafgw6_weIPQN3
  priority: 102
  providerName: Directory of Open Access Journals
Title Regionalization of hydrological model parameters using gradient boosting machine
URI https://www.proquest.com/docview/2623842230
https://doaj.org/article/1b22559ba4d3488a90028801de8f9c02
Volume 26
WOSCitedRecordID wos000751424000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAGF
  databaseName: Copernicus Publications
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: RKB
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: http://publications.copernicus.org/open-access_journals/open_access_journals_a_z.html
  providerName: Copernicus Gesellschaft
– providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: DOA
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVPQU
  databaseName: Continental Europe Database
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: BFMQW
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/conteurope
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Earth, Atmospheric & Aquatic Science Database
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: PCBAR
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/eaasdb
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Engineering Database
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: M7S
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: http://search.proquest.com
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Environmental Science Database
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: PATMY
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/environmentalscience
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: BENPR
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 1607-7938
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0028862
  issn: 1607-7938
  databaseCode: PIMPY
  dateStart: 20090601
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LaxsxEBaNU2guTZ80bWpEKZQeFu9qH9Keih0SGtqYxUkhPQk97ULjTWwnkH-fGVlr8KG59LSwmtVKGumb0Wg0Q8hnZvLaKF8ltbIiAZQ0sKRKAwzJgN6ZyuuQteQnH4_F5WXdRIPbMrpVdpgYgNq2Bm3kAwZyWhQgzNJv1zcJZo3C09WYQmOH7GKksqJHdkfH42ay2XIJUa3POxlPQLbHg0rQWorBDJAkYVUCKgC0jLEtwRTi9_8LpYPoOdn_30a_IM-j0kmH61nykjxx81fkWcx_Prt_TZqJmwabYLyVSVtPZ_d20SEjDQlzKMYJv0L_mSVFf_kphe_RY2xFQVdfogM1vQrOme4N-XVyfHH0PYm5FhJTiHSVCFFy4JE3OhW6tN7n2rjapXnu8MZa2Gdl2gFHS6dc6XnhrK8sQKnmyoEW-Jb05u3cvSM0K5iuclNbzn2hQf4pxFComIOuYgw_IINuoKWJgcgxH8ZfCRsSZI1E1khWSWCNRNYckK-bL67XQTgeoR0h7zZ0GD47vGgXUxlXo8w0w62UVoXNAcFUHaZKmlknfG1SqOQTcl5igIw5euBM1S385vR8IocViAdQbDLox5dI5Ftov1HxQgOMAsbU2qI83KKEFWy2i7vZIyOCYJe6qfP-8eIPZA_7jWahPDskvdXi1n0kT83d6s9y0Y8Loh9sDX30bD2Hd83w4uw3Pk_PGnjuTH6MHgA3SRoe
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxELZKi1QuvBEtBSzEQxys7Hp3be-BQwtEjZpGpRTRm7v22glSmy1JCsqf4jcy43gj5UBvPXDdnX3Y_vzNjD2eIeQ1t1lpKy9YWdWKAUtamFKFhQFJQd5Z4U2oWtKXg4E6PS2P1sif9iwMhlW2nBiIum4srpF3OOhplYMyS2IE5YGb_wb_bPqh9wkG8w3n3c8nH_dZLCHAbK6SGVOqkPBpb02iTFF7nxnrSpdkmcODWMF9SI2DHy1c5Qovc1d7UQNDGFm5FDMt8beXPxlWqcLd3Fiy4xbZUKLMYF5t7HUPv3xfunhKicX-KpcMbIm4MQpWUt4ZAXMxLhiYHNATnK8owlAv4F9aIai67r3_rZPuk7vRqKa7i1nwgKy58UOyGeu7j-aPyNGxG4Y1z3jqlDaejub1pGV-GgoCUcyDfoHxQVOK5wGGFJ7HiLgZBV9kigHi9CIEn7rH5NuNtPEJWR83Y_eU0DTnRmS2rKX0uQH9XqGOgBdLsMWslVuk0w6stjHROtb7ONfgcCEUNEJBc6EBChqhsEXeL5-4XCQZuUZ2D7GylMP04OFCMxnqyDY6NRxdRVPldQYMXZUBmklaO-VLm8BLXiHSNCYAGWOE0bC6gs_0vh7rXQHqDwy3FNrxLgr5Bv7fVvHABvQC5gxbkdxZkQSGsqu3W7TqyJDYpBaq29fffkk2908O-7rfGxw8I3ewD3AJLEt3yPpscuWek9v21-zHdPIiTkZKzm4a7X8B3Wpy5g
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxELaqFAEX3ohCAQuBEIdVdr0Pew8IpS0RUasoCiD1ZvxMkNpsm6Sg_DV-HTOON1IO9NYD193Zh-3P38zY4xlC3jKT10b5KqmVFQmwpIEpVRoYkAzknam8DlVLTvhwKE5P69EO-dOehcGwypYTA1HbxuAaeZeBnhYFKLO062NYxOio_-niMsEKUrjT2pbTWEPk2K1-g_u2-Dg4grF-x1j_87fDL0msMJCYQqTLRIiSw595o1OhS-t9ro2rXZrnDs9pBe8i0w7aUTrlSs8LZ31lgUA0Vy7DRExA_7uiAuEO2R0dHvTGG3dPiGq918p4AnZF3CQFi6noToHFElYlYH5ArzC2pRRD7YB_aYig9vr3_-cOe0DuRWOb9taz4yHZcbNH5E6s-z5dPSajsZuEtdB4GpU2nk5Xdt5qBBoKBVHMj36OcUMLiucEJhSex0i5JQUfZYGB4_Q8BKW6J-T7jTToKenMmpl7RmhWMF3lprac-0KD3leoO-DFHGw0Y_ge6baDLE1MwI51QM4kOGIIC4mwkKySAAuJsNgjHzZPXKyTj1wje4C42chh2vBwoZlPZGQhmWmGLqRWhc2BuVUdYJpm1glfmxRe8gZRJzExyAwRM1FX8JnB17HsVaAWwaDLoB3vo5Bv4P-Nigc5oBcwl9iW5P6WJDCX2b7dIldG5sQmtbB9fv3t1-Q24FmeDIbHL8hd7AJcGcuzfdJZzq_cS3LL_Fr-XMxfxXlJyY-bRvZfUVt7dA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Regionalization+of+hydrological+model+parameters+using+gradient+boosting+machine&rft.jtitle=Hydrology+and+earth+system+sciences&rft.au=Song%2C+Zhihong&rft.au=Xia%2C+Jun&rft.au=Wang%2C+Gangsheng&rft.au=She%2C+Dunxian&rft.date=2022-01-31&rft.pub=Copernicus+GmbH&rft.issn=1027-5606&rft.volume=26&rft.issue=2&rft.spage=505&rft_id=info:doi/10.5194%2Fhess-26-505-2022&rft.externalDBID=ISR&rft.externalDocID=A691102417
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1607-7938&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1607-7938&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1607-7938&client=summon