On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC

Sahoo, S; Dey, S; Dhar, A; Debsarkar, A; Pradhan, B

On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC

Sahoo, S Dey, S Dhar, A Debsarkar, A Pradhan, B

Permalink

Publication Type:: Journal Article
Citation:: Ecological Indicators, 2019, 106
Issue Date:: 2019-11-01

Closed Access

	Filename	Description	Size
	ECOLIND-11014.pdf	Submitted Version	4.78 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Sahoo, S	en_US
dc.contributor.author	Dey, S	en_US
dc.contributor.author	Dhar, A	en_US
dc.contributor.author	Debsarkar, A	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.accessioned	2020-04-10T06:34:09Z
dc.date.available	2020-04-10T06:34:09Z
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	Ecological Indicators, 2019, 106	en_US
dc.identifier.issn	1470-160X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139946
dc.description.abstract	© 2019 Elsevier Ltd Assessing the impact of climate variability is important for water resources planning and management. In the present study, climate model data were utilized in conjunction with the hydrological model to analyze the effect of climate change on projected streamflow and groundwater recharge values for the Dwarakeswar-Gandherswari basin, India. Regional Climate Model (RCM) data [Representative Concentration Pathway (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5)] were considered for future climate change scenarios. Five bias correction methods [linear scaling (LS), local intensity scaling (LOCI), power transformation (PWTR), distribution mapping (DM) and variance scaling (VARI)] were applied for RCM based precipitation and temperature data. Projected Land Use and Land Cover (LULC) values were obtained from Dyna-CLUE model. Discharge data (1990–2016) was utilized for model calibration and validation purpose. Total twelve scenarios (4 RCPs per year for the years 2030, 2050 and 2080) were considered. The results showed increasing trend in simulated discharge for the months June to September and reverse trend for the months October to December. The results also showed that groundwater recharge increased for the maximum number of sub-watersheds for the interval 2016–2030 compared to 2016–2050 and 2016–2080 under all RCPs. Uncertainties in streamflow were quantified in terms of exceedance probability and recurrence interval. ALPHA_BF was the most sensitive parameter for the river basin. However, gross increase in groundwater recharge was observed for all the scenarios. These results can be effectively utilized for irrigation planning purpose.	en_US
dc.relation.ispartof	Ecological Indicators	en_US
dc.relation.isbasedon	10.1016/j.ecolind.2019.105440	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Ecology	en_US
dc.title	On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC	en_US
dc.type	Journal Article
utslib.citation.volume	106	en_US
utslib.for	03 Chemical Sciences	en_US
utslib.for	05 Environmental Sciences	en_US
utslib.for	06 Biological Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	106	en_US

Abstract:

© 2019 Elsevier Ltd Assessing the impact of climate variability is important for water resources planning and management. In the present study, climate model data were utilized in conjunction with the hydrological model to analyze the effect of climate change on projected streamflow and groundwater recharge values for the Dwarakeswar-Gandherswari basin, India. Regional Climate Model (RCM) data [Representative Concentration Pathway (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5)] were considered for future climate change scenarios. Five bias correction methods [linear scaling (LS), local intensity scaling (LOCI), power transformation (PWTR), distribution mapping (DM) and variance scaling (VARI)] were applied for RCM based precipitation and temperature data. Projected Land Use and Land Cover (LULC) values were obtained from Dyna-CLUE model. Discharge data (1990–2016) was utilized for model calibration and validation purpose. Total twelve scenarios (4 RCPs per year for the years 2030, 2050 and 2080) were considered. The results showed increasing trend in simulated discharge for the months June to September and reverse trend for the months October to December. The results also showed that groundwater recharge increased for the maximum number of sub-watersheds for the interval 2016–2030 compared to 2016–2050 and 2016–2080 under all RCPs. Uncertainties in streamflow were quantified in terms of exceedance probability and recurrence interval. ALPHA_BF was the most sensitive parameter for the river basin. However, gross increase in groundwater recharge was observed for all the scenarios. These results can be effectively utilized for irrigation planning purpose.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/139946