CoChat
Sign Up
Cited by 153Hydrology and Watershed Management StudiesHydrology and Drought AnalysisSoil Moisture and Remote Sensing

The DOI matches its Crossref record.

Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios

Chanapathi, Tirupathi, Thatikonda, Shashidhar·2020·Science of The Total Environment
DOI 10.1016/j.scitotenv.2020.137736
Open this paper in CoChat — free account

Abstract

The climate and Land Use/Land Cover (LULC) changes evince the considerable impact on water balance components by altering the hydrological processes. So, the present work focuses on the evaluation of the combined impact of both the climate and LULC changes along with and without water storage structures on water balance components of the Krishna river basin, India under present and future scenarios with the help of Soil Water and Assessment Tool (SWAT). Sequential Uncertainty Fitting algorithm (SUFI-2) was used for the model calibration and validation, which were carried out at the Vijayawada gauge station. The coefficient of determination (R 2 ) and Nash-Sutcliffe efficiency (NSE) values obtained during the calibration period were 0.63 and 0.61, respectively, whereas, in validation, these values were found to be 0.61 and 0.56, indicates satisfactory results. The results showed that the model simulations and performance were significantly influenced by the presence of water storage structures, whereas the LULC changes were effective at the sub-watershed level. Future LULC maps of 2025, 2055, and 2085 were simulated from the Cellular Automata (CA) Markov Chain model, and they were used along with future climate projections to investigate its impact on water balance components. The climate model projects an increase of water balance components specifically, surface runoff, streamflow, and water yield, except for evapotranspiration in the future. Whereas, the future LULC changes…

Overview

Integrity signals

Registry

Crossref · 10.1016/j.scitotenv.2020.137736

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 62

  1. Abbaspour, 2015, A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model, J. Hydrol., 524, 733, 10.1016/j.jhydrol.2015.03.027 · DOI · Paper page
  2. Adhikari, 2012, Simulating forest cover changes of Bannerghatta National Park based on a CA-Markov model: a remote sensing approach, Remote Sen - Basel, 4, 3215, 10.3390/rs4103215 · DOI
  3. Algeet-Abarquero, 2015, Implications of land use change on runoff generation at the plot scale in the humid tropics of Costa Rica, Catena, 135, 263, 10.1016/j.catena.2015.08.004 · DOI
  4. Ali, 2017, Modeling urban land cover growth dynamics based on land change modeler (LCM) using remote sensing: a case study of Gurgaon, India, Advances in Computational Sciences and Technology, 10, 2947
  5. Anand, 2018, Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model, Sci. Total Environ., 644, 503, 10.1016/j.scitotenv.2018.07.017 · DOI
  6. Arnold, 1998, Large area hydrologic modeling and assessment part I: model development 1, JAWRA J Am Water Resour As, 34, 73, 10.1111/j.1752-1688.1998.tb05961.x · DOI · Paper page
  7. Babar, 2015, Streamflow response to land use – land cover change over the Nethravathi River Basin, India, J. Hydrol. Eng., 20, 10.1061/(ASCE)HE.1943-5584.0001177 · DOI
  8. Chanapathi, 2018, Analysis of rainfall extremes and water yield of Krishna river basin under future climate scenarios, J Hydrol Reg Stud, 19, 287, 10.1016/j.ejrh.2018.10.004 · DOI
  9. Clerici, 2019, Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes, Sci. Total Environ., 10.1016/j.scitotenv.2019.06.275 · DOI
  10. Crooks, 2001, Assessment of land use change in the Thames catchment and its effect on the flood regime of the river, Phy Chem Earth, Pt B, 26, 583, 10.1016/S1464-1909(01)00053-3 · DOI
  11. Das, 2003, Chemical weathering in the Krishna Basin and Western Ghats of the Deccan Traps, India: rates of basalt weathering and their controls, Geochim Cosmochim Ac, 69, 2067, 10.1016/j.gca.2004.10.014 · DOI
  12. Deng, 2015, Impacts of land use and land cover changes on surface energy and water balance in the Heihe River Basin of China, 2000–2010, Phy and Chem Earth, Pt A/B/C, 79, 2, 10.1016/j.pce.2015.01.002 · DOI
  13. Dong, 2014, Relative effects of human activities and climate change on the river runoff in an arid basin in northwest China, Hydrol. Process., 28, 4854, 10.1002/hyp.9982 · DOI
  14. Gashaw, 2018, Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia, Sci. Total Environ., 619, 1394, 10.1016/j.scitotenv.2017.11.191 · DOI
  15. Genxu, 2014, Impact of land-use change on hydrological processes in the Maying River basin, China, Sci China Ser D, 49, 1098
  16. Ghimire, 2014, Negative trade-off between changes in vegetation water use and infiltration recovery after reforesting degraded pasture land in the Nepalese Lesser Himalaya, Hydrol Earth Syst Sc, 18, 4933, 10.5194/hess-18-4933-2014 · DOI
  17. Gosain, 2011, Climate change impact assessment of water resources of India, Curr. Sci., 101, 356
  18. Gosain, 2014, Climate change impact assessment on hydrology of Indian river basins, Curr. Sci., 90, 346
  19. Heo, 2015, Water resources response to climate and land-cover changes in a semi-arid watershed, New Mexico, USA, Terr. Atmos. Ocean. Sci., 26, 463, 10.3319/TAO.2015.03.24.01(Hy) · DOI
  20. House-peters, 2011, Modeling the impact of land use and climate change on neighborhood-scale evaporation and nighttime cooling: a surface energy balance approach, Landscape Urban Plan, 103, 139, 10.1016/j.landurbplan.2011.07.005 · DOI
  21. Hu, 2005, Effects of climate and landcover change on stream discharge in the Ozark Highlands, USA, Environ. Model. Assess., 10, 9, 10.1007/s10666-004-4266-0 · DOI
  22. Jeuland, 2014, Water resources planning under climate change: assessing the robustness of real options for the Blue Nile, Water Resour. Res., 50, 2086, 10.1002/2013WR013705 · DOI
  23. Jiang, 2011, Quantifying the effects of climate variability and human activities on runoff from the Laohahe basin in northern China using three different methods, Hydrol. Process., 25, 2492, 10.1002/hyp.8002 · DOI
  24. Jodar-Abellan, 2019, Impact of land use changes on flash flood prediction using a sub-daily SWAT model in five Mediterranean ungauged watersheds (SE Spain), Sci. Total Environ., 657, 1578, 10.1016/j.scitotenv.2018.12.034 · DOI
  25. Joorabian Shooshtari, 2017, Impacts of future land cover and climate change on the water balance in northern Iran, Hydrolog Sci J, 62, 2655, 10.1080/02626667.2017.1403028 · DOI
  26. Karvonen, 1999, A hydrological model for predicting runoff from different land use areas, J. Hydrol., 217, 253, 10.1016/S0022-1694(98)00280-7 · DOI
  27. Kulkarni, 2014, Assessing hydrological response to changing climate in the Krishna Basin of India, J Earth Sci Clim Change, 5, 1
  28. Kundu, 2017, Individual and combined impacts of future climate and land use changes on the water balance, Ecol. Eng., 105, 42, 10.1016/j.ecoleng.2017.04.061 · DOI · Paper page
  29. Ma, 2008, Analysis of impacts of climate variability and human activity on streamflow for a river basin in arid region of northwest China, J. Hydrol., 352, 239, 10.1016/j.jhydrol.2007.12.022 · DOI
  30. Me, 2015, Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand, Hydrol Earth Syst Sc, 19, 4127, 10.5194/hess-19-4127-2015 · DOI
  31. Mendoza, 2015, Effects of hydrologic model choice and calibration on the portrayal of climate change impacts, J Hydrometeorology, 16, 762, 10.1175/JHM-D-14-0104.1 · DOI
  32. Metzger, 2014, Soil evaporation under different types of land use in southern African savanna ecosystems, J Plant Nutr Soil Sc, 177, 468, 10.1002/jpln.201300257 · DOI
  33. Mishra, 2016, Hydrologic sensitivity of Indian sub-continental river basins to climate change, Glob. Planet. Chang., 139, 78, 10.1016/j.gloplacha.2016.01.003 · DOI
  34. Mishra, 2014, Soil moisture droughts under the retrospective and projected climate in India, J. Hydrometeorol., 15, 2267, 10.1175/JHM-D-13-0177.1 · DOI
  35. Naef, 2002, A process based assessment of the potential to reduce flood runoff by land use change, J. Hydrol., 267, 74, 10.1016/S0022-1694(02)00141-5 · DOI
  36. Neris, 2012, Vegetation and land-use effects on soil properties and water in fi ltration of Andisols in Tenerife (Canary Islands, Spain), Catena, 98, 55, 10.1016/j.catena.2012.06.006 · DOI
  37. Neupane, 2015, Estimating the effects of potential climate and land use changes on hydrologic processes of a large agriculture dominated watershed, J. Hydrol., 529, 418, 10.1016/j.jhydrol.2015.07.050 · DOI
  38. Nikam, 2018, Analyzing future water availability and hydrological extremes in the Krishna basin under changing climatic conditions, Arab. J. Geosci., 11, 581, 10.1007/s12517-018-3936-1 · DOI
  39. Pai, 2014, Development of a new high spatial resolution (0.25 × 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, Mausam, 65, 1, 10.54302/mausam.v65i1.851 · DOI
  40. Paulo, 2017, Simulation of stream flow and hydrological response to land-cover changes in a tropical river basin, Catena, 162, 166
  41. Pechlivanidis, 2011, Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications, Global NEST J, 13, 193
  42. Pontius, 2000, Quantification error versus location error in comparison of categorical maps, Photogramm Eng Rem S, 66, 1011
  43. Rahman, 2015, An independent and combined effect analysis of land use and climate change in the upper Rhone River watershed, Switzerland, Appl. Geogr., 63, 264, 10.1016/j.apgeog.2015.06.021 · DOI
  44. Ramesh, 1988, Temporal, spatial and size variation in the sediment transport in the Krishna River basin, India, J. Hydrol., 98, 53, 10.1016/0022-1694(88)90205-3 · DOI
  45. Rodriguez-Lloveras, 2015, Patterns of runoff and sediment production in response to land-use changes in an ungauged Mediterranean catchment, J. Hydrol., 531, 1054, 10.1016/j.jhydrol.2015.11.014 · DOI
  46. Sahoo, 2018, Impact of water demand on hydrological regime under climate and LULC change scenarios, Environ. Earth Sci., 77, 341, 10.1007/s12665-018-7531-2 · DOI
  47. Sajikumar, 2015, Impact of land cover and land use change on runoff characteristics, J. Environ. Manag., 161, 460, 10.1016/j.jenvman.2014.12.041 · DOI
  48. Sharma, 2019, Impact of rainfall variability and anthropogenic activities on streamflow changes and water stress conditions across Tapi Basin in India, Sci. Total Environ., 687, 885, 10.1016/j.scitotenv.2019.06.097 · DOI
  49. Shi, 2013, Effects of land-use and climate change on hydrological processes in the upstream of Huai River, China, Water Resour Manag, 27, 1263, 10.1007/s11269-012-0237-4 · DOI
  50. Shrestha, 2017, Assessing climate change impacts on fresh water resources of the Athabasca River Basin, Canada, Sci. Total Environ., 601, 425, 10.1016/j.scitotenv.2017.05.013 · DOI
  51. Shrestha, 2018, Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand, Sci. Total Environ., 643, 1610, 10.1016/j.scitotenv.2018.06.306 · DOI
  52. Tan, 2019, A review of SWAT studies in Southeast Asia: applications, challenges and future directions, Water, 11, 914, 10.3390/w11050914 · DOI
  53. Tsarouchi, 2014, Historical and future land-cover changes in the Upper Ganges basin of India, Int. J. Remote Sens., 35, 3150, 10.1080/01431161.2014.903352 · DOI
  54. Van Griensven, 2006, Methods to quantify and identify the sources of uncertainty for river basin water quality models, Water Sci. Technol., 53, 51, 10.2166/wst.2006.007 · DOI
  55. VanShaar, 2002, Effects of land-cover changes on the hydrological response of interior Columbia River basin forested catchments, Hydrol. Process., 16, 2499, 10.1002/hyp.1017 · DOI
  56. Wagner, 2013, An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India, Hydrol. Earth Syst. Sci., 17, 2233, 10.5194/hess-17-2233-2013 · DOI
  57. Wang, 2014, Individual and combined effects of land use/cover and climate change on Wolf Bay watershed streamflow in southern Alabama, Hydrol. Process., 28, 5530, 10.1002/hyp.10057 · DOI
  58. White, 1997, Cellular automata as the basis of integrated dynamic regional modelling, Environ Plann B, 24, 235, 10.1068/b240235 · DOI
  59. Williams, 1969, Flood routing with variable travel time or variable storage coefficients, T ASAE, 12, 100, 10.13031/2013.38772 · DOI
  60. Yang, 2019, Application of multivariate recursive nesting bias correction, multiscale wavelet entropy and AI-based models to improve future precipitation projection in upstream of the Heihe River, Northwest China, Theor. Appl. Climatol., 137, 323, 10.1007/s00704-018-2598-y · DOI
  61. Yin, 2017, Long term quantification of climate and land cover change impacts on streamflow in an Alpine River Catchment, northwestern China, Sustainability, 9, 1278, 10.3390/su9071278 · DOI
  62. Zhang, 2018, Comparison of baseline period choices for separating climate and land use/land cover change impacts on watershed hydrology using distributed hydrological models, Sci. Total Environ., 622, 1016, 10.1016/j.scitotenv.2017.12.055 · DOI

Cited by

Sorted by citations

See all 153

Metadata

Authors
Chanapathi, T., & Thatikonda, S.
Affiliations
Indian Institute of Technology Hyderabad (IN)
Corresponding author
Thatikonda, Shashidhar
ORCID
Venue
Science of The Total Environment
Publisher
Elsevier BV
Published
2020-06
Volume
721
Pages
137736
ISSN
0048-9697 (ISSN-L)
DOI
10.1016/j.scitotenv.2020.137736
OpenAlex
W3010591963
Cited by
153
References
0
Citations by year
2026: 18 · 2025: 28 · 2024: 27 · 2023: 35 · 2022: 22 · 2021: 17 · 2020: 5
FWCI
6.14
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • The Science of The Total Environment (publishedVersion)
  • PubMed (publishedVersion)
  • Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) (submittedVersion)
Funders
Ministry of Human Resource Development
Topics
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
Keywords
climate change impacts, land use and land cover change, hydrological forecasting, Krishna River basin, water balance components, water storage structures, surface runoff, river discharge, water yield, evapotranspiration, future climate scenarios, RCP4.5, RCP8.5, SWAT model

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios. https://app.cochat.ai/papers/10.1016/j.scitotenv.2020.137736
The paper
Chanapathi, T., & Thatikonda, S. (2020). Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2020.137736