Integrated approach to simulate hydrological responses to land use dynamics and climate change scenarios employing scoring method in upper Narmada basin, India

Pandey, Brij Kishor; Khare, Deepak; Kawasaki, Akiyuki; Meshesha, Tesfa Worku

doi:10.1016/j.jhydrol.2021.126429

Cited by 20 publications

(4 citation statements)

References 59 publications

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“…With the implementation of TSS and RM approaches, the best‐performed GCMs were chosen; subsequently, those GCMs were used to structure the MME and MME‐ML algorithms. Interestingly, the study envisaged the best‐performed five CMIP6 GCMs as user‐defined climate models at the DRB, reciprocating the principles adopted in earlier research (Pandey et al ., 2019; 2021). Additionally, during MME development, the concept prescribed in the past studies (viz., Salman et al ., 2018; Wang et al ., 2018; Ahmed et al ., 2019a) was also followed.…”

Section: Methodsmentioning

confidence: 99%

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Dey

Sahoo

Kumar

et al. 2022

Intl Journal of Climatology

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Multimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basinscale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME-integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (T max ), and minimum temperature (T min ) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME-RF and MME-SVM approach has also been observed while capturing the spatial pattern as IMD-observed with well representation of climate indices for both wet and dry seasons. Future projections with MME-SVM and MME-RF suggested a possible rise in mean annual P in the range of 1.4-15% and 6.8-39% with an increasing trend in temperature (T max , T min ) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices.

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Section: Methodsmentioning

confidence: 99%

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Dey

Sahoo

Kumar

et al. 2022

Intl Journal of Climatology

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“…Pandey et al [64] examined the hydrological response to both land use and land cover change and climate change in the upper Narmada basin, India. Their results revealed a reduction in evapotranspiration percentage of 4.19% due to land use changes between 2010 and 2030.…”

Section: Evapotranspirationmentioning

confidence: 99%

Assessment of Hydrological Responses to Land Use and Land Cover Changes in Forest-Dominated Watershed Using SWAT Model

Ware,

Chang,

Lee

et al. 2024

Water

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Recognizing how human activities affect hydrological systems is vital for the sustainable preservation and effective management of water resources in the watershed. Hence, this paper focuses on the hydrological response to land use and land cover (LULC) change scenarios in the Anyang watershed, South Korea. We obtained LULC data maps for the years 2000, 2013, and 2022 from the local government, revealing significant changes over the years. Agricultural lands experienced a 6.2% increase from 2000 to 2022, and pastureland expanded by 8.67% over two decades. The SWAT model was utilized to assess the impact of LULC on the hydrological components of the study watershed. Model calibration and validation for each LULC change were carried out using the SWAT-CUP program, considering the recorded streamflow information of the region. An excellent agreement was reached between the simulated and measured streamflow in both the calibration and validation stages under various LULC conditions. The Nash–Sutcliffe model efficiency (NSE), the objective function, demonstrated values of 0.9, 0.89, and 0.89 during the calibration for 2000, 2013, and 2022, respectively, in the LULC scenario, while for the validation, we obtained values of 0.82, 0.78, and 0.80 for 2000, 2013, and 2022, respectively. Our findings indicate that the surface runoff rise contributed much to the water yield increase over the two decades compared to the other components in terms of the water yield, while the contribution of evapotranspiration (ET) to the watershed hydrological cycle declined by 1.66% from 2000 to 2022. The southeastern sub-basin part showed a high groundwater recharge distribution due to agricultural land, rice area, and forest area changes.

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“…When exploring the impact of LUCC, the researchers adopted the 10-year average climate in the base period and the LUCC in 2015 in the change period. The formula is [43][44][45]:…”

Section: Contribution Rate Of Climate and Lucc To Changes In Water Co...mentioning

confidence: 99%

Spatio-Temporal Dynamics of Water Conservation Service of Ecosystems in the Zhejiang Greater Bay Area and Its Impact Factors Analysis

Zhang

Yang

2022

Sustainability

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The Zhejiang Greater Bay Area (ZGBA) is the main functional area of water conservation in Zhejiang Province, China. It has 44.2% of the ecological red line area of Zhejiang Province. There are many mountains, plains, water systems, and tidal flat resources. It is an essential ecological barrier in the water supply area of the Hang-Jia-Hu area and the northern Zhejiang area. This paper aimed to clarify the water conservation services and influencing factors in the ZGBA, so as to provide reference and policy inspiration for local improvement of water resources. Based on the improved water balance method, the temporal and spatial dynamic changes of water conservation in the ZGBA from 2000 to 2019 were estimated, the impact of climate and land use and land cover change on water conservation was assessed, and geographic detectors were employed to explore the main influencing factors of water conservation. The following results can be summarized from this research: (1) The water conservation level of the ZGBA is relatively high and the water conservation amount showed a phased upward trend from 2000 to 2019; (2) The main reason for the significant increase in the level of water conservation from 2010 to 2019 was the increase in precipitation by 11% in the next 10 years compared with the previous 10 years, and land use exerted a low negative impact on water conservation; (3) Precipitation and evapotranspiration were the main single-factor influencing factors on water conservation, and the interactions between precipitation and vegetation/terrain were the main multi-factor influencing factors on water conservation.

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Integrated approach to simulate hydrological responses to land use dynamics and climate change scenarios employing scoring method in upper Narmada basin, India

Cited by 20 publications

References 59 publications

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin

Assessment of Hydrological Responses to Land Use and Land Cover Changes in Forest-Dominated Watershed Using SWAT Model

Spatio-Temporal Dynamics of Water Conservation Service of Ecosystems in the Zhejiang Greater Bay Area and Its Impact Factors Analysis

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