Analysing model disparity in diagnosing the climatic and human stresses on runoff variability over India

Sinha, Jhilam; Das, Jew; Goyal, Manish Kumar

doi:10.1016/j.jhydrol.2019.124407

Cited by 22 publications

(15 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interactions between social and ecological system are complex and non-linear [66]. Besides land use change, human activities that also exert great influences on runoff include the following: irrigation [67,68], construction and operation of dams and reservoirs [69,70], water conservancy projects [71], utilization of groundwater [72], and urban drainage pipe systems [73]. This paper focused on evaluating the impact of land use change on surface runoff and the other human and environmental factors that may affect surface runoff require further exploration in the future.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Yun-yun

Zhang

2020

Land

View full text Add to dashboard Cite

The change in land use during the process of urbanization affects surface runoff and increases flood risk in big cities. This study investigated the impact of land use change on surface runoff in Beijing’s central area during the period of rapid urbanization from 1984 to 2019. Land use maps of 1984, 1999, 2009, and 2019 were generated by image classification of Landsat images. Surface runoffs were calculated with the Soil Conservation Service curve number (SCS-CN) model. Correlation analysis was used to identify the dominant factor of land use change affecting surface runoff. The result showed that the variation trend of surface runoff was consistent with the trend of impervious land in Beijing’s central area, which increased during 1984~2009 and decreased during 2009~2019. Correlation analysis showed that changes in surface runoff were most strongly correlated with changes in impervious surfaces when compared with the correlation of runoff with other types of land use. The results of this study may provide a reference for city flood control and urban planning in fast growing cities worldwide, especially in developing countries.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Yun-yun

Zhang

2020

Land

View full text Add to dashboard Cite

show abstract

“…CC BY 4.0 License. Xiangyu et al, 2020;Song et al, 2020;Sinha et al, 2020;Li et al, 2020d;Li et al, 2020a;Deng et al, 2020;Zhang et al, 2019a;Young et al, 2019;Xin et al, 2019;Wang et al, 2019;Lv et al, 2019;Liu et al, 2019c;Lee and Yeh, 2019;Kazemi et al, 2019;He et al, 2019c;He et al, 2019b;He et al, 2019a;Wang et al, 2018;Xu et al, 2014). We argue and demonstrate herein that the two widely accepted parametric Budyko equations are non-unique, meaning they are only two of many possible single-parameter Budyko equations.…”

Section: Introductionmentioning

confidence: 59%

“…The widely held interpretations of explicit curves representing individual catchment behaviour and the catchment-specific parameter representing biophysical/landscape features has led to the development of methods to determine the sensitivity of rainfall partitioning to climate and/or landscape changes for individual catchments (Roderick and Farquhar, 2011;Wang and Hejazi, 2011;Yang and Yang, 2011;Wang et al, 2016b;Zhou et al, 2016;Shen et al, 2017;Zhang et al, 2016;Yeh and Tsao, 2020;Zhang et al, 2020;Sinha et al, 2020;Ning et al, 2020b;Liu et al, 2020;Li et al, 2020e;Li et al, 2020a;Liu et al, 2019a;Li et al, 2019;Yang et al, 2018; https://doi.org/10.5194/hess-2020-584 Preprint. Discussion started: 13 November 2020 c Author(s) 2020.…”

Section: Current Interpretations Of Explicit Budyko Curves and The Pamentioning

confidence: 99%

“…et al, 2018b; Li et al, 2018;Xiangyu et al, 2020) and multiple methods for decomposing anthropogenic and climatic impacts on rainfall partitioning (Wang and Hejazi, 2011;Xing et al, 2018b;Jaramillo et al, 2018;Mo et al, 2018;Sun et al, 2014;Jiang et al, 2015;Liang et al, 2015;Huang et al, 2016;Zhang et al, 2020;Yeh and Tsao, 2020;Xiangyu et al, 2020;Song et al, 2020;Sinha et al, 2020;Li et al, 2020d;Li et al, 2020a;Deng et al, 2020;Zhang et al, 2019a;Young et al, 2019;Xin et al, 2019;Wang et al, 2019;Lv et al, 2019;Liu et al, 2019c;Lee and Yeh, 2019;Kazemi et al, 2019;He et al, 2019c;He et al, 2019b;He et al, 2019a;Wang et al, 2018;Xu et al, 2014). Additionally, these interpretations have led numerous studies to pursue predictive relationships for the catchment-specific parameter based on various biophysical features ( Table S1 in the Supplemental Information) (Yang et al, 2007;Donohue et al, 2012;Yang et al, 2009;Shao et al, 2012;Li et al, 2013;Xu et al, 2013;Cong et al, 2015;Yang et al, 2016;…”

Section: Current Interpretations Of Explicit Budyko Curves and The Pamentioning

confidence: 99%

“…This interpretation has motivated profound efforts to understand the relationship between biophysical features and catchment-specific parameters (Yang et al, 2007;Donohue et al, 2012;Yang et al, 2009;Shao et al, 2012;Li et al, 2013;Xu et al, 2013;Cong et al, 2015;Yang et al, 2016;Zhang et al, 2018;Abatzoglou and Ficklin, 2017;Xing et al, 2018a;Zhao et al, 2020;Ning et al, 2020b;Li et al, 2020c;Li et al, 2020b;Zhang et al, 2019b;Ning et al, 2019;Bai et al, 2019). Numerous studies have also focused on determining the sensitivity of rainfall partitioning to climatic and/or land use changes (Roderick and Farquhar, 2011;Wang and Hejazi, 2011;Yang and Yang, 2011;Wang et al, 2016b;Zhou et al, 2016;Shen et al, 2017;Zhang et al, 2016;Yeh and Tsao, 2020;Zhang et al, 2020;Sinha et al, 2020;Ning et al, 2020b;Liu et al, 2020;Li et al, 2020e;Li et al, 2020a;Liu et al, 2019a;Li et al, 2019;Yang et al, 2018;Xing et al, 2018b;Li et al, 2018;Xiangyu et al, 2020), as well as on deriving causal attribution to changes in this partitioning (Wang and Hejazi, 2011;Xing et al, 2018b;…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reinterpreting the Budyko Framework

Reaver¹,

Kaplan²,

Klammler³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. The Budyko framework posits that a catchment's long-term mean evapotranspiration (E) is primarily governed by the availabilities of water and energy, represented by long-term mean precipitation (P) and potential evapotranspiration (E0), respectively. This assertion is supported by the distinctive clustering pattern that catchments take in Budyko space. Several semi-empirical, non-parametric curves have been shown to generally represent this clustering pattern but cannot explain deviations from the central tendency. Parametric Budyko equations attempt to generalize the non-parametric framework, through the introduction of a catchment-specific parameter (n or w). Prevailing interpretations of Budyko curves suggest that the explicit functional forms represent trajectories through Budyko space for individual catchments undergoing changes in aridity index, (E0/P), while n and w values represent catchment biophysical features; however, neither of these interpretations arise from the derivation of the Budyko equations. In this study, we re-examine, reinterpret, and test these two key components of the current Budyko framework both theoretically and empirically. In our theoretical test, we use a biophysical model for E to demonstrate that n and w values can change without invoking changes in landscape biophysical features and that catchments are not required to follow Budyko curve trajectories. Our empirical test uses data from 728 reference catchments in the United Kingdom and United States to illustrate that catchments rarely follow Budyko curve trajectories and that n and w are not transferable between catchments or across time for individual catchments. This non-transferability implies n and w are proxy variables for E/P, rendering the parametric Budyko equations under-determined and lacking of predictive ability. Finally, we show that the parametric Budyko equations are non-unique, suggesting their physical interpretations are unfounded. Overall, we conclude that, while the shape of Budyko curves generally captures the global behavior of multiple catchments, their specific functional forms are arbitrary and not reflective of the dynamic behavior of individual catchments.

show abstract

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Das

Umamahesh

2021

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

The present study aims to answer the following two research questions using the future outputs from 19 GCMs of the novel NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. (a) What will be the possible variability of future meteorological drought properties during the 21st century? (b) What will be the possible influence of temperature in modulating the drought properties? In order to answer the above questions, five different drought properties namely frequency, severity, duration, peak, and areal spread are analysed for future (2022–2099) using two meteorological drought indices such as Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for short (3‐month scale) and long (12‐month scale) term drought conditions. The essential outcomes from the study indicate a warming and wetting climate over Maharashtra during the 21st century. With an increase in monthly precipitation, severity and duration of future meteorological droughts are likely to decrease in long‐term drought condition. Conversely, in the short‐term condition, the properties are expected to increase. The drought properties like peak and areal spread are expected to increase for both short and long‐term drought conditions in most of the regions. Temperature plays a vital role in modulating the meteorological droughts, and the present study notices the same in most of the cases in all the drought properties, especially in drought severity, duration, and peak. All the division are identified as hotspot regions during the mid and later part of the 21st century for short‐term drought condition. The number of hotspot regions decreases in long‐term drought condition as compared to short‐term drought condition.

show abstract

Analysing model disparity in diagnosing the climatic and human stresses on runoff variability over India

Cited by 22 publications

References 45 publications

Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Reinterpreting the Budyko Framework

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Contact Info

Product

Resources

About