Evaluation of streamflow changes due to climate variation and human activities using the Budyko approach

Kazemi, Hamideh; Sarukkalige, Ranjan; Badrzadeh, Honey

doi:10.1007/s12665-019-8735-9

Cited by 21 publications

(15 citation statements)

References 51 publications

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“…Hydrological modelling and the Budyko method, were employed to separate the impacts of climate variation and human activities on mean annual streamflow change in the KRB. The total change in streamflow (∆Q) can be assumed to depend on climate variation (∆Q c ) and human activity (∆Q h ) [23][24][25]:…”

Section: Change Detectionmentioning

confidence: 99%

“…The pre-change period was divided into calibration and validation periods. The calibration parameters (Table A4) were adapted according to the manual, catchment characteristics, and literature [24,25,30,33]. After calibration and validation of the model for the pre-change period, the HBV-light model was used to simulate flow of the post-change period, keeping parameters constant.…”

Section: Assessment Of Streamflow Changes Using the Hbv Hydrological Modelmentioning

confidence: 99%

See 1 more Smart Citation

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

Kazemi

Hashemi

Maghsood

et al. 2021

Water

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This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.

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Section: Change Detectionmentioning

confidence: 99%

Section: Assessment Of Streamflow Changes Using the Hbv Hydrological Modelmentioning

confidence: 99%

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

Kazemi

Hashemi

Maghsood

et al. 2021

Water

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“…When the common denominator P is removed from the evaporative fraction, only a weak correlation of aridity with inferred evapotranspiration remains, whereas a very strong relationship of climate aridity with streamflow is present for the MOPEX catchments F I G U R E 3 The Budyko curve does not explain all spatial differences in catchment water balances (panel a) and those factors appear to relate to catchment properties as deviations and parametric curve parameters relate to specific properties (panels b and c). However, neither parametric curve parameters nor anomalies can be readily compared between catchments and across aridity gradients or to attribute causes of such changes in the historical record (e.g., Berghuijs, Larsen, Van Emmerik, & Woods, 2017;Jaramillo & Destouni, 2014;Kazemi, Sarukkalige, & Badrzadeh, 2019;Roderick & Farquhar, 2011;X. Wang, 2014;D.…”

Section: How Appropriate Is Budyko For Describing and Predicting Chmentioning

confidence: 99%

“…In recent years, the Budyko framework is frequently being used to estimate how streamflow and evapotranspiration respond to change, or to attribute causes of such changes in the historical record (e.g., Berghuijs, Larsen, Van Emmerik, & Woods, 2017; Jaramillo & Destouni, 2014; Kazemi, Sarukkalige, & Badrzadeh, 2019; Roderick & Farquhar, 2011; X. Wang, 2014; D. Wang & Hejazi, 2011). The (mathematical) specifics of such studies vary, but all approaches are founded on the assumption that catchments follow a (parametric) Budyko curve when aridity changes, and that consequently all other movements in the Budyko space are caused by other factors.…”

Section: Introductionmentioning

confidence: 99%

Unanswered questions on the Budyko framework

Berghuijs

Gnann

Woods

2020

Hydrological Processes

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“…Alterations in rainfall regime propagate through the hydrologic cycle, and thus induce changing streamflow regime. Impacts of climate change and anthropogenic activities on streamflow regime have drawn considerable attention recently [18][19][20][21][22][23]. A vast literature has thus been devoted to exploring non-stationarity in streamflow series worldwide [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

Shiau

Liu

2021

Water

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This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.

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Evaluation of streamflow changes due to climate variation and human activities using the Budyko approach

Cited by 21 publications

References 51 publications

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation

Unanswered questions on the Budyko framework

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

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