A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: A case study in a typical semi-arid North China basin

Zhang, Ke; Ruben, Gebdang Biangbalbe; Li, Xin; Li, Zhijia; Yu, Zhongbo; Xia, Jun; Dong, Zengchuan

doi:10.1016/j.envsoft.2020.104704

Cited by 81 publications

(43 citation statements)

References 58 publications

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“…Land use, which is the main interface between the torrential rainfalls and the ground surface, has an important influence on the runoff velocity [ 66 ]. For the present study, the land use layer was taken from the Corine Land Cover 2018 database.…”

Section: Datamentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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Section: Datamentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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“…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%

“…Second, we revisit the parametric Budyko equations that are currently interpreted by most authors to represent more generalized forms of the non-parametric Budyko equations (Budyko, 1974), and which can thus be used to separate the effects of changes in the average climate (i.e., changes in aridity index ) on ̅ from the effects of all other biophysical features (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; https://doi.org/10.5194/hess-2020-584 Preprint. Discussion started: 13 November 2020 c Author(s) 2020.…”

Section: Introductionmentioning

confidence: 99%

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Reinterpreting the Budyko Framework

Reaver¹,

Kaplan²,

Klammler³

et al. 2020

Preprint

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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.

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A comprehensive assessment framework for attributing trends in streamflow and groundwater storage to climatic and anthropogenic changes: A case study in the typical semi‐arid catchments of southern India

Nune

George

Western

et al. 2021

Hydrological Processes

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The clearest signs of hydrologic change can be observed from the trends in streamflow and groundwater levels in a catchment. During 1980During -2007 declines in streamflow (À3.03 mm/year) and groundwater levels (À0.22 m/year) were observed in Himayat Sagar (HS) catchment, India. We examined the degree to which hydrologic changes observed in the HS catchment can be attributed to various internal and external drivers of change (climatic and anthropogenic changes). This study used an investigative approach to attribute hydrologic changes. First, it involves to develop a model and test its ability to predict hydrologic trends in a catchment that has undergone significant changes. Second, it examines the relative importance of different causes of change on the hydrologic response. The analysis was carried out using Modified Soil and Water Assessment Tool (SWAT), a semidistributed rainfall-runoff model coupled with a lumped groundwater model for each sub-catchment. The model results indicated that the decline in potential evapotranspiration (PET) appears to be partially offset by a significant response to changes in rainfall. Measures that enhance recharge, such as watershed hydrological structures, have had limited success in terms of reducing impacts on the catchment-scale water balance. Groundwater storage has declined at a rate of 5 mm/y due to impact of land use changes and this was replaced by a net addition of 2 mm/y by hydrological structures. The impact of land use change on streamflow is an order of magnitude larger than the impact of hydrological structures and about is 2.5 times higher in terms of groundwater impact. Model results indicate that both exogenous and endogenous changes can have large impacts on catchment hydrology and should be considered together. The proposed comprehensive framework and approach demonstrated here is valuable in attributing trends in streamflow and groundwater levels to catchment climatic and anthropogenic changes. K E Y W O R D S climate and anthropogenic changes, coupled catchment model, potential evapotranspiration decline, streamflow and groundwater trends, watershed development

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A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: A case study in a typical semi-arid North China basin

Cited by 81 publications

References 58 publications

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Reinterpreting the Budyko Framework

A comprehensive assessment framework for attributing trends in streamflow and groundwater storage to climatic and anthropogenic changes: A case study in the typical semi‐arid catchments of southern India

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