Assessing the impact of climate variability and human activities on streamflow variation

The predictions of flood hazard over the design life of a hydrological project are of great importance for hydrological engineering design under the changing environment. The concept of a nonstationary flood hazard has been formulated by extending the geometric distribution to account for time-varying exceedance probabilities over the design life of a project. However, to our knowledge, only time covariate is used to estimate the nonstationary flood hazard over the lifespan of a project, which lacks physical meaning and may lead to unreasonable results. In this study, we aim to strengthen the physical meaning of nonstationary flood hazard analysis by investigating the impacts of climate change and population growth. For this purpose, two physical covariates, i.e., rainfall and population, are introduced to improve the characterization of nonstationary frequency over a given design lifespan. The annual maximum flood series of Xijiang River (increasing trend) and Weihe River (decreasing trend) are chosen as illustrations, respectively. The results indicated that:(1) the explanatory power of population and rainfall is better than time covariate in the study areas;(2) the nonstationary models with physical covariates possess more appropriate statistical parameters and thus are able to provide more reasonable estimates of a nonstationary flood hazard; and (3) the confidences intervals of nonstationary design flood can be greatly reduced by employing physical covariates. Therefore, nonstationary flood design and hazard analysis with physical covariates are recommended in changing environments.

Section: Study Areasmentioning

confidence: 99%

Nonstationary Flood Hazard Analysis in Response to Climate Change and Population Growth

Yan

et al. 2019

“…It is generally known that the processes of runoff generation are heavily influenced by a host of factors such as precipitation, temperature, evaporation, underlying surface, and human activity [3,4]. Due to the uncertainty of these factors, runoff series tend to be nonlinear, non-stationary, and even chaotic [5,6].…”

Section: Introductionmentioning

confidence: 99%

An EMD-Based Chaotic Least Squares Support Vector Machine Hybrid Model for Annual Runoff Forecasting

Zhao

Chen

et al. 2017

Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction accuracy, a novel hybrid model based on the empirical mode decomposition (EMD) for annual runoff forecasting is proposed and applied in this paper. Firstly, the original annual runoff series is decomposed into a limited number of intrinsic mode functions (IMFs) and one trend term based on the EMD, which makes the series stationary. Secondly, it will be forecasted by a least squares support vector machine (LSSVM) when the IMF component possesses chaotic characteristics, and simulated by a polynomial method when it does not. In addition, the reserved trend term is predicted by a Gray Model. Finally, the ensemble forecast for the original runoff series is formulated by combining the prediction results of the modeled IMFs and the trend term. Qualified rate (QR), root mean square errors (RMSE), mean absolute relative errors (MARE), and mean absolute errors (MAE) are used as the comparison criteria. The results reveal that the EMD-based chaotic LSSVM (EMD-CLSSVM) hybrid model is a superior alternative to the CLSSVM hybrid model for forecasting annual runoff at Shangjingyou station, reducing the RMSE, MARE, and MAE by 39%, 28.6%, and 25.6%, respectively. To further illustrate the stability and representativeness of the EMD-CLSSVM hybrid model, runoff data at three additional sites, Zhaishang, Fenhe reservoir, and Lancun stations, were applied to verify the model. The results show that the EMD-CLSSVM hybrid model proved its applicability with high prediction precision. This approach may be used in similar hydrological conditions.

“…The percentage of flood-season precipitation variation can also be responsible for annual streamflow reduction with a range of 7.36% (Tang-Tou) to 21.88% (Source); (3) Annual streamflow was more sensitive to annual precipitation than temperature in the humid region, and the opposite situation was observed in the arid region. The sensitivities to intra-annual climate variation increased after 2000 for all regions, and the increase was more significant in Tou-Long and Long-Hua regions.importance for a better understanding of the hydrologic mechanisms, which is beneficial for planning suitable adaptation strategies and water management.There are various methods to separate the impacts of climate change and anthropogenic interference on streamflow, mainly including catchment experiments, hydrological models, and statistical methods [9]. Catchment experiments are the most rigorous empirical research design for estimating the effects of land use on aquatic systems [10], but they can be influenced by the variation in experimental conditions and the presentation of results [11].…”

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confidence: 99%

“…Hundecha et al applied a conceptual rainfall-runoff model to 95 catchments in the Rhine basin to model the effect of land use change on runoff [18]. Statistical methods such as streamflow elasticity have also been used in regions specifically with available long-term climate and hydrologic data [9,19,20]. Tian et al used regression analysis to illustrate runoff decline via comparison of precipitation-runoff correlation for the period prior to and after sharp runoff decline [21].The semiarid and arid Yellow River Basin (YRB) is the main source of surface water in the northwest and northern part of China.…”

mentioning

confidence: 99%

“…There are various methods to separate the impacts of climate change and anthropogenic interference on streamflow, mainly including catchment experiments, hydrological models, and statistical methods [9]. Catchment experiments are the most rigorous empirical research design for estimating the effects of land use on aquatic systems [10], but they can be influenced by the variation in experimental conditions and the presentation of results [11].…”

mentioning

confidence: 99%

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Analysis of Natural Streamflow Variation and Its Influential Factors on the Yellow River from 1957 to 2010

Wang

Dong

et al. 2018

In this study, variation characteristics of hydrometeorological factors were explored based on observed time-series data between 1957 and 2010 in four subregions of the Yellow River Basin. For each region, precipitation-streamflow models at annual and flood-season scales were developed to quantify the impact of annual precipitation, temperature, percentage of flood-season precipitation, and anthropogenic interference. The sensitivities of annual streamflow to these three climatic factors were then calculated using a modified elasticity coefficient model. The results presented the following:(1) Annual streamflow exhibited a negative trend in all regions; (2) The reduction of annual streamflow was mainly caused by a precipitation decrease and temperature increase for all regions before 2000, whereas the contribution of anthropogenic interference increased significantly-more than 45%, except for Tang-Tou region after 2000. The percentage of flood-season precipitation variation can also be responsible for annual streamflow reduction with a range of 7.36% (Tang-Tou) to 21.88% (Source); (3) Annual streamflow was more sensitive to annual precipitation than temperature in the humid region, and the opposite situation was observed in the arid region. The sensitivities to intra-annual climate variation increased after 2000 for all regions, and the increase was more significant in Tou-Long and Long-Hua regions.importance for a better understanding of the hydrologic mechanisms, which is beneficial for planning suitable adaptation strategies and water management.There are various methods to separate the impacts of climate change and anthropogenic interference on streamflow, mainly including catchment experiments, hydrological models, and statistical methods [9]. Catchment experiments are the most rigorous empirical research design for estimating the effects of land use on aquatic systems [10], but they can be influenced by the variation in experimental conditions and the presentation of results [11]. Most relevant studies indicate that catchment streamflow decreased significantly after afforestation and increased after deforestation [10,12,13]. Hydrological models, both distributed and lumped, have been widely used [7,[14][15][16]. Hu et al. applied the water and energy budget-based distributed hydrological model (WEB-DHM) to diagnose and quantify climate and human impacts on streamflow change [17]. Hundecha et al. applied a conceptual rainfall-runoff model to 95 catchments in the Rhine basin to model the effect of land use change on runoff [18]. Statistical methods such as streamflow elasticity have also been used in regions specifically with available long-term climate and hydrologic data [9,19,20]. Tian et al. used regression analysis to illustrate runoff decline via comparison of precipitation-runoff correlation for the period prior to and after sharp runoff decline [21].The semiarid and arid Yellow River Basin (YRB) is the main source of surface water in the northwest and northern part of China. The annual streamflow is a...