Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China

Qiu, Dexun; Wu, Changxue; Mu, Xingmin; Zhao, Guangju; Gao, Peng

doi:10.1007/s11769-022-1271-7

Cited by 9 publications

(2 citation statements)

References 64 publications

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“…Yang et al (2014) found that climate factors could promote runoff in Northwest China. Qiu et al (2022) found that the magnitude and frequency of extreme precipitation in the WRB have strengthened in the past 60 years and showed a strong aggregation mode. In addition, in some areas, higher potential evapotranspiration and temperature will also intensify the hydrological cycle Miao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018

Qiu

et al. 2022

J. Arid Land

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Exploring the current runoff characteristics after the large-scale implementation of the Grain for Green (GFG) project and investigating its sensitivities to potential drivers are crucial for water resource prediction and management. Based on the measured runoff data of 62 hydrological stations in the Weihe River Basin (WRB) from 2006 to 2018, we analyzed the temporal and spatial runoff characteristics in this study. Correlation analysis was used to investigate the relationships between different runoff indicators and climate-related factors. Additionally, an improved Budyko framework was applied to assess the sensitivities of annual runoff to precipitation, potential evaporation, and other factors. The results showed that the daily runoff flow duration curves (FDCs) of all selected hydrological stations fall in three narrow ranges, with the corresponding mean annual runoff spanning approximately 1.50 orders of magnitude, indicating that the runoff of different hydrological stations in the WRB varied greatly. The trend analysis of runoff under different exceedance frequencies showed that the runoff from the south bank of the Weihe River was more affluent and stable than that from the north bank. The runoff was unevenly distributed throughout the year, mainly in the flood season, accounting for more than 50.00% of the annual runoff. However, the trend of annual runoff change was not obvious in most areas. Correlation analysis showed that rare-frequency runoff events were more susceptible to climate factors. In this study, daily runoff under 10%-20% exceeding frequencies, consecutive maximum daily runoff, and low-runoff variability rate had strong correlations with precipitation, aridity index, and average runoff depth on rainy days. In comparison, daily runoff under 50%-99% exceeding frequencies, consecutive minimum daily runoff, and high-runoff variability rate had weak correlations with all selected impact factors. The sensitivity analysis results suggested that the sensitivity of annual runoff to precipitation was always higher than that to potential evaporation. The runoff about 87.10% of the selected hydrological stations were most sensitive to precipitation changes, and 12.90% were most sensitive to other factors. The spatial pattern of the sensitivity analysis indicated that in relatively humid southern areas, runoff was more sensitive to potential evaporation and other factors, and less sensitive to precipitation.

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Section: Introductionmentioning

confidence: 99%

Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018

Qiu

et al. 2022

J. Arid Land

Self Cite

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“…Over the past 25 years, researchers have expanded the application of artificial intelligence methods in forecasting (Mathbout et al, 2018a). Among them, the Long Short-Term Memory (LSTM) network has addressed this issue and overcome the long-term dependence problem of Recurrent Neural Networks (RNN), being widely applied in studies related to runoff, precipitation, and drought prediction (Qiu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Drought Characteristics Prediction Using a Hybrid Machine Learning Model with Correction

Xue,

Luo,

et al. 2024

Preprint

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Drought prediction is a crucial aspect of drought risk assessment, playing a significant role in effectively minimizing losses associated with droughts. While many previous studies have focused on the variations of drought indices, understanding the impact of drought events requires a transformation into drought characteristics such as severity and duration. These drought features provide insights into the processes and hazards associated with drought events. The main objectives of this study are twofold: improving prediction accuracy and evaluating model performance based on drought characteristics.By decomposing the data into high and low-frequency sequences and effectively addressing these with optimally chosen models, a hybrid model (LSL) model is constructed.This model incorporates an error correction sequence to reduce errors during the prediction process and improve accuracy. Simultaneously, various aspects of drought characteristics, such as duration, start and end times, intensity, and peak value, were analyzed to enhance drought event prediction and impact assessment. The results show that, the LSL model is effective in predicting the drought class and all aspects of drought characteristics. Overall, the LSL model can be well applied to the prediction of drought events.

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Performance evaluation of CMIP6 GCMs for the projections of precipitation extremes in Pakistan

et al. 2023

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Extreme weather events are more detrimental to human culture and ecosystems than typical weather patterns. A multimodel ensemble (MME) of the top-performing global climate models (GCMs) to simulate 11 precipitation extremes was selected using a hybrid method to project their changes in Pakistan. It also compared the bene ts of using all GCMs compared to using only selected GCMs when projecting precipitation extremes for two future periods (2020-2059) and (2060-2099) for four shared socioeconomic pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.Results showed that EC-Earth3-Veg, MRI-ESM2-0 and NorESM2-MM performed best among GCMs in simulating historical and projecting precipitation extremes. Compared to the MME of all GCMs, the uncertainty in future projections of all precipitation indices using the selected GCMs was signi cantly smaller. The MME median of the selected GCMs showed increased precipitation extremes over most of Pakistan. The greater increases were in one-day maximum precipitation by 6-12 mm, ve-day maximum precipitation by 12-20 mm, total precipitation by 40-50 mm, 95th percentile precipitation events by greater than 30 mm, 99th percentile precipitation events by more than 9 mm, days when precipitation ≥ 4 mm by 0-4 days, days when precipitation ≥ 10 mm by 2-6 days, days when precipitation ≥ 20 mm by 1-3 days, and precipitation intensity by 1 mm/day, consecutive wet days by one day, consecutive dry days by 0-4 days in the northern high elevated areas for SSP5-8.5 in the late future. These results emphasize the greater in uence of climate change on precipitation extremes in the northern, high-elevation areas, which provide the majority of the country's water. This emphasizes the necessity to adopt suitable climate change mitigation strategies for sustainable development, particularly in the country's northern regions.

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Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China

Cited by 9 publications

References 64 publications

Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018

Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018

Drought Characteristics Prediction Using a Hybrid Machine Learning Model with Correction

Performance evaluation of CMIP6 GCMs for the projections of precipitation extremes in Pakistan

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