Analysis of the impact of precipitation and temperature on the streamflow of the Ürümqi River, Tianshan Mountain, China

Liu, Y C; Zhao, Kan; Liu, Y; Yin, Xiaoran; Labat, David

doi:10.1088/1755-1315/191/1/012135

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…To measure the cross-covariance of climatic indices and rainfall, we used cross-wavelet transform (XWT). Wavelet coherence (WTC) used to detect significant coherence between the time series [50]. The in-phase and out-of-phase relationship between the time series is obtained from the wavelet coherence phase xy (a, ) [51].…”

Section: Methodsmentioning

confidence: 99%

Influence of climatic indices (AMO, PDO, and ENSO) and temperature on rainfall in the Northeast Region of India

2020

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This study explores the spatiotemporal rainfall variability in the Northeast region of India combined with an analysis of the effects of temperature and natural climatic indices [El-Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multi-decadal Oscillation (AMO)] on rainfall. The multi-timescale features of rainfall time series for periods 1871-2016 were analysed based on the continuous wavelet transform method. Cross-wavelet and wavelet coherence explore cross-correlation between temperature, natural climatic indices, and rainfall during 1950-2016. Results of the continuous wavelet transform revealed a significant cycle of 12 months on a monthly scale and 2-8-year period on an annual and seasonal scale. The cross-wavelet and wavelet coherence results showed that temperature affects rainfall only on intra-annual (0.5-1 years) scale. ENSO affects rainfall on both intra-annual (0.5-1 years) and inter-annual (1-10 years) scale. PDO affects rainfall on both inter-annual (1-10 years) and decadal (10-21 years) scale. AMO affects rainfall only on the decadal (10-21 years) scale. The result that comes out from this study is useful for climate change assessment, agriculture, and water resources management points of view of the Northeast region of India.

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Section: Methodsmentioning

confidence: 99%

Influence of climatic indices (AMO, PDO, and ENSO) and temperature on rainfall in the Northeast Region of India

2020

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“…These advantages have led many ood warnings to use satellite precipitation products as input to hydrological models (Agarwal et al, 2020;Gao et al, 2018; Y. Liu et al, 2018;Sulugodu & Deka, 2019). In addition to precipitation products, Numerical Weather Prediction (NWP) has become a tool in operational meteorological research and forecasting in recent years (Lu et al, 2020;Mayer & Yang, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)

Sarvestan,

Barati,

Shamsipour

et al. 2024

Nat Hazards

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Flood early warning requires rainfall data with a high temporal and spatial resolution for ood risk analysis to simulate ood dynamics in all small and large basins. However, such high-quality data are still very scarce in many developing countries. In this research, in order to identify the best and most up-to-date rainfall estimation tools for early ood forecasting in arid and semi-arid regions, the northeastern region of Iran with 17 meteorological stations and four rainfall events was investigated. The rainfall products of satellites (PERSIANN-CDR and GSMaP, ERA5, GPM CHIRPS) along with the most widely used microphysical schemes of Weather Research and Forecasting (WRF) model (Purdue-Lin (Lin), WRF Single-Moment class 3, 6 (WSM3, WSM6), and WRF Double-Moment class 6 (WDM6).were used for rainfall modeling. The e ciency of each of these models to forecasting the amount of rainfall was veri ed by four methods: Threat Scores (TS), False Alarm Ratio (FAR), Hit Rate (H), and False Alarm (F). Analysis of research ndings showed that the WRF meteorological model has better accuracy in rainfall modeling for the next 24 hours. In this model, Lin's microphysical scheme has the highest accuracy, and its threat score (TS) quantity is up to 98% e cient in some stations. The best accuracy of satellite products for estimating the amount of rainfall is up to 50%. This accuracy value is related to the satellite product (ERA5). In this method, an 18 km distance from the ground station is the best distance for setting up the space station, which is used for input to hydrological/hydraulic models. Based on the results of this research, by using the connection of the WRF model with hydrology/hydraulic models, it is possible to predict and simulate rainfall-runoff up to 72 hours before its occurrence. Also, by using these space stations, the amount of rainfall is estimated for the entire area of the basin and an early ood warning is issued.

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Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods

Yeditha

Kasi

Rathinasamy

et al. 2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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An accurate and timely forecast of extreme events can mitigate negative impacts and enhance preparedness. Real-time forecasting of extreme flood events with longer lead times is difficult for regions with sparse rain gauges, and in such situations, satellite precipitation could be a better alternative. Machine learning methods have shown promising results for flood forecasting with minimum variables indicating the underlying nonlinear complex hydrologic system. Integration of machine learning methods in extreme event forecasting motivates us to develop reliable flood forecasting models that are simple, accurate, and applicable in data scare regions. In this study, we develop a forecasting method using the satellite precipitation product and wavelet-based machine learning models. We test the proposed approach in the flood-prone Vamsadhara river basin, India. The validation results show that the proposed method is promising and has the potential to forecast extreme flood events with longer lead times in comparison with the other benchmark models.

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Analysis of the impact of precipitation and temperature on the streamflow of the Ürümqi River, Tianshan Mountain, China

Cited by 4 publications

References 45 publications

Influence of climatic indices (AMO, PDO, and ENSO) and temperature on rainfall in the Northeast Region of India

Influence of climatic indices (AMO, PDO, and ENSO) and temperature on rainfall in the Northeast Region of India

Evaluation of the performance of satellite products and microphysical schemes with the aim of forecasting early flood warnings in arid and semi-arid regions (a case study of northeastern Iran)

Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods

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