Nonstationarity in the occurrence rate of floods in the Tarim River basin, China, and related impacts of climate indices

Abstract

Amplification of floods in the Xinjiang, China, has been observed, but reports on their changing properties and underlying mechanisms are not available. In this study, occurrence rates of floods in the Tarim River basin, the largest inland arid river basin in China, were analyzed using the Kernel density estimation technique and bootstrap resampling method. Also analyzed were the occurrence rates of precipitation extremes using the POT (Peak over Threshold)-based sampling method. Both stationary and non-stationary models were developed using GAMLSS (Generalized Additive Models for Location, Scale and Shape) to model flood frequency with time, climate index, precipitation and temperature as major predictors. Results indicated: (1) two periods with increasing occurrence of floods, i.e., the late 1960s and the late 1990s with 3 considerable fluctuations around 2-3 flood events during time intervals between the late 1960s and the late 1990s; (2) changes in the occurrence rates of floods were subject to nonstationarity. A persistent increase of flood frequency and magnitude was observed during the 1990s and reached a peak value; (3) AMO (Atlantic Multidecadal Oscillation) and AO (Atlantic Oscillation) in winter were the key influencing climate indices impacting the occurrence rates of floods. However, NAO (North Atlantic Oscillation) and SOI (South Oscillation Index) are two principle factors that influence the occurrence rates of regional floods. The AIC (Akaike Information Criterion) values indicated that compared to the influence of climate indices, occurrence rates of floods seemed to be more sensitive to temperature and precipitation changes. Results of this study are important for flood management and development of mitigation measures.

Non‐stationarities in the occurrence rate of heavy precipitation across China and its relationship to climate teleconnection patterns

“…Thus the nonstationarities in the occurrence of extreme events such as floods, heavy precipitation events and tropical storms have received more attention, because estimations of these extreme event occurrence rates are related to model the dynamics of many ecological, social and economic processes (Villarini et al ., , ; Silva et al ., ; Gu et al ., , ; Mumby et al ., ). The major aims of the present study are to carry out an exploratory analysis on the variability of frequency of heavy precipitation across China by concerning the occurrence rates of heavy precipitation events (HPOR), and ascertain if the variability obeys the assumption of nonstationarity.…”

Investigating Relationships Between Australian Flooding and Large‐Scale Climate Indices and Possible Mechanism

“…This indicates that variation of flood across Australia is closely connected to the climate system and the climate indices have a remarkable impact on the variation FMF through their effects on atmospheric circulation. The change of SST has played a key role to the variation of extreme precipitation and floods events (Gu et al, ; Liu et al, ; Meredith et al, ; Zhang et al, ). However, generating floods are determined by multiple factors such as rainfall intensity, antecedent soil moisture, catchment area, catchment soil properties, and catchment slope.…”

“…The reliability of flood predictions is mainly dependent on the selected climate indices (Mallakpour & Villarini, 2016). It is challenging to choose the dominant climate index for predicting flooding (Gu et al, 2016;Risko & Martinez, 2014). However, identifying the dominant climate index is important for investigating the physical causalities behind the observed relationships (Gu et al, 2016;Mallakpour & Villarini, 2016;Xiao et al, 2015;Zhang et al, 2015).…”

“…It is challenging to choose the dominant climate index for predicting flooding (Gu et al, 2016;Risko & Martinez, 2014). However, identifying the dominant climate index is important for investigating the physical causalities behind the observed relationships (Gu et al, 2016;Mallakpour & Villarini, 2016;Xiao et al, 2015;Zhang et al, 2015). Therefore, the second objective of this study is to find the dominant climate index that can express the relationships between climate indices and FMF.…”

Low Flow Regimes of the Tarim River Basin, China: Probabilistic Behavior, Causes and Implications

“…Based on daily precipitation and temperature dataset of 23 gauging stations and annual streamflow data of five hydrological stations covering 1960-2005, Zhang et al (2010) analyzed spatio-temporal variations of climatic change and associated impacts on water resource and found that annual streamflows of headstream rivers, except Aksu River, did not exhibit significant trends [17,18]. analyzed the intra-annual, seasonal and inter-seasonal clustering of floods and investigated possible impacts of climate indices on the occurrence rates of floods [19,20]. Li et al (2016) developed a new agricultural drought index, which is based on the Variable Infiltration Capacity (VIC) model coupled with an irrigation scheme and a reservoir module.…”