The upper tail of a flood frequency distribution is always specifically concerned with flood control. However, different model selection criteria often give different optimal distributions when the focus is on the upper tail of distribution. With emphasis on the upper-tail behavior, five distribution selection criteria including two hypothesis tests and three information-based criteria are evaluated in selecting the best fitted distribution from eight widely used distributions by using datasets from Thames River, Wabash River, Beijiang River and Huai River. The performance of the five selection criteria is verified by using a composite criterion with focus on upper tail events. This paper demonstrated an approach for optimally selecting suitable flood frequency distributions. Results illustrate that (1) there are different selections of frequency distributions in the four rivers by using hypothesis tests and information-based criteria approaches. Hypothesis tests are more likely to choose complex, parametric models, and information-based criteria prefer to choose simple, effective models. Different selection criteria have no particular tendency toward the tail of the distribution; (2) The information-based criteria perform better than hypothesis tests in most cases when the focus is on the goodness of predictions of the extreme upper tail events. The distributions selected by information-based criteria are more likely to be close to true values than the distributions selected by hypothesis test methods in the upper tail of the frequency curve; (3) The proposed composite criterion not only can select the optimal distribution, but also can evaluate the error of estimated value, which often plays an important role in the risk assessment and engineering design. In order to decide on a particular distribution to fit the high flow, it would be better to use the composite criterion.

Climate change and human activities are the primary drivers influencing changes in runoff dynamics. However, current understanding of future hydrological processes under scenarios of gradual climate change and escalating human activities remains uncertain, particularly in tropical regions affected by deforestation. Based on this, we employed the SWAT model coupled with the near future (2021–2040) and middle future (2041–2060) global climate models (GCMs) under four shared socioeconomic pathways (SSP1-2.6 (SSP1 + RCP2.6), SSP2-4.5 (SSP2 + RCP4.5), SSP3-7.0 (SSP3 + RCP7.0), and SSP5-8.5 (SSP5 + RCP8.5)) from the CMIP6 and the CA-Markov model to evaluate the runoff response to future environmental changes in the Dingan River Basin (DRB). The quantification of the impacts of climate change and land use change on future runoff changes was conducted. The results revealed a non-significant increasing trend in precipitation during the historical period (1999–2018). Furthermore, all three future scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) exhibited an upward trend in precipitation from 2021 to 2060. Notably, the SSP5-8.5 scenario demonstrated a highly significant increase (P < 0.01), while the SSP2-4.5 scenario displayed a non-significant decreasing trend. The future precipitation pattern exhibits a decrease during spring and winter, while showing an increase in summer and autumn. The temperature exhibited a significant increase (P < 0.05) across the four future scenarios, with amplitudes of 0.24 °C/(10 years), 0.36 °C/(10 years), 0.36 °C/(10 years), and 0.50 °C/(10 years) for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 respectively. The future trend of land use change entails a continuous increase in cultivated land and a corresponding decrease in artificial forest land. By 2032, the area of cultivated land is projected to witness a growth of 4.10%, while artificial forest coverage will experience a decline of 4.45%. Furthermore, by 2046, the extent of cultivated land is anticipated to expand by 4.41%, accompanied by a reduction in artificial forest cover amounting to 5.39%. The average annual runoff during the historical period was 53.35 m³/s, and the Mann-Kendall (MK) trend test showed that it exhibited a non-significant increasing trend. Compared with the historical period, the comprehensive impact of climate change and land use will cause changes in the runoff by 0.49%, 1.98%, − 3.13%, and 3.65% for the scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 in the near future, and − 3.24%, 1.30%, − 3.75% and 18.24% in the middle future respectively. The intra-annual variations in future runoff suggest an earlier peak and a more concentrated distribution of runoff during the wet season (May to October). Compared to historical periods, the total runoff in the wet season under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios increased by 6.53%, 8.91%, 7.17%, and 7.39%, respectively. The research findings offer significant insights into the future hydrological processes in tropical regions, while also serving as a valuable reference for watershed water resource management and disaster control.

The stationarity assumption of hydrological processes has long been compromised by human disturbances in river basins. The traditional hydrological extreme-value analysis method, i.e., “extreme value theory” which assumes stationarity of the time series, needs to be amended in order to adapt to these changes. In this paper, taking the East River basin, south China as a case study, a framework was put forward for selection of a suitable distribution curve for non-stationary flood series by using the time-varying moments (TVM). Data used for this study are the annual maximum daily flow of 1954–2009 at the Longchuan, Heyuan and Boluo Stations in the study basin. Five types of distribution curves and eight kinds of trend models, for a combination of 40 models, were evaluated and compared. The results showed that the flood series and optimal distribution curves in the East River basin have been significantly impacted by a continuously changing environment. With the increase of the degree of human influence, the thinner tails of distributions are more suitable for fitting the observed flow data, and the trend models are changed from CP (mean and standard deviation fitted by parabolic trend model) to CL (mean and standard deviation fitted by linear trend model) from upstream to downstream of the catchment. The design flood flow corresponding to a return period of more than 10 years at the Longchuan, Heyuan and Boluo Stations was overestimated by more than 28.36%, 53.24% and 26.06%, respectively if the non-stationarity of series is not considered and the traditional method is still used for calculation. The study reveals that in a changing environment, more advanced statistical methods that explicitly account for the non-stationarity of extreme flood characteristics are required.

Different regions exhibit different response patterns of hydrological process changes under changing environments. The hydrological response mechanism of underlying surface changes in tropical rainforest regions remains uncertain, so there is an urgent need to study the causes of hydrological changes in typical tropical watersheds. The sequential clustering analysis and Pettitt-Mann-Whitney test were employed to detect abrupt change points of runoff series for the Wanquan River Basin in the tropics, and the series was then divided into pre-impact period (1967–1990) and post-impact period (1991–2014). And the Soil and Water Assessment Tool (SWAT) model was used to simulate the runoff to quantify the impact of climate change, land use change and other human activities on runoff, which the latter two collectively referred to as “human activities” in this study. The findings reveal: 1) runoff series showed an abrupt change and a downward trend around the year 1990; the SWAT model has a good simulation in the Wanquan River Basin during the pre-impact period; the coefficients of determination (R²) for the calibration and validation periods are both 0.91, and the Nash-Sutcliffe model efficiency coefficients (NSE) for the calibration and validation periods are 0.89 and 0.86, respectively. 2) There was a considerable change in land use between 1967 and 2014, as evidenced by a decrease of 29.54% in natural forest and an increase of 54.90% in rubber. These land use changes were mostly caused by the transformation of tropical rainforests into rubber forests and orchards. 3) Runoff declined in the post-impact period from the pre-impact period, with climate change, land use change, and other human activities contributing 44.05%, 10.83%, and 45.12% to the runoff change, respectively. The conversion of tropical rain forests to rubber forests and orchards has indirectly led to a reduction in runoff. These results can provide a reference for understanding the evolution of water cycle for other tropical rivers.