Annual Streamflow Series Research Articles

Quantitative analysis for the response of streamflow variation to driving factors in seven major basins across China

1.2 billion people and 90% of the national GDP are concentrated in China's seven river basins (RB7). Streamflow variation of RB7 caused by climate change and human disturbance posing a severe challenge to the water resources security situation in China. Quantifying streamflow changes under these two stresses is crucial for deeply understanding hydrologic process and reasonably utilizing water resource. The present study analyzed the trends of data series with Sen’s slope estimator and Mann-Kendall test, and quantified the influences of precipitation (P), potential evapotranspiration (ET0) and underlying surface parameter (ω) on streamflow variation by Budyko hypothesis-based elasticity coefficient method in 9 catchments controlled by mainstream hydrological stations from seven major river basins across China. Results showed that: Annual streamflow in LRB, YRB1, and HaiRB displayed significant downward trends of −0.16, −0.67 and −0.53 mm·y−1 during 1961–2020. Except for HuaiRB, the annual streamflow series of other catchments experienced significant mutations during 1984–2000. The elasticity coefficient εP ranged from 1.65 to 3.56, εET0 ranged from −2.56 to −0.65, and εω ranged from −3.79 to −0.52. A higher sensitivity of streamflow to P and ω, comparing to ET0. Streamflow in the north basins was more sensitive to driving factors than in the south basins across China. Variations in annual streamflow in LRB and YZRB were mainly ascribed to the changes in P, with the contribution rates ranging 65.1–162%, while the underlying surface change dominated the streamflow reduction in SHRB, HaiRB, HuaiRB, YRB, and PRB, with the contribution rates ranging 60.0–163%. ET0 had a weak effect on streamflow change in most basins. The change of underlying surface ω was largely due to vegetation cover changing, urbanization expanding, water consumption increasing and water conservancy constructing.

Trend analysis of observed precipitation, temperature, and streamflow for Hadejia-Nguru wetlands catchment, Nigeria

This study investigated trends in hydro-climate data (precipitation, maximum/minimum temperatures, and streamflow) for the period 1980–2016 around a semi-arid Hadejia-Nguru wetlands (HNWs) in Northern Nigeria. Four meteorological stations (Bauchi, Kano, Hadejia, and Nguru) and three streamflow gauge statins (Hadejia, Katagum, and Gashua) were used to cover the HNWs’ catchment. The data was checked for normality using Anderson–Darling and Shapiro–Wilk tests. Pettit’s test was used to check for homogeneity in the data series, and absolute homogenization, using RHtestsV4 software, was applied to homogenize and pre-whiten the data series. Trend analysis was carried out using modified Mann–Kendall and Sen’s slope tests. Results revealed increasing positive trends at all the stations for monthly minimum, average annual minimum, average annual maximum, and annual average temperature data series, while monthly maximum temperature series of Bauchi and Kano stations indicated the presence of an increasing trend with magnitudes 0.0035 °C and 0.0019 °C; and no trend was detected for Hadejia and Nguru stations. For monthly precipitation, no trends were detected at all stations. However, an increasing trend was detected at Bauchi, Hadejia, and Nguru stations for mean annual precipitation with magnitude 7.7960 mm/year, 8.1766 mm/year, and 5.7214 mm/year, respectively. A decreasing trend was detected for monthly and annual streamflow series at Hadejia gauge station with magnitude − 0.0115 m3/s/month and − 3.7037 m3/s/year, respectively; and no trend was detected for monthly and annual streamflow series at Katagum and Gashua gauge stations. The trend analysis may be helpful for planning and management of water resources in the HNWs.

Wavelet transform-based trend analysis of streamflow and precipitation in Upper Blue Nile River basin

Study regionUpper Blue Nile River Basin, Ethiopia Study focusThis study aims to determine possible trends in monthly, seasonal and annual precipitation and streamflow series. The Discrete Wavelet Transform (DWT) was used to obtain detailed information about the time-frequency conditions of six meteorological and five streamflow stations during 1984–2014. The Mann-Kendall test was also applied to the original and the DWT components to determine which periodicities are dominant in reproducing the observed trends. The combined DWT and Mann-Kendall test has not been explored in the UBNRB context. We used three criteria to determine a suitable wavelet, decomposition level, and signal extension mode. From these criteria, the bias ratio between the wavelet approximation series and the original time series was proposed and explored in this study. New hydrological insights for the regionResults show that almost all the precipitation stations experience an insignificant but increasing trend, whereas the streamflow stations exhibit an increasing trend at a 5% significance level. The combined use of DWT and the Mann-Kendall test show that periodic events up to 4 years are more influential in affecting the observed trends.

Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations

Abstract. Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly sectoral water use data set (GMSWU), coupled with the dead capacity data of the large reservoirs within the LR basin and the Budyko hypothesis framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that started in October 2004. The annual streamflow and annual mean temperature time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant decreasing trend. (2) The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was due to HAs, such as the construction of hydropower stations within the study area. (3) The comparison with the other two methods showed that the CR of CC calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new framework proposed in this study were within 5 %. Therefore, the newly proposed framework, which considers the uncertainty of hydrological simulations, can accurately quantify the CR of CC and HAs to streamflow changes. (4) The quantitative results calculated by using the simulation results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other words, our novel framework could effectively solve the calculation errors caused by the “equifinality for different parameters” of hydrological simulations. (5) The results of this case study also showed that the reduction in the streamflow in June and November was mainly caused by decreased precipitation and increased evapotranspiration, while the changes in the streamflow in other months were mainly due to HAs such as the regulation of the constructed reservoirs. In general, the novel quantitative framework that considers the uncertainty of hydrological simulations presented in this study has validated an efficient alternative for quantifying the CR of CC and HAs to streamflow changes.

Exploring the Factors Affecting Streamflow Conditions in the Han River Basin from a Regional Perspective

Considerable attention has recently been focused on the impacts of climate change and human activities on river streamflow conditions. This study explored these effects using three hydrological modeling techniques such as multi-regression, a two-parameter hydrological model, and hydrological sensitivity analysis, followed by trend analysis and change point detection. The non-parametric Mann-Kendall test was used to analyze the trends in hydro-meteorological parameters. The non-parametric Pettitt test and double cumulative curve techniques were used to identify change points in annual streamflow series during 1978–2014. After determining the change point year to be 1997, the series were split into two parts: a pre-change (natural) period (1978–1997) and a post-change (human-induced) period (1998–2014). The hydrological models were calibrated and estimated for the pre-change (natural) period, which provided the relative change in annual streamflow for the post-change (human-induced) period. The contribution of climate variability ranged from 36.3% to 55.9%, and human activities accounted for 44.5% to 63.7% of streamflow variability. These results suggest that human activities are more impactful than climate variability. The outcomes of this study show that streamflow in the basin was influenced by climate variability, but human actions were also major driving forces in altering the streamflow.

Streamflow stationarity in a changing world

Whether river flows remain stationary is of great concern to hydrologists, water engineers, and society in general, yet is subject to substantial debate. Here we provide the first comprehensive assessment of the long-term stationarity of annual streamflow for 11 069 catchments globally. Our observation-based evidence shows that the long-term annual streamflow remains stationary in 79% of catchments with minimal human disturbance, indicating that historical climate change alone has not led to non-stationarity in annual streamflow series in most catchments. In direct contrast, we found streamflow has remained stationary in only 38% of those catchments where substantial human interventions have occurred. These results demonstrate the scale of the human impact on the freshwater system, and highlight the ongoing need for dealing with the impacts of direct human interventions to ensure successful water management into the future.

TIME SERIES TRENDS OF STREAMFLOW AND RAINFALL IN THE SANTO ANTÔNIO RIVER BASIN, BRAZIL

Detecting trends in streamflow and rainfall series can have great significance for proper water resource management. Thus, the objective of this study was to analyze the trends of historical streamflow and rainfall series using nonparametric statistical tests. A historical series of pluviometric and fluviometric gauges, which belong to the hydrometeorological network of the Brazilian Water National Agency in the Santo Antônio River Basin, Brazil, from 1985 to 2014 were used. By applying statistical tests, it was found that the time series are independent and random, and from the total 24 rainfall gauges evaluated, 12 presented nonstationary behavior, exhibiting mostly decreasing trends. Based on the six fluviometric gauges used for the annual streamflow series, only the annual data of one gauge tended to decrease to the minimum streamflow. However, for the monthly series, three gauges showed decreasing trends between July and September. This decrease in streamflow may be a consequence of rainfall reductions, high water demand, and changes in land use and cover.

Mapping abrupt streamflow shift in an abrupt climate shift through multiple change point methodologies: Brazil case study

ABSTRACT Three change-point methodologies were used to detect changes in the mean value of annual streamflow series and analyse simultaneous changes in large-scale global sea surface temperature (SST) oscillations. To verify the relationship between the variables we used wavelet coherence analysis. A preliminary detection skill test was performed using asynthetic series and Pruned Exact Linear Time (PELT) presented the best results among the methods used (Pettitt test, Bai and Perron algorithm) when combined with a penalty selection via the Changepoints for a Range of Penalties (CROPS) method. However, the use of classical penalty functions resulted in a poor performance of PELT. The three methods showed an extremely high convergence rate (> 90%) for the correct change points and a smaller rate for false positives (< 24%). Changes in the streamflow mean value coincided with phase shift of the low-frequency indices Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), also corroborated by the wavelet results. Most of the changes can be associated with phase shift impacts in the South Atlantic Convergence Zone.

Regional flood frequency analysis and uncertainties: Maximum streamflow estimates in ungauged basins in the region of Lavras, MG, Brazil

Regionalization techniques are an important alternative to overcome the scarcity of streamflow data and to provide adequate estimates for flood management. This work aims to generate a maximum streamflow regional function and evaluate the uncertainties in estimates for a basin in the South of Minas Gerais, Brazil. Six streamflow gauging stations, that presented stationarity and no trends according to the Mann-Kendal test, were used to develop the regional function by the dimensionless curve method. The annual maximum streamflow series were adjusted to the probability density functions (PDFs) 2-parameter Log-Normal, 3-paramenter Log-Normal, Gumbel, and Gamma. Then, the goodness-of-fit of the PDFs was verified by the Anderson-Darling test. For the regional analysis, heterogeneity and discordancy measures verified the homogeneity of the region, and the jack-knifing technique evaluated the predictive performance of the regional function by the coefficient c of Camargo and Sentelhas. The uncertainty analysis for at-site and regional function estimates was assessed by Monte Carlo simulations and bootstrapping. The performance of the regional function was classified as optimum for most of the watersheds, but as good when considering the prediction in certain return periods. The uncertainties were larger in ungauged basins, especially for greater return periods and drainage areas. The regional function developed can be employed in ungauged sites, but also to improve data in poorly monitored watersheds. Thus, this study demonstrates the adequate applicability of regionalization in ungauged watersheds and provides a simple alternative for maximum streamflow estimates in order to promote sustainable flood risk management.

Comparing the Performance of an Autoregressive State-Space Approach to the Linear Regression and Artificial Neural Network for Streamflow Estimation

Accurate streamflow estimation remains a great challenge although diverse modeling techniques have been developed during recent decades. In contrast to the process based models, the empirical data driven methods are easy to operate, require low computing capacity and yield fairly accurate outcomes, among which the state space (STATE) approach takes use of the temporal structures inherent in streamflow series and serves as a feasible solution for streamflow estimation. Yet this method has rarely been applied, neither its comparison with other methods. The objective was to compare the performance of an autoregressive STATE approach to the traditional multiple linear regression and artificial neural network in simulating annual streamflow series of 15 catchments located in the Loess Plateau of China. Annual data of streamflow (Q), precipitation (P) and potential evapotranspiration (PET) during 1961 ~ 2013 were collected. The results show that STATE was generally the most accurate method for Q estimation, explaining almost 90% of the total variance averaged over all the 15 catchments. The estimation of streamflow relied on its own of the previous year for most catchments. Besides, the impacts of P and PET on the temporal distribution of streamflow were almost equal. Missing data were estimated using the STATE method, which allowed inter annual trend analysis of the streamflow. Significant downward trends were manifested at all the 15 catchments during the study period and the corresponding slopes ranged from 0.24 to 1.71 mm/y. These findings hold important implications for hydrological modelling and management in China’s Loess Plateau and other arid and semi-arid regions

Power law distribution characteristics of daily streamflow in the Yellow River Basin under a changing environment

Abstract Natural streamflow series usually exhibit a power law tail with a magnitude–frequency relationship; however, it is not known how the power law distribution of daily streamflow varies under the influence of climate change and human activities. Based on the annual streamflow series of Toudaoguai, Longmen, Sanmenxia, Huayuankou, Gaocun, and Lijin stations located in the Yellow River in China, the Mann–Kendall and Mann–Whitney–Pettitt methods were used to identify change-points. Thereafter, the power law distributions of the daily streamflow series before and after abrupt changes were studied based on the two-parameter power law distribution method. The results reveal that, firstly, under the influence of human activities, abrupt changes have taken place in the streamflow series of the Yellow River, mainly in the storage year of large reservoirs. Secondly, the daily streamflow series at four out of six stations obey a power law distribution, however, with short tails. The power law characteristics of the series before the abrupt change are essentially consistent with the entire streamflow series; however, these characteristics gradually disappear after abrupt changes. Thirdly, human activities are the main factors underlying the variations in the power law distribution of daily streamflow in different periods. This study provides a way for the study of streamflow changes in the Yellow River and may also offer a scientific basis for water resources development and ecological restoration.

Stationarity in Annual Daily Maximum Streamflow Series in the Hydrographic Basin of the Upper Juruá River, Western Amazon

The present study has the objective of investigating the hypothesis of stationary behavior in the streamflow regime of the upper Juruá river basin. For this purpose, the annual maximum daily flow data recorded at stations located in Cruzeiro do Sul and Marechal Thaumaturgo, AC, Brazil, were analyzed using the Mann-Kendall, Pettitt, and Spearman’s Rho tests. The results show sufficient evidence to conclude that, in Cruzeiro do Sul, the flow rates present an ascending temporal behavior, while in Marechal Thaumaturgo it is descending. However, this tendency is not significant (p&lt;0.05), allowing to conclude in favor of the stationarity hypothesis in the streamflow series of the referred basin. Thus, one of the physical implications associated to the results obtained may be the conclusion that the streamflow regime in the Upper Juruá basin has not changed significantly over time.

Quantifying the Impacts of Climate Change, Coal Mining and Soil and Water Conservation on Streamflow in a Coal Mining Concentrated Watershed on the Loess Plateau, China

The streamflow has declined significantly in the coal mining concentrated watershed of the Loess Plateau, China, since the 1970s. Quantifying the impact of climate change, coal mining and soil and water conservation (SWC), which are mainly human activities, on streamflow is essential not only for understanding the mechanism of hydrological response, but also for water resource management in the catchment. In this study, the trend of annual streamflow series by Mann-Kendall test has been analyzed, and years showing abrupt changes have been detected using the cumulative anomaly curves and Pettitt test. The contribution of climate change, coal mining and SWC on streamflow has been separated with the monthly water-balance model (MWBM) and field investigation. The results showed: (1) The streamflow had an statistically significant downward trend during 1955–2013; (2) The two break points were in 1979 and 1996; (3) Relative to the baseline period, i.e., 1955–1978, the mean annual streamflow reduction in 1979–1996 was mainly affected by climate change, which was responsible for a decreased annual streamflow of 12.70 mm, for 70.95%, while coal mining and SWC resulted in a runoff reduction of 2.15 mm, 12.01% and 3.05mm, 17.04%, respectively; (4) In a recent period, i.e., 1997–2013, the impact of coal mining on streamflow reduction was dominant, reaching 29.88 mm, 54.24%. At the same time, the declining mean annual streamflow induced through climate change and SWC were 13.01 mm, 23.62% and 12.20 mm, 22.14%, respectively.

Environmental Flow Assessment Considering Inter- and Intra-Annual Streamflow Variability under the Context of Non-Stationarity

A key challenge to environmental flow assessment in many rivers is to evaluate how much of the discharge flow should be retained in the river in order to maintain the integrity and valued features of riverine ecosystems. With the increasing impact of climate change and human activities on riverine ecosystems, the natural flow regime paradigm in many rivers has become non-stationary conditions, which is a new challenge to the assessment of environmental flow. This study presents a useful framework to (1) detect change points in runoff time series using two statistical methods (Mann-Kendall test method and heuristic segmentation method), (2) adjust data of the changed period against the original flow series into a stationary condition using a procedure of reconstruction; and (3) incorporate inter- and intra-annual streamflow variability with adjusted streamflow to evaluate environmental flow. The Jialing to Han inter-basin water transfer project was selected as the case study. Results indicate that a change point of 1994 was identified, revealing that the stationarity of annual streamflow series is invalid. The variations of reconstructed streamflow series are roughly consistent with original streamflow series, especially in the maximum/minimum values and rise/fall rates, but the mean value of reconstructed streamflow series is increased. The reconstructed streamflow series would further serve to eliminate the non-stationary of original streamflow, and incorporating the inter- and intra-annual variability would upgrade the ecosystem fitness. Selecting different criteria for the conservation of riverine ecosystems can have significantly different consequences, and we should not focus on the protection of specific objectives that will inevitably affect other aspects. This study provides a useful framework for environmental flow assessment and can be applied to a wide range of instream flow management approaches to protect the riverine ecosystem.

Simulation of time series using periodic gamma autoregressive models

AbstractPeriodic autoregressive models are frequently used to model hydrologic series. In the literature, annual streamflow series are approximated by normal distribution. However, for short periods (daily, weekly, monthly) this is no longer the case, particularly due to the data skewness. A new class of first‐order model was, therefore, studied in an attempt to overcome this problem. The model has an autoregressive structure and can be additive, multiplicative, or hybrid, but with gamma marginal distribution. Furthermore, the classical model assumes that the method of moments is effective for parameters estimation. For the first time, this paper undertakes a complete analysis of the hybrid model for this context and the novelty lies in the parameter estimation via maximization of the likelihood function. For an application in the Brazilian case, the additive model proved to be more effective than previously reported and the method allows high orders and skewed distributions.

Homogeneity analysis of streamflow records in arid and semi-arid regions of northwestern Iran

Homogeneity analysis of the streamflow time series is essential to hydrological modeling, water resources management and climate change studies. In this study, five absolute homogeneity tests and one clustering approach were used to determine the homogeneity status of the streamflow time series (over the period 1960–2010) in 14 hydrometric stations of three important basins (i.e., Aras River Basin, Urmia Lake Basin and Sefid-Roud Basin) in northwestern Iran. Results of the Buishand range test, von Neumann ratio test, cumulative deviation test, standard normal homogeneity test and Pettitt test for monthly streamflow time series detected that about 42.26%, 38.09%, 33.33%, 39.28% and 68.45% of the streamflow time series were inhomogeneous at the 0.01 significance level, respectively. Streamflow time series of the stations located in the eastern parts of the study area or within the Urmia Lake Basin were mostly homogeneous. In contrast, streamflow time series in the stations of the Aras River Basin and Sefied-Roud Basin showed inhomogeneity at annual scales. Based on the overall classification for the monthly and annual streamflow series, we determined that about 45.60%, 11.53% and 42.85% of the time series were categorized into the ‘useful’, ‘doubtful’ and ‘suspect’ classes according to the five absolute homogeneity tests. We also found the homogeneity patterns of the streamflow time series by using the clustering approach. The results suggested the effectiveness of the clustering approach for homogeneity analysis of the streamflow time series in addition to the absolute homogeneity tests. Moreover, results of the absolute homogeneity tests and clustering approach indicated obvious decreasing change points of the streamflow time series in the 1990s over the three basins, which were mostly related to the hydrological droughts.

Regional flood frequency analysis using L‐moments for geographically defined regions: An assessment in Brazil

The devastating effects of floods, combined with scarce data sets, have stimulated the development of hydrological regionalisation techniques. The present study proposed an evaluation of the L‐moments based index‐flood procedure, coupled with watershed grouping based on geographical convenience for regionalisation of maximum streamflows. A pioneer analysis for South America, addressing the geographical classification method adopted by National Water Agency of Brazil (ANA), was conducted considering over 100 watersheds in southern Brazil. Nonstationary and discordant maximum annual streamflow (MAS) series were removed with the aid of the Mann–Kendall test and discordancy measure, whereas the heterogeneity measure was used to check regional homogeneity. The best regional distribution was identified by the Z DIST goodness‐of‐fit measure. Finally, multiple nonlinear regressions, considering morphological and meteorological watershed characteristics, were performed to obtain better index‐flood estimates. It was concluded that: (a) the proposed methodological strategy provided satisfactory estimation of design floods; (b) area, mean slope, stream gradient, and flow length, were the most satisfactory explanatory variables; (c) the fitted equations stand out as a state‐of‐art alternative for the scarce hydrological monitoring in southern Brazil; and (d) the hydrological boundaries defined by ANA might not be the most adequate approach from a regional point of view.

Nonstationarity in maximum annual daily streamflow series from Southern Brazil

ABSTRACT Extreme streamflow values estimates are important for flood risk assessment and also for the design and operation of hydraulic structures. The behavior of this hydrological variable is under climate and land use changes effects and river’s course modifications caused by construction and operation of large reservoirs. The assumption of stationarity commonly adopted in flood frequency and magnitude analysis studies is questionable under such circumstances. In this work we identified nonstationary annual maximum streamflow series corresponding to fluviometric gauges located in Southern Brazil. A nonstationary frequency model was applied to those series and the results were compared with those of a stationary model. We also evaluated the presence of abrupt changes in the series. The results indicate that 75 of 157 series of Southern Brazil may be considered nonstationary, most of which are in the Iguazu, the Paranapanema and the Uruguay basins. For a planning horizon equal to 10 years, the return period of the present 100 years flood changes to 48-75 years when considering the nonstationary model, respectively. Abrupt changes were identified mainly as occurring in the 70’s.

At-Site Flood Frequency Analysis Coupled with Multiparameter Probability Distributions

Abstract The proper design of hydraulic structures as well as river basin management are directly dependent on adequate estimates of maximum streamflow, preferably obtained from long historical series. However, the scarce hydrological monitoring, recurrent in developing countries and the need for estimates associated with high return periods (RPs) have led to the use of estimation methods based statistical procedures, such as at-site flood frequency analysis. This study presents a framework for at-site flood frequency analysis coupled with multiparameter probability distribution functions (PDFs) (GEV, LN3, PE3, GLO, GPA, KAP and WAK), in which all the statistical procedures are derived from L-moments, in order to investigate the applicability of these PDFs in comparison to those of 2-parameters (EV1, LN2 and Gamma). The modeling framework was evaluated considering 106 maximum annual streamflow (MAS) series for the Rio Grande do Sul State - Brazil. PDFs’ goodness-of-fit was studied in accordance with the Anderson-Darling test. It can be concluded that: i) the multiparameter distributions, especially KAP and WAK, had performance superior to the traditional 2-parameter distributions, providing a greater number of historical series better adjusted by such multiparameter PDFs; ii) shorter series were usually better represented by GEV when compared to the other PDFs, which is an important characteristic when long historical series are not frequently available; and iii) the quantile estimates derived from multiparameter PDFs presented lower Relative Absolute Error, thus emphasizing the importance of using such PDFs in water resources management and engineering projects.

Comprehensive evaluation of multiple methods for assessing water resources variability of a lake–river system under the changing environment

Abstract The inherent regularities of hydrological processes will not be stationary under the changing environment, making the evaluation of hydrological characteristics and trend forecasting face great uncertainty. Based on the observed streamflow series in the Poyang Lake catchment during the period 1960–2013, this study investigated the evolution characteristics and future trend of water resources. The extracted trend component by wavelet decomposition and the reliability of trend forecasting by rescaled range analysis (R/S analysis) were verified and assessed by a non-parametric statistical test. Results indicate a change point at 1995 when the trend of annual streamflows across the catchment changed. Periodic components indicated that the lake basin seems to enter into an increasing process under inter-decadal scale, while decreasing under inter-annual scale in the near future. R/S analysis revealed that annual streamflow series of most stations in Poyang Lake show ‘weaker’ or ‘very weak’ persistence. For the whole study period, Hurst exponents of most stations are less than 0.5 (0.224–0.339), therefore, opposite from the increasing trend of historical series, decreasing trend is forecasted for the basin. However, due to the effect of intensified human activities in the Fuhe sub-basin, the reliability of R/S analysis on trend forecasting is disturbed at Lijiadu station.