Annual Runoff Time Series Research Articles

A comprehensive assessment of runoff dynamics in response to climate change and human activities in a typical karst watershed, southwest China

The Chengbi River Basin is a typical karst watershed in Southwest China. Understanding the effects of climate change (CC) and human activities (HAs) on hydrological process is important for regional water resources management and water security. However, a comprehensive assessment of the effects of CC and HAs on runoff dynamics at different time scales in the Chengbi River Basin is still lacking. To address these needs, we used Budyko Mezentsev-Choudhurdy-Yang and Slope change ratio of accumulative quantity methods to assess the contribution of the changing environment to annual and intra-annual runoff changes in the Chengbi River Basin. The results indicated that annual runoff time series was divided into the base phase Ta (1980–1996) and the change phase Tb (1997–2019). Compared to the natural status in Ta, the relative contributions of CC and HAs to the runoff increase in Tb were 154.86% and −54.86%. In addition, the shift in intra-annual runoff occurred in 2007 and was mainly caused by HAs, with a contribution rate of 76.22%. The increase in annual runoff in Tb could be attributed to the positive contribution of rainfall. Changes in rainfall and reservoir construction altered the original state of intra-annual runoff. Furthermore, the high degree of heterogeneity in the surface karst zone increased the runoff coefficient. The spatial unsaturation of the subsurface water-bearing media and rainfall patterns caused a significant lag effect in the response of surface runoff to rainfall. This study can help researchers and policy makers to better understand the response of karst runoff to changing environment and provide insights for future water resources management and flood control measures.

Hydrological Response of the Kunhar River Basin in Pakistan to Climate Change and Anthropogenic Impacts on Runoff Characteristics

Pakistan is amongst the most water-stressed countries in the world, with changes in the frequency of extreme events, notably droughts, under climate change expected to further increase water scarcity. This study examines the impacts of climate change and anthropogenic activities on the runoff of the Kunhar River Basin (KRB) in Pakistan. The Mann Kendall (MK) test detected statistically significant increasing trends in both precipitation and evapotranspiration during the period 1971–2010 over the basin, but with the lack of a statistically significant trend in runoff over the same time-period. Then, a change-point analysis identified changes in the temporal behavior of the annual runoff time series in 1996. Hence, the time series was divided into two time periods, i.e., prior to and after that change: 1971–1996 and 1997–2010, respectively. For the time-period prior to the change point, the analysis revealed a statistically significant increasing trend in precipitation, which is also reflected in the runoff time series, and a decreasing trend in evapotranspiration, albeit lacking statistical significance, was observed. After 1996, however, increasing trends in precipitation and runoff were detected, but the former lacked statistical significance, while no trend in evapotranspiration was noted. Through a hydrological modelling approach reconstructing the natural runoff of the KRB, a 16.1 m3/s (or 15.3%) reduction in the mean flow in the KRB was simulated for the period 1997–2010 in comparison to the period 1971–1996. The trend analyses and modeling study suggest the importance of anthropogenic activities on the variability of runoff over KRB since 1996. The changes in streamflow caused by irrigation, urbanization, and recreational activities, in addition to climate change, have influenced the regional water resources, and there is consequently an urgent need to adapt existing practices for the water requirements of the domestic, agricultural and energy sector to continue being met in the future.

Determination of variation uncertainty in runoff time series at multi-temporal scales

Abstract In order to survey the possible periodic, uncertainty and common features in runoff with multi-temporal scales, the empirical mode decomposition (EMD) method combined with the set pair analysis (SPA) method was applied, with data observed at Zhangjiashan hydrological station. The results showed that the flood season and annual runoff time series consisted of four intrinsic mode function (IMF) components, and the non-flood season time series exhibited three IMF components. Moreover, based on the different coupled set pairs from the time series, the identity, discrepancy, and contrary of different periods at multi-temporal scales were determined by the SPA method. The degree of connection μ between the flood season and annual runoff periods were the highest, with 0.94, 0.77, 0.7 and 0.73, respectively, and the μ between the flood periods and the non-flood periods were the lowest, with 0.66, 0.46, 0.24 and 0.24, respectively. Third, the maximum μ of each SPA appeared in the first mode function. In general, the different extractive periods decomposed by the EMD method reflected the average state of Jinghe River. Results also verified that runoff suffered from seasonal and periodic fluctuations, and fluctuations in the short-term corresponded to the most important variable. Therefore, the conclusions drawn in this study can improve water resources regulation and planning.

Uniform discrete wavelet spectrum for detection of hydrologic variability at multiple timescales

Detecting the signals of hydrologic variability, including both periodicities and trends, at multi-timescales with evaluation of their statistical significance is one of the primary goals in hydrology studies, but it is also a challenging task. While the discrete wavelet spectrum (DWS) has been widely used to meet this purpose, it has limitations in practical applications, especially with the difficulty in detecting the spatial heterogeneity in hydrologic variability. In this study, a uniform DWS method is developed by determining the spectral values of the reference DWS, which are used as a uniform criterion for significance evaluation to identify hydrologic signals and compare their spatial heterogeneity. Application of the uniform DWS method to three annual runoff (and the corresponding precipitation) time series in the Yarlung Tsangpo River basin indicates that the significance of the runoff variability increases from down- to up-streams. It is shown that the uncertainty becomes large when the variability at small timescales is considered in short hydrologic time series. It is also demonstrated that hydrologic variability at long timescales usually tends to have a non-monotonic trend and that a monotonic trend would misrepresent the true underlying behavior. The uniform DWS method proposed here, by identifying the periodicities, non-monotonic and monotonic trend with significance evaluation, has the potential for a much wider use in hydrologic and climate sciences.

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

Spatiotemporal variations in frozen ground and their impacts on hydrological components in the source region of the Yangtze River

The source region of the Yangtze River (SRYR), located on the eastern Tibetan Plateau, is an essential part of the Asian Water Tower and plays an important role in the downstream water resources. Significant changes in frozen ground caused by increases in air temperature have been widely reported in the past several decades, which has greatly affected regional runoff. This study evaluated the spatiotemporal variations in frozen ground and hydrological components by utilizing a geomorphology-based eco-hydrological model (GBEHM) and investigated the reasons for runoff changes based on the Budyko framework. The results showed that the area with an elevation range of 4700–4800 m located in the permafrost region was the main source area of runoff generation from 1981 to 2015. Compared with the permafrost region, the seasonally frozen ground (SFG) region had a larger ratio of annual evapotranspiration to annual precipitation, although the aridity indices in the two regions were very similar. From 1981 to 2015, the mean value of the maximum frozen depth of SFG (MFDSFG) decreased by 12.3 cm/10 a and the mean value of the active layer thickness (ALT) of permafrost increased by 4.2 cm/10 a. The annual runoff in the SFG region decreased, while that in the permafrost region increased. Runoff change was more sensitive to precipitation change in the higher altitude regions that were mainly covered by permafrost than in the lower altitude regions that were mainly covered by the SFG, while the evapotranspiration change in the transition zone was more sensitive to climate change. An abrupt change in the annual runoff time series was detected in 1989, 2004, and 2004 in the SFG region, the permafrost region and the entire SRYR, respectively, and the annual runoff change from period 1 (1981 to change point) to period 2 (change point + 1 to 2015) were −25.7 mm, 33.8 mm and 25.8 mm respectively. Frozen ground degradation contributed changes of −15.0 mm, −8.8 mm and −11.6 mm to the annual runoff in the SFG region, the permafrost region and the entire SRYR, respectively. This result implied that frozen ground degradation had a negative impact on regional runoff in the SRYR. These findings deepen our understanding of frozen ground and its hydrological changes and are helpful for water resource management in the SRYR.

Qualitative Approach for Assessing Runoff Temporal Dependence Through Geometrical Symmetry

Currently, noticeable changes in traditional hydrological patterns are being observed on the short and medium-term. These modifications are adding a growing variability on water resources behaviour, especially evident in its availability. Consequently, for a better understanding/knowledge of temporal alterations, it is crucial to develop new analytical strategies which are capable of capturing these modifications on its temporal behaviour. This challenge is here addressed via a purely stochastic methodology on annual runoff time series. This is performed through the propagation of temporal dependence strength over the time, by means of Causality, supported by Causal Reasoning (Bayes’ theorem), via the relative percentage of runoff change that a time-step produces on the following ones. The result is a dependence mitigation graph, whose analysis of its symmetry provides an innovative qualitative approach to assess time-dependency from a dynamic and continuous perspective against the classical, static and punctual result that a correlogram offers. This was evaluated/applied to four Spanish unregulated river sub-basins; firstly on two Douro/Duero River Basin exemplary case studies (the largest river basin at Iberian Peninsula) with a clearly opposite temporal behaviour, and subsequently applied to two watersheds belonging to Jucar River Basin (Iberian Peninsula Mediterranean side), characterised by suffering regular drought conditions.
 Keywords: Causal reasoning, Theorem of Bayes, Temporal dependence propagation, Runoff time series, Water resources management

Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on Gravitational Search Algorithm and Variational Mode Decomposition

Accurate annual runoff prediction plays an important role in modern water resources planning and management. Here, a hybrid model using evolutionary extreme learning machine and variational mode decomposition (VMD) is developed to forecast annual runoff time series. In the proposed method, the VMD method is first used to decompose the original streamflow into a series of disjoint subcomponents; second, each subcomponent is forecasted by constructing an appropriate extreme learning machine model while the gravitational search algorithm is adopted to tune the model parameters; finally, the aggregated output generated by the forecasting results of all the models is treated as the final simulated output. The annual runoff data series of three huge hydropower reservoirs in China are chosen to testify the performance of the proposed forecasting model. The results show that the developed model can outperform several traditional methods with respect to the employed statistical indexes. Thus, the decomposition-ensemble idea is helpful to yield accurate and stable forecasting results, while the proposed forecasting method can provide strong technical support for operators in water resources and power systems.

Study on the variation characteristics of annual rainfall and runoff in Oujiang River Basin of Zhejiang Province

In this paper, Mann-Kendall method and wavelet analysis method are used to analyze the annual rainfall and runoff data in Oujiang River Basin of Zhejiang Province, and the following conclusions are obtained: in the past 50 years, the annual average rainfall series increase and the annual runoff series decrease in Oujiang River Basin. The reason for this is that in 1960s, many hydraulic structures were built near Xuren station, such as river dams, which led to the rapid decline of runoff in the flood season. The results of Morlet wavelet analysis show that there is significant multi-time scale variation characteristic in the annual average rainfall and runoff series. The periodicity of the annual rainfall series exhibits three types of variation trends for different time-scales, i.e. 22-to 30-year scale, 10-to 22-year scale, and 3-to 10-year scale. While the periodicity of the annual runoff time series exhibits three types of variation trends for different time-scales, i.e. 15-to 25-year scale, 8-to 15-year scale, and 3-to 8-year scale.

The variation changes of the precipitation – runoff relationship

The variation of the rainfall-runoff relationship t will lead to the disappearance of the consistency assumption of engineering hydrology, which will influence the planning, design, operation management and development and utilization of water resources. Therefore, the variation diagnosis of the rainfall -runoff relationship has become one of the hot topics and key issues in this area. In this study, the variation points of the precipitation-runoff relationship are defined, the difference between the variation point and the mutation point is distinguished, and the method of variation classification is proposed based on the the dimensionless mean and variation coefficients. Then, a comprehensive diagnosis system of the rainfall-runoff relationship variation is constructed on the basis of systematically summarying and analyzing of the diagnosis method of rainfall - runoff relationship variation at home and abroad. Taking the Weihe River Basin as a case study, the comprehensive diagnosis system is verified by applying it to test the change point the annual runoff time series at the Huaxian hydrological station. And the results show that the comprehensive diagnosis method proposed in this paper is scientific and reasonable.

Understanding the Main Causes of Runoff Change by Hydrological Modeling: A Case Study in Luanhe River Basin, North China

In the traditional point of view, if there is a significant decreasing trend for a runoff time series, while no significant trend for a precipitation series is present, then an unreliable conclusion will be made that the land surface change is the main contributor to the runoff change. To test it, we selected four sub-watersheds in the Luanhe river basin as the study areas where land use has changed severely. We first detected the long-term rainfall and runoff trend by the Mann–Kendall test, Sen’s slope, and the moving average method, and found that the runoff had a decreasing trend at the 0.05 significance level, while the rainfall had no significant trend in all sub-watersheds. Then an orderly cluster analysis and moving T test method were used to detect the change point of the runoff series. We quantified the contributions of the land surface change and climate variability based on Soil and Water Assessment Tool (SWAT), and the contribution of climate variability accounted for more than 50%, which implies that climate change is the main factor of runoff decrease in the study areas. To further test this, a trend analysis of a reconstructed annual runoff time series under undisturbed conditions has been done. The results showed that in some sub-watersheds, although rainfall series had no significant decreasing trend, the runoff series had significant downward trend. This can be explained by the nonlinear relationship between rainfall and runoff. This study came to a different conclusion from the common view, which observes that runoff decrease is mainly caused by land surface change if rainfall series lacks a significantly decreasing trend.

A Hybrid Model for Annual Runoff Time Series Forecasting Using Elman Neural Network with Ensemble Empirical Mode Decomposition

Because of the complex nonstationary and nonlinear characteristics of annual runoff time series, it is difficult to achieve good prediction accuracy. In this paper, ensemble empirical mode decomposition (EEMD) coupled with Elman neural network (ENN)—namely the EEMD-ENN model—is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The annual runoff time series from four hydrological stations in the lower reaches of the four main rivers in the Dongting Lake basin, and one at the outlet of the lake, are used as a case study to test this new hybrid model. First, the nonstationary and nonlinear original annual runoff time series are decomposed to several relatively stable intrinsic mode functions (IMFs) by using EEMD. Then, each IMF is predicted by using ENN. Next, the predicted results of each IMF are aggregated as the final prediction results for the original annual runoff time series. Finally, five statistical indices are adopted to measure the performance of the proposed hybrid model compared with a back propagation (BP) neural network, EEMD-BP, and ENN models—mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (R) and Nash–Sutcliffe coefficient of efficiency (NSCE). The performance comparison results show that the proposed hybrid model performs better than the BP, EEMD-BP or ENN models. In short, the developed hybrid model can provide a significant improvement in annual runoff time series forecasting.

Annual runoff prediction of the upstream of Heihe River Basin, China

A prediction method of annual runoff is proposed for the upstream Heihe River Basin. The Mann-Kendall test, order cluster analysis, variance analysis and R/S analysis methods were employed to investigate trends, mutations, periods and persistence of annual runoff time series, respectively. Based on various components, the linear superposition method was applied to forecast annual runoff. The Nash-Sutcliffe efficiency coefficient (NSE), the mean absolute percentage error (MAPE) and the percentage of pass (POP) were utilized to evaluate the simulation and prediction effects of annual runoff. Moreover, the calibration period was between 1958 and 2010, while the validation period was from 2011 to 2015. The results show that i) the annual runoff time series had a significant increasing trend (0.65×108 m3/10a), a probable mutation at 2006 and two significant periods (6 years and 22 years) in the calibration period; ii) the randomization of annual runoff time series was improved after removing the trend, mutation and periodic components, with the Hurst exponents 0.52; and iii) the simulation effects of calibration period (NSE=0.74, MAPE=6.6% and POP=90.6%) and the prediction effects of validation period (MAPE=6.5%, POP=80%) jointly indicate that the annual runoff prediction method is suitable in this river basin.

Improving Forecasting Accuracy of Annual Runoff Time Series Using RBFN Based on EEMD Decomposition

Annual runoff forecasting is one of the most important applications for effective reservoir management. Given the time-varying and non-linear characteristics of river runoff data, a novel hybrid model is proposed to improve the forecasting accuracy. First, the original data of runoff is decomposed into a number of intrinsic mode functions (IMFs) and one residual term using the ensemble empirical mode decomposition (EEMD) method. Then, these sub-series are modeled respectively by radial basis function network (RBFN) model. Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff time series. The proposed hybrid model is examined by predicting the annual runoff of Maduwang station in Bahe River, China. The comparison results indicate that the proposed hybrid model can effectively enhance RBFN approach for annual runoff series forecasting accuracy and it is superior to the commonly used model like auto-regressive integrated moving average (ARIMA) and back-propagation network (BPNN).

Auto Regressive and Ensemble Empirical Mode Decomposition Hybrid Model for Annual Runoff Forecasting

Annual runoff forecasting remains a difficult task due to its complicated non-stationary characteristics. To solve this difficulty and improve the prediction accuracy, this paper proposes a novel hybrid model for annual runoff forecasting. This model is based on the ensemble empirical mode decomposition (EEMD) and Auto-Regressive (AR). And it is suitable for non-stationary time series. The proposed model is tested using the annual runoff data from four hydrologic stations in the upper reaches of the Fenhe River basin in China. The non-stationary original annual runoff time series is first decomposed into a limited number of intrinsic mode functions (IMFs) and one trend term using EEMD technique for making the time series stationary. Then, these IMFs are forecasted by establishing corresponding optimum AR models only stationary processes, and trend term is predicted by quadratic polynomial equation. At last, the prediction results of the modeled IMFs and trend term are summed to formulate an ensemble forecast for the original runoff series. The performance of the EEMD-AR hybrid model is compared with EMD-AR and single AR models. Results indicate that EEMD-AR hybrid model gives better accuracy in predicting annual runoff in the study area when compared to other models.

Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

A New Method of Change Point Detection Using Variable Fuzzy Sets Under Environmental Change

Change point detection is an effective tool to identity whether the hydrological data are of consistency. In this paper, Pettitt test was first used to detect change point for annual rainfall and runoff time series in 6 selected sub-watersheds of Luanhe river basin in Northeast part of China. Then we presented a method to detect change point according to the law of mutual change of quality and quantity in variable fuzzy sets. We chose the mean of time series as assessment index as in other change point detection methods, and defined 95 and 5 % quantiles of the time series as the supremum and infimum respectively. We selected a reference period (for example, the first 10 points of the time series) as the stationary period, and after the reference period, we checked the mean value of the time series point by point. We used this method in the 6 sub-watersheds of Luanhe river basin. The results of the 2 methods showed that most annual rainfall time series had no change point, and some annual runoff time series had change point in 1979 or 1981. Comparison of the 2 methods was made, and it indicated that Pettitt test provided reference for variable fuzzy sets method, but the latter provided more reasonable results than Pettitt test in this study. This method can also be used in other natural time series.

Quantitatively analyze the impact of land use/land cover change on annual runoff decrease

Annual runoff in Luanhe river basin was detected a downward trend and caused water crisis in Tianjin, China. To quantify the decreased runoff volume, Mann–Kendall test and Pettitt test were employed to check whether there existed significant trend and change points for annual rainfall and runoff time series in Panjiakou reservoir basin and 8 sub-watersheds. It was found that the annual runoff time series had a significant downward trend at 5 % confidence level, and the change point was at 1979 in Panjiakou reservoir watershed. Then double mass curve of annual rainfall and annual runoff was plotted, and two lines were fitted before and after 1979, respectively. Based on this method, the comprehensive effects of land use/land cover change on annual runoff were estimated. To further quantify the contributions of each main factor to annual runoff decrease, water stored in check dams and social water use in different periods were surveyed first. And then multi-linear regression was used to develop the relations between annual runoff and the driven factors. Water area decrease was identified to be the main factor contributing to annual runoff reduction. The results in this study can provide valuable information for water resources planners and policy makers.

Trends and Shifts in Time Series of Rainfall and Runoff in the Gambia River Watershed

For several decades, climate change and climate variability issues and their impacts on the hydrological regime of rivers have constituted a major topic for hydroclimatological sciences research and water resources planning policies. Understanding of these issues needs enough long time series of rainfall and runoff data covering a large period, and a comprehensive diagnosis of the existing trends and shifts in these time series of data. This can be done by applying robust statistical tests to relevant rainfall and runoff time annual series. The aim of this paper is to highlight the effect of climate change in the Gambia River Basin and its impacts on the availability of the water resources of this basin. To reach this objective, we have selected runoff time series of the Gambia River Basin at Mako, Kedougou Diaguéri streamgauges and rainfall time series at Koulountou’s rain gauge. Statistical tests for shift detection presented in the Khronostat software, such as Pettit, Hubert and Buishand ellipse tests are first used, Mann Kendall test for annual trend are then applied to check whether trends exist or not in these times series. When the null hypothesis of no trend is rejected, the non parametric Sen’s test is then applied to validate the Mann Kendall trend test and to estimate the magnitude of the trend and its direction. Tests for homogeneity show an increasing shift for rainfall time series of Koulountou raingauge and for runoff time series of Mako and Diaguéri and a decreasing shift for Kedougou streamgauge. According to the Mann Kendall trend test, there is an upward trend for Koulountou rainfall time series, and Mako and Diaguéri runoff time series, and a downward trend for Kedougou annual runoff time series. The Buishand ellipse and the Hubert test indicate generally the same year of the beginning of the shift. Interesting perspectives for decision makers in evaluation and precise management of water resources and water projects in the Gambia River basin are offered as well.