Streamflow Time Research Articles

Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

AbstractThe objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

Accounting for Temporal Variability of Streamflow in Estimates of Travel Time

Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to that account for the effects of streamflow variability on travel time and compare our estimates with to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15-minute interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60 to 90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because heterogeneous surficial geology, hydro-climatology, and vegetation and applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.

Current and future projections of glacier contribution to streamflow in the upper Athabasca River Basin

The Athabasca River Basin in Alberta, Canada, is of international significance, and understanding water supply is critically important to the sustainability of the region. In the upper Athabasca River Basin, glaciers covered 272 km2 (2.8% of the watershed) as of 2010, but less than 50 km2 are projected to remain by the end of the twenty-first century. This study investigated glacier contributions to streamflow for a baseline (1981–2010) period and two climate change scenarios (RCP 4.5 and RCP 8.5, 2011–2100) using a hydrological model that explicitly accounts for glacier retreat. Simulated discharge from glaciers (i.e. including all glacier ice, firn and snow melt) contributed 4%–6% of annual streamflow and 20%–30% in August during the baseline period. The model projects an increase in glacier discharge in the coming decades with a later decline by the middle of the twenty-first century. Although glacier discharge is projected to increase in the short term, this water source is non-renewable barring a stabilization of air temperatures in the region, something not currently projected by any climate model. While streamflow in the upper Athabasca River Basin was projected to increase on an annual time scale, streamflow during the late-summer months was projected to decrease by up to 58% by 2100. These simulations follow findings from across western Canada and globally, highlighting that ‘peak water’ is not solely driven by changes in glacier discharge and is also responsive to changes in climate. Late summer is a period when water demands are the highest, water shortages already occur, water quality is the most sensitive and the ability to navigate throughout watercourses in the basin is limited. These findings emphasize the importance of changes in streamflow timing and seasonality and present major challenges for water management in the Athabasca River Basin and elsewhere in glacierized catchments over the coming decades.

Hydrologic alteration and potential ecosystemic implications under a changing climate in the Chitral River, Hindukush region, Pakistan

Abstract This study investigates changes in the hydrologic regime of the Chitral River, Hindukush–Karakoram–Himalayan (HKH) region, Pakistan. Different statistically based methods were used to assess climate change-induced hydrologic alterations that can possibly impact aquatic habitat in the study region. The hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) was calibrated, validated, and applied to predict streamflow in the Chitral River basin. The HBV model was forced with the ensemble of four general circulation models under different representative concentration pathway emission scenarios to generate future streamflow under climate change conditions in the basin for the mid-twenty-first century. The results of this study show that hydrologic regimes in the study area, expressed by the magnitude, duration, frequency, timing, and rate of streamflow, are likely to alter in the future. Positive (i.e., with increased frequency) hydrologic alteration is projected for most flow parameters under all scenarios for the 2021–2050 period compared with values observed during the historical period (1976–2005). These hydrologic alterations might have impacts on fish and migratory bird species in the study area. This research can be helpful in providing practical information for more effective water resources and aquatic ecosystem management in the HKH region.

Constraining water age dynamics in a south‐eastern Australian catchment using an age‐ranked storage and stable isotope approach

AbstractImproving our knowledge of the travel times of water through catchments is critical for the management and protection of water resources and to improve our understanding of fundamental catchment behaviour. In this study we use the age‐ranked storage framework StorAge Selection (SAS) to investigate travel times in the Corin catchment, a headwater catchment in the south‐east of Australia covered by native Eucalyptus species. Few studies have applied the SAS framework globally and in energy‐intensive areas where catchment losses are heavily in favour of evapotranspiration relative to streamflow. A combination of observed and modelled values of oxygen‐18 (δ18O), the stable isotope in water, are used to constrain storage selection preferences of streamflow and evapotranspiration and the size of the catchment active storage. The highest performing parameter combinations that could reproduce δ18O in streamflow were dependent on a strong preference for young water in evapotranspiration, and a mixture of weak young and old water preference in streamflow. The mean travel time of streamflow over the study period 2007–2019, weighted by the flow rate, is limited to within a probable range of 2.81–9.77 years. The size of the active storage, a key parameter in the SAS framework, was poorly identified, and in combination with the isotopic inputs into the model, contributed to the uncertainty of the results. We discuss the implications of the results with respect to the study area, as well as within the context of SAS research globally and identify ways to improve the modelling process.

Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.

Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach

Abstract. Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but as yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets (PRSim.wave) which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of the kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States. We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional water management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential. Highlights. Stochastic simulation of continuous streamflow time series using an empirical, wavelet-based, spatio-temporal model in combination with the parametric kappa distribution. Generation of stochastic time series at multiple sites showing temporal short- and long-range dependence, non-stationarities, and spatial dependence in extreme events. Implementation of PRSim.wave in R package PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization.

Trends in Global Flood and Streamflow Timing Based on Local Water Year

AbstractAnalysis of flood and streamflow timing has recently gained prominence as a tool for attribution of climatic changes to flooding. Such studies generally apply circular statistics to the day of maximum flow in a calendar year and use nonparametric linear trend tests to investigate changes in flooding on a local or regional scale. Here we investigate both the center timing of streamflow and the day of maximum flow using a local water year. For each station, the start of the water year is defined as the month of lowest average monthly streamflow. This definition of water year prevents ambiguity in the direction of computed trends and enables flood and streamflow timing to be described by a normal distribution. Using the assumption of normality, we calculate the historical trend in both flood and streamflow timing using linear regression. While shifts in flood and streamflow timing are consistent with climate change and are shifting in a similar direction, shifts in the timing of the annual maxima flood are approximately three times that of streamflow timing. The results here have implications for water resources and environmental management where streamflow and flood timing are critical to planning. The applicability of the normal approximation to flood and streamflow timing will enable future analyses to use parametric statistics.

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

Five centuries of reconstructed streamflow in Athabasca River Basin, Canada: Non-stationarity and teleconnection to climate patterns

Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489–2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the applications of available tree ring predictors. Based on results of goodness-of-fit tests, the HBR developed was skillful and reliable in reconstructing the streamflow of ARB. From five centuries of reconstructed streamflow for ARB, five or six abrupt change points are detected. The streamflow time series obtained from a backward moving, 46-year window for six gauging sites in ARB vary significantly over five centuries (1489–2006) and at times could exceed the 90% and/or 95% confidence intervals, denoting significant non-stationarities. Apparently changes in the mean state and the lag-1 autocorrelation of reconstructed streamflow across the gauging sites can be similar or radically different from each other. These nonstationary features imply that the default stationary assumption is not applicable in ARB. Further, the reconstructed streamflow shows statistically significant oscillations at interannual, interdecadal and multidecadal time scales and are teleconnected to climate patterns such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO). A composite analysis shows that La Niña (El Niño), cold (warm) PDO, and cold (warm) AMO events are typically associated with increased (decreased) streamflow anomalies of ARB. The reconstructed streamflow data provides us the full range of streamflow variability and recurrence characteristics of extremes spanned over five centuries from which it is useful for us to evaluate and manage the current water systems of ARB more effectively and a better risk analysis of future droughts of ARB.

Insights on Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, {\lambda} of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of {\lambda} and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.

Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R2 = 0.88; NS = 0.88; RMSE = 142.30 (m3/s); MAE = 88.94 (m3/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R2 = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m3/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.

Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

The effect of seasonal variation in precipitation and evapotranspiration on the transient travel time distributions

Precipitation (P), plant water use, and evaporation from the soil surface control the travel time of streamflow (Q) and evapotranspiration (ET) in a complex way. However, the impact of soil moisture and energy availability on the travel time distribution (TTD) of evaporated and transpired waters are yet less understood. In this study, we investigate how the seasonal variability of P and ET in terms of phase shift and rate influences the temporal dynamics of TTDs. To this end, we choose four contrasting climate types described as in-phase P and ET, out-of-phase P and ET, year-round constant P with seasonal ET, and year-round constant ET with seasonal P. We use a physically-based hydrological model to simulate dominant processes in the water and energy cycles as well as subsurface flow velocity, which are subsequently used in a Lagrangian particle-tracking model to characterize the age distribution of water particles in space and time. The results prove that the soil moisture availability than the ET rate imposes a stronger control on the TTD of transpired water in all climate scenarios. Plants indicate an overall tendency towards younger water particles unless in periods with a pronounced dry condition when old waters are more available in the deeper soil layers. In particular, the climate scenario with out-of-phase P and ET yields the highest percentage of old particles taken up by plants. Furthermore, ET age sampling mechanism shows hysteresis against the ET rate, with an opposite direction in climates with in-phase and out-of-phase P and ET. Our results also suggest that the ratio between the median travel time of Q and the median age of water storage has a dual relationship with the Q rate, indicating the existence of a threshold behavior that distinguishes the direct and inverse storage effects based on the storage volume.

Signature-based multi-modelling and multi-objective calibration of hydrologic models: Application in flood forecasting for Canadian Prairies

Multi-modelling aims to make use of the strengths of single hydrologic models to improve the accuracy of simulating the watershed system behavior. Considering hydrological signatures such as the flow duration curve segmentation in the calibration of each hydrologic model leads to a better parameter identifiability. In this study, a novel weighted average model-wrapper based on flow duration curve segmentation is introduced to aggregate the calibrated models into a multi-model. The proposed framework is applied to develop a model-wrapper of the Upper Assiniboine River Basin for flood forecasting upstream of the Shellmouth reservoir in the Prairie region of Canada. The HEC-HMS, HBV-EC, HSPF, and WATFLOOD hydrologic models that are being used at the Hydrologic Forecast Centre of Manitoba Infrastructure for operational inflow forecasting are calibrated using signature-based multi-objective optimization. These models have significantly different structural complexities. The calibration of each of these models is set up as three simulation-optimization problems with different objective functions to balance the model capability in simulating multiple important hydrological signatures. Results show that the model-wrapper outperforms each of the single calibrated models that are of operational use at Manitoba Infrastructure, e.g. NSE improved from 0.44 for the best individual model to 0.76 for the model-wrapper in the calibration period. Moreover, the weights associated with each hydrologic model component indicate the contribution rate of the individual models to the model-wrapper in high-flow, mid-flow, and low-flow portions of streamflow time series. Quantifying the contribution of each model component provides a deeper insight into model selection strategy, especially when a component has minimal or no contribution, e.g. HEC-HMS and HBV-EC in this paper, to the model-wrapper performance in all ranges of streamflow simulation compared to other model components.

Optimizing climate model selection for hydrological modeling: A case study in the Maumee River basin using the SWAT

This study develops a new method for selecting the most representative climate model for use in the Soil and Water Assessment Tool (SWAT) and evaluates this method in the Maumee River Basin. Downscaled temperature and precipitation data from 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) models were used to drive the SWAT. Our model selection approach uses a sensitivity analysis to evaluate the impacts of temperature and precipitation on streamflow and to determine the optimal weights for both variables. This weighting scheme is then used to rank the performance of all climate models and identify which ones are most representative of climate forcing required by the hydrological model. The results of our sensitivity analysis indicate that the response of streamflow to precipitation is consistent throughout the year, while the response to temperature is more complex. During the warm season, higher temperatures cause evapotranspiration to increase and streamflow to decrease; during the cool season, higher temperatures cause more snowmelt and this causes streamflow to increase. Based on our weighting scheme, the GISS-E2-R was identified as the most representative climate model in the Maumee River Basin. We then compared the performance of SWAT-simulated streamflow using all CMIP5 models. The most representative climate model ranked first based on correlation and mean absolute error, and second based on Nash-Sutcliffe efficiency, while the model selected using a simple average method was ranked fifth, fourth and twelfth, respectively. Based on the evaluation of other key variables, such as sediment transport, nitrogen and phosphorus, the optimal climate model also has a higher correlation and a more representative standard deviation than the other CMIP5 models. The selected model also performs better than the 32-model ensemble mean based on correlation and Taylor’s skill score. When applying this method at different time scales, the responding time of streamflow to climate factors needs to be considered. The model selection method that was developed in this paper can be applied by hydrological modelers to select the most representative climate models for evaluating the impacts of climate change in other watersheds around the world.

Investigating the impacts of climate change and human activities on hydrological drought using non-stationary approaches

Climate variation and human activities are two prime drivers influencing the hydrological cycle and the stationarity of hydrologic systems. Hydrological drought is caused by significant negative variation from normal hydrologic conditions, influenced by climate variation and human activities. Traditional methods employed for quantifying the impacts of climate change and human activities are based on the assumption of stationarity, which is no longer valid under the current changing environment. In this study, a heuristic segmentation method was used to identify the change point in streamflow time series by considering its non-linearity, and trend analysis was performed on hydro-meteorological variables before and after the change point. A non-stationary Standardized Runoff Index (SRINS) was constructed using Generalized Additive Models in Location, Scale, and Shape (GAMLSS). Finally, the influences of climate change and human activities on hydrological drought were quantified at various time scales. The results reveal change points in the streamflow time series after the 1990s. Significant decreases in precipitation and streamflow were also observed after the change points in all study watersheds, whereas the trend of potential evapotranspiration increased at a higher rate after the change points. The impact of climate change on the seasonal (three-month) time scale was greater in the winter and spring seasons, whereas the impact of human activities was significantly higher in the summer and autumn seasons. The influence of climate change on hydrological drought was dominant at longer (6- and 12-month) time scales, whereas human activities accounted for 25.6% and 20% of the changes in watersheds #1018 and #1019 on the 12-month time scale, respectively.

STATIONARITY OF EXTREME STREAMFLOW TIME SERIES FOR FLOOD CONTROL OPERATION IN HYDROPOWER RESERVOIRS

DOI: 10.12957/cadest.2019.50174 The Brazilian Power Sector consider flood prevention in the operation by reserving during the rainy season empty volumes in its hydropower reservoirs. The currently used flood control methodology assumes seasonal stationarity for the naturalized daily streamflow records calculated by the Brazilian National Operator System (NOS/ONS). Considering that climatic and/or land uses and occupation changes may be altering hydrological regime, this article investigates the stationarity of maximum annual streamflow time series of duration compatible with flood events at the hydropower plants located in the Parana river basin. Keywords : Statistical Tests, Extreme Streamflow Time Series Stationarity, Hydropower, Flood Control Studies.

Non‐linear quickflow response as indicators of runoff generation mechanisms

AbstractLinking quickflow response to subsurface state can improve our understanding of runoff processes that drive emergent catchment behaviour. We investigated the formation of non‐linear quickflows in three forested headwater catchments and also explored unsaturated and saturated storage dynamics, and likely runoff generation mechanisms that contributed to threshold formation. Our analyses focused on two reference watersheds at the Coweeta Hydrologic Laboratory (CHL) in western North Carolina, USA, and one reference watershed at the Susquehanna Shale Hills Critical Zone Observatory (SHW) in Central Pennsylvania, USA, with available hourly soil moisture, groundwater, streamflow, and precipitation time series over several years. Our study objectives were to characterise (a) non‐linear runoff response as a function of storm characteristics and antecedent conditions, (b) the critical levels of shallow unsaturated and saturated storage that lead to hourly flow response, and (c) runoff mechanisms contributing to rapidly increasing quickflow using measurements of soil moisture and groundwater. We found that maximum hourly rainfall did not significantly contribute to quickflow production in our sites, in contrast to prior studies, due to highly conductive forest soils. Soil moisture and groundwater dynamics measured in hydrologically representative areas of the hillslope showed that variable subsurface states could contribute to non‐linear runoff behaviour. Quickflow generation in watersheds at CHL were dominated by both saturated and unsaturated pathways, but the relative contributions of each pathway varied between catchments. In contrast, quickflow was almost entirely related to groundwater fluctuations at SHW. We showed that co‐located measurements of soil moisture and groundwater supplement threshold analyses providing stronger prediction and understanding of quickflow generation and indicate dominant runoff processes.