Discharge Time Series Research Articles

Exploring Temporal Dynamics of River Discharge Using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables, e.g., precipitation, has a significant impact on the temporal distribution of the river discharge in recent days, making the discharge forecasting even more complicated for diversified water-related issues, e.g., flood prediction and irrigation planning. In order to predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to seven days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and R2 value of 0.93 with one day lead time. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records

AbstractIn this paper, we introduce a relatively simple data‐driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference‐based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large‐sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization.

Wavelet analysis of hydro-climatic time-series and vegetation trends of the Upper Aragón catchment (Central Spanish Pyrenees)

Water managers and researchers noted with concern a nearly generalized decline in Mediterranean rivers discharge over the last decades. Changes in climatic forces (precipitation and air temperature) and land use and land cover (LULC) changes characterized by re-vegetation and greenness are the two most possible explanations for this discharge decline. The direct impact on river discharge stemming from these changes is difficult to assess and their role is generally studied separately. Here, we use the method of wavelet transformation to interpret the time-scale dependency of catchment discharge concerning the uneven temporal climatic fluctuations and re-vegetation processes. We analyzed the temporal variation of concurrent air temperature, precipitation and river discharge time-series for the Upper Aragón catchment, located in the Central Spanish Pyrenees. A long-term database collected over 60 years (1956–2020) was used. Land cover maps corresponding to different decades were used and the results indicated that the catchment experienced a significant increase in the area covered by mixed and broadleaf forests, mostly as a consequence of land abandonment. We show how temperature slightly increased and precipitation moderately decreased. However, catchment discharge experienced a sharp decline in its magnitude and also changes in its temporal variability dynamics. The relevance of the seasonal time-scales with regard to the available discharge is reduced, which strengthens the importance of the inter-annual time-scales for the catchment discharge dynamics. Furthermore, the catchment storage-discharge cycle at inter-annual time-scales is also reduced. Such changes can mostly be attributed to the changes in plant coverage, with an increasing weight in shaping hydrological processes at catchment scale due to the greenness effect. As such, we conclude that LULC changes have played a dominant role on the river discharge dynamics. Climatic trends, on the contrary, have been small, and they have played a secondary role in the decline of river discharge. Future research can use these observations to constrain the pace of upcoming water demands based on the available water resources at Mediterranean catchment scale.

Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence

Statistical dependency measures such as Kendall’s Tau or Spearman’s Rho are frequently used to analyse the coherence between time series in environmental data analyses. Autocorrelation of the data can, however, result in spurious cross correlations if not accounted for. Here, we present the asymptotic distribution of the estimators of Spearman’s Rho and Kendall’s Tau, which can be used for statistical hypothesis testing of cross-correlations between autocorrelated observations. The results are derived using U-statistics under the assumption of absolutely regular (or β-mixing) processes. These comprise many short-range dependent processes, such as ARMA-, GARCH- and some copula-based models relevant in the environmental sciences. We show that while the assumption of absolute regularity is required, the specific type of model does not have to be specified for the hypothesis test. Simulations show the improved performance of the modified hypothesis test for some common stochastic models and small to moderate sample sizes under autocorrelation. The methodology is applied to observed climatological time series of flood discharges and temperatures in Europe. While the standard test results in spurious correlations between floods and temperatures, this is not the case for the proposed test, which is more consistent with the literature on flood regime changes in Europe.

Assessing the Performance of SHETRAN Simulating a Geologically Complex Catchment

Despite recent progress in terms of cheap computing power, the application of physically-based distributed (PBD) hydrological codes still remains limited, particularly, because some commercial-license codes are expensive, even under academic terms. Thus, there is a need for testing the performance of free-license PBD codes simulating complex catchments, so that cheap and reliable mechanistic modelling alternatives might be identified. The hydrology of a geologically complex catchment (586 km2) was modelled using the free-license PBD code SHETRAN. The SHETRAN evaluation took place by comparing its predictions with (i) discharge and piezometric time series observed at different locations within the catchment, some of which were not taken into account during model calibration (i.e., multi-site test); and (ii) predictions from a comparable commercial-license code, MIKE SHE. In general, the discharge and piezometric predictions of both codes were comparable, which encourages the use of the free-license SHETRAN code for the distributed modelling of geologically complex systems.

Combination of Wilcoxon test and scatter diagram for trend analysis of hydrological data

• An alternative trend analysis method is proposed in this study. • The approach includes combination of scatter diagram and Wilcoxon test. • Suggested methodology provides visual representation and numerical results. • The method can be compared with both Şen’s ITA and Mann-Kendall results. In this study, time series data is divided into two equal parts, as in the basic principle of the Innovative Trend Analysis (ITA) method. However, unlike the ITA, the data are not ordered when comparing the halves to avoid any change in the time series. Wilcoxon Signed Rank Test is used to consider the differences between the two equal halves and to determine if there is any trend in time series. A graphical presentation is provided like ITA approach and is expressed as the Mann-Kendall (MK) test for trend detection at a given confidence interval (significance level). In the application phase, minimum, maximum and average discharge time series measured at Yakabaşı, Söğütlühan, Arıcılar, and Topluca stations along the northern line from west to east of Turkey are used. The trend conditions of the data are determined by the proposed method and compared with ITA and MK tests results. No trend is observed in 8 out of 12 cases for stations at the 95% confidence interval. While decreasing trends are detected in the maximum discharge of Yakabaşı station, the minimum discharge of Arıcılar station, and the average discharge of Yakabaşı station, an increasing trend is detected in the average discharge records of Topluca station. Here, an easy-to-use model for decision-makers is proposed that presents numerical as well as graphical analyses of trend identification studies.

Comparative study on stream flow prediction using the GMNN and wavelet-based GMNN

Abstract Flood flow forecasting is essential for mitigating damage in flood-prone areas all over the world. Advanced actions and methodology to optimize peak flow criteria can be adopted based on forecasted discharge information. This paper applied the models of the integrated wavelet, multilayer perceptron (MLP), time-delay neural network (TDNN), and gamma memory neural network (GMNN) to predict hourly river-level fluctuations, including storage rate change variable. Accordingly, the researchers initially used the discrete wavelet transform to decompose the water discharge time-series into low- and high-frequency components. After that, each component was separately predicted by using the MLP, TDNN, and GMNN models. The performance of the proposed models, namely wavelet–MLP, wavelet–TDNN, and wavelet–GMNN, was compared with that of single MLP, TDNN, and GMNN models. This analysis affirms that precision is better in the case of integrated models for forecasting river reach levels in the study region. Furthermore, multiple inputs–multiple outputs (MIMO) networks (MIMO-1 artificial neural network (ANN) and MIMO-2 ANN), along with multiple inputs–single output (MISO) ANN were employed for obtaining flow forecasts for several sections in a river basin. Model performances were also evaluated using the root mean squared error having less than 10% of the average mean value, with the coefficient of correlation being more than 0.91 and with the peak flow criteria showing the chances of flash floods being low to moderate with values not more than 0.15.

Changes in precipitation and discharge in a Mediterranean catchment as a response to climate change and human activities

AbstractThe Mediterranean region is considered to be highly affected by climate change with rainfall deficits leading to a significant decrease in river discharge. This study aims to assess the impacts of climate change on water resources in a Mediterranean catchment, namely the Bas-Loukkos catchment (Morocco), where pressure on the water resources is already present due to intensive hydro-agricultural development and is likely to increase. Mann–Kendall, Pettitt and Buishand tests were used to analyze trends and detect breakpoints in discharge and precipitation time series over the period 1960–2018. The precipitation–specific discharge relationships has been analysed by the double-mass curve (DMC). The analyses highlighted a decreasing trend in precipitation. A significant breakpoint was detected in early 1970s, with mean annual precipitation decreasing by 16–26% after this period. Discharge decreased by approximately 35% beginning in the late 1970s/early 1980s. The DMC showed different patterns between ‘undisturbed’ sub-catchments and two intensively managed sub-catchments. Wettest Mediterranean catchments are often considered as future water reservoirs to support part of the water needs in arid catchments. This study highlights that such catchments may already be impacted by climate changes, with discharge decreasing, and by water human activities that exert a major pressure on water resources.

A lumped parameter model to evaluate the relevance of excess air as a tracer of exchanged flows between transmissive and capacitive compartments of karst systems

The objective of this study was to evaluate the relevance of using excess air (EA) for the characterization of drain/matrix exchange in karst systems using a rainfall discharge model coupled with the simulation of EA measured at the outlet of the studied system. The conceptual model assumes a linear relationship between the formation of EA and the increase of hydrostatic pressure in the capacitive part of the aquifer. The simulated EA at the spring consists of the mixing of water circulating in the different compartments of the aquifer, with their own EA signature. The analysis is performed taking as an example the Durzon karst system (Larzac, France). The modeling is applied using daily rainfall discharge time series and 18 EA measurements at the main outlet of the karst system within 3 hydrological cycles. The main modeling results show that EA variations measured at the karst spring can be explained by recharge processes and exchange between conduit and matrix. EA measurements at the spring thus contain valuable information about the flow dynamics within the aquifer. Furthermore, results show that the use of EA measurements, despite their sparse temporal resolution, allows for reducing uncertainties in the estimation of some parameters of the reservoir model used for the simulation of karst spring discharge.

Adaptation of river channels to a wetter or drier climate: Insights from the Lower Pilcomayo River, South America

Climate change has a direct influence on both hydrology and floodplain vegetation of water courses, which are key players in river morphodynamics. The river system response to climate change is complex and the effects of non-linear interactions between alterations in water, sediment and vegetation remain incompletely understood. Which of these components becomes dominant in shaping the river channel when climate becomes drier or wetter? To answer this question, we investigate the cross-sectional response of sand-bed rivers to climate change focusing on channel width and depth, which respond directly to changes in boundary stresses. Thanks to the exceptional availability of long time-series of daily discharge and cross-sectional profiles, the Pilcomayo River is an ideal living lab for this investigation. We constructed a two-dimensional model of the river using the open-source state-of-the-art, structured Delft3D code. The highly dynamic behaviour and quick morphological adaptation of the Pilcomayo allowed reducing the period of time covered by the simulations because the river cross-section adapts its morphology to a new value of the water discharge within hours or days, which is crucial for modelling investigations. Calibration and validation were successfully performed by comparison with historical data. We considered several scenarios representing current, dryer and wetter climates. The results show that a dryer climate reduces the river channel depth and enlarges the width. A wetter climate increases the channel depth but produces negligible widening. Vegetation, sparser with a drier climate and denser with a wetter climate, is found to control the channel width. This analysis is unique and shows which alterations can be expected in alluvial sand-bed rivers with natural vegetated banks due to climate change.

Dynamic mass loss from Greenland's marine-terminating peripheral glaciers (1985–2018)

AbstractGlobal glacier mass balance decreased rapidly over the last two decades, exceeding mass loss from the Greenland and Antarctic Ice Sheets. In Greenland, peripheral glaciers and ice caps (GICs) cover only ~5% of Greenland's area but contributed ~20% of the island's ice mass loss between 2000 and 2018. Although Greenland GIC mass loss due to surface meltwater runoff has been estimated using atmospheric models, mass lost to changes in ice discharge into oceans (i.e., dynamic mass loss) remains unquantified. We use the flux gate method to estimate discharge from Greenland's 585 marine-terminating peripheral glaciers between 1985 and 2018, and compute dynamic mass loss as the discharge anomaly relative to the 1985–98 period. Greenland GICs discharged between 2.94 ± 0.23 and 4.03 ± 0.23 Gt a−1from 1985 to 1998, depending on the gap-filling method, and abruptly increased to 5.10 ± 0.21 Gt a−1from 1999 to 2018. The resultant ~1–2 Gt a−1dynamic mass loss was driven by synchronous widespread acceleration around Greenland. The mass loss came predominantly from the southeast region, which contains 39% of the glaciers. Although changes in discharge over time were small relative to surface mass-balance changes, our speed and discharge time series suggest these glaciers may quickly accelerate in response to changes in climate.

Suspended sediment and discharge dynamics in a glaciated alpine environment: identifying crucial areas and time periods on several spatial and temporal scales in the Ötztal, Austria

Abstract. Glaciated high-alpine areas are fundamentally altered by climate change, with well-known implications for hydrology, e.g., due to glacier retreat, longer snow-free periods, and more frequent and intense summer rainstorms. While knowledge on how these hydrological changes will propagate to suspended sediment dynamics is still scarce, it is needed to inform mitigation and adaptation strategies. To understand the processes and source areas most relevant to sediment dynamics, we analyzed discharge and sediment dynamics in high temporal resolution as well as their patterns on several spatial scales, which to date few studies have done. We used a nested catchment setup in the Upper Ötztal in Tyrol, Austria, where high-resolution (15 min) time series of discharge and suspended sediment concentrations are available for up to 15 years (2006–2020). The catchments of the gauges in Vent, Sölden and Tumpen range from 100 to almost 800 km2 with 10 % to 30 % glacier cover and span an elevation range of 930 to 3772 m a.s.l. We analyzed discharge and suspended sediment yields (SSY), their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. We complemented our analysis by linking the observations to satellite-based snow cover maps, glacier inventories, mass balances and precipitation data. Our results indicate that the areas above 2500 m a.s.l., characterized by glacier tongues and the most recently deglaciated areas, are crucial for sediment generation in all sub-catchments. This notion is supported by the synchronous spring onset of sediment export at the three gauges, which coincides with snowmelt above 2500 m but lags behind spring discharge onsets. This points at a limitation of suspended sediment supply as long as the areas above 2500 m are snow-covered. The positive correlation of annual SSY with glacier cover (among catchments) and glacier mass balances (within a catchment) further supports the importance of the glacier-dominated areas. The analysis of short-term events showed that summer precipitation events were associated with peak sediment concentrations and yields but on average accounted for only 21 % of the annual SSY in the headwaters. These results indicate that under current conditions, thermally induced sediment export (through snow and glacier melt) is dominant in the study area. Our results extend the scientific knowledge on current hydro-sedimentological conditions in glaciated high-alpine areas and provide a baseline for studies on projected future changes in hydro-sedimentological system dynamics.

Fitting a superposition of Ornstein–Uhlenbeck process to time series of discharge in a perennial river environment

Classical Ornstein–Uhlenbeck (ou) processes are Lévy-driven linear stochastic models with exponentially decaying autocorrelation functions which do not always fit more slowly decaying real time series data. A superposition of ou processes (known as a supou process) is proposed to overcome this issue for application to river discharge time series data. The discharge data has a sub-exponential autocorrelation function and this is captured by the supou process based on the mean reversion speed generated by a Gamma distribution. All the parameters of the supou process are identified by matching the autocorrelation and the first to fourth statistical moments of the discharge data. The empirical and modelled histograms of the discharge data are comparable with each other.

Alternative stable states and hydrological regime shifts in a large intermittent river

Non-perennial rivers and streams make up over half the global river network and are becoming more widespread. Transitions from perennial to non-perennial flow are a threshold-type change that can lead to alternative stable states in aquatic ecosystems, but it is unknown whether streamflow itself is stable in either wet (flowing) or dry (no-flow) conditions. Here, we investigated drivers and feedbacks associated with regime shifts between wet and dry conditions in an intermittent reach of the Arkansas River (USA) over the past 23 years. Multiple lines of evidence suggested that these regimes represent alternative stable states, including (a) significant jumps in discharge time series that were not accompanied by jumps in flow drivers such as precipitation and groundwater pumping; (b) a multi-modal state distribution with 92% of months experiencing no-flow conditions for <10% or >90% of days, despite unimodal distributions of precipitation and pumping; and (c) a hysteretic relationship between climate and flow state. Groundwater levels appear to be the primary control over the hydrological regime, as groundwater levels in the alluvial aquifer were higher than the stream stage during wet regimes and lower than the streambed during dry regimes. Groundwater level variation, in turn, was driven by processes occurring at both the regional scale (surface water inflows from upstream, groundwater pumping) and the reach scale (stream–aquifer exchange, diffuse recharge through the soil column). Historical regime shifts were associated with diverse pressures including network disconnection caused by upstream water use, increased flow stability potentially associated with reservoir operations, and anomalous wet and dry climate conditions. In sum, stabilizing feedbacks among upstream inflows, stream–aquifer interactions, climate, vegetation, and pumping appear to create alternative wet and dry stable states at this site. These stabilizing feedbacks suggest that widespread observed shifts from perennial to non-perennial flow will be difficult to reverse.

Study of trends in the time series of maximum water discharges in the Tisza basin rivers within Ukraine

Study of trends in the time series of maximum water discharges in the Tisza basin rivers within Ukraine

Optimizing the Management of Multireservoir Systems Under Shifting Flow Regimes

AbstractOver the past few decades, significant research efforts have been devoted to the development of tools and techniques to improve the operational effectiveness of multireservoir systems. One of those efforts focuses on the incorporation of relevant hydrologic information into reservoir operation models. This effort is particularly relevant in regions characterized by low‐frequency climate signals, where time series of river discharges exhibit regime‐like behavior. Failure to properly capture such regime‐like behavior yields suboptimal operating policies, especially in systems characterized by large storage capacity such as large multireservoir systems. Hidden Markov Modeling is a class of hydrological models that can accommodate both overdispersion and serial dependence in time series, two essential hydrological properties that must be captured when modeling a system where the climate is switching between different states (e.g., dry, normal, and wet). In terms of reservoir operation, Stochastic Dual Dynamic Programming (SDDP) is one of the few optimization techniques that can accommodate both system and hydrologic complexity, that is, a large number of reservoirs and diverse hydrologic information. However, current SDDP formulations are unable to capture the long‐term persistence of the streamflow process found in some regions. In this paper, we present an extension of the SDDP algorithm that can handle the long‐term persistence and provide reservoir operating policies that explicitly capture regime shifts. Using the Senegal River Basin as a case study, we illustrate the potential gain associated with reservoir operating policies tailored to climate states.

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

A summary of environmental radioactivity research studies by members of the Japan Society of Nuclear and Radiochemical Sciences

Abstract Many scientists who are members of the Japan Society of Nuclear and Radiochemical Sciences have been involved in academic activities in response to the Fukushima Daiichi Nuclear Power Plant accident. Projects had been implemented that include determining radionuclides in environmental samples, identifying the distribution of radionuclides by large-scale soil monitoring, tracing radionuclide discharge time series, clarifying environmental dynamics of radionuclides, etc. For the last 10 years, these results have been shared and discussed in annual workshops partly sponsored by the society. This review summarizes the studies yielding these results, and they include reconstruction of the 131I distribution on soil by long-lived 129I analysis, reconstruction of the radioactive plume transport, identification of biological resuspension sources, discovery and characterization of cesium particles, and parameterization of the environmental behavior of radiocesium for dose assessment.

Laboratory modelling of urban flooding

This paper presents two datasets obtained from laboratory experiments of urban flooding in a street network performed at the University of Liège. The experimental model represents a part of a synthetic urban district that consists of three inlets, three outlets and several three- and four- branches crossroads. The following experimental data was produced: (i) dataset 1: time-series of flow depths at model inlets and time-series of discharges at model outlets for a two-branch junction model, a two-branch bifurcation model and a district model. The datasets were generated by varying the upstream and downstream boundary conditions, i.e. flooding conditions; (ii) dataset 2 includes the same data type as dataset 1 complemented by 2D surface velocity measured using the non-intrusive LSPIV technique for eight urban form configurations in the district model. The collected data enable improving the understanding of the effect of urban forms on the urban flood processes. These two datasets are valuable for validating and improving numerical or analytical models of urban flooding and may contribute to flood risk management and flood-resilient urban design.

딥러닝 알고리즘의 성능을 향상하기 위한 최적 모니터링 주기 결정: 남강 유역의 사례를 중심으로

The intention of this study is to address the effect of temporal resolution of input data on the prediction accuracy of deep learning algorithms. A popular watershed model Soil & Water Assessment Tool (SWAT) was used to generate time series of discharge and 9 water quality variables over different time intervals (i.e., 1, 2, 3, and 7 days) for three years (i.e., 2015-2017). We then assessed the performance of two deep learning algorithms, recurrent neural network (RNN) and gated recurrent unit (GRU), on those prepared data sets. Our finding showed that the prediction accuracy of the RNN algorithm increased progressively with the time resolution of input data from weekly data to daily data. The RNN also achieved its superior performance on the daily data, regardless of dependent variables. The only exception was the variable carbonaceous biochemical oxygen demand. When a comparison was made between those algorithms, the RNN algorithm was found to have slightly better accuracy than the GRU algorithm. Collectively, all these results revealed that the temporal resolution of input data was strongly responsible for changes in the performance of deep learning, specifically for those used in time series prediction.