Streamflow Series Research Articles

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Streamflow Intermittence in Europe: Estimating High‐Resolution Monthly Time Series by Downscaling of Simulated Runoff and Random Forest Modeling

AbstractKnowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry and ecosystem services. We present a novel modeling approach to estimate monthly time series of streamflow intermittence at high spatial resolution at the continental scale. Streamflow intermittence is quantified at more than 1.5 million river reaches in Europe as the number of no‐flow days grouped into five classes (0, 1–5, 6–15, 16–29, 30–31 no‐flow days) for each month from 1981 to 2019. Daily time series of observed streamflow at 3706 gauging stations were used to train and validate a two‐step random forest modeling approach. Important predictors were derived from time series of monthly streamflow at 73 million 15 arc‐sec (∼500 m) grid cells that were computed by downscaling the 0.5 arc‐deg (∼55 km) output of the global hydrological model WaterGAP, which accounts for human water use. Of the observed perennial and non‐perennial station‐months, 97.8% and 86.4%, respectively, were correctly predicted. Interannual variations of the number of non‐perennial months at non‐perennial reaches were satisfactorily simulated, with a median Pearson correlation of 0.5. While the spatial prevalence of non‐perennial reaches is underestimated, the number of non‐perennial months is overestimated in dry regions of Europe where artificial storage abounds. Our model estimates that 3.8% of all European reach‐months and 17.2% of all reaches were non‐perennial during 1981–2019, predominantly with 30–31 no‐flow days. Although estimation uncertainty is high, our study provides, for the first time, information on the continent‐wide dynamics of non‐perennial rivers and streams.

140-year daily ensemble streamflow reconstructions over 661 catchments in France

Abstract. The recent development of FYRE (French hYdroclimate REanalysis) Climate, a high-resolution ensemble daily reanalysis of precipitation and temperature covering the 1871–2012 period and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J (Génie Rural à 6 Paramètres Journaliers) lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions, HydRE (Hydrological REconstruction), spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty using forcings from all 25 ensemble members provided by FYRE Climate and (2) the streamflow measurement error by perturbing observations used during the calibration. Further, the hydrological model error based on the relative discrepancies between observed and simulated streamflow has been added to derive the HydREM (Hydrological REconstruction with Model error) streamflow reconstructions. These two reconstructions are compared to other hydrological reconstructions with different meteorological inputs, hydrological reconstructions from a machine learning algorithm, and independent and dependent observations. Overall, the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing us to better understand the variability in past hydrology over France.

Enhanced multi-step streamflow series forecasting using hybrid signal decomposition and optimized reservoir computing models

This study evaluates the use of different time series decomposition methods in association with echo state networks (ESNs), deep echo state networks (DeepESNs), and next generation reservoir computing (NGRC) models to predict the natural flow of water in two hydropower reservoirs with horizons of one, seven, fourteen, and twenty-one steps ahead. The Coyote optimization algorithm is used to adjust the hyperparameters of the ESNs. The use of reservoir computing (RC) methods with Coyote optimization algorithm, a swarm intelligence approach, is compared with the use of variational mode decomposition (VMD), empirical wavelet transform, and empirical mode decomposition. Analysis of the results shows that the use of signal decomposition methods in association with RC can improve the accuracy and performance of the prediction model. The results are compared using different statistical metrics, showing that, in most cases, decomposition methods can improve the forecasting performance of RC. Overall, NGRC outperformed ESN and DeepESN in most of evaluated cases. The best results were obtained using VMD for the forecast horizons of 7, 14, and 21 days ahead, with a decrease in mean absolute error in a range from 43.70% to 88.88% compared to all the other models. Using a Diebold–Mariano test, the results show that the use of VMD is statistically significant in all the cases, with a significant value of 1% in all tested cases of ESN forecasting.

Frequency of Italian Record-Breaking Floods over the Last Century (1911–2020)

This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy between 1911 and 2020. Our research employs an extensive dataset of 522 annual maximum series (AMS) of streamflow observed across Italy between 1911 and 2020. We consider three time intervals (1911–2020, 1911–1970, and 1971–2020), and we define pooling-groups of AMSs based on (a) hydrological (e.g., catchment size, mean annual precipitation, etc.) and (b) spatial proximities of the gauged sites. First, within each group and for each time period, we compute the regional average number of record-breaking events (NRbins). Second, with a series of resampling experiments that preserve the spatial correlation among the AMSs, we test the hypothesis that NRbins result from a group of stationary sequences. Our results show spatially coherent patterns of an increasing number of record-breaking floods in central and in northeastern Italy over the last 50 years. In the same time interval, significant deviations in the regional number of record-breaking events from what would be expected for stationary flood sequences seem to be more common in drier climates or at higher altitudes, while the catchment size does not seem to be a meaningful descriptor.

Trends of meteorological and hydrological droughts and associated parameters using innovative approaches

Climate change and drought have profound effects on hydro-meteorological series. In addition to spatial, these effects could be on annual, seasonal, monthly, or daily temporal scales. In the literature, seasonal Mann-Kendall and Kendall Tau, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Runoff Index are mostly used to detect seasonal effects (autumn, winter, spring, and summer), despite some restrictive assumption. Innovative Polygon Trend Analysis (IPTA) method developed from Innovative Trend Analysis (ITA) analyzes monthly effects on hydro-meteorological variables without restrictive assumptions. In this study, the IPTA method is revised and developed as Periodic Innovative Polygon Trend analysis (P-IPTA) to analyze hydro-meteorological series in periods of 1, 3, 6, 9, and 12 months instead of one-month duration. The method turns to the IPTA for one-month evaluations. Also, ITA method is improved by adding the frequencies for each drought classification (F-ITA). For the precipitation and water availability (Istanbul, Türkiye) and stream flow (Danube River, Romania) series, the P-IPTA method has been used in addition to the SPI, SPEI, and SDI methods to detect the trends in meteorological and hydrological droughts, and their associated parameters. There are generally decreasing trends, increasing drought frequencies, and decreasing wet event frequencies in the study areas. As the period lengths of the hydro-meteorological series increase, drought becomes more evident. Unlike these methods, the method results are consistent with the F-ITA SPI, SPEI, and SDI graphs and can give drought and wet periods. Similarly, the new P-IPTA method will enable researchers to investigate seasonal effects not only on hydro-meteorological series but also on any variable.

Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti

Abstract. Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

Future Changes in Floods, Droughts, and Their Extents in the Alps: A Sensitivity Analysis With a Non‐Stationary Stochastic Streamflow Generator

AbstractSpatially compounding droughts and floods challenge water management and may become more severe in a warming climate. However, the influence of climate change on widespread hydrologic extremes remains largely unknown because they are neither well represented in observations nor in models. Here, we present a non‐stationary stochastic streamflow generator that captures streamflow dependencies between different catchments and represents seasonal covariations of streamflow with temperature. We run this model for 925 nearly natural catchments in the Alps to generate daily streamflow time series for a reference period and three warming levels of 1, 2, and 3°C. Then, we identify drought and flood events, determine their spatial extents, and assess changes in all of these characteristics. This sensitivity analysis for different warming levels suggests that changes in flood characteristics and spatial extent are substantially weaker than those in drought characteristics and spatial extent. While floods show changes in their timing toward earlier in the year, simulated changes in magnitude, volume, duration, and spatial extent are negligible. In contrast, droughts show changes not just in timing but also intensity, deficit, duration, and extent. Specifically, droughts are projected to intensify, last longer, and slightly increase in spatial extent, with the magnitude of change increasing with the warming level. These projected changes highlight the need to develop adaptation strategies in particular for droughts. Such adaptation strategies should go beyond local adaptation and consider that extreme events become more widespread in a warming world.

Ground deformation monitoring via PS-InSAR time series: An industrial zone in Sacco River Valley, central Italy

Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced technique enabling effective ground deformation monitoring. In this study, PS-InSAR time series of Sentinel-1 ascending and descending orbits for period 2015–2022 are utilized for an industrial zone in Sacco River Valley, Central Italy. The Sequential Turning Point Detection (STPD) is applied to estimate the trend turning points and their directions in PS-InSAR time series. In addition, river flow and climate time series for Sacco River near the industrial zone are analyzed using the coefficient of determination and the Least-Squares Cross-Wavelet Analysis (LSCWA) to investigate their potential impact on ground deformation. A significant land subsidence was observed prior to Fall 2016 likely as the result of drought and excessive water extraction followed by land uplift after Fall 2017 likely due to groundwater rebound. The LSCWA showed statistically significant seasonal coherency between precipitation and streamflow in 2018 when relatively much higher precipitation and streamflow were observed in this year compared to 2016 and 2017, potentially contributing to the land uplift in the study zone during 2018. These results not only highlight the capabilities of STPD for detecting trend turning points and LSCWA for analyzing streamflow and precipitation time series but also can help policymakers and stakeholders for developing a sustainable city and environment.

Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model

Study regionYellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. Study focusWe introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short-Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) streamflow data. Multiscale (Catchment, River) attributes were incorporated into the DI LSTM to represent human disturbances on land surface. We employed this method to reconstruct daily streamflow series in 60 human-regulated catchments across the Yellow River Basin, and identified the sensitivity of the DI LSTM model to the multiscale attributes. New hydrological Insights for the RegionOur findings revealed that the DI LSTM model achieved favourable performance in streamflow estimation, with the highest Kling-Gupta efficiency (KGE) reaching up to 0.9, outperforming the Regular LSTM model, which was forced by meteorological variables. Multiscale attributes can enhance the DI model performance, particularly in large catchments with significant human activities. A two-step validation demonstrated the high accuracy of the reconstructed streamflow data across the Yellow River Basin, as the KGEs for streamflow estimation in 40 catchments are over 0.6. In summary, the DI LSTM model shows great potential for reconstructing streamflow in human-regulated catchments in arid regions. The reconstructed daily streamflow data contribute valuable insights for monitoring changing hydrological conditions, especially in regions lacking extensive streamflow monitoring networks.

Evaluation of instream ecological flows based on hydrological alteration in the Upper Huai River, China

Abstract Natural flow regime (instream ecological flow) is a vital element of ecological hydrology, serving a crucial role in the fundamental functions of river ecosystems. Intense human activities, especially reservoir operation, have unavoidably altered the flow regime of the Upper Huai River, leading to further impacts on river ecosystems. It is essential to quantify hydrological alterations in flow regimes and their associated impacts on river ecosystems for effective river water management. Ecological flow indicators, namely ecological deficit and surplus, were analyzed to assess instream ecological flow. The overall degree of alteration (Do) and the Dundee Hydrological Regime Alteration Method (DHRAM) were utilized to evaluate the degree of hydrological alteration. Additionally, the Shannon Index (SI) was employed to estimate the impact of hydrological alterations on ecological diversity in this study. The results reveal that the streamflow series underwent mutation in 1987, leading to a decrease in ecological surplus and an increase in ecological deficit. The overall alteration degree is 32%, with a DHRAM level of 3, signifying low hydrological alteration and moderate ecological risk in the region. Furthermore, the biodiversity of the river has markedly declined due to human activities following the alteration.

Past and Projected Future Droughts in the Upper Colorado River Basin

AbstractDrought has affected much of the western United States since about the year 2000, including the Upper Colorado River Basin (UCRB). Using a time series of UCRB streamflow derived from a tree‐ring based reconstruction of UCRB streamflow for the years 1 CE through 1905 CE, together with naturalized UCRB streamflow values for 1906 CE through 2021 CE, we identify 51 drought events, including the 2000–2021 drought. Although the recent 2000–2021 drought has been relatively severe, it is not the most severe of the UCRB drought events we identified. Results also indicate that natural variability combined with projected climate warming can result in UCRB drought events that are more severe than any drought since 1 CE.

What controls the tail behaviour of flood series: rainfall or runoff generation?

Abstract. Many observed time series of precipitation and streamflow show heavy-tail behaviour. For heavy-tailed distributions, the occurrence of extreme events has a higher probability than for distributions with an exponentially receding tail. If we neglect heavy-tail behaviour we might underestimate the magnitude of rarely observed, high-impact events. Robust estimation of upper-tail behaviour is often hindered by the limited length of observational records. Using long time series and a better understanding of the relevant process controls can help with achieving more robust tail estimations. Here, a simulation-based approach is used to analyse the effect of precipitation and runoff generation characteristics on the upper tail of flood peak distributions. Long, synthetic precipitation time series with different tail behaviour are produced by a stochastic weather generator. These are used to force a conceptual rainfall–runoff model. In addition, catchment characteristics linked to a threshold process in the runoff generation are varied between model runs. We characterize the upper-tail behaviour of the simulated precipitation and discharge time series with the shape parameter of the generalized extreme value (GEV) distribution. Our analysis shows that runoff generation can strongly modulate the tail behaviour of flood peak distributions. In particular, threshold processes in the runoff generation lead to heavier tails. Beyond a certain return period, the influence of catchment processes decreases and the tail of the rainfall distribution asymptotically governs the tail of the flood peak distribution. Beyond which return period this is the case depends on the catchment storage in relation to the mean annual rainfall amount.

Probabilistic Forecast of Ecological Drought in Rivers Based on Numerical Weather Forecast from S2S Dataset

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts.

Hydrologic connectivity assessment among natural hydrosystem components using emerging entropy and evolutionary computing methods

Abstract Understanding the interactions among the natural components of hydrological systems is essential for managing water resources, addressing environmental concerns, and mitigating the impacts of natural events such as floods and droughts. In this study, long-term (1993–2019) hydrologic connectivity among precipitation, humidity, evaporation, surface runoff, minimum/maximum temperature, and their interactions with groundwater level (GWL) in the southeastern Caspian Sea region was assessed using mutual information theory and the state-of-the-art jittered genetic programming approach. While the former was used to find dominant components, their effective time delay, and the power of potential nonlinear interactions, the latter was utilized to extract an explicit relation between the GWL and the most influential components. The data were gathered from several piezometers, meteorological stations, and a stream gauge available in the study area. The results showed an overall positive trend in the GWL with an increasing rate since 2007 that reflects the influence of artificial recharge infrastructures built in the study area. Statistical connectivity analyses demonstrated that historical precipitation and streamflow series have the least impact on the temporal variation of the average GWL.

The Value of Large‐Scale Climatic Indices for Monthly Forecasting Severity of Widespread Flooding Using Dilated Convolutional Neural Networks

AbstractSpatially co‐occurring floods pose a threat to the resilience and recovery of the communities. Their timely forecasting plays a crucial role for increasing flood preparedness and limiting associated losses. In this study we investigated the potential of a dilated Convolutional Neural Network (dCNN) model conditioned on large‐scale climatic indices and antecedent precipitation to forecast monthly severity of widespread flooding (i.e., spatially co‐occurring floods) in Germany with 1 month lead time. The severity was estimated from 63 years of daily streamflow series as the sum of concurrent exceedances of at‐site 2‐year return periods within a given month across 172 mesoscale catchments (median area 516 km2). The model was trained individually for the whole country and three diverse hydroclimatic regions to provide insights on heterogeneity of model performance and flood drivers. Our results showed a considerable potential for forecasting widespread flood severity using dCNN especially as the length of training series increases. However, event‐based evaluation of model skill indicates large underestimation for rainfall‐generated floods during dry conditions despite overall lower severity of these events compared to the rain‐on‐snow floods. Feature attribution and wavelet coherence analyses both indicated considerable difference in the major flood drivers in three regions. While the flooding in North‐Eastern region is strongly affected by the Baltic Sea, the North‐Western region is affected more by global patterns associated with the El‐Niño activity. In the Southern region in addition to global patterns we detected the effect of the Mediterranean Sea, while antecedent precipitation plays a less important role in this region.

Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression

AbstractA large literature on the sensitivity of streamflow response to climate variations has emerged over the past several decades, but the underlying mechanisms are not fully understood. The usual approaches to this problem have been simple and do not fully address its complexity, which involves the individual and joint effects of large‐scale climate modes and smaller‐scale weather features on streamflow volumes, the presence of persistence in these time series and their effects on antecedent conditions. Ongoing improvements in observational and reanalysis datasets and online access allow for better quantification of the connections between streamflow response, climate variability modes and regional weather features. The purpose of this paper is to determine whether Bayesian networks can be used to better identify key factors and their associated pathways leading to streamflow generation. A Markov blanket approach is described and illustrated using monthly streamflow series recorded at eight gauging stations within or immediately adjacent to the coastal region of eastern mainland Australia. The method is compared and contrasted with conventional multiple linear regression with discussion around this type of approach as part of a growing interest in machine learning applications. One example is used to illustrate the application of both approaches to a streamflow series exhibiting strong non‐stationarity. Results for the other streamflow series are included in a concise synthesis. Overall, the results suggest that Bayesian networks have several desirable features in terms of transparency, interpretability and explanatory insight. The findings from this study lend support to the use of Bayesian networks for modelling connections between streamflow volume and the variability of climate and regional weather. This improved understanding of the key controls of streamflow variability is intended to help address growing needs around informing social, cultural, economic and ecological aspects of water planning and management.

Hydrological changes in Serra da Mantiqueira Range watersheds (Southeast Brazil)

The Serra da Mantiqueira Environmental Protection Area is one of the most important Atlantic Forest biodiversity hotspots in the World, and one of the main water sources for the Brazilian Southeast Region. In this study, we analyze the existence and characteristics of streamflow changes in two watersheds (Sapucaí-Mirim and Sapucaí-Guaçu River watersheds) of the Serra da Mantiqueira Range, and if these changes are due to climate change, alterations of land cover and/or external factors. Mann-Kendall and Pettit tests were used to detect changes in streamflow, rainfall and evapotranspiration data series, at different time scales. The characteristics and magnitude of land cover changes were evaluated from multi-temporal maps, produced from satellite images and the supervised classification technique. The results show an increase in winter rainfall at the two watersheds studied. No changes were detected in the streamflows of the Sapucaí-Mirim River Basin. The summer streamflow of Sapucaí-Guaçu River watershed reduced at a rate of −0.035 m³.s−1. year−1. In addition, there were sudden streamflow changes in the summer (from the year 1967) and autumn (from the year 1988) in Sapucaí-Guaçu watershed. Furthermore, the reference summer evapotranspiration of the Sapucaí-Guaçu watershed showed an increasing trend and an abrupt increase (in 1988), besides a sudden reduction of actual evapotranspiration in 2006. The integrated analysis of these results, together with the changes in land cover and population, allow us to conclude that the reductions in summer and autumn streamflows at the Sapucaí-Guaçu watershed were probably due to the increase in the water withdrawal for public supply, added to the effects of urbanization on the hydrology of the watershed.

Daily Streamflow of Argentine Rivers Analysis Using Information Theory Quantifiers.

This paper analyzes the temporal evolution of streamflow for different rivers in Argentina based on information quantifiers such as statistical complexity and permutation entropy. The main objective is to identify key details of the dynamics of the analyzed time series to differentiate the degrees of randomness and chaos. The permutation entropy is used with the probability distribution of ordinal patterns and the Jensen-Shannon divergence to calculate the disequilibrium and the statistical complexity. Daily streamflow series at different river stations were analyzed to classify the different hydrological systems. The complexity-entropy causality plane (CECP) and the representation of the Shannon entropy and Fisher information measure (FIM) show that the daily discharge series could be approximately represented with Gaussian noise, but the variances highlight the difficulty of modeling a series of natural phenomena. An analysis of stations downstream from the Yacyretá dam shows that the operation affects the randomness of the daily discharge series at hydrometric stations near the dam. When the station is further downstream, however, this effect is attenuated. Furthermore, the size of the basin plays a relevant role in modulating the process. Large catchments have smaller values for entropy, and the signal is less noisy due to integration over larger time scales. In contrast, small and mountainous basins present a rapid response that influences the behavior of daily discharge while presenting a higher entropy and lower complexity. The results obtained in the present study characterize the behavior of the daily discharge series in Argentine rivers and provide key information for hydrological modeling.