Streamflow Time Research Articles

High resolution spatiotemporal patterns of flow at the landscape scale in montane non‐perennial streams

AbstractIntermittent and ephemeral streams in dryland environments support diverse assemblages of aquatic and terrestrial life. Understanding when and where water flows provide insights into the availability of water, its response to external controlling factors, and potential sensitivity to climate change and a host of human activities. Knowledge regarding the timing of drying/wetting cycles can also be useful to map critical habitats for species and ecosystems that rely on these temporary water sources. However, identifying the locations and monitoring the timing of streamflow and channel sediment moisture remains a challenging endeavor. In this paper, we analyzed daily conductivity from 37 sensors distributed along 10 streams across an arid mountain front in Arizona (United States) to assess spatiotemporal patterns in flow permanence, defined as the timing and extent of water in streams. Conductivity sensors provide information on surface flow and sediment moisture, supporting a stream classification based on seasonal flow dynamics. Our results provide insight into flow responses to seasonal rainfall, highlighting stream reaches very reactive to rainfall versus those demonstrating more stable streamflow. The strength of stream responses to precipitation are explored in the context of surficial geology. In summary, conductivity data can be used to map potential stream habitat for water‐dependent species in both space and time, while also providing the basis upon which sensitivity to ongoing climate change can be evaluated.

The hydroclimate niche: A tool for predicting and managing riparian plant community responses to streamflow seasonality

AbstractHabitat suitability is a consequence of interacting environmental factors. In riparian ecosystems, suitable plant habitat is influenced by interactions between stream hydrology and climate, hereafter referred to as “hydroclimate”. We tested the hypothesis that hydroclimate variables would improve the fit of ecological niche models for a suite of riparian species using occurrence data from the western United States. We focus on the climate conditions (temperature, precipitation and vapor pressure deficit) during the months of lowest and highest streamflow as integrative hydroclimate metrics of resource and stress levels. We found that the inclusion of hydroclimate variables improved model fit for all species in the western USA dataset. We then tested the utility of the improved habitat suitability models by projecting them onto a regulated segment of the Colorado River to assess potential impacts of streamflow seasonality on vegetation metrics of management concern. Species frequency derived from independent survey data in the Colorado River segment was significantly higher for species with predicted suitable habitat than for species without predicted suitable habitat. Under different simulated hydrographs for the Colorado River, overall species richness was predicted to be greatest with peak streamflows during summer, and native‐to‐non‐native species ratios were predicted to be greatest with lowest streamflows in winter. Summer high flows were particularly associated with higher predicted habitat suitability for species that have increased in cover over recent decades (e.g., Pluchea sericea, Baccharis species). We conclude that hydroclimate covariates can be useful tools for predicting how riparian vegetation communities respond to changes in the seasonal timing of low and high streamflows.

Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature

The transfer of the hydrological information between catchments is founded on the definition of hydrological similarity, which is in turn strictly connected to the features to be regionalised. In order to characterise the catchment behaviour in the streamflow generation processes, the similarity should reflect also the interaction between meteorological forcings and river streamflow time series. While previous hydrological research has identified basins with similar meteorological forcings (i.e. similarity of climate) or with similar streamflow time-series (i.e. similarity of runoff response), the present work proposes, for the first time, to quantify the interaction between the entire time-series of different forcing data and streamflow observations, to be considered as a novel hydrological signature and used as a catchment similarity metric.In particular, the present study proposes the use of a multi-variate entropy-based measure, the so-called transfer entropy, a time-asymmetric quantity which analyses the interaction between different signals. The concept of transfer entropy is applied for identifying the dominant hydrological processes occurring in a catchment, measuring the transfer of information from different meteorological forcings over the catchment to the corresponding observed time series of daily streamflow at the basin outlet. The resulting transfer entropy values are then used as signatures to characterise the main catchment dynamics, and a classification of the basins region is obtained assuming that similar values of transfer entropy correspond to hydrologically similar basins.The methodology is tested on a densely-gauged set of more than 200 catchments across Austria and the outcomes of the approach are evaluated against a set of morpho-climatic catchment attributes and typical streamflow signatures.Despite the limitations, the method is able to distinguish the predominant or partial role of snow melt and evapotranspiration across the dataset, helping to assess differences in catchment response time and to highlight the role of very high orographic precipitation in catchments with a dominant snow regime. The study demonstrates the potential of transfer entropy as complementary to consolidated streamflow signatures for assessing hydrological similarity and for quantifying the connection between different catchment processes.

Hybrid global gridded snow products and conceptual simulations of distributed snow budget: evaluation of different scenarios in a mountainous watershed.

Considering snowmelt in mountainous areas as the important source of streamflow, the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes. The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models. To tackle these challenges, Global Gridded Snow Products (GGSPs) are introduced, which effectively simplify the identification of the spatial characteristics of snow hydrological variables. This research aims to investigate the performance of multi-source GGSPs using multi-stage calibration strategies in hydrological modeling. The used GGSPs were Snow-Covered Area (SCA) and Snow Water Equivalent (SWE), implemented individually or jointly to calibrate an appropriate water balance model. The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow. The results showed that using GGSPs as complementary information in the calibration process, besides streamflow time series, could improve the modeling accuracy compared to the conventional calibration, which is only based on streamflow data. The SCA with NSE, KGE, and RMSE values varying within the ranges of 0.47–0.57, 0.54–0.65, and 4–6.88, respectively, outperformed the SWE with the corresponding metrics of 0.36–0.59, 0.47–0.60, and 5.22–7.46, respectively, in simulating the total streamflow of the watershed. In addition to the superiority of the SCA over SWE, the two-stage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy. On the other hand, the consistent contribution of snowmelt to the total generated streamflow (ranging from 0.9 to 1.47) and the ratio of snow melting to snowfall (ranging from 0.925 to 1.041) in different calibration strategies and models resulted in a reliable simulation of the model.

A role for water markets in enhancing water security in the western United States?: Lessons from the Walker River Basin

AbstractIn many semi-arid, snow-fed river systems, climate change is shifting the timing and quantity of streamflow; at the same time, changing water use priorities are introducing additional demands on water supplies. These dynamics challenge water security across the globe. Water markets – economic instruments used to reallocate water via voluntary trade – may be used to adapt to these changes, though their implementation remains limited. To understand how water markets may enhance water security in the western United States, we assess diverse actors’ perceptions of water allocation institutions broadly, as well as their preferences for different water market designs, in the empirical context of the Walker River Basin. This 4,200-square mile watershed, located on the California-Nevada border, exemplifies many key regional water management challenges. Through an analysis of 30 in-depth interviews, we find that actors across sectors desire changes to traditional water allocation institutions, preferably at the local level, and view markets as an acceptable tool for reallocation. Despite identified legal and social challenges, markets that facilitate temporary water trading are generally preferred. While limited to the context of a single basin, these findings provide lessons for designing water markets to enhance water security in basins regionally and beyond.

Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.

Identifying potential systemic changes in a regulated river: A diagnostic methodology based on whiteness property of innovation sequences

Traditional hydrologic routing schemes are structured in reference to a pre-assumed time-invariant inflow-storage-outflow function. In light of this, potential systemic changes, arising from river regulation, cannot be accommodated in such a static model structure. As such, a diagnostic methodology is developed in this study to examine the presence of potential systemic changes and recognize time spans in which the adopted inflow-storage-outflow function is inadequate to represent the functional behaviour of the system. This is done objectively by developing a two-step change-points detection algorithm based on the whiteness property of the innovation sequence. Using four carefully designed numerical experiments, our findings reveal that the proposed methodology (a) detects the non-stationary behaviour in streamflow time series associated with systemic changes objectively, (b) tracks the pathway of structural evolution of a hydrologic routing model properly, and (c) identifies time periods automatically wherein a river routing model structure needs to be updated.

Wpływ zmian klimatu na zasoby wody

In the last few years there has been significant interest in Climate change. It has found the direct impact on surrounding world as well as on water resources. The rise in temperature associated with climate change results in a general decrease in the proportion of precipitation falling as snow and, as a result, a decrease in the duration of snow cover in many areas. With a shift from spring snow melt to winter runoff, this has implications for the timing of streamflow in such areas. The number of people living in water-stressed countries would rise. These changes threaten the quality of drinking water, rising water levels, and drying up, which also has implications for quality of life and food production. All of the above have a devastating effect on our ecosystem. It should be noted that the impact of climate change on water resources is also the impact of the community, such as environmental or social (health security, future existence). Scientists from all across the world already agree that the global climate change is the result of human activity.

Risk of Climate Change for Hydroelectricity Production in China Is Small but Significant Reductions Cannot Be Precluded for More Than a Third of the Installed Capacity

AbstractTo support the implementation of China's carbon neutrality target, this study assesses the impact of future climate change on hydroelectricity production (HP) in China. We developed a hydropower database for mainland China, which covers 92% of the installed capacity indicated by national statistics (352 GW in 2018). An HP model was applied to simulate monthly HP by 3,521 conventional plants at different levels of global warming (1°, 1.5°, 2°, and 3°C above preindustrial) using an ensemble of monthly streamflow time series computed by five global hydrological models (GHMs), where each global hydrological model was driven by four global climate models (GCMs). With a probability of 80%, limiting global warming to 1.5°C will constrain changes of national HP to the range of −1.4%–2.5% as compared to near‐present climate (1°C). In case of a 3°C world, this range more than doubles. HP is projected to likely increase in the two provinces Sichuan and Yunnan that encompass more than half of China's installed capacity. However, with a probability of about 10%, HP may decrease in a 3°C world by more than 15% in five provinces, and 40% of the installed capacity may suffer from an HP decrease of more than 5%. The uncertainty of projected HP changes is mainly due to the GCMs. We found that relative changes in annual HP and streamflow often differ in magnitude and direction as HP depends on seasonal streamflow patterns and installed capacities that determine the maximum flow through the turbines.

First steps in the ecological flow determining for Latvian rivers

Abstract This study analyses the impact of small hydropower plants (HPP) on fish in three Latvian transboundary river basin districts. The MesoHABSIM habitat simulation model was applied to seven Latvian lowland rivers regulated by HPP, four of which belong to the salmonid river type and three to the cyprinid type. Daily stream flow time series for 1961–2018 were used for flow regime calculations in reference (unimpacted) and altered (impacted by HPP operation) flow conditions. Conditional habitat suitability model/criteria for fish were used to assess a potentially available habitat at different flow conditions. Brown trout (Salmo trutta) for salmonid rivers and chub (Squalius cephalus) for cyprinid rivers were selected as case examples to show habitat suitability for different stream types. The authors found significant differences in habitat availability for salmonid and cyprinid rivers, indicating that ecological flows must be calculated separately for fast- and slow-flowing rivers. This study is the first attempt in Latvia to set ecological flow values not only using hydrological calculations but also biological data as an indicator of ecological changes.

Trends and Climate Elasticity of Streamflow in South-Eastern Brazil Basins

Trends in streamflow, rainfall and potential evapotranspiration (PET) time series, from 1970 to 2017, were assessed for five important hydrological basins in Southeastern Brazil. The concept of elasticity was also used to assess the streamflow sensitivity to changes in climate variables, for annual data and 5-, 10- and 20-year moving averages. Significant negative trends in streamflow and rainfall and significant increasing trend in PET were detected. For annual analysis, elasticity revealed that 1% decrease in rainfall resulted in 1.21–2.19% decrease in streamflow, while 1% increase in PET induced different reductions percentages in streamflow, ranging from 2.45% to 9.67%. When both PET and rainfall were computed to calculate the elasticity, results were positive for some basins. Elasticity analysis considering 20-year moving averages revealed that impacts on the streamflow were cumulative: 1% decrease in rainfall resulted in 1.83–4.75% decrease in streamflow, while 1% increase in PET induced 3.47–28.3% decrease in streamflow. This different temporal response may be associated with the hydrological memory of the basins. Streamflow appears to be more sensitive in less rainy basins. This study provides useful information to support strategic government decisions, especially when the security of water resources and drought mitigation are considered in face of climate change.

Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magni-tude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleocli-matic reconstructions. We aimed to determine the fractal behaviour and the long-range de-pendence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as sug-gested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal dis-tribution and a generalized Hurst–Kolmogorov (HK) dependence structure.

Temporal and spectral governing dynamics of Australian hydrological streamflow time series

We use new and established methodologies in multivariate time series analysis to study the dynamics of 414 Australian hydrological stations’ streamflow. First, we analyse our collection of time series in the temporal domain, and compare the similarity in hydrological stations’ candidate trajectories. Then, we introduce a Whittle Likelihood-based optimization framework to study the collective similarity in periodic phenomena among our collection of stations. Having identified noteworthy similarity in the temporal and spectral domains, we introduce an algorithmic procedure to estimate a governing hydrological streamflow process across Australia. To determine the stability of such behaviours over time, we conclude by studying the evolution of the governing dynamics and underlying time series with time-varying applications of principal components analysis (PCA) and eigenspectrum analysis.

Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does

Abstract. Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS20) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2=0.85), suggesting that a 1 d earlier DOS20 corresponds with a 1 d earlier DOQ25 and 0.7 d earlier DOQ50. Empirical projections of future DOS20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS20 will occur on average 11±4 d earlier per 1 ∘C of warming. However, DOS20 in colder watersheds (mean November–February air temperature, TNDJF<-8 ∘C) is on average 70 % more sensitive to climate change than in warmer watersheds (TNDJF>0 ∘C). Moreover, empirical projections of DOQ25 and DOQ50 based on DOS20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.

Studying the Changes in the Hydro‐Meteorological Components of Water Budget in Lake Urmia

AbstractAbrupt changes in the Lake Urmia water level have been addressed in many studies, and yet the link between the water level decline and hydro‐meteorological variables in the basin is a major topic for debate between researchers. In this study, a set of data‐driven techniques is used to investigate the components of the water budget in Lake Urmia. Then, the rate of monthly depth differences (DD), precipitation (P), evaporation (E), potential groundwater head (G), and streamflow (Q) time series between 1974 and 2014 are used in the analysis. Several scenarios and strategies are developed by considering the major changes in the year‐2000, which is believed to be the initiation of the hydrological encroachment in the basin. Simple water budget (WB), dynamic regression (DR), and symbolic regression (SR) techniques are used to simulate the DD with consideration to P, E, G, and Q. Alternatively, the effect of the year 1997 as the potential base‐line for the initiation of significant meteorological trends in the basin is investigated. Conducted analysis showed that the DR models of an autoregressive moving average together with multiple exogenous inputs provide an approximate R2: 0.7 as the best alternative among the selected models. It is shown that the Q and G depict abrupt changes compared to the P and E, while either the year 1997 (climate effect) or the year 2000 (encroachment effect) is considered as the baseline in the study.

How Climate Extremes Influence Conceptual Rainfall-Runoff Model Performance and Uncertainty

Rainfall-runoff models are frequently used for assessing climate risks by predicting changes in streamflow and other hydrological processes due to anticipated anthropogenic climate change, climate variability, and land management. Historical observations are commonly used to calibrate empirically the performance of conceptual hydrological mechanisms. As a result, calibration procedures are limited when extrapolated to novel climate conditions under future scenarios. In this paper, rainfall-runoff model performance and the simulated catchment hydrological processes were explored using the JAMS/J2000 model for the Berg River catchment in South Africa to evaluate the model in the tails of the current distribution of climatic conditions. An evolutionary multi-objective search algorithm was used to develop sets of parameters which best simulate “wet” and “dry” periods, providing the upper and lower bounds for a temporal uncertainty analysis approach to identify variables which are affected by these climate extremes. Variables most affected included soil-water storage and timing of interflow and groundwater flow, emerging as the overall dampening of the simulated hydrograph. Previous modeling showed that the JAMS/J2000 model provided a “good” simulation for periods where the yearly long-term mean precipitation shortfall was <28%. Above this threshold, and where autumn precipitation was reduced by 50%, this paper shows that the use of a set of “dry” parameters is recommended to improve model performance. These “dry” parameters better account for the change in streamflow timing of concentration and reduced peak flows, which occur in drier winter years, improving the Nash-Sutcliffe Efficiency (NSE) from 0.26 to 0.60 for the validation period 2015–2018, although the availability of climate data was still a potential factor. As the model performance was “good” (NSE > 0.7) during “wet” periods using parameters from a long-term calibration, “wet” parameters were not recommended for the Berg River catchment, but could play a large role in tropical climates. The results of this study are likely transferrable to other conceptual rainfall/runoff models, but may differ for various climates. As greater climate variability drives hydrological changes around the world, future empirically-based hydrological projections need to evaluate assumptions regarding storage and the simulated hydrological processes, to enhanced climate risk management.

Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.

Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.

Regional, multi-decadal analysis on the Loire River basin reveals that stream temperature increases faster than air temperature

Abstract. Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (105 km2 with 52 278 reaches). Stream temperature increased for almost all reaches in all seasons (mean =+0.38 ∘C decade−1) over the 1963–2019 period. Increases were greatest in spring and summer, with a median increase of + 0.38 ∘C (range =+0.11 to +0.76 ∘C) and +0.44 ∘C (+0.08 to +1.02 ∘C) per decade, respectively. Rates of stream temperature increases were greater than for air temperature across seasons for the majority of reaches. Spring and summer increases were typically greatest in the southern part of the Loire basin (up to +1 ∘C decade−1) and in the largest rivers (Strahler order ≥5). Importantly, air temperature and streamflow could exert a joint influence on stream temperature trends, where the greatest stream temperature increases were accompanied by similar trends in air temperature (up to +0.71 ∘C decade−1) and the greatest decreases in streamflow (up to −16 % decade−1). Indeed, for the majority of reaches, positive stream temperature anomalies exhibited synchrony with positive anomalies in air temperature and negative anomalies in streamflow, highlighting the dual control exerted by these hydroclimatic drivers. Moreover, spring and summer stream temperature, air temperature, and streamflow time series exhibited common change points occurring in the late 1980s, suggesting a temporal coherence between changes in the hydroclimatic drivers and a rapid stream temperature response. Critically, riparian vegetation shading mitigated stream temperature increases by up to 0.16 ∘C decade−1 in smaller streams (i.e. < 30 km from the source). Our results provide strong support for basin-wide increases in stream temperature due to joint effects of rising air temperature and reduced streamflow. We suggest that some of these climate change-induced effects can be mitigated through the restoration and maintenance of riparian forests.

Dynamic quantile regression for trend analysis of streamflow time series

AbstractWater availability is an essential factor in maintaining the integrity of ecological processes and is a source of socio‐economic development. However, socio‐economic development increases the pressure on water resources. Thus, understanding the flow regime in a watershed is essential to correct water resource management. In this study, we propose using dynamic quantile regression (DQR) to analyse trends in streamflow time series. DQR is a subset of the general class of semi‐parametric quantile regression models, which is tailored for time series modelling. It allows for autoregressive dynamics and modelling of trending behaviour and seasonal fluctuations. With a single model, it is possible to estimate the impacts of predictor variables on any given quantile of the response distribution instead of simply evaluating such effects on the mean response, as is typically done in other statistical approaches. In other words, it is possible to gain knowledge on how predictors impact the magnitude of streamflow in wet (upper quantiles) and dry seasons (lower quantiles) separately. We used DQR to model a streamflow time series of 27 gauges, distributed in the Araguaia watershed in central Brazil. The results show that, except for a single gauge (Alto Araguaia), there are downward streamflow trends, thus non‐stationary behaviour. The model yielded excellent data fits (pseudo‐R2 above 0.80), and it was possible to obtain a confidence interval for each slope. In our analysis, the usefulness of DQR modelling for assessing trends in streamflow time series is shown and, consequently, for achieving efficient water resource management.