Distinguishing streamflow trends caused by changes in climate,forest cover,and permafrost in a large watershed in northeastern China

Understanding how rivers respond to changes in land cover, climate, and subsurface conditions is critical for sustainably managing water resources and ecosystems. In this study, long‐term hydrologic, climate, and satellite data (1973–2012) from the Upper Tahe River watershed (2359 km²) in the Da Hinggan Mountains of northeast China were analysed to quantify the relative hydrologic effects of climate variability (system input) and the combined influences of forest cover change and permafrost thaw (system characteristics) on average annual streamflow (system response) using 2 methods: the sensitivity‐based method and the Kendall–Theil robust line method. The study period was subdivided into a forest harvesting period (1973–1987), a forest stability period (1988–2001), and a forest recovery period (2002–2012). The results indicated that the combined effects of forest harvesting and permafrost thaw on streamflow (+ 47.0 mm) from the forest harvesting period to the forest stability period was approximately twice as large as the effect associated with climate variability (+20.2 mm). Similarly, from the forest stability period to the forest recovery period, the decrease in average annual streamflow attributed to the combined effects of forest recovery and permafrost thaw (?38.0 mm) was much greater than the decrease due to climate variability (?22.2 mm). A simple method was used to separate the distinct impacts of forest cover change and permafrost thaw, but distinguishing these influences is difficult due to changes in surface and subsurface hydrologic connectivity associated with permafrost thaw. The results highlight the need to consider multiple streamflow drivers in future watershed and aquatic ecosystem management. Due to the ecological and hydrological susceptibility to disturbances in the Da Hinggan Mountains, forest harvesting will likely negatively impact ecohydrological processes in this region, and the effects of forest species transition in the forest recovery process should be further investigated.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.     

Characterizing spatial and temporal variation in 18O and 2H content of New Zealand river water for better understanding of hydrologic processes

Time series of hydrogen and oxygen stable isotope ratios (δ²H and δ¹⁸O) in rivers can be used to quantify groundwater contributions to streamflow, and timescales of catchment storage. However, these isotope hydrology techniques rely on distinct spatial or temporal patterns of δ²H and δ¹⁸O within the hydrologic cycle. In New Zealand, lack of understanding of spatial and temporal patterns of δ²H and δ¹⁸O of river water hinders development of regional and national-scale hydrological models. We measured δ²H and δ¹⁸O monthly, together with river flow rates at 58 locations across New Zealand over a two-year period. Results show: (a) general patterns of decreasing δ²H and δ¹⁸O with increasing latitude were altered by New Zealand's major mountain ranges; δ²H and δ¹⁸O were distinctly lower in rivers fed from higher elevation catchments, and in eastern rain-shadow areas of both islands; (b) river water δ²H and δ¹⁸O values were partly controlled by local catchment characteristics (catchment slope, PET, catchment elevation, and upstream lake area) that influence evaporation processes; (c) regional differences in evaporation caused the slope of the river water line (i.e., the relationship between δ²H and δ¹⁸O in river water) for the (warmer) North Island to be lower than that of the (cooler, mountain-dominated) South Island; (d) δ²H seasonal offsets (i.e., the difference between seasonal peak and mean values) for individual sites ranged from 0.50‰ to 5.07‰. Peak values of δ¹⁸O and δ²H were in late summer, but values peaked 1 month later at the South Island sites, likely due to greater snow-melt contributions to streamflow. Strong spatial differences in river water δ²H and δ¹⁸O caused by orographic rainfall effects and evaporation may inform studies of water mixing across landscapes. Generally distinct seasonal isotope cycles, despite the large catchment sizes of rivers studied, are encouraging for transit time analysis applications.     

Hammond Hill Research Catchment: Supporting hydrologic investigations of rooting zone and vegetation water dynamics under climate change

The Hammond Hill Research Catchment (HH) is a small (120 ha), temperate, second order tributary to Six Mile Creek, Cayuga Lake, and the Great Lakes (42.42°, −76.32°). The HH has been monitored since January 2017 for the purpose of understanding how recent infiltration mixes with antecedent soil water on hillslope forest floors and the spatial and temporal patterns of Root Water Uptake (RWU) by temperate northeastern US tree species (eastern hemlock [Tsuga canadensis], American beech [Fagus grandifolia], and sugar maple [Acer saccharum]). These data are informing us about the hydrologic consequences of anticipated tree species composition change and supporting the development of more refined ecohydrological models. The glaciated catchment is underlain by a shallow confining siltstone layer (1–1.5 m depth) and densely covered with an approximately 60 year old regrowth mixed species forest of hemlock, beech, and other deciduous tree species common to the northeastern US. Current datasets from the HH include precipitation snow water equivalent, discharge, and associated isotopic water compositions, δ²H & δ¹⁸O. Measurements of (top 10 cm) soil water content, as well as bulk soil water and hemlock and beech xylem isotopic compositions are made at several locations across a topographic wetness gradient. The near-term role of the HH is to support an understanding of the environmental and ecological drivers of plant RWU competition. All data from the HH are publicly available.     

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data‐demanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low‐land natural rivers, lake outlets, snow‐fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross‐validation conducted over a 30‐year period indicates that they can be suitably adopted for long‐term analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments

The synthesis of experimental understanding of catchment behaviour and its translation into qualitative perceptual models is an important objective of hydrological sciences. We explore this challenge by examining the cumulative understanding of the hydrology of three experimental catchments and how it evolves through the application of different investigation techniques. The case study considers the Huewelerbach, Weierbach and Wollefsbach headwater catchments of the Attert basin in Luxembourg. Subsurface investigations including bore holes and pits, analysis of soil samples and Electrical Resistivity Tomography measurements are presented and discussed. Streamflow and tracer data are used to gain further insights into the streamflow dynamics of the catchments, using end‐member mixing analysis and hydrograph separation based on dissolved silica and electrical conductivity. We show that the streamflow generating processes in all three catchments are controlled primarily by the subsolum and underlying bedrock. In the Huewelerbach, the permeable sandstone formation supports a stable groundwater component with little seasonality, which reaches the stream through a series of sources at the contact zone with the impermeable marls formation. In the Weierbach, the schist formation is relatively impermeable and supports a ‘fill and spill’‐type of flow mechanism; during wet conditions, it produces a delayed response dominated by pre‐event water. In the Wollefsbach, the impermeable marls formation is responsible for a saturation‐excess runoff generating process, producing a fast and highly seasonal response dominated by event water. The distinct streamflow generating processes of the three catchments are represented qualitatively using perceptual models. The perceptual models are in turn translated into quantitative conceptual models, which simulate the hydrological processes using networks of connected reservoirs and transfer functions. More generally, the paper illustrates the evolution of perceptual models based on experimental fieldwork data, the translation of perceptual models into conceptual models and the value of different types of data for processes understanding and model representation. Copyright © 2014 John Wiley & Sons, Ltd.     

Spatial aggregation of time‐variant stream water ages in urbanizing catchments

We calibrated an integrated flow–tracer model to simulate spatially distributed isotope time series in stream water in a 7.9‐km² catchment with an urban area of 13%. The model used flux tracking to estimate the time‐varying age of stream water at the outlet and both urbanized (1.7 km²) and non‐urban (4.5 km²) sub‐catchments over a 2.5‐year period. This included extended wet and dry spells where precipitation equated to >10‐year return periods. Modelling indicated that stream water draining the most urbanized tributary was youngest with a mean transit time (MTT) of 171 days compared with 456 days in the non‐urban tributary. For the larger catchment, the MTT was 280 days. Here, the response of urban contributing areas dominated smaller and more moderate runoff events, but rural contributions dominated during the wettest periods, giving a bi‐modal distribution of water ages. Whilst the approach needs refining for sub‐daily time steps, it provides a basis for projecting the effects of urbanization on stream water transit times and their spatial aggregation. This offers a novel approach for understanding the cumulative impacts of urbanization on stream water quantity and quality, which can contribute to more sustainable management. Copyright © 2015 John Wiley & Sons, Ltd.     

A modelling framework for evaluation of the hydrological impacts of nature‐based approaches to flood risk management,with application to in‐channel interventions across a 29‐km2 scale catchment in the United Kingdom

Nature‐based approaches to flood risk management are increasing in popularity. Evidence for the effectiveness at the catchment scale of such spatially distributed upstream measures is inconclusive. However, it also remains an open question whether, under certain conditions, the individual impacts of a collection of flood mitigation interventions could combine to produce a detrimental effect on runoff response. A modelling framework is presented for evaluation of the impacts of hillslope and in‐channel natural flood management interventions. It couples an existing semidistributed hydrological model with a new, spatially explicit, hydraulic channel network routing model. The model is applied to assess a potential flood mitigation scheme in an agricultural catchment in North Yorkshire, United Kingdom, comprising various configurations of a single variety of in‐channel feature. The hydrological model is used to generate subsurface and surface fluxes for a flood event in 2012. The network routing model is then applied to evaluate the response to the addition of up to 59 features. Additional channel and floodplain storage of approximately 70,000 m³ is seen with a reduction of around 11% in peak discharge. Although this might be sufficient to reduce flooding in moderate events, it is inadequate to prevent flooding in the double‐peaked storm of the magnitude that caused damage within the catchment in 2012. Some strategies using features specific to this catchment are suggested in order to improve the attenuation that could be achieved by applying a nature‐based approach.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.