scholarly journals Stream discharge depends more on the temporal distribution of water inputs than on yearly snowfall fractions for a headwater catchment at the rain-snow transition zone

2021 ◽  
Author(s):  
Leonie Kiewiet ◽  
Ernesto Trujillo ◽  
Andrew Hedrick ◽  
Scott Havens ◽  
Katherine Hale ◽  
...  
Keyword(s):  
Transition Zone ◽  
Temporal Distribution ◽  
Low Flow ◽  
Stream Discharge ◽  
Stream Drying ◽  
Distributed Modelling ◽  
Spatially Distributed ◽  
Dry Out ◽  
Annual Discharge ◽  
Headwater Catchment

Abstract. Climate warming affects snowfall fractions and snowpack storage, displaces the rain-snow transition zone towards higher elevations, and impacts discharge timing and magnitude as well as low-flow patterns. However, it remains unknown how variations in the spatial and temporal distribution of precipitation at the rain-snow transition zone affect discharge. To investigate this, we used observations from eleven weather stations and snow depths measured in one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semi-arid, 1.8 km2 headwater catchment at the rain-snow transition zone. We focused on surface water inputs (SWI; the summation of rainfall and snowmelt) for four years with contrasting climatological conditions (wet, dry, rainy and snowy) and compared simulated SWI to measured discharge. We obtained a strong spatial agreement between snow depth from the lidar survey and model (r2: 0.88), and a median Nash-Sutcliffe Efficiency (NSE) of 0.65 for simulated and measured snow depths for all modelled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the four years, with north-facing slopes producing 1.09 to 1.25 times more SWI than south-facing slopes, and snow drifts producing up to six times more SWI than the catchment average. We found that discharge in a snowy year was almost twice as high as in a rainy year, despite similar SWI. However, years with a lower snowfall fraction did not always have lower annual discharge nor earlier stream drying. Instead, we found that the dry-out date at the catchment outlet was positively correlated to the snowpack melt-out date. These results highlight the heterogeneity of SWI at the rain-snow transition zone and emphasize the need for spatially distributed modelling or monitoring of both the snowpack and rainfall.

scholarly journals An Assessment of the Recent Evolution of the Streamflow in a Near-Natural System: A Case Study in the Headwaters of the Mero Basin (Galicia, Spain)

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 97
Author(s):  
M. L. Rodríguez-Blanco ◽  
M. M. Taboada-Castro ◽  
M. T. Taboada-Castro
Keyword(s):  
Historical Context ◽  
Low Flow ◽  
Upward Trend ◽  
Nw Spain ◽  
Stream Discharge ◽  
System A ◽  
High Flows ◽  
The Mean ◽  
Headwater Catchment

Observational trend analysis is fundamental for documenting changes in river flows and placing extreme events in their longer-term historical context. Observations from near-natural catchments, i.e., with little or no alteration by humans, are of great importance in detecting and attributing streamflow trends. The purpose of this study is to analyze the annual and seasonal trends of stream discharge (mean, low and high flows) in a headwater catchment in NW Spain, i.e., in the wettest corner of the Iberian Peninsula. The results showed no significant decrease in the mean annual stream discharge. However, significantly lower summer and autumn mean stream discharge and an increase in low flow period were detected, in addition to lesser autumn high flow. The rainfall pattern followed an upward trend, but was not significant. This different pattern shown by rainfall and discharge indicates that is not sufficient to explain the observed trend in stream discharge. Air temperature, most notably by enhancing evapotranspiration, may explain the altered patterns of stream discharge.

Characterizing denitrification in the aquifer and transit times based on spatially-distributed measurements in streams

2020 ◽  
Author(s):  
Camille Vautier ◽  
Christophe Petton ◽  
Ronan Abhervé ◽  
Gilles Pinay ◽  
Anniet Lavermann ◽  
...  
Keyword(s):  
Residence Time ◽  
Current Knowledge ◽  
Regional Scale ◽  
Low Flow ◽  
Catchment Scale ◽  
Fractured Bedrock ◽  
Stream Discharge ◽  
Common Control ◽  
Transit Times ◽  
Spatially Distributed

<p>Human activity has more than doubled reactive nitrogen delivery to Earth’s ecosystems. In the past several decades, efforts have been made to reduce agricultural inputs of nitrogen, but the decrease of the nitrate concentration in rivers is also controlled by natural processes, especially by flow and denitrification in the aquifer. Yet, with current knowledge, it remains difficult to characterize transit times and groundwater denitrification rates at scales relevant for mitigation actions (catchment scale to regional scale).</p><p>Data directly obtained in piezometers generally display large variabilities without any obvious correlation to any landscape, geological or geomorphological characteristics. Here we propose an alternative method based on in-stream measurements to get a representative view of the role of the aquifer in the temporary storage and degradation of nitrates. We performed spatially-distributed measurements in low-order streams within a 35 km2 agricultural catchment underlain by a crystalline, fractured bedrock aquifer. Measurements were performed during low-flow. Stream discharge and radon activity were used to determine the groundwater discharge into the streams. Silica was used as an age-tracer [1]. Nitrate concentrations and isotopic ratios allowed to characterize the denitrification in the aquifer.</p><p>Results show that in-stream measurements provide a representative view of transport and denitrification in the aquifer. They highlight that the scale of homogenization is larger than the studied catchment, and reveal an unexpected correlation between the mean residence time and the characteristic denitrification time. This allows to hypothesize a common control on residence time and denitrification in the aquifer, that could be exercised by the depth of the weathered zone. Unraveling such a correlation could be a first step towards a global characterization of aquifer processes through geophysical imagery methods.</p><p> </p><p>[1] Marcais, J., et al. 2018. Dating groundwater with dissolved silica and CFC concentrations in crystalline aquifers. STOTEN.</p>

How sensitive is discharge at the rain-snow transition zone to the spatial and temporal distribution of surface water inputs?

2021 ◽  
Author(s):  
Leonie Kiewiet ◽  
Katherine Hale ◽  
Scott Havens ◽  
Ernesto Trujillo ◽  
Andrew Hedrick ◽  
...  
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Transition Zone ◽  
Snow Depth ◽  
Shallow Subsurface ◽  
Snow Covered Area ◽  
Covered Area ◽  
Spatially Distributed ◽  
High Resolution Satellite Imagery ◽  
Dry Out

<p>Changes in rain/snowfall apportionments are already being observed in mountain environments because of climate change. Increases in temperatures are leading to the displacement of rain-snow transition zones towards higher elevations, and are impacting snowpack storage, discharge timing and magnitude and low-flow patterns. To assess sensitivity of discharge to such changes, we investigated variability in surface water inputs (SWI = snowmelt + rainfall) in a semi-arid, 1.8 km<sup>2</sup> headwater catchment in the rain-snow transition zone in Idaho (USA). We used a spatially distributed snowpack model (iSnobal/Automated Water Supply Model, AWSM) to investigate catchment SWI during four years (2005, 2010, 2011, 2014) with contrasting climatological conditions, and compared these results to measured streamflow and soil moisture. Results are evaluated using continuous measurements of snow depths at eleven weather stations, one lidar snow depth survey, and high-resolution satellite imagery (PSScene4Band) used to quantify the persistence of the snowpack across the catchment. We found that the model results agreed well with the spatial (r<sup>2</sup>: 0.86 in 2009 compared to lidar-derived snow depths) and temporal (median Nash-Sutcliffe Efficiency for normalized snow depths: 0.76 compared to weather station snow depth measurements) variations of the snowpack. The model results suggested that simulated snow-covered area was a good predictor for simulated SWE (range r<sup>2</sup>: 0.60 to 0.78 for all modeled years) during most of the snow-covered season, which indicates the usefulness of snow-covered area to quantify SWE at the rain-snow transition zone. We found that snow drifting and aspect-controlled processes caused large differences in snow depths across the watershed, with some snowdrifts producing SWI that was 3x greater than from nearby low elevation, south-facing slopes. In years with a lower snow fraction of total precipitation, the spatial distribution of SWI was much more homogeneous and stream discharge in spring time was lower, even though significant rainstorms occurred during that time. Indeed discharge response to SWI varied by season: in late spring/early summer, discharge was produced when basin-wide shallow subsurface storage exceeded ~150mm whereas in late fall/early winter, discharge was most responsive to precipitation after the shallow subsurface storage exceeded 250-300 mm. This indicates the importance of contributions from other, possibly deeper, flow paths, and is also consistent with the observation that years with a lower snow fraction did not have lower discharge nor earlier stream drying in summer. Nonetheless, the dry-out date at the catchment outlet was positively correlated to the last day at which there was snow present in the catchment as derived from the model results for the simulated years, and for four additional years (2016-2019) for years in which the high-resolution satellite imagery was available. This indicates the importance of snowdrifts for sustaining streamflow and the need for spatially-distributed modeling of the snowpack at the rain-snow transition zone, rather than using basin-average values. While extensive data may be required to understand the breadth of catchment responses in rain-snow transition zone, some critical parameters such as dry-out date can be determined from high-resolution satellite images.</p>

Relationship of upstream migrating adult Atlantic salmon (Salmo salar) and stream discharge within Catamaran Brook, New Brunswick

2007 ◽  
Vol 64 (3) ◽  
pp. 563-573 ◽  
Author(s):  
Sean C Mitchell ◽  
Richard A Cunjak
Keyword(s):  
Body Size ◽  
Atlantic Salmon ◽  
New Brunswick ◽  
Salmo Salar ◽  
Predictive Ability ◽  
River System ◽  
Low Flow ◽  
Stream Discharge ◽  
Atlantic Salmon Salmo Salar ◽  
Adult Abundance

Stream discharge has long been associated with abundance of returning adult spawning salmonids to streams and may also affect body size distribution of adult salmon as low flows interfere with returns of larger-bodied fish. We examined these relationships of abundance and body size within Catamaran Brook, a third-order tributary to the Miramichi River system of New Brunswick, Canada, to investigate the causes of a declining trend in annual returns of Atlantic salmon (Salmo salar) to this stream. Regression models of adult abundance, proportion of the run as grilse, and body size of returning adults as functions of maximum daily stream discharge during the period of upstream spawner migration were constructed. Adult abundance shows a logarithmic relationship with stream discharge and provides good predictive ability, while appearing to not be significantly related to adult abundance in the larger Miramichi system. The proportion as grilse in the run and female body size are also logarithmically related to stream discharge, with low flow years being very influential in the regressions. These relationships of Atlantic salmon population abundance and body size characteristics have implications with respect to stock integrity and production of the following generation.

scholarly journals Regional hydrology controls stream microbial biofilms: evidence from a glacial catchment

2004 ◽  
Vol 1 (1) ◽  
pp. 497-531 ◽  
Author(s):  
T. J. Battin ◽  
A. Wille ◽  
R. Psenner ◽  
A. Richter
Keyword(s):  
Landscape Heterogeneity ◽  
Low Flow ◽  
Detection Rates ◽  
Stream Communities ◽  
Stream Discharge ◽  
Carbon Production ◽  
Microbial Biofilms ◽  
Short Period ◽  
Alpine Stream

Abstract. Glaciers are highly responsive to global warming and important agents of landscape heterogeneity. While it is well established that glacial ablation and snowmelt regulate stream discharge, linkage among streams and streamwater hydrogeochemistry, the controls of these factors on stream microbial biofilms remain insufficiently understood. We investigated glacial (metakryal, hypokryal), groundwater-fed (krenal) and snow-fed (rhithral) streams – all of them representative for alpine stream networks – and present evidence that these hydrologic and hydrogeochemical factors differentially affect sediment microbial biofilms. Average microbial biomass and bacterial carbon production were low in the glacial streams, whereas bacterial cell size, biomass, and carbon production were higher in the tributaries, most notably in the krenal stream. Whole-cell in situ fluorescence hybridization revealed reduced detection rates of the Eubacteria and higher abundance of α-Proteobacteria in the glacial stream, a pattern that most probably reflects the trophic status of this ecosystem. Our data suggest low flow during the onset of snowmelt and autumn as a short period (hot moment) of favorable environmental conditions with pulsed inputs of allochthonous nitrate and dissolved organic carbon, and with disproportional high microbial growth. Krenal and rhithral streams with more constant and favorable environments serve as possible sources of microbes and organic matter to the main glacial channel during periods (e.g. snowmelt) of elevated hydrologic linkage among streams. Ice and snow dynamics have a crucial impact on microbial biofilms, and we thus need better understanding of the microbial ecology and enhanced consideration of critical hydrological episodes in future models predicting alpine stream communities.

scholarly journals Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

2017 ◽  
Author(s):  
Diana Lucatero ◽  
Henrik Madsen ◽  
Jens C. Refsgaard ◽  
Jacob Kidmose ◽  
Karsten H. Jensen
Keyword(s):  
Hydrological Model ◽  
Predictive Distribution ◽  
Low Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Initial State ◽  
Lower Accuracy ◽  
Spatially Distributed ◽  
Streamflow Forecasts ◽  
Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

Performance and sensitivity of a spatially distributed hydrological conceptual flood model with snow components.

2021 ◽  
Author(s):  
François Colleoni ◽  
Catherine Fouchier ◽  
Pierre-André Garambois ◽  
Pierre Javelle ◽  
Maxime Jay-Allemand ◽  
...  
Keyword(s):  
Significant Proportion ◽  
Slow Flow ◽  
Modeling Tools ◽  
Snow Modeling ◽  
Distributed Modelling ◽  
Distributed Framework ◽  
Mountainous Catchments ◽  
Spatially Distributed ◽  
Flow Components ◽  
Spatially Varying

<p>In France, flash floods are responsible for a significant proportion of damages caused by natural hazards, either human or material. Hence, advanced modeling tools are needed to perform effective predictions. However for mountainous catchments snow modeling components may be required to correctly simulate river discharge.</p><p>This contribution investigates the implementation and constrain of snow components in the spatially distributed SMASH* platform (Jay-Allemand et al. 2020). The goal is to upgrade model structure and spatially distributed calibration strategies for snow-influenced catchments, as well as to investigate parametric sensitivity and equifinality issues. First, the implementation of snow modules of varying complexity is addressed based on Cemaneige (Valery et al. 2010) in the spatially distributed framework. Next, tests are performed on a sample of 55 catchments in the French North Alps. Numerical experiments and global sensitivity analysis enable to determine pertinent combinations of flow components (including a slow flow one) and calibration parameters. Spatially uniform or distributed calibrations using a variational method (Jay-Allemand 2020) are performed and compared on the dataset, for different model structures and constrains. These tests show critical improvements in outlet discharge modeling by adding slow flow and snow modules, especially considering spatially varying parameters. Current and future works focus on testing and improving the constrains of snow modules and calibration strategy, as well as potential validation and multiobjective calibration with snow signatures gained from in situ or satellite data. </p><p>*SMASH: Spatially-distributed Modelling and ASsimilation for Hydrology, platform developped by INRAE-Hydris corp. for operational applications in the french flood forecast system VigicruesFlash</p>

scholarly journals Spatial and temporal distribution of Yunnan snub-nosed monkey, Rhinopithecus bieti, indices

Mammalia ◽  
2019 ◽  
Vol 83 (2) ◽  
pp. 103-109
Author(s):  
Rong Fu ◽  
Li Li ◽  
ZhongHua Yu ◽  
Eve Afonso ◽  
Patrick Giraudoux
Keyword(s):  
Large Scale ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Homogeneous Distribution ◽  
Non Invasive ◽  
Species Of Conservation Concern ◽  
Spatially Distributed ◽  
Elusive Species ◽  
Conservation Concern ◽  
Group Distribution

Abstract Studying elusive species of conservation concern might be difficult for technical and ethical reasons. However, censuses can be based on the observation of activity indices. When coupled to non-invasive genetic methods this approach can provide extremely precise information about population size, individual movements and diseases. However, the design of optimal sampling is dependent on a knowledge on group distribution and possible variations of detectability of index targets. The aim of this study was to document the distribution of Yunnan snub-nosed monkey indices in space and time in that perspective. Based on transects carried out across the range of a fed population and on counts along the trail across the range of a wild group, we show that 2–3 day stays of a group in a place of some hectares were sufficient to get an homogeneous distribution of indices. Furthermore, the number of indices found were dependent on both pig presence and season. On the other hand, on a large scale of 100 km2 indices were spatially distributed as nested clusters. Indices distribution indicated a strong preference towards southern slopes and altitudes ranging between 2900 and 3400 m. Those observations pinpoint the importance of considering spatial scale to organise sampling designed to estimate population distribution.

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