scholarly journals Comparing the runoff decompositions of small testbed catchments: end-member mixing analysis against hydrological modelling

2021 ◽  
Author(s):  
Andrey Bugaets ◽  
Boris Gartsman ◽  
Tatiana Gubareva ◽  
Sergei Lupakov ◽  
Andrey Kalugin ◽  
...  
Keyword(s):  
Auxiliary Information ◽  
Hydrological Modelling ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Benchmark Analysis ◽  
In Situ Observations ◽  
Data Generalization ◽  
Runoff Events ◽  
Mountain Taiga

Abstract. This study is focused on the comparison of catchment streamflow composition simulated with three well-known rainfall-runoff (RR) models (ECOMAG, HBV, SWAT) against hydrograph decomposition onto the principal constituents evaluated from End-Member Mixing Analysis (EMMA). There used the data provided by the short-term in-situ observations at two small mountain-taiga experimental catchments located in the south of Pacific Russia. All used RR models demonstrate that two neighboring small catchments disagree significantly in mutual dynamics of the runoff fractions due to geological and landscape structure differences. The geochemical analysis confirmed the differences in runoff generation processes at both studied catchments. The assessment of proximity of the runoff constituents to the hydrograph decomposition with the EMMA that makes a basis for the RR models benchmark analysis. We applied three data aggregation intervals (season, month and pentad) to find a reasonable data generalization period ensuring results clarity. In terms of runoff composition, the most conformable RR model to EMMA is found to be ECOMAG, HBV gets close to reflect specific runoff events well enough, SWAT gives distinctive behavior against other models. The study shows that along with using the standard efficiency criteria reflected proximity of simulated and modelling values of runoff, compliance with the EMMA results might give useful auxiliary information for hydrological modelling results validation.

scholarly journals Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1872 ◽  
Author(s):  
Lu Yi ◽  
Wanchang Zhang ◽  
Xiangyang Li
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Eastern China ◽  
Hydrological Modelling ◽  
Heavy Rainfall Event ◽  
In Situ Observation ◽  
Rainfall Runoff ◽  
Study Case

To compare the effectivenesses of different precipitation datasets on hydrological modelling, five precipitation datasets derived from various approaches were used to simulate a two-week runoff process after a heavy rainfall event in the Wangjiaba (WJB) watershed, which covers an area of 30,000 km2 in eastern China. The five precipitation datasets contained one traditional in situ observation, two satellite products, and two predictions obtained from the Numerical Weather Prediction (NWP) models. They were the station observations collected from the China Meteorological Administration (CMA), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG), the merged data of the Climate Prediction Center Morphing (merged CMORPH), and the outputs of the Weather Research and Forecasting (WRF) model and the WRF four-dimensional variational (4D-Var) data assimilation system, respectively. Apart from the outlet discharge, the simulated soil moisture was also assessed via the Soil Moisture Active Passive (SMAP) product. These investigations suggested that (1) all the five precipitation datasets could yield reasonable simulations of the studied rainfall-runoff process. The Nash-Sutcliffe coefficients reached the highest value (0.658) with the in situ CMA precipitation and the lowest value (0.464) with the WRF-predicted precipitation. (2) The traditional in situ observation were still the most reliable precipitation data to simulate the study case, whereas the two NWP-predicted precipitation datasets performed the worst. Nevertheless, the NWP-predicted precipitation is irreplaceable in hydrological modelling because of its fine spatiotemporal resolutions and ability to forecast precipitation in the future. (3) Gauge correction and 4D-Var data assimilation had positive impacts on improving the accuracies of the merged CMORPH and the WRF 4D-Var prediction, respectively, but the effectiveness of the latter on the rainfall-runoff simulation was mainly weakened by the poor quality of the GPM IMERG used in the study case. This study provides a reference for the applications of different precipitation datasets, including in situ observations, remote sensing estimations and NWP simulations, in hydrological modelling.

Ingredients of German flood events

2020 ◽  
Author(s):  
Ralf Merz ◽  
Larisa Tarasova ◽  
Stefano Basso
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Rainfall Intensity ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Regional Variability ◽  
Large Variability ◽  
Runoff Events

<p>Floods can be caused by a large variety of different processes, such as short, but intense rainfall bursts, long rainfall events, which are wetting up substantial parts of the catchment, or rain on snow cover or frozen soils. Although there is a plethora on studies analysing or modelling rainfall-runoff processes, it is still not well understood, what rainfall and runoff generation conditions are needed to generate flood runoff and how these characteristics vary between catchments. In this databased approach we decipher the ingredients of flood events occurred in 161 catchments across Germany. For each catchment rainfall-runoff events are separated from observed time series for the period 1950-2013, resulting in about 170,000 single events. A peak-over-threshold approach is used to select flood events out of these runoff events. For each event, spatially and temporally distributed rainfall and runoff generation characteristics, such as snow cover and soil moisture, as well as their interaction are derived. Then we decipher those event characteristics controlling flood event occurrence by using machine learning techniques.</p><p>On average, the most important event characteristic controlling flood occurrence in Germany is, as expected, event rainfall volume, followed by the overlap of rainfall and soil moisture and the extent of wet areas in the catchment (area with high soil moisture content). Rainfall intensity is another important characteristic. However, a large variability in its importance is noticeable between dryer catchments where short rainfall floods occur regularly and wetter catchments, where rainfall intensity might be less important for flood generation. To analyse the regional variability of flood ingredients, we cluster the catchments according to similarity in their flood controlling event characteristics and test how good the flood occurrence can be predicted from regionalised event characteristics. Finally, we analyse the regional variability of the flood ingredients in the light of climate and landscape catchment characteristics.</p>

Geochemical transformation of forested catchments following bark beetle infestation: Evidence from EC hysteresis during rainfall-runoff events

2021 ◽  
Author(s):  
Ye Su ◽  
Jakub Langhammer
Keyword(s):  
Bark Beetle ◽  
Tree Mortality ◽  
Forest Cover ◽  
Scale Effects ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Event Scale ◽  
Decadal Scale ◽  
Runoff Events

<p>Forest disturbance resulting from bark beetle infestation is becoming a widespread phenomenon due to climate warming and changing precipitation patterns. Such disturbance could result in alterations of streamflow and stream geochemistry. Our previous study found that these changes developed relatively rapidly after infestation and have long-lasting (decadal-scale) effects. Furthermore, infestation-induced changes in event-scale dynamics of in-stream electric conductivity (EC) – discharge (Q) relations were found to be considerable, impacting even the annual average EC-values. In this study, therefore, all rainfall-induced runoff events occurring during an 11-year period were identified and their distinct EC-Q relations were evaluated. The evaluation was done based on 10-min high-frequency monitoring of Q and EC and in four experimental catchments (~4 km<sup>2</sup> each; located in the Sumava Mountains, Central Europe), having different forest cover (disturbance) stages. Furthermore, snapshot sampling was carried out to map EC and chemical parameters (N, DOC, etc.) in different hydrological landscape units (riparian area, hillslope, and terrace) and in multiple vertical layers of soil (surface, soil, and groundwater). Results showed that after infestation the EC-Q hysteresis loops at the event-scale shifted from positive to negative relationships, implying changes in the subsurface chemical composition and runoff patterns. Specifically, healthy forest systems required event flows to mobilize substances in the soil and groundwater systems as the groundwater level rose into the relatively conductive, shallow part of the soil profile during an event. Such flush-driven systems were known for their release of large fractions of total annual in-stream substance loads showing a positive EC-Q relationship. By contrast, after infestation-induced tree mortality, the mobilization and downward percolation of nutrients and carbon from litter and decomposing needles may be considerable even during moderate rain and infiltration events. When the system is flooded under event conditions, substance-enriched soil water and groundwater may be mixed with and diluted by low-salinity event water, leading to a negative EC-Q relationship.  This study exemplifies how EC monitoring techniques can be used as an alternative to high-cost geochemical monitoring in quantifying complex rainfall-runoff processes as well as runoff generation processes, allowing for long monitoring periods at high temporal resolution and reasonable costs.</p>

scholarly journals Diel fluctuations of viscosity-driven riparian inflow affect streamflow DOC concentration

2017 ◽  
Author(s):  
Michael P. Schwab ◽  
Julian Klaus ◽  
Laurent Pfister ◽  
Markus Weiler
Keyword(s):  
High Frequency ◽  
Riparian Zone ◽  
Rainfall Runoff ◽  
Complex Interplay ◽  
Lower Viscosity ◽  
Doc Concentration ◽  
Diel Fluctuations ◽  
Runoff Events ◽  
Headwater Catchment

Abstract. Diel fluctuations of streamwater DOC concentrations are generally explained by a complex interplay of different instream processes. We measured the light absorption spectrum of water and DOC concentrations in-situ and with high-frequency by means of a UV-Vis spectrometer during 18 months at the outlet of a forested headwater catchment in Luxembourg (0.45 km2). We generally observed diel DOC fluctuations with a maximum in the afternoon during days that were not affected by rainfall-runoff events. We identified an increased inflow of terrestrial DOC to the stream in the afternoon, causing the DOC maxima in the stream. The terrestrial origin of the DOC was derived from the SUVA-254 (specific UV absorbance at 254 nm) index, which is a good indicator for the aromaticity of DOC. In the studied catchment, the only possible process that can explain the diel DOC input variations towards the stream is the so-called viscosity effect. The water temperature in the upper parts of the riparian zone is increasing during the day, leading to a lower viscosity and therefore a higher hydraulic conductivity. Consequently, more water from areas that are rich in terrestrial DOC passes through the riparian zone and contributes to streamflow in the afternoon. We believe that not only diel instream processes, but also viscosity driven diel fluctuations of terrestrial DOC input should be considered for explaining diel DOC patterns in streams.

scholarly journals Diel fluctuations of viscosity-driven riparian inflow affect streamflow DOC concentration

2018 ◽  
Vol 15 (7) ◽  
pp. 2177-2188 ◽  
Author(s):  
Michael P. Schwab ◽  
Julian Klaus ◽  
Laurent Pfister ◽  
Markus Weiler
Keyword(s):  
High Frequency ◽  
Riparian Zone ◽  
Stream Water ◽  
Rainfall Runoff ◽  
Lower Viscosity ◽  
Doc Concentration ◽  
Diel Fluctuations ◽  
Runoff Events ◽  
Headwater Catchment

Abstract. Diel fluctuations of stream water DOC concentrations are generally explained by a complex interplay of different instream processes. We measured the light absorption spectrum of water and DOC concentrations in situ and with high frequency by means of a UV–Vis spectrometer during 18 months at the outlet of a forested headwater catchment in Luxembourg (0.45 km2). We generally observed diel DOC fluctuations with a maximum in the afternoon during days that were not affected by rainfall–runoff events. We identified an increased inflow of terrestrial DOC to the stream in the afternoon, causing the DOC maxima in the stream. The terrestrial origin of the DOC was derived from the SUVA-254 (specific UV absorbance at 254 nm) index, which is a good indicator for the aromaticity of DOC. In the studied catchment, the most likely process that can explain the diel DOC input variations towards the stream is the so-called viscosity effect. The water temperature in the upper parts of the saturated riparian zone is increasing during the day, leading to a lower viscosity and therefore a higher hydraulic conductivity. Consequently, more water from areas that are rich in terrestrial DOC passes through the saturated riparian zone and contributes to streamflow in the afternoon. We believe that not only diel instream processes, but also viscosity-driven diel fluctuations of terrestrial DOC input should be considered to explain diel DOC patterns in streams.

In situ observations of electromigration induced void behavior

1995 ◽  
Vol 53 ◽  
pp. 244-245
Author(s):  
T. Marieb ◽  
J. C. Bravman ◽  
P. Flinn ◽  
D. Gardner ◽  
M. Madden
Keyword(s):  
Electron Microscopy ◽  
Void Nucleation ◽  
Plan View ◽  
Transmission Electron ◽  
Nucleation Sites ◽  
Microelectronics Industry ◽  
In Situ Observations ◽  
Backscatter Electron Detector ◽  
Voltage Scanning

Electromigration and stress voiding have been active areas of research in the microelectronics industry for many years. While accelerated testing of these phenomena has been performed for the last 25 years[1-2], only recently has the introduction of high voltage scanning electron microscopy (HVSEM) made possible in situ testing of realistic, passivated, full thickness samples at high resolution.With a combination of in situ HVSEM and post-testing transmission electron microscopy (TEM) , electromigration void nucleation sites in both normal polycrystalline and near-bamboo pure Al were investigated. The effect of the microstructure of the lines on the void motion was also studied.The HVSEM used was a slightly modified JEOL 1200 EX II scanning TEM with a backscatter electron detector placed above the sample[3]. To observe electromigration in situ the sample was heated and the line had current supplied to it to accelerate the voiding process. After testing lines were prepared for TEM by employing the plan-view wedge technique [6].

scholarly journals In situ observations of coral spawning and spawn slick at Lankayan Island, Sabah, Malaysia

2021 ◽  
Vol 51 (1) ◽  
Author(s):  
Sze Hoon Gan ◽  
Zarinah Waheed ◽  
Fung Chen Chung ◽  
Davies Austin Spiji ◽  
Leony Sikim ◽  
...  
Keyword(s):  
Coral Spawning ◽  
In Situ Observations

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Pelagic deep-sea fauna observed on video transects in the southern Norwegian Sea

Polar Biology ◽  
2021 ◽  
Author(s):  
Philipp Neitzel ◽  
Aino Hosia ◽  
Uwe Piatkowski ◽  
Henk-Jan Hoving
Keyword(s):  
First Record ◽  
Observation System ◽  
In Situ Observation ◽  
Norwegian Sea ◽  
Video Recordings ◽  
In Situ Observations ◽  
Abundance And Distribution ◽  
Oceanic Food Webs ◽  
Deep Sea Fauna

AbstractObservations of the diversity, distribution and abundance of pelagic fauna are absent for many ocean regions in the Atlantic, but baseline data are required to detect changes in communities as a result of climate change. Gelatinous fauna are increasingly recognized as vital players in oceanic food webs, but sampling these delicate organisms in nets is challenging. Underwater (in situ) observations have provided unprecedented insights into mesopelagic communities in particular for abundance and distribution of gelatinous fauna. In September 2018, we performed horizontal video transects (50–1200 m) using the pelagic in situ observation system during a research cruise in the southern Norwegian Sea. Annotation of the video recordings resulted in 12 abundant and 7 rare taxa. Chaetognaths, the trachymedusaAglantha digitaleand appendicularians were the three most abundant taxa. The high numbers of fishes and crustaceans in the upper 100 m was likely the result of vertical migration. Gelatinous zooplankton included ctenophores (lobate ctenophores,Beroespp.,Euplokamissp., and an undescribed cydippid) as well as calycophoran and physonect siphonophores. We discuss the distributions of these fauna, some of which represent the first record for the Norwegian Sea.

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