On the dialog between experimentalist and modeler in catchment hydrology

The dialog between experimentalist and modeler in catchment hydrology has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of dominant runoff processes—thus there is often much more information content on the catchment than we use for calibration of a model. While modelers often appreciate the need for 'hard data' for the model calibration process, there has been little thought given to how modelers might access this 'soft' or process knowledge. We present a new method where soft data (i.e., qualitative knowledge from the experimentalist that cannot be used directly as exact numbers) are made useful through fuzzy measures of model-simulation and parameter-value acceptability. We developed a three-box lumped conceptual model for the Maimai catchment in New Zealand, a particularly well-studied process-hydrological research catchment. The boxes represent the key hydrological reservoirs that are known to have distinct groundwater dynamics, isotopic composition and solute chemistry. The model was calibrated against hard data (runoff and groundwater-levels) as well as a number of criteria derived from the soft data (e.g. percent new water, reservoir volume, etc). We achieved very good fits for the three-box model when optimizing the parameter values with only runoff (Reff=0.93). However, parameter sets obtained in this way showed in general a poor goodness-of-fit for other criteria such as the simulated new-water contributions to peak runoff. Inclusion of soft-data criteria in the model calibration process resulted in lower Reff-values (around 0.84 when including all criteria) but led to better overall performance, as interpreted by the experimentalist’s view of catchment runoff dynamics. The model performance with respect to soft data (like, for instance, the new water ratio) increased significantly and parameter uncertainty was reduced by 60% on average with the introduction of the soft data multi-criteria calibration. We argue that accepting lower model efficiencies for runoff is 'worth it' if one can develop a more 'real' model of catchment behavior. The use of soft data is an approach to formalize this exchange between experimentalist and modeler and to more fully utilize the information content from experimental catchments

Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model

For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat-based surface water extent observations can inform the calibration of a lumped bucket-type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April-October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely-available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher-resolution imagery will be helpful for model calibration for more sites, especially when time series length increases

Evaluating the effects of alternative model structures on dynamic storage simulation in heterogeneous boreal catchments

Estimating dynamic storage as a metric can be used to make an overall assessment of catchment resilience to extreme weather events such as droughts and floods. Because of the complexity of direct empirical measurements, bucket-type hydrological models can be a suitable tool to simulate the catchment storage across a broad range of scales as they require minimal input data. However, these models consist of one or more conceptual structures based on several linear or nonlinear reservoirs and connections between these reservoirs. Therefore, choosing the most appropriate model structure to represent storage-discharge functioning in catchments is difficult. To bridge this gap, this study evaluated the performance of three different HBV model structures on 14 heterogeneous boreal catchments classified into four distinct catchment categories. The results showed that the three-bucket structure performed better in larger catchments with deeper sediment soils. In contrast, a single reservoir structure is sufficient to predict the storage-discharge behavior for a lake-influenced catchment with lower elevation above the stream network. Moreover, our results indicate that while the estimates of mean catchment storage varied between the different model structures, the ranking between the catchments largely agreed for the different structures. Hence, our results suggest that instead of a single model structure, using an ensemble averaging approach would not only better address the structural uncertainty but also facilitate further storage comparison between different catchments. Finally, based on Spearman rank correlation results, we found that catchment size and sediment soil were positively correlated with dynamic storage estimation

Characterizing multiple roles of pannier during embryogenesis, as revealed with an augmented fluorescent live imaging toolkit, in the beetle Tribolium castaneum.

Embryogenesis in the beetle Tribolium castaneum relies on the morphogenetic movements of the two extraembryonic membranes, amnion and serosa. It is due to their function and concerted development that the embryo is able to survive. During formation of both membranes, their movements include tissue expansion, as well as intra-tissue fusion and inter-tissue separation. In late development, the coordinated withdrawal and subsequent degeneration of both membranes facilitates embryonic dorsal closure, during which the amnion replaces the serosa as a transient yolk cover. To learn more about the amnion, the transcription factor Tc pannier (Tc-pnr). The wild type expression pattern of Tc-pnr was determined and the observed defects after parental RNA interference were analyzed. Tc-pnr deficient embryos display a pronounced hole in the dorsal cuticle and a characteristic bending towards the dorsal side. This specific phenotype is the result of a defect during dorsal closure. In Drosophila melanogaster, the dorsal hole is due to a loss of Dm decapentaplegic (Dm-dpp) expression in the dorsal most cells. It could be shown that in Tribolium, Tc-pnr and Tc-dpp are co-expressed in corresponding cells of the dorsal ectoderm as well. This suggests that Tc-pnr has an essential function in dorsal closure. After loss of Tc-pnr expression in the amnion, where it was shown to be expressed at least until mid embryogenesis, the amnion displays various defects around the process of dorsal closure. Intriguingly, the amnion ruptures ectopically and withdraws independent from the serosa. To investigate the disturbed interplay between the serosa and the amnion, an existing enhancer trap line expressing EGFP in the amnion was modified, by using a combination of CRISPR/Cas9 and homology directed repair, to express DsRed2 instead. The DsRed2 expressing amnion line can now be crossed to another line expressing EGFP in the serosa

Downsizing parameter ensembles for simulations of rare floods

For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed

Chemische Triebkräfte : Von der Verbrennung zum Herzschlag

Was hat eine einfache chemische Verbrennung mit dem Herzschlag zu tun? Diese Frage soll im Rahmen einer physikalisch-chemischen Experimentalvorlesung beantwortet werden. Dabei fügen sich anscheinend weit voneinander entfernte Phänomene in ein klar strukturiertes Bild. Es wird deutlich, dass der menschliche Körper aus thermodynamischer Sicht eine biologische \u27Wärmekraftmaschine\u27 ist, deren Wirkungsgrad höher als der Wirkungsgrad mechanische Wärmekraftmaschinen (\u27Dampfmaschinen\u27). Spröde anmutende thermodynamische Sätze füllen sich mit Leben - die Begriffe \u27Energie, Wärme, Entropie\u27 werden anschaulich. Anhand einfacher Demonstrationsexperimente wird ein Bogen über 250 Jahre Forschung im Bereich der chemischen Energiewandlung gespannt

Self-guided smartphone excursions in university teaching—experiences from exploring “Water in the City”

Like many other university teachers, we were faced with an unprecedented situation in spring 2020, when we had to cancel on-site teaching and excursions due to the Covid-19 pandemic. However, we were in the fortunate position that we had already started to develop a smartphone-based self-guided excursion on the topic of “Water in the City”. We accelerated this development and used it to replace the traditional group excursion in our Bachelor level introductory course in Hydrology and Climatology. The excursion of this course is visited by around 150 students each year. Because the student feedback was overall very positive, we used the self-guided excursion again in 2021 and plan to continue to use it in the coming years. In this paper, we describe the excursion, discuss the experiences of the students and ourselves, and present recommendations and ideas that could be useful for similar excursions at other universities

The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data

Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects