scholarly journals Commercial microwave links for urban drainage modelling: The effect of link characteristics and their position on runoff simulations

2019 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Temporal Dynamics ◽  
Model Performance ◽  
Telecommunication Networks ◽  
Rainfall Runoff ◽  
Data Set ◽  
Quantitative Precipitation Estimates ◽  
Runoff Dynamics ◽  
Urban Rainfall Runoff ◽  
Commercial Microwave Links ◽  
Heavy Rainfalls

Commercial microwave links (CMLs), radio connections widely used in telecommunication networks, can provide path-integrated quantitative precipitation estimates (QPEs) which could complement traditional precipitation observations. This paper assesses the ability of individual CMLs to provide relevant QPEs for urban rainfall-runoff simulations and specifically investigates the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km2) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for urban rainfall-runoff predictions and subsequent modelling performance is assessed by comparing simulated runoffs with measured stormwater discharges. The results show that model performance is related to both the sensitivity of CML to rainfall and CML position. The bias propagated into the runoff predictions is inversely proportional to CML path length. The effect of CML position is especially pronounced during heavy rainfalls, when QPEs from shorter CMLs, located within or close to catchment boundaries, better reproduce runoff dynamics than QPEs from longer CMLs extending far beyond the catchment boundaries. Interestingly, QPEs averaged from all available CMLs best reproduce the runoff temporal dynamics. Adjusting CML QPEs to three rain gauges located 2-3 km outside of the catchment substantially reduces the bias in CML QPEs. Unfortunately, this compromises the ability of the CML QPEs to reproduce runoff dynamics during heavy rainfalls. More experimental case studies are necessary to provide specific recommendations on CML preprocessing methods tailored to different water management tasks, catchments and CML networks.

Commercial microwave links in urban rainfall-runoff modelling: Two different approaches to removing the bias

2020 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Wave Attenuation ◽  
Temporal Dynamics ◽  
Estimation Methods ◽  
Rainfall Runoff ◽  
Geophysical Research ◽  
Urban Catchment ◽  
Individual Level ◽  
The Individual ◽  
Commercial Microwave Links ◽  
Heavy Rainfalls

<p>Commercial microwave links (CMLs) are point-to-point radio connections widely used as cellular backhaul and thus very well covering urbanized areas. They can provide path-integrated quantitative precipitation estimates (QPEs) as they operate at frequencies where radio wave attenuation caused by raindrops is almost proportional to rainfall intensity. Pastorek et al. (2019b) demonstrated the feasibility of using CML QPEs to predict rainfall-runoff in a small urban catchment. Unfortunately, runoff volumes were highly biased, mostly for QPEs from short CMLs, although the temporal runoff dynamics were predicted very well, especially during heavy rainfall events. It was also shown that, for the heavy rainfalls, reducing the bias by adjusting the CML QPEs to traditional rainfall measurements (Fencl et al., 2017) leads to less accurate reproduction of the runoff temporal dynamics.</p><p>Current understanding is that the bias in CML QPEs is often caused by imprecise estimation of wet antenna attenuation (WAA), which is a complex process influenced by many physical phenomena, including radome hardware or positioning of the outdoor unit. However, traditional WAA estimation methods are typically unable to take into account all the individual-level factors. We proposed (Pastorek et al., 2019a) to estimate WAA separately for each of the examined CMLs by using discharge measurements at the outlet of a small urban catchment and showed that this approach can reduce the bias in CML QPEs, leading to generally satisfying performance of rainfall-runoff models, mainly for heavy rainfalls.</p><p>In the presented study, we evaluate the effect of the method proposed in Pastorek et al. (2019a) (method i) on rainfall-runoff modelling in more detail and compare it to the method of Fencl et al. (2017) (method ii). For a case study in Prague-Letňany, Czech Rep., a calibrated rainfall-runoff model is used to predict discharges at the outlet of the small urban catchment (1.3 km<sup>2</sup>) using QPEs from 16 CMLs. First results confirm that minimizing the bias in CML QPEs using method i is convenient mainly for heavy rainfalls, as Nash-Sutcliffe efficiency is considerably higher in this case for all but one CML (on average 0.65; only 0.40 for method ii). Moreover, method i preserves the information about the rainfall temporal dynamics during heavy rainfalls better than method ii for most of the individual CMLs (correlation coefficient with observed runoffs on average 0.83 for method i and 0.78 for method ii). Next steps should include generalization for other case studies, including an exploratory analysis of the potential mismatches.</p><p> </p><p>References</p><p>Fencl, M., Dohnal, M., Rieckermann, J., Bareš, V., 2017. Gauge-adjusted rainfall estimates from commercial microwave links. Hydrol. Earth Syst. Sci. 21, 617–634.</p><p>Pastorek, J., Fencl, M., Rieckermann, J. and Bareš, V., 2019b. Commercial microwave links for urban drainage modelling: The effect of link characteristics and their position on runoff simulations. Journal of environmental management 251, 109522.</p><p>Pastorek, J., Fencl, M., and Bareš, V., 2019a. Calibrating microwave link rainfall retrieval model using runoff observations. Geophysical Research Abstracts 21, EGU2019-10072.</p><p> </p><p>This study was supported by the project no. 20-14151J of the Czech Science Foundation and by the project of the Czech Technical University in Prague no. SGS19/045/OHK1/1T/11.</p>

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 2 (3) ◽  
pp. 639-690 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Model Performance ◽  
Source Area ◽  
Subsurface Water ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. Through flux exchanges among the different spatial domains of the REW, surface and subsurface water interactions are fully coupled. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work is the first full application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

scholarly journals Gauge-adjusted rainfall estimates from commercial microwave links

2017 ◽  
Vol 21 (1) ◽  
pp. 617-634 ◽  
Author(s):  
Martin Fencl ◽  
Michal Dohnal ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Telecommunication Networks ◽  
Monitoring Networks ◽  
Mobile Telecommunication ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Set Up ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links

Abstract. Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of ground truth from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error  < 7 %, Nash–Sutcliffe efficiency coefficient  >  0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

scholarly journals Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: A comparison of LSTM-based models with four lumped conceptual models

2021 ◽  
Author(s):  
Thomas Lees ◽  
Marcus Buechel ◽  
Bailey Anderson ◽  
Louise Slater ◽  
Steven Reece ◽  
...  
Keyword(s):  
Great Britain ◽  
Performance Improvement ◽  
Conceptual Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Published Data ◽  
Rainfall Runoff ◽  
Data Set ◽  
Process Understanding ◽  
Long Short Term Memory

Abstract. Long short-term memory models (LSTMs) are recurrent neural networks from the emerging field of Deep Learning (DL), which have shown recent promise when predicting time-series especially when data are abundant. Rainfall-runoff modelling presents a challenge, yet accurate hydrological models are vital for flood forecasting, hazard impact assessment, and to assess the potential effects of climate change on floods and water resources. In this study, we compare the performance of two DL-based models, a LSTM and an Entity Aware LSTM (EA LSTM). The DL models were trained using a newly published data set, CAMELS-GB, for a sample of 518 catchments across Great Britain. To identify spatial and seasonal patterns in model performance, we compare the DL models against benchmark outputs from four lumped conceptual models recently configured for rainfall-runoff modelling in Great Britain. Our findings show that the LSTM models simulate discharge with consistently high model performance scores, including in catchments typically considered difficult to model. The LSTM achieves a mean catchment NSE of 0.88 (0.86 for the EALSTM), which represents a performance improvement of 10 %–16 % compared with the benchmark conceptual models. Seasonal and spatial patterns indicate that the largest performance improvement relative to the benchmark is in the drier summer months and in drier catchments in the South East of England. By comparing LSTMs with conceptual models, we diagnose possible reasons for their different performance. We suggest that LSTMs offer useful predictive capability for rainfall-runoff modelling in Great Britain and elsewhere and note their value to support process understanding in locations where processes are less well understood.

scholarly journals Rainfall-runoff modelling in a catchment with a complex groundwater flow system: application of the Representative Elementary Watershed (REW) approach

2005 ◽  
Vol 9 (3) ◽  
pp. 243-261 ◽  
Author(s):  
G. P. Zhang ◽  
H. H. G. Savenije
Keyword(s):  
River Basin ◽  
Overland Flow ◽  
Subsurface Flow ◽  
Model Performance ◽  
Source Area ◽  
Watershed Hydrology ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based ◽  
Infiltration Excess

Abstract. Based on the Representative Elementary Watershed (REW) approach, the modelling tool REWASH (Representative Elementary WAterShed Hydrology) has been developed and applied to the Geer river basin. REWASH is deterministic, semi-distributed, physically based and can be directly applied to the watershed scale. In applying REWASH, the river basin is divided into a number of sub-watersheds, so called REWs, according to the Strahler order of the river network. REWASH describes the dominant hydrological processes, i.e. subsurface flow in the unsaturated and saturated domains, and overland flow by the saturation-excess and infiltration-excess mechanisms. The coupling of surface and subsurface flow processes in the numerical model is realised by simultaneous computation of flux exchanges between surface and subsurface domains for each REW. REWASH is a parsimonious tool for modelling watershed hydrological response. However, it can be modified to include more components to simulate specific processes when applied to a specific river basin where such processes are observed or considered to be dominant. In this study, we have added a new component to simulate interception using a simple parametric approach. Interception plays an important role in the water balance of a watershed although it is often disregarded. In addition, a refinement for the transpiration in the unsaturated zone has been made. Finally, an improved approach for simulating saturation overland flow by relating the variable source area to both the topography and the groundwater level is presented. The model has been calibrated and verified using a 4-year data set, which has been split into two for calibration and validation. The model performance has been assessed by multi-criteria evaluation. This work represents a complete application of the REW approach to watershed rainfall-runoff modelling in a real watershed. The results demonstrate that the REW approach provides an alternative blueprint for physically based hydrological modelling.

scholarly journals Assessing the impact of PET estimation methods on hydrologic model performance

2020 ◽  
Author(s):  
Dilhani Ishanka Jayathilake ◽  
Tyler Smith
Keyword(s):  
Potential Evapotranspiration ◽  
Model Performance ◽  
Monte Carlo Sampling ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Rainfall Runoff ◽  
Data Set ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

scholarly journals Rainfall-Runoff modelling using Long-Short-Term-Memory (LSTM) networks

2018 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Claire Brenner ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Short Term Memory ◽  
Regional Scale ◽  
Model Performance ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Soil Moisture Accounting ◽  
Long Short Term Memory

Abstract. Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a novel data driven approach, using the Long-Short-Term-Memory (LSTM) network, a special type of recurrent neural networks. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model, in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.

scholarly journals Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

2018 ◽  
Vol 22 (11) ◽  
pp. 6005-6022 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Claire Brenner ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Short Term Memory ◽  
Regional Scale ◽  
Model Performance ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Soil Moisture Accounting ◽  
Long Short Term Memory

Abstract. Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.

scholarly journals Gauge-Adjusted Rainfall Estimates from Commercial Microwave Links

2016 ◽  
Author(s):  
Martin Fencl ◽  
Michal Dohnal ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Telecommunication Networks ◽  
Monitoring Networks ◽  
Mobile Telecommunication ◽  
Urban Centers ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links

Abstract. Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centers, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates from CMLs (QPEs) are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as many operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of "ground truth" from RGs with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to one hour i) provide precise high-resolution QPEs (rel. error 0.75) and ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it also substantially reduce bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space-time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

scholarly journals Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jagadish Sankaran ◽  
Harikrushnan Balasubramanian ◽  
Wai Hoh Tang ◽  
Xue Wen Ng ◽  
Adrian Röllin ◽  
...  
Keyword(s):  
Single Molecule ◽  
Temporal Dynamics ◽  
Growth Factor Receptor ◽  
Super Resolution ◽  
Actin Binding ◽  
Biological Knowledge ◽  
Data Set ◽  
Epidermal Growth ◽  
Molecular Brightness ◽  
Super Resolution Microscopy

AbstractSuper-resolution microscopy and single molecule fluorescence spectroscopy require mutually exclusive experimental strategies optimizing either temporal or spatial resolution. To achieve both, we implement a GPU-supported, camera-based measurement strategy that highly resolves spatial structures (~100 nm), temporal dynamics (~2 ms), and molecular brightness from the exact same data set. Simultaneous super-resolution of spatial and temporal details leads to an improved precision in estimating the diffusion coefficient of the actin binding polypeptide Lifeact and corrects structural artefacts. Multi-parametric analysis of epidermal growth factor receptor (EGFR) and Lifeact suggests that the domain partitioning of EGFR is primarily determined by EGFR-membrane interactions, possibly sub-resolution clustering and inter-EGFR interactions but is largely independent of EGFR-actin interactions. These results demonstrate that pixel-wise cross-correlation of parameters obtained from different techniques on the same data set enables robust physicochemical parameter estimation and provides biological knowledge that cannot be obtained from sequential measurements.

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