Displays

Precipitation measurement biases arise from both instrumental and environmental factors. For Tipping Bucket Rain-gauges (TBRs) the underestimation bias due to the employed mechanical principle was largely described in the literature and considered in recent measurement quality standards (e.g. EN 17277:2019), while wind has been recognized as the main environmental factor affecting the measurement. Precipitation Measurements Biases (PMBs) are largely understated and propagate through the modelling of hydrological processes at the catchment scale, affecting the results of hydrological simulation. The present work addresses the propagation of PMBs within a distributed hydrological model applied to the case study of the Seveso river basin, a highly urbanized catchment of about 200 km² located north of the city of Milan (Italy), which experienced a number of severe floods in the last years. To this aim, four TBRs located within the Seveso catchment area were tested using a field portable calibrator in order to quantify their mechanical bias. The calibrator allows generating constant water flows, which serve as the reference, equivalent to three rainfall rates of 50, 100 and 200 mm/h for a gauge with collecting area of 1000 cm². Furthermore, the wind-induced error was considered using a numerical Collection Efficiency curve obtained from the computational fluid-dynamic simulation of cylindrical gauges. Flow discharge was simulated using a spatially distributed hydrological model fed by the ARPA Lombardia tipping bucket precipitation network and including PMBs correction techniques. The results are compared to the discharge observations in specific section along the Seveso river and the influence of the PMBs is evaluated. Results show that, for high intensity rainfall events, when TBR measurements are subject to larger underestimation, the bias of peak discharge can be up to about 5 %. Of the same magnitude is the impact of the wind that mostly affects events with low precipitation intensity.

References:

EN 17277:2019 - Hydrometry - Measurement requirements and classification of rainfall intensity measuring instruments, European Committee for Standardization, 2019.

How to cite: Ravazzani, G., Scurati, A. R., Stagnaro, M., Cauteruccio, A., Lanza, L. G., Cislaghi, M., Rondanini, C., and Calabrese, M.: A case study of the propagation of precipitation measurement biases into a distributed hydrological model for the Seveso river basin , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9356, https://doi.org/10.5194/egusphere-egu2020-9356, 2020.

Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment. 

This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05°). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.

How to cite: Duan, Z., Duggan, E., Qing, Y., and Tuo, Y.: Assessing the performance of radar-based and satellite precipitation products in hydrological modelling with SWAT in Vils Basin, Germany , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11018, https://doi.org/10.5194/egusphere-egu2020-11018, 2020.

Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in >200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR) and (3) combination of multiple sources (MSWEP V2). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984-2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

How to cite: Fallah Maraghi, A., Oh, S., and Orth, R.: Climate-dependent propagation of precipitation uncertainty into the water cycle, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-416, https://doi.org/10.5194/egusphere-egu2020-416, 2020.

With the expected increase in flooding due to climate change, accurate estimation of precipitation and the resulting modelled hydrographs are an essential requirement for reliable flood forecasts. At present, most radar rainfall adjustment methods require raingauge data to increase the accuracy of the precipitation estimates. One disadvantage is that raingauges only measure precipitation at a given point and usually there are a relatively small number of these points in a typical catchment (and some smaller catchments may not have any). River discharge from the catchment integrates the influence of catchment-wide precipitation and can often be more accurately measured than the areal rainfall, especially in areas with a sparse raingauge network. Here, we present a non-raingauge radar adjustment method that utilises discharge data only to adjust radar precipitation estimates for input to hydrological models. This method allows a hydrological model to adjust its treatment of precipitation input, through an additional model parameter, by comparing the observed and modelled hydrographs. An additional advantage of this method is that it can be also applied to adjust any form of precipitation input (e.g. radar, raingauge or satellite)  to produce more accurate hydrograph estimates. This proposed method is comparable to a traditional radar raingauge adjustment method over a number of catchments and hydrological models, for both peak flows and for the entire hydrograph. Additionally, this method allows for the adjusted of catchment averaged raingauge precipitation measurements to correct for any possibly errors due to using point data i.e. spatial density or representative issues. This results in a substantial improvement in discharge estimation compared to the un-adjusted raingauge measurements.

How to cite: O'Loughlin, F. and Bruen, M.: QPE adjustment using river discharge, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17491, https://doi.org/10.5194/egusphere-egu2020-17491, 2020.

Commercial microwave links (CMLs) can be used for quantitative precipitation estimation. The measurement technique is based on the exploitation of the close to linear relationship between the attenuation of the signal level by rainfall and the path averaged rain rate. At a temporal resolution of one minute, the signal level of almost 4000 CMLs distributed all over Germany is being recorded since August 2017, resulting in one of the biggest CML data sets available for scientific purposes. A crucial step for retrieving rainfall information from this large CML data set is to accurately detect rainy periods in the time-series, a process which is hampered by strong signal fluctuations, occasionally occurring even when there is no rain. In our study, we evaluate the performance of convolutional neural networks (CNNs) to distinguish between rainy and non-rainy signal fluctuations by recognizing their specific patterns. CNNs make use of many layers and local connections of neurons to recognize patterns independent of their location in the time-series. We designed a custom CNN architecture consisting of a feature extraction and classification part with 20 layers of neurons and 1.4 x 10⁵ trainable parameters. To train the model and validate the results we refer to the gauge-adjusted radar product RADOLAN-RW, provided by the German meteorological service. Despite not being an absolute truth, it provides robust information about rain events at the CML locations at an hourly time resolution. With only 400 CMLs used for training and 3504 for validation, we find that CNNs can learn to recognize different signal fluctuation patterns and generalize well to sensors and time periods not used for training. Overall we find a good agreement between the CML and weather radar derived rainfall information by detecting on average 87 % of all rainy and 91 % of all non-rainy periods.

How to cite: Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Big commercial microwave link data: Detecting rain events with deep learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18009, https://doi.org/10.5194/egusphere-egu2020-18009, 2020.

Rainfall plays a critical role in the hydrological cycle, being the main downward forcing. It is well known that rainfall exhibits large variability in space and time due to the storm dynamics and its interaction with the topography. It is a difficult task to reconstruct the rainfall over an area accurately. Rainfall is usually collected through rain gauges, disdrometers, and weather radars. Rain gauges and disdrometers provide quite accurate measurements of rainfall on the ground, but at a single site, while weather radars provide an indication of rainfall field variability in space, even if their use is restricted to plain areas.

Recently, unconventional observations have been considered for the monitoring of rainfall. These consist in signal attenuation measurements induced by rain on a mesh of point-to-point commercial microwave links (CML). These data, integrated with the ones collected by a network of conventional rain gauges, can provide further information about rainfall dynamics leading to improvements in hydrological modelling, which requires accurate description of the rainfall field.

The work we are going to describe is part of MOPRAM (MOnitoring Precipitation through a Network of RAdio links at Microwaves), a scientific project funded by Fondazione Cariplo (see also the EGU abstract of Nebuloni et al., 2020). Here we use rainfall data, obtained both from a rain gauge network and from signal attenuation measurements, into a hydrological model in order to evaluate the improvement in the hydrological modelling due to a better description of the rainfall field. We consider a semi-distributed rainfall-runoff model and we apply it to the Mallero catchment (Western Rhaetian Alps, Northern Italy), with the outlet located in Sondrio. This catchment is equipped with 13 microwave links and a network of 13 rain gauges.

Firstly, we implement and test the Rain field Reconstruction Algorithm (RRA), which retrieves the 2D rainfall field from CML data through a tomographic inversion technique, developed by D’Amico et al., 2016. By RRA we generate synthetic rainfall maps from attenuation data measured by 13 links located in the Mallero basin, for a few historical events in the period 2016-2019. To improve the accuracy of rainfall field reconstruction, we also integrate the reconstructed maps with on ground data from 13 rain gauges. These maps are used as input to the hydrological rainfall-runoff model. Finally, we compare the observed discharge with the calculated one using the hydrological model and different rainfall inputs.

How to cite: Cazzaniga, G., De Michele, C., Deidda, C., D'Amico, M., Ghezzi, A., Nebuloni, R., and Sileo, A.: Calculating the hydrological response of a mountain catchment using conventional and unconventional (CML) rainfall observations: the case study of Mallero catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16368, https://doi.org/10.5194/egusphere-egu2020-16368, 2020.

In view of the likely increase of thunderstorm and extreme precipitation events under climate change scenarios, alternatives to improve the estimates of rainfall and the understanding of the runoff response to extreme events are relevant, especially in areas with low or absent radar or raingauge coverage. Efforts in this direction have resulted, for example, on the Global Precipitation Measurement (GPM) products, which offer potentially useful estimates of precipitation over relatively fine spatial and temporal scales. With the launch of GOES 16 satellite, with its new Geostationary Lightning Mapper (GLM) instrument and improved visible and infrared imagery (with the Advanced Baseline Imager - ABI), new possibilities emerge in the analysis of (severe) convective precipitation and its impact on runoff. In this work, we analyze the relationship between lightning activity and rainfall, with the aim to estimate how total lightning data can be used as proxy of (heavy) precipitation estimates. GLM data is evaluated against weather radar in three different ways: (1) based on a Gaussian Kernel method; (2) using a simple dot-count approach, and (3) using the operational GLM gridded product, built on the ABI fixed grid (2 x 2 km). Two sample strategies are evaluated: a pixel-based comparison and a comparison method that extracts statistics inside polygons (using watersheds). For all cases, both group and flash data from GLM are used. The study area focuses on the southeastern and central-west regions of Brazil, where developments towards enhanced flood nowcasting and warning systems capabilities have been carried out in order to anticipate flash floods and prevent flood damages in the future.

How to cite: Bourscheidt, V. and Ramos, M.-H.: On the use of Geostationary Lightning Mapper data as proxy for heavy precipitation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20588, https://doi.org/10.5194/egusphere-egu2020-20588, 2020.

Efficiently dam operation is necessary to secure water resources and to respond to floods. For the dam operation, the amount of dam inflow should be accurately calculate. Rainfall information is important for the amount of dam inflow estimation and prediction therefore rainfall should be observed accurately. However, it is difficult to observe the rainfall due to poor density of rain gauges because of the dam is located in the mountainous region. Moreover, ground raingauges are limitted to localized heavy rainfall, which is increasing in frequency due to climate changes. The advantage of radar is that it can obtain high-resolution grid rainfall data because radar can observe the spatial distribution of rainfall. The radar rainfall are less accurate than ground gauge data. For the accuracy improvement of radar rainfall, many adjustment methods using ground gauges, have been suggested. For dam basin, because the density of ground gauge is low, there are limitations when apply the bias adjustment methods. Especially, the localized heavy rainfall occurred in the mountainous area depending on the topography. In this study, we will develop a radar rainfall adjustment method considering the orographic effect. The method considers the elevation to obtain kriged rainfall and apply conditional merging skill for the accuracy improvement of the radar rainfall. Based on this method, we are going to estimate the mean areal precipitation for hydropower dam basin. And, we will compare and evaluate the results of various adjustment methods in term of mean areal precipitation and dam inflow.

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)

How to cite: Yoon, S., Shin, H., and Choi, G.: Mean areal rainfall improvement using radar rainfall estimation technique by considering geographic character for dam operation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1972, https://doi.org/10.5194/egusphere-egu2020-1972, 2020.

Estimation of dam inflow using rainfall needs for efficient and timely operation of dam. Accuracy rainfall data is important to estimate dam inflow. Currently, rainfall pattern has volatile temporal and spatial distribution. Dam inflow based on rainfall gauged data is inadequate for operating hydroelectric dam. Radar rainfall has been used as an alternative because radar data provides spatially distributed rainfall. In this study, we estimated inflow discharge for hydroelectric dam using both radar and rain gauged data to find a case to improve the accuracy. Hydrological modeling have been adopted to estimate inflow and based on rainfall data collected from 2018 to 2019.

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)

How to cite: Choi, G., Shin, H., and Yoon, S.: Research on dam inflow analysis based on radar rainfall data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2116, https://doi.org/10.5194/egusphere-egu2020-2116, 2020.

The characteristics of the watershed are important to reduce hydrologic disasters, such as the risk of dam flooding. In other words, quantitative precipitation estimation(QPE) is important to manage water resources in large regions. Both radar and rain gauged data are used to improve QPE. This study is dealt with suggesting the best location of additional rain gauged stations to be installed in order to improve QPE as entropy theory. Conditional entropy is used to quantitatively evaluate the location of additional gauged stations to be installed given the existing rainfall network. Because radar produces high-resolution precipitation estimates, it can be used to identify the high entropy points to reduce rainfall uncertainty. The data were collected from May 2018 to August 2019 in the Bukhan river dam basin. Road networks were also considered for the establishment for a practical approach.

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD

(No. 2018-Tech-20)

How to cite: Kwon, T. and Yoon, S.: The recommended location of rain gauge stations based on radar, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2969, https://doi.org/10.5194/egusphere-egu2020-2969, 2020.

Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.

*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)

How to cite: Yoon, S., Kim, J., and Kwon, T.: Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2957, https://doi.org/10.5194/egusphere-egu2020-2957, 2020.

Hydrological modelling depends strongly on precipitation input. Especially, to simulate very localized heavy precipitation events, models require precipitation information with a high spatial and temporal resolution. In order to study the influence of precipitation station densities and interpolation schemes on hydrological model performance, we use gauge data from the highly dense station network WegnerNet (www.wegenernet.org). The WegenerNet is located in the southeastern Alpine forelands of Austria. It measures precipitation and other meteorological variables at a 5-min time sampling with about 150 climate stations in an area of about 22 km x 16 km (i.e ~ one station per 2 km²). We complement these data by the operational networks of the Austrian weather and hydrographic services (ZAMG and AHYD), leading to a total of 158 stations.

This highly dense station network permits us to analyze the precipitation data uncertainty for specific short and long duration events over the lower Styrian Raab catchment (about 500 km²) and its sub-catchments (about 10 to 50 km²). For modeling, we employ the process-based model WaSiM (www.wasim.ch) with a 100 m x 100 m spatial and a 30 minutes temporal resolution. We calibrated the model with all 158 precipitation stations and inverse distance weighting (IDW) interpolation scheme; this simulation is used as our reference run (Ref-158). We performed further simulations with only stations from ZAMG, the 5-Stations case, also include the stations from AHYD (adding another 3 stations), the 8-Stations case, and step by step including stations from the WegenerNet, the 16-Stations, 32-Stations and 64-Stations cases. For each simulation, we compared three interpolation schemes: two IDW setups and Thiessen polygons. Our study focuses on short duration, local extremes (convective events in 2009, 2010, 2011), but for comparison also includes long duration frontal extreme events.

Our results suggest that for the runoff simulation with dense precipitation stations (Ref-158) the effect of the interpolation scheme is negligible. By contrast, modeling with low-resolution precipitation data obtained from less than 10 stations, the interpolation scheme leads to deviations of over 20% in terms of peak flow. These deviations are especially pronounced for the short duration events. For the total Styrian Raab catchment, the 32-Stations case is as good as the Ref-158 case, independent of the interpolation scheme (mostly smaller than 10% deviation). For the long duration events and the IDW interpolation scheme even, the 5-Stations case is sufficient. For the smaller catchments, the peak flow is much more event-dependent. More stations do not necessarily lead to less deviation to the reference and no clear under- or over-estimation is visible.

How to cite: Hohmann, C., Oh, S., Kirchengast, G., Foelsche, U., and Rieger, W.: Impact of spatial resolution and interpolation schemes of precipitation data on hydrological modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7264, https://doi.org/10.5194/egusphere-egu2020-7264, 2020.

Accurate precipitation measurement is crucial for hydrological modeling and weather forecasting. During the last decade, considerable progress has been made in satellite-based precipitation products that could be a potential source as inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively examined the performance of several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, with emphasis on their performance based on elevation and extreme events. The analysis has been conducted at daily time-scales over Austria for the period June 2014–December 2015, using a dense network of gauges (882 stations) as a reference. Since Austria characterized by complex terrain and a big difference in altitude over the country, the annual mean precipitation range significantly varies with elevation and climate conditions. Therefore, the microclimate can be created due to rapid changes in elevation which cause obstruct the air mass movement or this rapid changes in elevation can cause the updraft of the air mass over the mountains to create orographic rainfall.

The results showed that the number of extreme days is double over the Alpine area in comparison to low altitude regions. The 90% percentile level of wet days (P ≥ 0.1 mm) as the R90th of the stations showed the maximum values at high-elevation areas (Alpine mountains). The spatial distribution of the R90th for MSWEP, IMERG-V05B, and –V06a were rather similar to observations with higher number of days for the precipitation threshold above 90th percentile over the south part of Austria. In contrast, ERA5 underestimated the frequency of the extreme events over the big part of the south region, while showed higher number of extreme days over north Austria. Moreover, SM2RAIN, displayed underestimation of the R90th, almost over the whole country. It was evident that with the increase of elevation, the mean RMSE, MAE, and bias increase and CC decreases. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06A and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain.

Reference: Sharifi, E., Eitzinger, J., Dorigo, W. (2019). Performance of the State-Of-The-Art Gridded Precipitation Products Over Mountainous Terrain. A Regional Study Over Austria. Remote Sensing 11(17), 2018, https://doi.org/10.3390/rs11172018.

How to cite: Sharifi, E., Dorigo, W., and Eitzinger, J.: Error assessment of precipitation products based on the elevations and extreme events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3462, https://doi.org/10.5194/egusphere-egu2020-3462, 2020.

The systematic and random errors in different remotely sensed (RS) precipitation products varies spatially and seasonally.  Error characterisation of the satellite precipitation products is vital for improved hydrologic and climatic modelling as precipitation is the key component of surface and subsurface hydrologic system. In this study, a new approach is developed for the bias correction of different satellite and processed rainfall products across Western Ghats region of India. The Western Ghats are mountainous ranges of about 1600 Kms length parallel to west coast of peninsular India, which consists the largest tropical rainforest in India.  Many studies have reported that most of the RS rainfall products are underestimated in Western Ghats region. In the present study, a multiplicative error distribution model for the entire Western Ghats for each of the RS precipitation products used is developed. Quality controlled interpolated gridded rain gauge data from Indian Meteorological Department (IMD) is used as the base. The IMD rainfall data is cross validated with available rain gauge data in the Western Ghats region. The bias correction of four multisatellite high-resolution precipitation products namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation products, 3B42 version 7 and TMPA-3B42RT (Real Time) version 7 and Precipitation data from NASA (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) is performed in this study. The multiplicative monthly bias factor for each grid cell of Western Ghats is generated with the IMD rainfall as reference and it is found that the monthly multiplicative error for Western Ghats fluctuates around a common mean for each of the grid cell. Based on this a rainfall multiplicative error distribution is generated for each month for the Western Ghats regions. Systematic errors in rainfall were corrected using this distribution and the efficacy of error-corrected rainfall is evaluated with the help of conceptual rainfall-runoff models. The results depict that the proposed method helps to reduce the bias in different rainfall products and provide improved runoff estimations at Western Ghats.

How to cite: Kunnath-Poovakka, A. and T Iype, E.: A Method for Bias Correction of Remotely Sensed Precipitation across Western Ghats Region of India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8431, https://doi.org/10.5194/egusphere-egu2020-8431, 2020.

Precipitation is one of the most important inputs of meteorological and hydrological models and also flood warning systems. Thus, accurate estimation of rainfall is essential for improving the reliability of the models and systems. Although remote sensing (RS) techniques for rainfall estimation (e.g., weather radars and satellite microwave imagers) have improved significantly over the last decades, rain gauges are still more reliable and widely used for this purpose and also for the evaluation of RS estimates. Since the characteristics of a rainfall event can change rapidly in space and time, the accuracy of rain gauge estimation is highly dependent on the spatial and temporal resolution of the gauge network.

The main aim of this study is to evaluate the ability of the Integrated Nowcasting through Comprehensive Analysis (INCA) of the Central Institute for Meteorology and Geodynamics (ZAMG) to detect and estimate rainfall events. This is done by using 12 years of data from a very dense rain gauge network, the WegenerNet Feldbach region, as a reference, and comparing its data to the INCA analyses. INCA rainfall analysis data are based on a combination of ZAMG ground station data, weather radar data, and high-resolution topographic data. The system provides precipitation rate data with a 1 km spatial grid resolution and 15 minutes temporal resolution. The WegenerNet includes 155 ground stations, almost uniformly spread over a moderate hilly orography area of about 22 km × 16 km.

After removing outliers and scale WegenerNet data to 1 km, the accuracy of INCA to detect and estimate rainfall events was investigated using 12 years of the dataset. The results show that INCA can detect rainfall events relatively well. It was found that INCA overestimates the rainfall amount between 2012 and 2014, and generally overestimates precipitation for light rainfall events. For heavy rainfall events, however, an underestimation of INCA is prominent in most events. Based on the results, the difference between INCA and WegenerNet estimates is relatively higher during the wet season in the summer half-year (May-September). It is worth pointing out that INCA performs better in detecting and estimating rainfall around the two ZAMG stations located within the study area.

How to cite: Ghaemi, E., Foelsche, U., Kann, A., Kirchengast, G., and Fuchsberger, J.: Comprehensive evaluation of precipitation analyses using a very dense rain gauge network in southeast Austria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16576, https://doi.org/10.5194/egusphere-egu2020-16576, 2020.

Abstract.

Climate change impact studies typically require a reference climatological dataset providing a baseline period to assess future changes.  The reference dataset is also used to perform bias correction of climate model outputs.  Various reliable precipitation datasets are now available over regions with a high-density network of weather stations such as over most parts of Europe and in the United States.  In many of the world’s regions, the low-density of observation stations (or lack thereof) renders gauge-based precipitation datasets highly uncertain.  Satellite, reanalysis and merged products can be used to overcome this limitation.   However, each dataset brings additional uncertainty to the reference climate. This study compares ten precipitation datasets over 1091 African catchments to evaluate dataset uncertainty contribution in climate change studies. The precipitation datasets include two gauged-only products (GPCC, CPC), four satellite products (TRMM, CHIRPS, PERSIANN-CDR and TAMSAT) corrected using ground-based observations, three reanalysis products (ERA5, ERA-I, and CFSR) and one merged product of gauge, satellite, and reanalysis (MSWEP).

Each of those datasets was used to assess changes in future streamflows. The climate change impact study used a top-down modelling chain using 10 CMIP5 GCMs under RCP8.5. Each climate projection was bias-corrected and fed to a lumped hydrological model to generate future streamflows over the 2071-2100 period. A variance decomposition was performed to compare GCM uncertainty and reference dataset uncertainty for 51 streamflow metrics over each catchment. Results show that dataset uncertainty is much larger than GCM uncertainty for most of the streamflow metrics and over most of Africa. A selection of the best performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to datasets, but remained comparable to that of GCMs in most cases. Results show also relatively small differences between datasets over a reference period can propagate to generate large amounts of uncertainty in the future climate. 

How to cite: Tarek, M., Brissette, F., and Arsenault, R.: Uncertainty of precipitation reference dataset for climate change impact studies , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11842, https://doi.org/10.5194/egusphere-egu2020-11842, 2020.

Manual throughfall gauges can not apply to explore the temporal properties of precipitation redistribution.  To follow the interception temporarily it is necessary to use automatic gauges. Commercial rainfall data-loggers are suitable but in a spatially heterogeneous environment, like agroforestry systems, need a large number to represent the spatial differences. To reduce the cost, we started to develop a microcontroller-based data logger.

As a test case, we develop new auxiliary equipment for an already working custom trough in our riparian alder plot. It is a large surface trough with a big container where water level change is sensed. This gauge works with a commercial data logger which will be used for validation purposes. The planned addition is a tipping bucket, which provides a digital signal directly. The simple task is to log the timestamp of the tips. After many iterations, an ARM
Cortex-M0+ based architecture was selected, which integrates all the necessary components of a simple data logger.  The development is fully open-source shared through git (https://github.com/kaliczp/hvlog). The presentation shares the experiences accumulated during the continuous development.

This research has been supported by the EFOP-3.6.2-16-2017-00018 in University of Sopron project and the corresponding author's work has been also supported by the János Bolyai Scholarship of the Hungarian Academy of Sciences and ÚNKP-19-4-I-4-SOE-4 New National Excellence
Program of the Ministry for Innovation and Technology.

How to cite: Kalicz, P., Csáki, P., Zagyvai-Kiss, K. A., and Gribovszki, Z.: Datalogger development for tipping-bucket throughfall measurement with open-source tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10303, https://doi.org/10.5194/egusphere-egu2020-10303, 2020.

The advancement of hydrological knowledge is dependent on observation data (Ex, Blume et al., 2017; Kirchner, 2006). Nevertheless, these needs may become financially unviable due to the high costs of monitoring, especially in developing countries with large territorial extensions as Brazil and scarce financial support for this purpose. Thus, it is not difficult to find large areas in Brazil without hydrological monitoring. Alternatively, satellite-based estimations have been explored to supply these deficiencies due to several advantages like measuring spatial variability, being rapidly and generally freely available on the Internet, and maintaining functionality even during catastrophic situations that can damage or temporarily shut down ground networks (e.g., flooding, overland effects of hurricanes). However, even if to calibrate these remote sensing products, the land-based monitoring is needed. In this light, citizen science where many volunteers can monitor more sites than a typical research team can be an interesting means of creating this information (Buytaert et al., 2014), although even for these volunteers, the cost could be a problem. In this perspective, we present here a low-cost framework developed and field-tested for monitoring hydrological variables (precipitation, humidity, temperature, barometric pressure, and water level) based on the Arduino Platform. The monitoring-set could be self-constructed by volunteers or distributed as assembly kits that, apart from providing reliable measures of the hydrological variables at a very low cost, can boost interest in monitoring and science. Reliability and quality tests showed that measures gathered with the developed monitoring-set are within commercial standards. However, as the Arduino Platform is open, which facilitates its application and diminish the costs, special care with the suppliers should be taken, as not all follow the same quality standards.

How to cite: Tassi, R., Minetto, B., Persh, C., Campos Pimentel, F., and Allasia, D.: Low-cost framework for hydrological monitoring in developing countries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12785, https://doi.org/10.5194/egusphere-egu2020-12785, 2020.

Accurate measurements of atmospheric precipitation play an important role in solving a large variety of water management problems. The relatively low spatial and temporal resolution of the monitoring network in remote areas puts significant constraints on its use in small-scale studies, where a high spatial and temporal resolution is a must. Until quite recently, the high cost of the commercial devices that have to be deployed to fill in the gaps in space and time domains was very often the main factor restricting the focus of both scientific research and commercial applications on larger scales. The first decades of the 21^st century brought about massive advancements in the field of low-cost electronics, sensors, and rapid prototyping techniques. Moreover, a number of open source software solutions came into existence that provides ready-to-use tools to store, analyse and transfer data. This inspired a large community of scientists and makers to build their own prototypes of measuring instruments or dataloggers, often for a fraction of the cost of the commercial devices that comply with their specific needs.

This study presents the process of the development and calibration of a low-cost rain gauge for measuring atmospheric precipitation. The prototype was designed as a two-chamber tipping-bucket rain gauge built around the Arduino open-source electronics platform. The advent of 3D printing enabled the rapid prototyping of the mechanical parts of the rain gauge, which are made of a durable ABS thermoplastic material. The study also presents the process of rain gauge calibration, with both volumetric and dynamic calibration procedures used. The rain gauge was set at a resolution of 0.5 mm with a standard deviation of  ±0.01 mm. The results of the dynamic calibration also showed that the behaviour of the rain gauge complies with that of the commercial devices.

The low cost and precision of this type of instrumentation make it ideal for applications in which there is a high risk of its being damaged or even lost. In addition, the open-source aspect of the project, its low-cost, and the relatively minor requirements for its construction make it a good candidate for use in citizen-science partnerships, which are becoming very popular, mainly due to their popularization benefits.             

How to cite: Brziak, A., Valent, P., and Kubáň, M.: Low cost precipitation measurement in remote areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13836, https://doi.org/10.5194/egusphere-egu2020-13836, 2020.

In recent years, so-called opportunistic sensors for measuring rainfall, are attracting more notice due to their broad availability and low financial effort for the scientific community. These sensors are existing devices or infrastructure, which were not intentionally built to measure rainfall, but can deliver rainfall information. One example of such an opportunistic measurement system are Commercial Microwave Links (CMLs), which provide part of the backbone of modern mobile communication. CMLs can deliver path-averaged rainfall information through the relation between rainfall and attenuation along their paths. Before such an opportunistic data source can be used, either as an individual or a merged data product, its performance compared to other rainfall products must be evaluated.

We discuss the selection of performance metrics, spatial and temporal aggregation and rainfall thresholds for the comparison between a German-wide CML network and a gauge-adjusted radar product provided by the German Weather Service. The CML data set consists of nearly 4000 CMLs with minutely readings from which we will present a year of data. 

First, we show the influence of the temporal aggregation on the comparability. With higher resolution, the impact due to small temporal deviations increases. Second, CMLs represent path-averaged rainfall information, while the radar product is gridded. We discuss the choice whether the comparison should be performed on the point, line or grid scale. This choice depends on the desired future applications which already should be considered when selection evaluation tools. Third, the decision to exclude rain rates below a certain threshold or the calculation of performance metrics for certain intervals gives us a more detailed insight in the behavior of both rainfall data sets.

How to cite: Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: How to evaluate rainfall estimation performance? - A discussion of metrics, thresholds and aggregations for one year of country-wide CML rainfall estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18095, https://doi.org/10.5194/egusphere-egu2020-18095, 2020.

The number of private meteorological stations with data available online through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation for high intensity events of different durations. Due to unknown biases of the observations, rainfall amounts of the secondary network are not considered directly. Instead, only their temporal order is assumed to be correct. The crucial step is to find the stations with informative measurements. This is done in two steps, first by selecting the locations using time series of indicators of high precipitation amounts. The remaining stations are checked whether they fit into the spatial pattern of the other stations. Thus it is assumed that the percentiles at the secondary network accurate. These percentiles are then translated to precipitation amounts using the distribution functions which were interpolated using the weather service data only. The suggested procedure was tested for the State of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amounts of 1, 3, 6, 12 and 24 hours. For each aggregations, nearly 200 intense events were evaluated. The results show that filtering the secondary observations is necessary, the interpolation error after filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest time aggregations.

How to cite: Seidel, J., El Hachem, A., and Bárdossy, A.: Using Data From Personal Weather Stations to Improive Precipitation Estimation and Interpolation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19499, https://doi.org/10.5194/egusphere-egu2020-19499, 2020.

Deterministic and stochastic spatial interpolation methods are widely used for the imputation of precipitation data to obtain gap-free datasets. Conceptually simple deterministic approaches using weighting methods that use Euclidean distances in spatial interpolation. Uses of probability space-based measures which include a measure from forecast verification and distribution similarity hypothesis test statistic values are evaluated in this study as possible replacements for Euclidean distances in weighting methods. Nonlinear optimization formulations are solved to obtain the best parameter values of the spatial interpolation methods. Long-term daily precipitation datasets from two climatic regions are used to impute missing precipitation data and several error and performance measures are used to assess the proposed methods. The proposed surrogates for Euclidean distances provide conceptually simple yet superior deterministic interpolation methods for improved estimation of missing precipitation data. Local and global variants of interpolations are evaluated. Preliminary results confirm the superiority of probability space-based methods for imputation of missing precipitation data at multiple temporal scales.

How to cite: Teegavarapu, R.: Precipitation Imputation using Optimal Probability Space-Based Interpolation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20930, https://doi.org/10.5194/egusphere-egu2020-20930, 2020.

In order to reveal sub-daily characteristics of precipitation, this study analyzes statistical characteristics of hourly precipitation by using statistical methods. The hourly precipitation observed data was collected from 34 representative precipitation stations of Sichuan Province of China during 2001-2007. The results show that the average one hour precipitation is 0.148 mm, and the probability density function of precipitation intensity y = 0.2235e-0.083x. The average daily precipitation time is 2.6 hours, and the probability of precipitation event each hour follows a sine wave. As well as the maximum probability time when precipitation occurs in a day is 3 AM to 4 AM, and the minimum probability time is 14 PM to 15 PM. It suggests a method for precipitation downscaling from daily into hourly.

How to cite: Huafang, L.: Analysis on sub-daily characteristics of one hour precipitation based on observed data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20975, https://doi.org/10.5194/egusphere-egu2020-20975, 2020.

We show that "an arrow of time", which is reflected by the joint distributions of successive variables in a stochastic process, may exist (or not) solely on grounds of marginal probability distributions, without affecting stationarity or involving the structural dependencies within the process. The temporal symmetry/asymmetry dichotomy thus revealed, is exemplified for the simplest case of stably-distributed Markovian recursions, where the lack of Gaussianity, even when the increments of the process are independent and identically distributed (i.i.d.) with symmetric marginal, is generating a break of temporal symmetry. We devise a statistical tool to evidence this striking result, based on fractional low-order joint moments, whose existence is guaranteed even for the case of "fat-tailed" strictly-stable distributions, and is thereby suited for parameterizing structural dependencies within such a process.

How to cite: Carsteanu, A. A. and Langousis, A.: Revealing a temporal symmetry/asymmetry dichotomy in a Markovian setting, and a parameterization based on fractional low-order joint moments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6582, https://doi.org/10.5194/egusphere-egu2020-6582, 2020.