Rainfall estimation using moving cars as rain gauges - Laboratory experiments

The spatial assessment of short time-step precipitation is a challenging task. Low density of observation networks, as well as the bias in radar rainfall estimation motivated the new idea of exploiting cars as moving rain gauges with windshield wipers or optical sensors as measurement devices. In a preliminary study, this idea has been tested with computer experiments (Haberlandt and Sester, 2010). The results have shown that a high number of possibly inaccurate measurement devices (moving cars) provide more reliable areal rainfall estimations than a lower number of precise measurement devices (stationary gauges). Instead of assuming a relationship between wiper frequency (W) and rainfall intensity (R) with an arbitrary error, the main objective of this study is to derive valid W-R relationships between sensor readings and rainfall intensity by laboratory experiments. Sensor readings involve the wiper speed, as well as optical sensors which can be placed on cars and are usually made for automating wiper activities. A rain simulator with the capability of producing a wide range of rainfall intensities is designed and constructed. The wiper speed and two optical sensors are used in the laboratory to measure rainfall intensities, and compare it with tipping bucket readings as reference. Furthermore, the effect of the car speed on the estimation of rainfall using a car speed simulator device is investigated. The results show that the sensor readings, which are observed from manual wiper speed adjustment according to the front visibility, can be considered as a strong indicator for rainfall intensity, while the automatic wiper adjustment show weaker performance. Also the sensor readings from optical sensors showed promising results toward measuring rainfall rate. It is observed that the car speed has a significant effect on the rainfall measurement. This effect is highly dependent on the rain type as well as the windshield angle. © 2013 Authjor(s).DFG/3504/5-

Areal rainfall estimation using moving cars - computer experiments including hydrological modeling

The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e. RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance, but also for use in hydrological modeling. The results show that the RCs considering measurement errors derived from laboratory experiments provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Even assuming higher uncertainties for RCs as obtained from the laboratory up to a certain level is observed practical.DFG/3504/5-

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%

Merging of rain gauge and radar data for various temporal resolutions and network density scenarios

In many cases there are only few data from a sparse rain gauge network available, that might not be sufficient for the modeling of hydrologic processes. Recently, the use of radar data became more common, although there is often a high bias compared to rain gauge data. Inaccuracies in radar sensing of precipitation are, for instance, related to spatial and temporal variations in the relationship between reflected energy of the radar beam and corresponding rainfall intensity. This work focuses on the best combination of radar and rain gauge data using geostatistical approaches. Three different merging techniques, i.e. kriging with an external drift, indicator kriging with an external drift and conditional merging have been evaluated by cross validation for the data of 90 rain gauges and a radar device, while ordinary kriging is used as the reference. The study area is located in Lower Saxony, Germany, and covers the measuring range of the radar station Hanover. The data used in this study comprise continuous time series over the time period from 2008 until 2010. Different temporal data resolutions and rain gauge network density scenarios have been investigated in order to obtain more general results. In addition, the effect of radar data quality on the interpolation result is analysed. First results show that the performance of all the merging techniques depends on the quality of radar data and in general smoothing of the gridded radar data is improving the interpolation. The merging quality varies highly from time step to time step, but is usually much better than the use of point information only (ordinary kriging). So far, a single best approach has not been identified. The benefit of the analysed merging methods in comparison to only using rain gauge data depends on temporal resolution, error in radar data, network density and other factors

Radar data bias correction implementing quantile mapping and investigation of its influence in a hydrological model

Weather radar is an important source of data for estimating rainfall rate with relatively high temporal and spatial resolution covering large areas. Although weather radar provides fine temporal and spatial resolution data, it is subject to different sources of error. Beside casual problems associated with radar, e.g. clutter and attenuation, weather radar either underestimates or overestimates the rainfall amount. Additionally, time steps with strangely high values result in destroying the structure of time series derived from radar data. In order to estimate areal precipitation for hydrological analyses, radar data could be merged with rain gauge network data. The merging product quality is strongly dependent on radar data quality. The main purpose of this study is to illustrate a method for improving radar data quality and to investigate the influence of radar data quality on merging products by means of cross validation. Quantile mapping on the two sources of data, the radar and rain gauge network, is implemented in this study to improve the radar data quality. After correcting the radar data, considering rain gauge data as the truth, the data is implemented into a hydrological model, HBV-IWW, to investigate the influence of the different input sources regarding model performance. It has been observed that implementing quantile mapping improves radar data quality significantly. On the other hand, using radar data after correction not only improves interpolation performances but also reveals other possible applications like disaggregation of daily rainfall data into finer temporal resolutions. Beside radar data quality, there are other factors influencing the model performance like network density and the applied interpolation technique. The study area is a mesoscale catchment located in Lower Saxony, northern Germany

Burning mouth syndrome: A comparative cross-sectional study

Background and Aim: Burning mouth syndrome (BMS) may be defined as a burning sensation in the oral mucosa usually unaccompanied by clinical signs. Multiple conditions have been attributed to a burning sensation. The aim of this study was to determine the role of age and sex in BMS. Materials and Methods: A total of 195 consecutive patients with BMS and 95 healthy patients without burning sensation were recruited in this study. Patients with BMS had experienced oral, burning sensations for at least 6 months without oral clinical signs, and with a normal blood count. Multiple logistic regression analyses were utilized to define the main predictors. Results: Menopause, candidiasis, psychological disorders, job status, denture, and dry mouth were significantly frequent in BMS patients. Multivariate logistic regression indicated age (odds ratio (OR) =1.12, 95% confidence interval (CI): 1.08–1.15, P < 0.0001) and sex (OR = 3.14, 95% CI: 1.4–6.7, P < 0.002) significantly increase the odds of BMS. Psychological disorders (OR = 3.39, 95% CI: 1.2–9.5, P < 0.02) and candidiasis remain as predictive factors. Ultimately, age was defined as a critical predictor. Moreover, we can therefore predict that a 60-year-old woman with psychological disorders is 25 times more likely to suffer from BMS than a man 10 years younger who has no psychological disorder. Conclusion: Age and sex were the main predictors in BMS. Psychological disorders and candidiasis were significantly associated with the occurrence of BMS

Areal rainfall estimation using moving cars as rain gauges - laboratory and field experiment

Areal precipitation estimation for fine temporal and spatial resolution is still a challenging task. Beside the fact that newly developed instrumentations, e.g. weather radar, provide valuable information with high spatial and temporal resolutions, they are subject to different sources of errors. On the other hand, recording rain gauges provide accurate point rainfall depth, but are still often poor in density. Equipping a car with a GPS device as well as sensors measuring rainfall makes it possible to implement cars on the streets as the moving rain gauges. Initial results from a modeling study assuming arbitrary measurement errors have shown that implementing a reasonable large number of inaccurate measurement devices (raincars) provide more reliable areal precipitations compared to the available rain gauge network. The purpose of this study is to derive relationships between sensor readings and rain rate in a laboratory and quantify the errors. Sensor readings involve wiper frequency and optical sensors which are on the cars to automate wiper activities. Besides, the influence of car speed on the sensor readings is investigated implementing a car-speed simulator. It has been observed that the manual wiper activity adjustment, according to front visibility, shows a strong relationship between rainfall intensity and wiper speed. Two optical sensors calibrated in laboratory showed a relatively strong relationship with the rain intensity recorded by a tipping bucket. A positive relationship between the velocity and the amount of water has been observed meaning that the higher the speed of a car, the higher the amount of water hitting the car. Additionally, some preliminary results of the field experiments are discussed

Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle

To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water Resource