Development and Assessment of Methods for Predicting Rainfall in Unmeasured Watersheds Using the Relation Between Observed and Sensor-Measured Rainfall

Recently, with the development of information and communication technology and the Internet of Things (IoT), observation technology using sensors is being applied in a variety of ways, such as using a sensor to observe rainfall in an unmeasured area. In this study, the relationship between the rainfall sensor signal (S) and the amount of rainfall (R) was developed through an experiment in an artificial rainfall generator, and the applicability was evaluated through outdoor observation. The coefficient of determination of the relational expression developed through the indoor experiment was 0.95, the mean absolute error was 2.66 mm/hr, the root mean square error was 3.87 mm/hr, the efficiency coefficient was 0.89, and the concordance index was 0.97, showing very high reliability. In the outdoor test results, the error rate was 7.96% when comparing the data from the rainfall sensors in vehicles and the precipitation station, which were not observed at the same location. Despite such errors, it is judged that accurate rainfall observation using a rainfall sensor is possible in an area where a precipitation station is not installed.

Synthetic rainfall for Germany based on simulations from two stochastic models

<p>For planning of urban drainage systems using hydrological models, long, continuous precipi-tation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative.</p><p>This contribution discusses the results of a research project, providing 5-minutes continuous stochastic point rainfall data for entire Germany for urban hydrological applications. Two different stochastic rainfall models are employed: a parametric stochastic model based on Alternating-Renewal processes and a non-parametric approach based on Resampling. Using rainfall observations from about 800 stations in Germany, the parameters of the models are regionalized. Rainfall and discharge characteristics are utilised for the evaluation of the model performance using a subset of 45 stations.</p><p>The results show, that stochastic rainfall from either of the models is better suited for urban hydrologic design, compared to the common practice scenario, where data from the nearest precipitation station is used. Notably, it could be shown that a mixture of generated rainfall from both models leads to a compensation of errors and further improves results, contrasted with using only data from one single model.</p>

Snow observations in Finland in support of designing nature-based solutions – from citizen observations to satellites

<p>As a representative example of an inland water system in northern Europe the Lake Puruvesi tends to suffer from nutrient and loads. Surface runoff caused by extreme precipitation or excessive, rapid snow melt produce nutrient leaching especially from heavily managed forests. Citizen science has potential to address to risk of eutrophication of Lake Puruvesi. Firstly, forest owners need to be engaged to the project to co-design and co-develop nature-based solutions including information about forest management options to reduce nutrient leaching. Secondly, to improve understanding and modelling  standard observational network should be enhanced. The extreme precipitation in the area is monitored throughout the year with, in particular, for this project established automatic precipitation station. Additionally, during the winter season a group of citizen volunteers measure manually snow depth and snow density in the catchment area of Lake Puruvesi. The precipitation and snow data collected with in-situ and satellite measurements are analyzed to indicate the relationships of the nutrient flows and precipitation in the area. Here we present the preliminary results from the measurement campaign from the period January-April 2020.</p>

Areas with Flood Potential Risk in the Lower Uz Catchement (Romania). Protection and Mitigation Measurements

The identification of areas with flood potential risk is important concerning the rational management of emergencies in case of floods. The most significant floods in the history of Romania occurred in the catchment basin of Siret (Uz River being an indirect tributary) and Prut. The analysis focused on the identification of flood potential risk index. The study involves the analysis of natural and anthropogenic physical and geographic factors: lithology, land declivity, soil texture, profile curvature and land use. The weighting of each analyzed factor for the contribution to floods was obtained using the AHP extension of the ArcGIS software. This methodology was applied for the lower Uz river basin, situated downstream from Lake Poiana Uzului. The catchment basin of Uz was affected by major floods in the summer of 2005, while the Uz River recorded a maximum historic discharge of 132 m3/s, at the precipitation station of Darmanesti, situated upstream from the Poiana Uzului reservoir. The consequences of the historic high water were serious. Extended surfaces within the major riverbed were flooded, numerous houses were partially damaged and some destroyed. The study highlights that the highest values of flooding index range between 3.96 and 4.71 and that they affect 14% of the entire surface of the studied area.

Evaluation of Satellite Based Precipitations and Their Applicability for Rainfall Runoff Modelling in Narayani Basin of Nepal

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31