Gauge Rainfall Research Articles

Comparison Study Of Measurement Results Between Rain Gauge 7052.0100 and Optical Rain Gauge 815 at Lapan Kototabang

In general, the population in Indonesia is a farmer. This causes farmers to need information about the weather to carry out agricultural activities. One way to obtain information about the weather is to use the Rain Gauge instrument. In this study, the Rain Gauge instrument at LAPAN Kototabang from 2020 to 2021 was compared with the Optical Rain Gauge instrument. This research uses descriptive research with secondary data in the form of rainfall intensity data that has been measured on the Rain Gauge and Optical Rain Gauge from LAPAN Kototabang. This research was conducted to analyze the output data from the Rain Gauge and Optical Rain Gauge in the form of rainfall intensity, rainfall accumulation, and time. From the data analysis, two results can be stated from this research. First, the analysis of the rainfall parameter data that has been processed shows that the rainfall in Kototabang in January - October 2020 and 2021 has a high rainfall value. This causes the intensity of rainfall in Kototabang to be categorized as heavy rain. The two accumulations of rainfall in 2020 and 2021 in the Kototabang area are 497.031 mm and 0.602 mm using the Rain Gauge. While the measurement using Optical Rain Gauge rainfall intensity is 0.207 mm and 0.221 mm. This proves that the intensity of rainfall in 2020 is greater than the intensity of rainfall in 2021.

Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.

Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America

This study aims to validate the accuracy of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset at representing climate variability over various time scales. The study is carried out in Antioquia, in northwestern Colombia, and uses statistically independent information provided by 75 rain gauges located at different sites from 1981 to 2018. In this study, statistical metrics are used to analyze the error structure of the CHIRPS data. Resilient methods for validation are included based on Gauss-Markov diagnostic problems and influence points, leverage, and outliers in the form of the trimmed least squares boot method and principal component analysis. To represent the performance of CHIRPS more accurately at estimating precipitation at different time scales, a comparison is made using 12 specific rain gauge located in different subregions of Antioquia, which had well-differentiated climatic characteristics. The results show that the accuracy of CHIRPS estimates is conditioned by geographic and climatic characteristics of the region in which precipitation events occur. CHIRPS works well in most weather conditions in Antioquia, even during the most intense periods of ENSO. However, it does better in the Andean subregions and underperforms in warmer regions. This study finds that CHIRPS is a rainfall data source that can be considered for analyzing seasonal and interannual variability and spatial precipitation patterns. It provides good spatio-temporal coverage and is particularly suitable for areas with few rainfall gauges. However, its accuracy is minimal for daily analysis.

Analysis of Debris Flow Triggering Conditions for Different Rainfall Patterns Based on Satellite Rainfall Products in Hengduan Mountain Region, China

Rainfall is the main factor that induces debris flow. Satellite rainfall products provide a new source of data in terms of debris flow-triggering conditions to overcome the lack of rainfall data coverage from ground-based rainfall gauges in large-scale mountainous regions. In this study, the applicability of four satellite rainfall products (CMORPH, GPM, MSWEP, and PERSIANN) in the Hengduan Mountain region (HMR) was evaluated with reference to ground observation data from 2000 to 2020. The critical rainfall and rainfall thresholds under different rainfall patterns and warning levels that trigger debris flows were analyzed according to the empirical cumulative distribution function (ECDF) and cumulative probability. The results showed that CMORPH (comprehensive indicator score (CI = 0.72) and GPM (CI = 0.70) performed better in the simulation of daily rainfall sequence consistency and extreme rainfall conditions in the study area. CMORPH also had the highest reconstruction rate for correctly capturing rainfall events that triggered debris flows, with a value of 89%. Approximately half of the rainfall patterns that cause debris flows are antecedent-effective-rainfall-dominated. Both intraday-rainfall-dominated and intraday-antecedent-rainfall-balanced patterns were below 30%. There were evident differences in the critical rainfall for different rainfall patterns under the same warning level. By comparing the results of previous studies on rainfall thresholds, it is believed that the results of this study confirm the application of satellite rainfall products; in addition, the calculated rainfall thresholds can provide a reference for the early warning of debris flows in the HMR. In general, this work is of great significance to the prediction and early warning of debris flow hazards.

SUBANG RADAR CAPPI DATA PROCESSING AND Z-R OPTIMIZATION FOR QUANTITATIVE PRECIPITATION ESTIMATES (QPE) OVER LANGAT RIVER BASIN

The main advantage of radar data over point gauge rainfall is its ability to provide continuous spatial and temporal resolution of rainfall details over a large area. Weather radar transmits electromagnetic wave, interacts with raindrops and reflects some of the intercepted power (backscattering) that is subsequently converted into rainfall intensity. Despite its advantages, indirect rainfall estimation using radar reflectivity factor suffers from various sources of error such as ground clutter, partial beam occultation, beam blockage and attenuation effects. Literatures on the use of radar quantitative precipitation estimates (QPE) in Malaysia have been increasing since the past 15 years ago. However, none of the previous work have detailed out the sources of radar data used and its processing for rainfall rate conversion. This paper will discuss the fundamentals in radar data acquisition and processing for rainfall input to a case study of Langat river basin, Malaysia. The methodology in raw radar data processing is decribed in details and the use of CAPPI data for rainfall estimation over Langat river basin is discussed. The findings indicate a good performance of the radar CAPPI data as an alternative source to the rainfall measurement for Langat river basin with correlation coefficient between radar rainfall and gauged rainfall ranging from 0.69 to 0.75. Improvement on radar rainfall estimates is also recommended by newly derived optimized Z-R equations based on monsoon season. The results presented in this study are encouraging, especially for the application of water resources management for the river basin.

Regional-scale evaluation of 14 satellite-based precipitation products in characterising extreme events and delineating rainfall thresholds for flood hazards

Gridded satellite-based rainfall products have not been so far evaluated for flood hazards monitoring through empirical methods, especially over large areas. Therefore, the main aims of this study are (i) to assess the quality of satellite-based precipitation products for identifying extreme rainfall events able to produce flood events, and (ii) to evaluate the use of satellite-based precipitation products for creating rainfall thresholds to support decision making for issuing flood warnings. Eight products fully based on satellite data (i.e., uncorrected) and six gauge-corrected products were evaluated based on ground-based data obtained from 583 sub-daily rainfall gauges and considering a catalogue of 551 flood occurrences in the state of São Paulo, Brazil, for a period of five years (2015–2019). The gauged values were compared with the precipitation products for six different time steps (3 h, 6 h, 12 h, 1 d, 3 d, and 10 d) and considering rainfall-duration thresholds for six non-exceedance probabilities (5%, 10%, 20%, 30%, 40%, and 50%). Results show that all analysed products tend to large underestimate the extreme rainfall events (i.e., when and where flood events were registered), mainly for sub-daily scales, with the best results found for two uncorrected products (i.e., PDIR-Now and GPM + SM2RAIN) considering 10-days accumulated precipitation. Considerable underestimations were also identified for the rainfall thresholds delineated by the satellite-based products, with the best performances obtained by CHIRP V2.0 (uncorrected) and IMERG-F (corrected) considering tolerance levels of 20%. Based on our findings, the rainfall satellite-based products dataset, even less accurate than the ground-based observations, can be applied, when multi-daily accumulated data are considered, as an alternative source of data for determining precipitation thresholds in some regions that present low-density of RAIN gauges but not replace the gauged data in regions with a high-density of rain gauges with sub-daily data available

Estimation of Intensity Duration Frequency for Ungauged Basin in Lampung Province, Indonesia

Frequency duration intensity (IDF) analysis was conducted to estimate the peak flow rate based on the minimum rainfall data collection station. Rainfall data used is data with high intensity that occurs in a short time from automatic rainfall recording stations. Currently, the availability and distribution of automatic rain recording stations in Lampung Province, Indonesia, are still limited. Therefore, this study aims to use the IDF approach in the ungauged basin area for areas with rainfall data that do not meet the hydrological analysis criteria by interpolating rainfall data from 126 manual rainfall measuring stations in Lampung Province, Indonesia. The research method includes analysis of rainfall intensity using the Mononobe equation at various durations and returns periods. Next, create a rainfall intensity map (isohyet) using ArcGis. Finally, compare the IDF analysis of daily rainfall data at 4 automatic rainfall gauge stations with the estimation results based on the intensity map (isohyet). Based on the results of data analysis, it is known that from the available 126 rainfall climatology stations, there are 113 rainfall climatology stations with complete data for 10 years and 13 rainfall climatology stations with incomplete data for 10 years. In addition, the study results show that 45.24% of the daily rainfall in Lampung province is in the low category, 53.97% is in the medium category, and 0.79% is in the high category. This study indicates that rainfall intensity data from climatological rainfall stations that do not meet the hydrological criteria can be found by interpolating rainfall intensity maps from the nearest rain climatology station that meet the hydrological analysis criteria. The relationship test of the actual rainfall intensity variable at 4 automatic rainfall gauge stations with the rainfall intensity from the map (isohyet) using MAPE showed satisfactory results.

Analysis of rainfall and streamflow trend and variability over Birr River watershed, Abbay basin, Ethiopia

The analysis of rainfall and streamflow trends provides useful information for the effective hydrological variables, water resource planning, and management systems. Trend analysis is important to understanding the performance and features of hydrological and climatic variables over a long timescale. The goal of this study is to identify and analyze existing relationships between rainfall and streamflow trends, as well as to investigate potential drivers of streamflow variability in the Birr River watershed. The Mann Kendall test, Pettitt test, Sen's slope estimator, and an indicator of hydrological alterations were used to investigate trends, detection of change points, and the linkage of existing rainfall and streamflow at monthly, seasonal, and annual timescales. The result of this study showed that most rainfall gauging stations did not reveal statistically significant changes. On the other hand, streamflow exhibited a statistically significant increasing and decreasing pattern. In all stations, the streamflow trends and change points were found to be inconsistent. The variations in streamflow without significant changes in rainfall suggested that other variables were influencing streamflow. These could be due to the changes in watershed physical elements, such as alterations in the dynamics of Land Use Land Cover (LULC), which are most likely to be responsible for observed changes in the streamflow fluctuations. The finding of this study can be used as a platform for further studies on the effect of watershed dynamics on hydrological flow variability.

Meteorological drought monitoring across the main river basins of Ethiopia using satellite rainfall product

BackgroundDrought is a recurrent phenomenon emerging from an inter-annual and intra-seasonal deficit of water across the atmosphere-to-aquifer continuum and these events are reported to be very severe in regions of Ethiopia. Availability of accurate precipitation observations significantly impacts the drought monitoring systems. These observations are scarce and sparsely distributed in countries like Ethiopia. To overcome such a problem, the use of satellite rainfall estimates with continuous and timely data at different spatio-temporal scales is opportune, provided their accuracy is well known. Among the currently available satellite rainfall products studies specifically in Ethiopia shown that Climate Hazard Group Infrared Precipitation with Station version 2 daily rainfall product (hereafter CHIRPSv2) has better performance and recommended as a valuable substitute for gauge rainfall data. Therefore, the current study focused on assessing the applicability of CHIRPSv2 for meteorological drought monitoring in Ethiopia. Due to the wide spatio-temporal variability of Ethiopia's climate, the performance of CHIRPSv2 rainfall product for meteorological drought monitoring has been assessed in selected river basins (Awash, Blue Nile, Baro, Danakil, Omo and Tekeze) of Ethiopia. For drought estimations, two well-known meteorological drought indices such as Effective Drought Index (EDI) and the Standardized Precipitation Index (SPI) have been utilized.ResultThe obtained results show that the CHIRPSv2 based EDI and SPI are able to identify all the historical drought events reported between 1982 and 2016 (such as 1984, 1992, 2003, 2009, 2011, 2012, and 2015). The time series plots of EDI and SPI values show that most of the drought events in the selected river basins occurred during their corresponding main and smaller rainy months. Detailed spatio-temporal investigations of the two worst drought years (1984 and 2011) and one drought-free year (2007) show that both the EDI and SPI could enable to identify the drought and drought-free areas correctly when compared with the available recorded historical droughts (RHD) across each river basin. Similarly, the temporal trends of EDI and SPI identified drought shows that frequency and severity of drought were higher during 1980s and 2000s than 1990s.ConclusionsSuch good agreement between the identified drought and historical drought indicates that CHIRPSv2 is a promising rainfall dataset, which could be used to develop drought monitoring and early warning system across different river basins of Ethiopia. Besides, the study helps to provide useful information for decision makers to implement different adaptation and mitigation measures of drought in the study area. The finding also will support to improve the existing drought monitoring and early warning system and to build resilience to drought at the river basin level.

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods and many gaps and failures at the daily scale. Thus, a hybrid model was developed based on an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) coupled with the maximum overlap discrete wavelet (MODWT) method to obtain precipitation estimates. Six rainfall gauge stations located in different biomes within the studied region were adopted, and satellite data (CMORPH) were used. The interval of data that was have used is 1998-2016. The precipitation data were evaluated by seasonal (wet and dry) periods. The results obtained demonstrated the good capacity of the MODWT-ANFIS model to simulate the daily precipitation. In this case, data entries lagged by 4days and 5days performed better, with Nash values close to 1.0 and mean square errors (MSE) below 0.1.

Probabilistic rainfall threshold of landslides in Data-Scarce mountainous Areas: A case study of the Bailong River Basin, China

The damage caused by rainfall-induced landslides has increased globally. The development of urbanisation has led to the expansion of residential areas in mountainous areas, and human activities have accelerated slope instability. However, the limited data records in mountainous zones have resulted in low-precision landslide rainfall thresholds. Establishing a landslide early warning model in areas with scarce data is an unresolved problem. This study uses the Bailong River Basin in western China as an example. By downscaling Tropical Rainfall Measuring Mission (TRMM) data to a daily resolution of 1 km and conditionally merging it with ground station rainfall, we obtain high-precision rainfall data to establish rainfall threshold curves based on Bayes' theorem and the frequency method. Subsequently, a landslide susceptibility map based on deep learning was integrated to construct a regional landslide early warning model. The results are as follows: First, compared with downscaling rainfall and rain gauge interpolation data, the daily rainfall data with a resolution of 1 km that combines the two has better overall performance and higher accuracy. Second, we generated the event-rainfall-and-duration threshold curves of 5%, 20%, and 50% probability by combining the frequency method and Bayesian theorem, and show that with a probability of landslide occurrence of 50%, while the intercept of Bayes' theorem is greater than that of the frequency method, the overall trend is consistent. Finally, by using a typical mass rainfall event to test the performance, the results show that the early warning capability of the model integrated with the rainfall threshold and landslide susceptibility map is more accurate than that of using a separate rainfall threshold. The outcomes of this research are expected to provide efficient support for early warning and risk management of geological disasters in mountainous areas with scarce data.

Observational Characteristics of Dual-Polarization Radar for Heavy Rainfall Events in Capital Area

In this study, the observational characteristics of dual-polarization radar for studying the heavy rainfall in the capital area were evaluated. To this end, three rainfall events that had caused significant flood damage due to the heavy rainfall from June to August 2020 were selected. The application of dual-polarization radar data revealed a significant change in precipitation during the analysis of observation characteristics of the affected capital area’s heavy rainfall. Moreover, the physical characteristics of reflectivity, differential reflectivity, specific differential phase, and copolar-correlation coefficient and their temporal dynamics in radar rainfall were compared to rain gauge rainfall. The radar reflectivity has been identified at the frequency of ≥ 40 dBZ due to the effects of heavy rainfall in the capital area, while the differential reflectivity has confirmed that the particle size was larger. The specific differential phase seemingly exhibited the most similar distribution to rain gauge rainfall, while exhibiting local characteristics of heavy rainfall in the capital area, according to the differential reflectivity result. The copolar-correlation coefficient confirmed the consistent observational performance of the dual-polarization radar for evaluation of heavy rainfall. Overall, the radar rainfall demonstrated good performance in simulating the peak time, and CSU-HIDRO estimated the rainfall with up to 32.4% more accuracy than JPOLE.

Citizen rain gauges improve hourly radar rainfall bias correction using a two-step Kalman filter

Abstract. The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse.

Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control. Significance Statement The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

Comparison between Satellite-derived Rainfall and Rain Gauge Observation over Peninsular Malaysia

Validation of the bias-corrected product of National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre Morphing Technique CMORPH-CRT was conducted using gridded rain gauge dataset of Wong et al. (2011) and rain gauge data from meteorological stations throughout Peninsular Malaysia. The CMORPH-CRT was compared for four contrasting topographic sub-regions of Peninsular Malaysia, i.e. west coast (WC), foothills of Titiwangsa range (FT), inland-valley (IN) and east coast (EC). CMORPH-CRT product with grid resolution of 8 km × 8 km at temporal resolution of 1-hour from 00Z January 1998 to 23Z December 2018 was utilized. The results show that CMORPH-CRT are in agreement with the rain gauge data. The CMORPH-CRT performed best over coastal sub-regions but it underestimated over FT sub-region and overestimated at IN. CMORPH-CRT tend to perform better in moderate rather than heavy rainfall events. For extreme weather events, the CMORPH-CRT had shown capability in observing the formation and decay of low-pressure system in Penang during 4th November 2017 and it is in agreement with rain gauge based SPI index i.e. drought conditions over Peninsular Malaysia.

Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products

Abstract. Accurate weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 d) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams (Selingue Dam, Markala Dam, Goronyo Dam, Bakolori Dam, Kainji Dam, Jebba Dam, Dadin Kowa Dam, and Lagdo Dam) in the Niger river basin using NASA's Integrated Multi-satellitE Retrievals (IMERG) Final Run merged satellite gauge rainfall observations. The results indicate that the accuracy of GFS forecast varies depending on climatic regime, lead time, accumulation timescale, and spatial scale. The GFS forecast has large overestimation bias in the Guinea region of the basin (wet climatic regime), moderate overestimation bias in the Savannah region (moderately wet climatic regime), but has no bias in the Sahel region (dry climate). Averaging the forecasts at coarser spatial scales leads to increased forecast accuracy. For daily rainfall forecasts, the performance of GFS is very low for almost all watersheds, except for Markala and Kainji dams, both of which have much larger watershed areas compared to the other watersheds. Averaging the forecasts at longer timescales also leads to increased forecast accuracy. The GFS forecasts, at 15 d accumulation timescale, have better performance but tend to overestimate high rain rates. Additionally, the performance assessment of two other satellite products was conducted using IMERG Final estimates as reference. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) merged satellite gauge product has similar rainfall characteristics to IMERG Final, indicating the robustness of IMERG Final. The IMERG Early Run satellite-only rainfall product is biased in the dry Sahel region; however, in the wet Guinea and Savannah regions, IMERG Early Run outperforms GFS in terms of bias.

Merging satellite rainfall estimates and daily rain gauge observations for improved flood simulation in MelkaKuntire catchment, upper Awash Basin, Ethiopia

Acquiring high-quality rainfall data is very important for studying catchment hydrology. This data can be obtained by merging Satellite Rainfall Estimates (SREs) and gauge observations as highlighted by several researchers. However, the application of these merged rainfall products for hydrological modeling has rarely been explored in a complex topographic region like Melkakuntire catchment, Ethiopia. Therefore, this study aims to assess the suitability of merged and unmerged SREs in simulating three flow conditions (daily streamflow, seasonal and high flows) using the HBV-light model. A total of nine SREs of which three from the unmerged (raw) SREs (IMERG v06B, TRMM 3B42v7, and TAMSAT v3), three from the downscaled-merged SREs (IMERG-D + Gauge, TRMM-D + Gauge, and TAMSAT-D + Gauge), and three from the undownscaled-merged SREs (IMERG + Gauge, TRMM + Gauge, and TAMSAT + Gauge) were used for the period 2003–2015. We used the Random Forest (RF) algorithm to merge SREs with gauges, with and without downscaling the spatial resolution of the selected SREs. For the RF algorithm, the gridded SREs and elevation values were used as the independent variables while daily gauge observations were considered as the dependent variable. Results show that the merged SREs outperform both the unmerged SREs and the gauge data in simulating all three flow conditions when the model is calibrated to each rainfall input independently. Similarly, the hydrologic model forced by merged SREs produces better performances while running the model with the baseline parameter values derived from gauges as well. Moreover, the suitability of SREs in simulating all three flow conditions is more improved if the model is calibrated to each SREs than to gauge observations. Overall, the downscaled-merged SREs produce the best result in closing the water budget of the study catchment, but further studies could be needed to validate these merged rainfall products, particularly the best performing IMERG-D + Gauge product, in other data-scare regions.

On the correction of multiple minute sampling rainfall data of tipping bucket rainfall recorders (Short Contribution)

In the last decades of the 1900s, the tipping bucket rainfall gauges (TBG) were used to record the sub-daily rainfall data. In the first period of the rainfall data recording, as a result of the lack of efficient data storage and data transmission, the sampling period of the TBG devices was chosen in a magnitude of 10–20 minutes. Consequently, there are historical datasets characterized by several minutes long sampling periods. Since the turn of the 2000s, the data handling has been revolutionized; the sampling period has diminished to one minute. There is a systematic error of the TBG technique which has been investigated since the middle of the 1900s. Between 2004 and 2008, a comprehensive research was performed to determine the correction equation for several TBG devices. These results can be utilized for the short sampling period measurements (one minute sampling), but for longer sampling period data, further corrections are needed. In this paper, a supplementary correction is presented. On the base of the mathematical determination of the correction factor, simple estimation will be proposed to be able to execute the necessary correction. After the presentation of the correction factor, a general correction factor is proposed for larger geographical regions and wide time span of the measurements. The revision of the historical rainfall data recorded by TBG devices can be important in several issues, such as the re-evaluation of intensity-duration-frequency (IDF) curves, and in other fields.

OUTLIER AND HOMOGENEITY ANALYSIS OF EXTREME RAINFALL SERIES IN KANO, NIGERIA

Data preparation is one of the most important steps in the field of hydrology since the results of any research depend on whether the input data were reliable and rightly collected or not. Reliability and quality of long-term rainfall data are required before carrying out any study in hydrology. For this reason, outlier identification techniques and homogeneity tests have proved helpful. This study aimed at carrying out outliers and homogeneity analysis of extreme rainfall data series for the Kano rainfall gauging station. XLSTAT, 2019 was used to identify outliers through Grubb’s test for outliers/two-tailed test and Dixon test for outliers/two-tailed statistic test and Standard Normal Homogeneity Test (SNHT), Buishand Range (BR) Test, Pettitt Test, and Von Neumann Ratio (VNR) Test were used for homogeneity analysis. Four outliers were identified and treated accordingly. The results of homogeneity tests showed that the maximum daily rainfall data of the Kano gauging station is homogenous and classified as useful. The maximum daily rainfall data of Kano station can be used for any study in water resources and environmental engineerings, such as in Development of Intensity Duration Frequency (IDF) Curves and Rainfall-Runoff Simulations.Keywords: Outliers test, homogeneity tests, rainfall data, XLSTAT 2019

Flash Flood Modeling Using HEC-RAS (2D) model on Wadi Reem in the western region, Kingdom of Saudi Arabia

Various infrastructures in Saudi Arabia are affecting by floods and rainfall that may damage constructions at urban areas due to water flows in the main valleys and streams. Asser Region in western Saudi Arabia is characterized by its high rainfall intensity that leads to flash floods. The present study aims to evaluate and identify potential floods in coastal areas of Wadi Reem basin and to show the urban areas exposed to flooding with flood water in the coastal city. Runoff calculation is an important part of regional runoff forecast and water resources evaluation model. The Soil Conservation Service (SCS) curve number (CN) model developed by the U.S. Department of Agriculture National Resources Conversion Service (NRCS) is the most popular and widely applied model for direct runoff estimation. The current work used HEC-RAS (2D) in order to simulate Flood map depth and velocity in Wadi Reem coastal areas. Watershed and its stream was delineated using digital elevation model (DEM) and morphological parameters calculated using Watershed Modeling System (WMS). Rainfall frequency analysis was performed for selected three rainfall gages using annual maximums of 24-h rainfall. This study deals with geographic information systems (GIS), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on Wadi Reem coastal area.