Rainfall Gauging Stations Research Articles

A Novel Hybrid Spatiotemporal Missing Value Imputation Approach for Rainfall Data: An Application to the Ratnapura Area, Sri Lanka

Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the accuracy of the final predictions. Hence, accurately estimating missing values by considering the spatiotemporal variations in data has become a crucial step in eco-hydrological modeling. The multi-layer perceptron (MLP) is a promising tool for modeling temporal variation, while spatial kriging (SK) is a promising tool for capturing spatial variations. Therefore, in this study, we propose a novel hybrid approach combining the multi-layer perceptron method and spatial kriging to impute missing values in rainfall data. The proposed approach was tested using spatiotemporal data collected from a set of nearby rainfall gauging stations in the Ratnapura area, Sri Lanka. Missing values are present in collected rainfall data consecutively for a considerably longer period. This pattern has scattered among stations discontinuously over five years. The proposed hybrid model captures the temporal variability and spatial variability of the rainfall data through MLP and SK, respectively. It integrates predictions obtained through both MLP and SK with a novel optimal weight allocation method. The performance of the model was compared with individual approaches, MLP, SK, and spatiotemporal kriging. The results indicate that the novel hybrid approach outperforms spatiotemporal kriging and the other two pure approaches.

Rainfall intensity model with spatialization of intensity-duration-frequency curve parameters - A case study for the state of Maranhão, Brazil

The characterization of intense rainfall in engineering projects is fundamental, mainly regarding the estimate of design flows necessary for designing hydraulic works. Intense rainfall events are commonly measured by Equations and curves that relate their intensity, duration, and frequency. Such relations, known as IDF, enable the hydrological characterization of a given region. This article presents a methodological design and results from both determination and spatialization of IDF curve parameters for the state of Maranhão. Historical series of maximum daily rainfalls obtained from National Water and Sanitation Agency (ANA) were used in 126 rainfall gauge stations and the Gumbel probability distribution estimated the maximum daily rainfall for 5, 10, 15, 25, 50, and 100 return periods. The Isozonal Method obtained the IDF correlations of intense rainfall events for 0,1. 1, and 24 h durations, and their performance were conducted by Nash-Sutcliffe R2 coefficient and Root Mean Square Relative Error (RMSE). “K, a, b, and c” parameters of intense rainfall equations were determined by optimization and convergence processes and their spatialization was carried out by interpolation by Inverse Distance Weighted (IDW), which enabled to determine the values of each parameter in regions without physical measurements of rainfall. Similarly, rainfall intensity was spatialized for the entire state. According to the results, the rainfall distribution in the state of Maranhão shows a variation in the indexes of precipitation, with the highest values found in areas located in central-southern, southwestern, and southeastern regions.

Performance of Different Parameterization Configurations of WRF-ARW Model during Heavy Rainfall over Mi Oya River Basin

Short-term intense precipitation is one of the hallmarks of climate change. Mi Oya River basin experiences severe seasonal floods annually, but the damage can be lessened by developing a numerical weather forecasting (NWF) model for the entire basin and incorporating it with effective reservoir management. Several areas in Sri Lanka have undergone NWF studies, however they are insufficient to determine the best physics schemes for the basin. This study investigates the Weather Research and Forecasting (WRF-ARW) model's predictability with varying three microphysics and two cumulus schemes to discover the optimal set of physics parameters for predicting heavy rainfall occurrences throughout the Southwest and Northeast monsoon seasons within a nested domain configuration. The WRF model's forecasting results at 3 km grid resolution were compared with four rainfall gauging stations in the basin for three rainfall events in May 2016, April 2018, and November 2015. Total Model Performance was derived for the evaluation utilizing bias, MAE, RMSE, Correlation Coefficient, and slope of each model's output data with observed rainfall data. After comparing the model output to data, WSM6 microphysics and Betts-Miller-Janjic cumulus with other default physics settings were determined to be the optimal physics combination to forecast weather across the region.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Stochastic modelling of the daily precipitation occurrence in the Tocantins-Araguaia hydrographic region, Amazon, Brazil

Evaluating the temporal and spatial variation of the occurrence of dry and rainy days is essential for planning the use of water resources. This article evaluated the behaviour of daily precipitation in the Tocantins-Araguaia hydrographic region (TAHR). The 1st-order stationary Markov chain (MC) of two states was used without considering the spatial correlation between the rainfall gauge stations. The developed model was efficient in estimating the occurrences of daily precipitation for the entire TAHR, as the maximum errors found were equal to RMSE: 14.1568, RSR: 0.0149, NSE: 0.9973 and PBIAS: -1.7650. However, the areas located in the Amazon-Cerrado Biome (Ecotone) require more attention due to the greater occurrence of consecutive drought days. Therefore, it is suitable to use MCs of higher orders for greater accuracy. In addition, the transition probabilities that led to rain conditions were greater when they were closer to the Atlantic Ocean, while the transition probabilities that led to drought conditions were greater in the areas located in the Cerrado biome.

Comparing surface water supply index and streamflow drought index for hydrological drought analysis in Ethiopia

Recently, floods and drought have become common natural hydroclimatic hazards in several countries. Consequently, the identification of an appropriate drought index is now a challenging task for researchers. It is obvious that there is not a single best drought index; rather a comparison of indices will give a relative option. The objective of this study was to compare two hydrological drought indices; the modified surface water supply index (M1SWSI) and streamflow drought index (SDI) over eight river basins, in Ethiopia. The M1SWSI and SDI value was computed from 1973 to 2014 using 34 streamflow stations, 42 rainfall gauge stations, and 3 lake-level data. The two indices results showed that the 1980s were the most severe drought years for all river basins. But for the case of Genale Dawa and Wabishebele basins, the drought severity increased from 2000 to 2014. Hydrological drought analysis using SDI has more drought occurrence frequency than M1SWSI. In all river basins from 1973 to 2014, there were a total of 18 severe drought events when using M1SWSI, but there were a total of 39 severe and 12 extreme drought events when using SDI. This implied that M1SWSI reduced the occurrence probability of severe drought by 53.85% and extreme drought by 100%. It is known that Ethiopia is stricken by extreme droughts in the last few decades. But M1SWSI doesn't detect those invidious drought events. In this study, SDI is found to be a better hydrological drought index. Therefore, policy and strategic planners, master plan developers, and decision-makers can use SDI to analyze historical and future hydrological drought trends to develop effective drought mitigation measures.

Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia

PurposeThe purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.Design/methodology/approachSelected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.FindingsThe spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R2 = 0.72) is better as compared to MSWEP (NSE = 0.69; R2 = 0.7), TRMM (NSE = 0.67, R2 = 0.68) and CHIRPS (NSE = 0.58 and R2 = 0.62).Practical implicationsCalibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.Originality/valueThis research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

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.

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.

Pluvial flooding: High-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms

Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T-year design storm generates the T-year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ∼ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2). The Safer_RAIN fast-processing algorithm based on digital elevation models (DEMs) is compared with the IBER 2D hydrodynamic model in four real storms by using 10-min precipitation fields. Precipitation recorded at rainfall-gauging stations was merged with continuous fields obtained from a meteorological radar station. Given the hydrostatic limitations of Safer_RAIN, the benchmarking results are adequate in terms of water depths in depressions. A long set of 10 000 synthetic storms that maintain the statistical properties of observations in Pamplona is generated. Safer_RAIN is used to simulate runoff response, and filling and spilling processes, in depressions for the 10 000 synthetic storms, obtaining the probability distribution of water depths in each cell. Maps of pluvial flood hazards are developed in the Pamplona metropolitan area for 10 return periods in the range from two to 500 years from such pixel-based series of simulated water depths. Bivariate return-period curves are estimated in a set of cells, showing that several storms can generate a given T-year pluvial flood with an increasing precipitation with storm duration that depends on the draining catchment soil characteristics. The methodology proposed is useful to develop maps of pluvial flood hazards in large multi-depression urban areas in reasonable computation times, identifying the main pluvial flood hotspots.

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

Definition of Ecological Flow Using IHA and IARI as an Operative Procedure for Water Management

It is widely recognized that the hydrological regime of natural flow plays a primary and crucial role in influencing the physical condition of habitats, which, in turn, determines the biotic composition and sustainability of aquatic ecosystems. The current hydro-ecological understanding states that all flow components might be considered as operational targets for water management, starting from base flows (including low flows) to high and flood regimes in terms of magnitude, frequency, duration, timing, and rate of change. Several codes have been developed and applied on different case studies in order to define common tools to be implemented for Eflow assessment. This work deals with the definition of an operative procedure for the evaluation of the Eflow monthly distribution to be adopted in a generic watercourse cross-section for sustainable surface water resource management and exploitation. The methodology proposes the application of the Indicators of Hydrologic Alteration methodology (IHA by TNC) coupled to the valuation of the Index of Hydrological Regime Alteration (IARI by ISPRA) as an operative tool to define the ecological flow in each monitoring cross-section to support sustainable water resource management and planning. The case study of the Agri River in Basilicata (Southern Italy) is presented. The analyses were carried out based on monthly discharge data derived by applying the HEC-Hydrological Modeling System at the basin scale using the daily rain data measurements obtained by the regional rainfall gauge stations and calibrated through the observed inlet water discharge registered at the Lago del Pertusillo reservoir station.

Applying support vector machines optimized by genetic algorithm for estimating the spatial distribution of mean annual precipitation

The main goal of the present study was to present an alternative spatial interpolation technique in order to estimate the mean annual precipitation distribution within a research area. The novel spatial interpolation technique combined evolutionary algorithms and data mining methods. Specifically, a support vector machine (SVM) was used to produce the mean annual precipitation map, the structural parameters of which (cost, epsilon, and gamma) were tuned by a genetic algorithm (GA). To evaluate the performance of the developed methodology, 80 rainfall gauge stations (covering the time period 1970–2010) located within the Macta basin, Northwestern Algeria, were analyzed. The conceptual model which was formulated in order to create the mean annual precipitation map had seven topographic variables, namely latitude, longitude, altitude, the minimum, maximum, and mean altitude within a radius of 5 km from each station and the distance from the coastline. To estimate the efficiency and accuracy of the developed model, three statistical criteria, the root mean square error, the mean square error, and the r square, had been used. The results were compared with the findings obtained by multi-linear regression analysis (MLR) with the optimized SVM model showing higher learning and predictive r square values (0.835 and 0.848, respectively) than the values obtained by the MLR model (0.248 and 0.388, respectively). Another goal was the examination of the spatio-temporal pattern concerning the mean annual precipitation. The spatial-temporal trend was estimated using the non-parametric Mann-Kendall approach and Sen’s slope estimator. The analysis revealed significant spatial variability with 14 stations showing statistical significant downward trend, reaching −6.62 mm/year, and only 1 station showing statistical significant upward trend, +3.62 mm/year. The knowledge of the spatio-temporal trend of mean annual precipitation is of high significance in studies concerning climate change, groundwater assessments, and flood-related applications.

Assessment of satellite products for filling rainfall data gaps in the Amazon region

AbstractRainfall data series with adequate quality and length are often incomplete or nonexistent. Thus, filling in rainfall gaps becomes necessary to complete databases. This article proposes the use of satellite products (TRMM—Tropical Rainfall Measuring Mission, CHIRPS—Climate Hazards Group InfraRed Precipitation with Stations and CMORPH—CPC Morphing Technique) to fill gaps in the rainfall historical series. The simple regression method, using satellite rainfall estimates, was tested to fill the missing data from 164 rainfall gauge stations in the Amazon region. Large dispersions were observed between rainfall data, with R2 ranging from 0.383 to 0.844, the best results were found in areas with less rainfall. As well, the greatest performance of the products was verified in the dry period, with r and d higher than 0.899 and 0.950, respectively. The product with the best representation in the region was CHIRPS, which had the lowest monthly values of mean absolute error (0.979 mm) and root mean square error (3.656 mm). The results confirm that the satellite estimates satisfactorily represent the seasonal variation of rainfall in the region, despite presenting cases of overestimation and underestimation of data. The higher performance of CHIRPS can be explained by the higher spatial resolution (0.05°), allowing for more accurate weather forecasts. In fact, CHIRPS has the CHPclim model, which adds other factors to the good product performance. These characteristics justify the better performance of the CHIRPS product for filling gaps in daily rainfall data in the Amazon region, favoring the best monthly rainfall estimates for each region state analyzed.Recommendations for Resource Managers Satellite products have been increasingly used for estimating rainfall data in regions with a low number of installed rainfall gauge stations. Thus, the assessment and selection of these products needs to be elaborated for the best decision making of water resource managers. Rainfall data are important to recognize the occurrence patterns for prediction of the climatic behavior of a region. Sectors such as agriculture and disaster prevention (droughts, floods, erosion of watersheds, and river silting) need knowledge of rainfall for planning, management, and mitigation. Knowledge of rainfall behavior is very important in the Amazon region. In this case, the dry season and temperatures have been increasing due to global climate change. These changes establish conditions for more intense fires, which increases the deforestation of the region.

The Performance of the Diurnal Cycle of Precipitation from Blended Satellite Techniques over Brazil

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels.

Evaluation of the Performance of High-Resolution Satellite Based Rainfall Products for Stream Flow Simulation

Precipitation data is an intrinsic parameter of rainfall-runoff simulation, since it is strongly hooked into the accuracy of the spatial and temporal representation of the precipitation. In areas where rainfall gauging stations are scarce, additional data sources could also be needed. Satellite platforms have provided as a satisfactory alternative because of their global coverage. Although a good range of satellite-based estimations of precipitation is out there, not all the satellite products are suitable for all regions. In addition, in data-scarce areas where interpolation schemes are applied, it becomes difficult to get an accurate performance assessment; another comparison method is required as rainfall-runoff models. Remotely-sensed estimates are to get realistic and reliable data to be accessed in water resource assessments. Therefore, there is a requirement to evaluate the accuracy of remote sensing techniques. Inter comparison between Satellite rainfall product and observed data were done using point to grid method selecting representative metrological stations. Inter comparison between Satellite rainfall product and observed data were done using point to grid method selecting representative metrological stations. TAMSAT shows the average value of R=0.87 and NS =0.764. Considering four categorical index POD, FAR, FB and HSS, the average value 0.71, 0.22, 0.92, and 0.66 respectively. For CHRIPS average R and NS are 0.88 and 0.755 respectively and categorical index POD, FAR, FB and HSS were 0.8,0.05, 0.85 and 0.81) respectively. The study model stream flow using both CHRIPS and TAMSAT rainfall products by using the SWAT model from 1983 to 2017. The model was calibrated from 1998 to 2003 and validated from 2004 to 2007 using SUFI-2 algorithm embodied in the SWAT-CUP. The Nash-Sutcliffe Efficiency (NSE), linear correlation coefficient (R) and BIAS indices were used to benchmark the model performance and shows very good result (having R2 and NS=0.71- 0.95 during calibration and 0.72-0.97 during validation.

Application of HEC-HMS Model for Flow Simulation for Dungun River Basin

Abstract: The uncertainty in the weather will cause droughts and extreme floods. It can affect agriculture, natural resources, ecosystems, and losses. To overcome water-related problems resulting from climate change and careful care should be given to operations management, reservoirs and water catchment. The purpose of this study is to develop a rainfall runoff model and to assess the runoff potential for the catchment and to calibrate and validate the model and using the calibrated values for future hydrological research. The Hydrological Modelling System (HEC-HMS) was used for the river basin of Sungai Dungun (1463.34 km2), Terengganu. To compute infiltration, transform, and flow routing, methods like Initial and Constant, Clark Unit hydrograph and lag routing were chosen, respectively. The rainfall data for this study were obtained from the Department of Irrigation and Drainage Malaysia (DID), covering from year 2007 to 2018. There are three rainfall gauging stations and one stream flow data stations in the study area. Rainfall-runoff simulation had been conducted using different data set for calibration and validation. Preliminary data shows that there is a clear difference between the observed and simulated peak flows. Model calibration with optimization methods and sensitivity analysis was performed to obtain the optimal parameters for this catchment. The values of the parameters obtained from the calibration show a reasonable difference in peak flow. Model validation using optimized parameter values showed reasonable difference in peak flow. Generally the results of the study showed a good simulation between observed and estimated value with NSE = 0.85, R2 = 0.86, Relative Error Peak = -4.14% and Relative Error Volume = -22.5%

Calibration of seasonal transfer equation (Z–R) by data of Doppler weather radar, rainfall gauging station and genetic algorithm method in the Abolabbas watershed (in southwest of Iran)

Abstract The observed radar reflectivity (Z) converts to rainfall intensity (R) by a transfer function. In the first stage, for calibration of collected data (with time step 15 minutes) by weather radar and determination of the best relation between Z and R, it applied a genetic algorithm (GA) to minimize the amount of root mean square error (RMSE). Although Z = 166R2 (the transfer function in the Khuzestan province of Iran) is an appropriate equation, the GA method distinguished that Z = 110R1.8 (from February to May) and Z = 126R2 (for other months) are the optimum transfer functions for the Abolabbas watershed in Iran. The mean of RMSE of optimum transfer equations is 0.59 mm/hr in the calibration stage and 0.85 mm/hr in the verification stage. In the second stage, the Hydrologic Modeling System (HEC-HMS model) used four types of precipitation data (extracted rainfall data from radar and the optimum transfer equations, Z = 166R2, Z = 200R1.6 and extracted rainfall data from rainfall gauging stations). The calibrated rainfall data by the optimum transfer equations can produce flood hydrographs in which their accuracy is similar to the accuracy of generated flood hydrographs by collected rainfall data of rainfall gauging stations. The mean of RMSE is 0.65 cubic metres per second and the mean or R2 is 0.89 for optimum transfer equations.

Investigating regional distribution for maximum daily rainfall in arid regions: case study in Saudi Arabia

The hydrologists need effective procedures to assist them in predicting the implications related to high rainfall risks, both at gauged and ungauged areas. The first step for any assessment is estimating the rainfall values associated with various return periods in years. This information is obtained using rainfall frequency analysis techniques based on observed maximum daily rainfall values. The existence of a regional distribution for the rainfall frequency within a certain region is considered as a precious information for the hydrologists to estimate—with confidence—the expected rainfall at high return periods. The aim of this research is to determine the regional statistical distribution for various regions in Saudi Arabia. Saudi Arabia was selected to represent arid regions as it covers a large area of the Arabian Peninsula in addition to the availability of rainfall data compared with other similar countries. The data of 394 rainfall gauging stations were included in the study, which covers all Saudi Arabia in its 13 main regions. The data was analyzed using multiple frequency analysis methods and was subjected to various statistical tests including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Anderson-Darling Criterion (AD), Station-Year Analysis, and Index Flood Method, in order to determine the best regional statistical distribution and define the homogenous regions using regional analysis. The study concluded that the Log-Pearson type III distribution was the best model to describe the distribution of the daily maximum rainfall in the region. Saudi Arabia was sub-divided into seven homogeneous regions. Contour maps for the 2-, 5-, 10-, 25-, 50-, and 100-year rainfall were produced to predict the rainfall at any point within the Kingdom of Saudi Arabia.