Validation of Satellite Daily Rainfall Estimates Over Indonesia

The sparse distribution of rainfall gauges in Indonesia can be supplemented with satellite products. However, due to the highly dynamic and random nature of rainfall in the region, it is crucial to evaluate the accuracy of these rainfall estimation data. To assess the accuracy of CHIRPS, GCPC, GsMap, and IMERG gridded rainfall data products, numerical comparisons using root mean square error (RMSE) and dichotomous method indicators such as percent correct (PC), false alarm ratio (FAR), bias score (BIAS), probability of false detection (POD), and critical success index (CSI) will be conducted. Analyzing 40 years of data reveals that the accuracy of satellite products is highest in the southern part of South Sulawesi Province, particularly along the west coast. The southern region shows better accuracy than the northern part, and the western area outperforms the eastern region. The accuracy of satellite rainfall estimates varies by location and time, with RMSE values increasing during periods of high rainfall, such as the peak of the rainy season, and decreasing during the dry season. Dichotomous parameters indicate a higher false alarm error compared to missing detections. Among the products, CHIRPS demonstrates the most consistent accuracy, maintaining performance close to the best values. Overall, CHIRPS outperforms the other products, followed by IMERG, GCPC, and GsMap. However, this ranking is subject to change over time and location, necessitating re-evaluation for each application and time period

ABSTRACTAccurate estimation of precipitation is crucial for crop yield assessment, flood and drought monitoring, and water structures management. Precipitation is subject to both temporal and spatial variability. While recording rain gauges support temporal resolution, they measure point rainfall and require dense network and application of interpolation techniques to provide spatial dimension. On the other hand, remote-sensing products cover regional and global spatial scales. Building upon the Tropical Rainfall Measuring Mission (TRMM) heritage, the Global Precipitation Measurement (GPM) mission is an international net of satellites that present the next-generation global observations of rain and snow at a spatial resolution of 0.1° × 0.1° with a half-hour temporal resolution. In this study, March–December 2014 3-hourly TRMM data (3B42V7) and half-hourly Integrated Multi-satellite Retrievals for GPM (IMERG) data are compared with the 3-hourly rain gauges data in Khorasan Razavi province, located in northwest of Iran. Coefficient of determination (R2), Bias, MBias, RBias, mean absolute error (MAE), root mean square error (RMSE) as well as probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics were measured for validation purposes. The result showed that correlation between IMERG data and rain gauge rainfall data is higher than those of 3B42V7 data. In addition, the values of MBias, Bias, and RBias confirmed that both of 3B42V7 and IMERG underestimated rainfall over the study area, whereas MBias of IMERG was higher than 3B42V7. Furthermore, MAE and RMSE values of 3B42V7 and IMERG were similar while IMERG evaluation turned out a better correlation coefficient (r) and POD than 3B42V7. This study showed that IMERG generally had reasonable agreement with the gauge data.

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).

Nowadays, precipitation estimations based on satellite products (PESPs) have become an alternative source of sparse rainfall gauge data for various hydrologic applications, especially in scarce data regions like Africa which suffer from a lack of surface monitoring resources. Hence, their accuracy and performances must be evaluated by climate variance, complex topography, and geographical position due to their variation from one region to another. The Blue Nile is considered the crucial tributary of the Nile River which provides the utmost share of the river flow. So, this study aims to evaluate high-resolution PESPs [Tropical Rainfall Measuring Mission product 3B42V7 (TRMM 3B42V7) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)] over the Blue Nile basin (BNB) using categorical metrics [probability of detection (POD), and false alarm ratio (FAR)] and continuous statistical indicators [root mean square error (RMSE), mean absolute error (MAE), relative bias (RE), and correlation coefficient (R)] to assess its capability of rainfall amount detection and event occurrence. Hydrological river basin environmental assessment model (Hydro-BEAM) distributed model is also employed to investigate their capabilities to predict streamflow over the BNB. The hydrological daily and monthly simulations were calibrated and validated at Khartoum and Eldeim station for the period 1998–2007. Further, a hydrological monthly simulation was performed at various stations in the Eastern Nile basin (ENB) within the period 2001–2007 using Hydro-BEAM to appraise the ability of both precipitation products to predict streamflow. Moreover, a trial is made to adopt Hydro-BEAM to consider into the runoff simulation the influence of exiting dams during the simulation periods along the ENB to avoid the uncertainness and inaccuracies in simulation results.

Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20% calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R2). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R2, Nash−Sutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R2 and NSE and a lower value of PBIAS. The FDC exhibited SPPs’ excellent performance during 20% to 40% exceedance time.

Reliable rainfall estimates are essential for understanding extreme rainfall patterns and supporting climate adaptation in vulnerable areas. In Ethiopia’s Jemma sub-basin, limited surface weather observations challenge accurate characterization of rainfall extremes, necessitating the use of satellite and reanalysis products. We evaluated five rainfall datasets, CHIRPS v2.0, TAMSAT v3.1, ERA5, MERRA v2, and MSWEP v2.8, for their ability to replicate daily rainfall, detect rainfall events, and represent rainfall indices across sub-tropical and temperate agro-ecological zones (AEZs). A point to pixel evaluation approach was used, and statistical metrics including continuous metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling Gupta efficiency (KGE), and correlation coefficient (R), and categorical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias index (FBI) were used to assess performance and assign ranks using a comprehensive rating index (CRI). Results show that MSWEP v2.8 is most reliable for daily rainfall across both AEZs, while CHIRPS v2.0 excels in capturing extreme rainfall in the sub-tropical AEZ. MSWEP v2.8 also performs well in the temperate AEZ but shows limitations for indices like R20mm in sub-tropical AEZ. ERA5 and MERRA v2 exhibit higher biases, lower resolution, and weaker correlations. These findings highlight the importance of selecting rainfall products based on indices and regional conditions. MSWEP v2.8 and CHIRPS v2.0 are recommended for flood and drought forecasting, water management, and agricultural planning. Integrating multiple datasets with local observations and establishing additional meteorological stations can further enhance rainfall assessments and modeling.

Droughts severely impact agriculture, food security, and water resources, particularly in data scarce regions like the Genale Dawa River Basin (GDRB). This study evaluates the performance of five satellite rainfall products including CHIRPSv2.0, RFE2.0, TAMSATv3.1, PERSIANN-CDR, and ARC2 over 2001-2020 using metrics such as Mean Bias, Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient (CC), Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). Furthermore, the studies examined the relationship between climate indices including the Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO) and drought variability. CHIRPSv2.0 demonstrated the highest accuracy, especially at Ginir (CC = 0.98) and Chewbet (CC = 0.75), and the lowest errors at Teferekella (MAE = 45, RMSE = 65). Based on a multi-criterion ranking approach, CHIRPSv2.0 was identified as the most reliable satellite-based product for daily rainfall estimation across the entire basin, exhibiting strong performance metrics (Lp = 0.433, CC = 0.84, POD = 0.85, CSI = 0.46). CHIRPSv2.0 was used to analyze drought spatiotemporal patterns using SPI-3 and SPI-6. Drought severity, frequency, and duration were highest in the west-central and northern GDRB, with major events occurring in 1988, 1991-1993, 2000, 2004, 2011, and 2012. Climate index analysis showed that AMO and NAO positive phases were associated with wetter conditions, while negative PDO and ENSO phases corresponded with drier periods, especially in central and eastern areas. These findings highlight CHIRPSv2.0's reliability for drought monitoring and its value for early warning and mitigation planning in the GDRB.

Over the past few decades, there has been a continuous increase in the public interest for solar energy as an alternative and cleaner source of energy. Therefore, it is not surprising that there is a similar interest in developing accurate models to forecast solar photovoltaic (PV) power production. Such models vary depending on the geographical location of PV sites and the seasons considered. The Philippines has yet to have a solar PV output forecasting model adapted to the country's local conditions. This study aims to evaluate the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) forecasting model as a tool for forecasting solar PV generation based on the seasonal characteristics of the country and identify which input parameters are significant for each season. This work used solar PV production data as an endogenous variable. Meanwhile, exogenous variables include in-situ solar irradiance data from solar power plants; and cloud cover, wind speed and direction, ambient temperature, precipitation, and relative humidity, which we extracted from ERA5 Reanalysis data. Datasets were divided based on the seasons as defined by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), namely, hot dry (HD), rainy (R), and cold dry (CD) seasons. Then, we performed a forecast on each season and one full year to assess the performance of the SARIMAX model. The best model per season was done based on the forecasting accuracy measured using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This work analyzed seasonal and year-round SARIMAX models for Baguio City, Philippines. Results show that the cold dry season got the highest accuracy value in terms of 2.26 MAE and 4.06 RMSE. Meanwhile, the rainy season had the lowest accuracy of 12.91 MAE and 16.16 RMSE. We can infer from our findings that seasonal forecasting is better during hot dry and cold dry seasons. We also found that the year-round forecasting model performs better than the rainy season model. From the significant parameters identified in our best models, our analysis showed that wind direction can be removed from all models; irradiation and relative humidity were significant for all seasons.

Accurate short-term rainfall-runoff prediction is very important for flood mitigation and the safety of infrastructures in Southern Thailand. This study aims to utilize both analysis and prediction of the runoff forecast by combining the wavelet technique with regression and artificial neural network. The daily rainfall and runoff data were collected from 1,031 days during January 2017 to October 2019 in Songkhla Lake Basin, Thailand. The performance of calibration and validation of the models is evaluated with appropriate statistical methods; coefficient of determination (R 2 ), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (ENS). The results of daily runoff series modeling indicated that the wavelet artificial neural network model performed the best among those models. This model showed the Coefficient of Determination, Nash-Sutcliffe Efficiency and Root mean Square Error in the value of 0.9999, 0.9998 and 0.0037, respectively. These values explained that the model can describe 99.99% of the variation of the current runoff in Songkhla Lake Basin.

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.

This study presents a probabilistic model for daily extreme rainfall. The Annual Maximum Series (AMS) data of daily rainfall in Makurdi was fitted to Generalized Extreme Value (GEV) distribution using Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (Bayesian MCMC) simulations. MLE is a reliable principle to derive an efficient estimator for a model as sample size approaches infinity. Results in this study show that despite the asymptotic requirement of the MLE, its performance can be improved when adopting Bayesian MCMC. The comparison between the performance of MLE and Bayesian MCMC methods using Percent Bias (PBIAS), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) proved Bayesian MCMC is the better method to estimate the distribution parameters of extreme daily rainfall amount in Makurdi. Based on the 36-year record of rainfall (1979-2014) in Makurdi, return levels for the next 10, 100, 500, 1000 and 10000 years were derived. Key words: Extreme daily rainfall, generalized extreme value distribution, parameter estimation, t-year return level, Makurdi.

In this paper, the monthly variation of Surface Water Vapour Density (SWVD) with meteorological parameters of monthly average daily mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours during the period of thirty-eight (38) years (1979 - 2016) for Akure (Latitude 7.280N, Longitude 5.300E, 375m above sea level) were investigated. The daily variation of surface water vapour density for the two distinct seasons considering two typical months in each during the period of year 2014 was examined. The results showed fluctuation in the amount of surface water vapour density in each day of the month for the period under investigation. The monthly average daily values indicated that the surface water vapour densities are greater during the rainy season than in the dry season. It was observed that the maximum average value of surface water vapour density of 20.1019 gm-3 occurred in the month of September during the rainy season and minimum value of 15.5110 gm-3 in the month of January during the dry season. The highest value of surface water vapour density was observed on the 4th of June, 2014 with 26.3320 gm-3 and the lowest on the 31st day of December, 2014 with 6.0276 gm-3. The comparison assessment of the developed two variable SWVD based models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). The developed multivariate correlation regression model that relates pressure and precipitable water vapour with R2 = 100%, MBE = -0.0177, RMSE = 0.0179, MPE = 0.1034, NSE = 99.9956% and IA = 99.9989% was found more suitable for surface water vapour density estimation with good fitting and therefore can be used for estimating surface water vapour density in Akure.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

An approach for improving the capability of a coupled meteorological and hydrological model for rainfall and flood forecasts

The limitations of good rainfall data due to constraints on direct measurements can be overcome by using satellite data or reanalysis data. The use of this data must, of course, go through a validation process first. This research aims to evaluate daily data from the Tropical Rainfall Measurement Mission (TRMM) version 3B42RT (TRMM_3B42RT) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data against data collected from 17 rain gauges in the Sumatra region South. Evaluation is carried out based on the Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) values between the data. In addition, the estimation capabilities of TRMM_3B42RT and ERA5 were evaluated based on the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) values. The results show a very high correlation between TRMM_3B42RT and ERA5 with rain gauge data, especially in terms of monthly data. These values (monthly data) for TRMM_3B42RT and ERA5 data are 0.3-0.9 and 0.2-0.9, respectively. The RMSE values of TRMM_3B42RT and ERA5 data in monthly analysis are 75-250mm/month and 100-180mm/month, respectively. The forecasting performance of TRMM_3B42RT and ERA5 shows good results, especially for moderate rainfall in daily data and heavy rainfall in monthly data. The results of this analysis show tha the monthly data TRMM_3B42RT is more in line with the station data and can be used in further research.