3B42 V7 Research Articles

An attention-mechanism-based deep fusion model for improving quantitative precipitation estimation in a sparsely-gauged basin

Improving quantitative precipitation estimation (QPE) in sparsely-gauged basins via merging remote sensing precipitation data and rain gauge data still remains a challenge since most existing merging models degrade when the rain gauge data become limited. To address the challenge, we propose an attention-mechanism-based deep learning model, Multi-Level Transformer Fusion (MLTF) model, which allows to capture the inner interactions among the multi-source input data (TRMM 3B42 V7 data, GridSat-B1 data, and DEM) in the sparsely-gauged basin. Taking the source region of the Yellow River basin (SRYRB) as a representative case study, we demonstrate the performance of the proposed model and compare it with conventional methods (e.g., Multiplicative Bias Correction, Additive Bias Removal, linear regression, and Kriging) and deep-learning-based models (CNN, CNN-LSTM). Results indicate that the merged precipitation in SRYRB produced by the MLTF model exhibits a RMSE reduction of 27.1 %, MAE decrease of 11.2 %, and CC increase of 19.2 % in comparison to the original TRMM data, outperforming all the selected comparative methods. Finally, an improved daily precipitation dataset during 1999–2019 with a spatial resolution of 0.05° is produced for the study area. This study proposes a new method for QPE improvement in a sparsely-gauged basin, which would provide valuable data support for regional hydrological study and water resources management.

Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 Satellite Precipitation Datasets in Arabian Arid Regions

Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate the adequacy of satellite datasets as an alternative to the rain gauges, the Kingdom of Saudi Arabia (KSA) is selected for the current study as a representative of the arid regions. KSA occupies most of the Arabian Peninsula and is characterized by high variability in topographic and climatic conditions. Five satellite precipitation datasets (SPDSs)—CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6—are evaluated versus 324 conventional rain-gauges’ daily precipitation measures. The evaluation is conducted based on nine quantitative and categorical metrics. The evaluation analysis is carried out for daily, monthly, yearly, and maximum yearly records. The daily analysis revealed a low correlation for all SPDSs (<0.31), slightly improved in the yearly and maximum yearly analysis and reached its highest value (0.58) in the monthly analysis. The GPM IMERG V6 and PERSIANN-CDR have the highest probability of detection (0.55) but with a high false alarm ratio (>0.8). Accordingly, in arid regions, the use of daily SPDSs in rainfall estimation will lead to high uncertainty in the obtained results. The best performance for all statistical metrics was found at 500–750 m altitudes in the central and northern parts of the study area for all satellites except minor anomalies. CMORPH dataset has the lowest centered root mean square error (RMSEc) for all analysis periods with the best results in the monthly analyses.

Optimizing simulation of summer precipitation by weather research and forecasting model over the mountainous southern Tibetan Plateau

The mountainous regions of southern Tibetan Plateau and surroundings (hereafter mountainous southern TP), mainly including Himalayas, Gangdise, Nyainqentanglha and Hengduan Mountains, are characterized by high mountains and a complex terrain, and play a crucial role in the redistribution of monsoon water vapor and consequently determine the spatial distribution of precipitation over the TP. However, numerical simulations generally have large precipitation deviation in this focus area. Therefore, optimizing simulation of summer precipitation by Weather Research and Forecasting (WRF) model with two-way nested domains was carried out. A matrix of 11 model experiments, including four cumulus convection (CU) schemes, four microphysics (MP) schemes and four planetary boundary layer (PBL) schemes, were designed. In situ observations from 44 stations, TRMM 3B42 v7 precipitation product, and statistical and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods were utilized for the model evaluation. The results showed that physical parameterization schemes, Kain-Fritsch-Cumulus Potential scheme (CU), Thompson scheme (MP), Mellor-Yamada-Janjic scheme (PBL), Unified Noah land surface model, and Eta similarity surface layer scheme, greatly reduced the summer precipitation simulation error of newly High Asia Refined analysis (HAR v2) over the mountainous southern TP, with the better reproduction of the spatiotemporal distribution and vertical variability of precipitation. Compared to NCEP-FNL reanalysis data, the performance of this model setup forced by ERA5 was notably improved, especially with perspective to the vertical variability of precipitation. Our findings suggested that summer precipitation simulation was more sensitive to the CU scheme, and the choice of Kain-Fritsch-Cumulus Potential scheme (ERA5 as initial conditions) significantly improved the precipitation simulation, especially with the lowest MAE (2.00 mm/d), RMSE (2.89 mm/d) and highest Rs (0.74). Further analysis implied that accurate description of shallow convection and proper suppression of deep convection should be considered in precipitation simulations over the TP, especially in mountainous regions. This study could provide a firm foundation for the production of more realistic dynamic downscaling precipitation dataset over the TP.

Hydro Statistical Assessment of TRMM and GPM Precipitation Products against Ground Precipitation over a Mediterranean Mountainous Watershed (in the Moroccan High Atlas)

The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V05 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R > 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash–Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions.

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Evaluation of Three High-Resolution Remote Sensing Precipitation Products on the Tibetan Plateau

Remote sensing precipitation products provide rich data for ungauged basins. Evaluating the accuracy and detection capability of remote sensing precipitation products is crucial before application. In this study, an index system in terms of quantitative differences, capturing capacity and precipitation distribution was constructed to evaluate three precipitation products, TRMM 3B42 V7, GPM IMERGE Final and CMORPH V1.0, at various temporal and spatial scales on the Tibetan Plateau from 2001 to 2016. The results show that the correlations among the three products were larger at the monthly scale than at the annual scale. The lowest correlations between the products and observation data were found in December. GPM performed the best at the monthly and annual scales. Particularly, the GPM product presented the best capability of detection of both precipitation and non-precipitation events among the three products. All three precipitation products overestimated 0.1~1 mm/day precipitation, which occurred most frequently. An underestimation of precipitation at 10~20 mm/day was observed, and this intensity accounted for the majority of the precipitation. All three precipitation products showed an underestimation in terms of the annual maximum daily precipitation. The accuracy of the same product varied in different regions of the Tibetan Plateau, such as the south, the southeast, eastern–central region and the northeast, and there was a certain clustering of the accuracies of neighboring stations. GPM was superior to TRMM and CMORPH in the southern Tibetan Plateau, making it recommended for applications.

AVALIAÇÃO DA PRECIPITAÇÃO DO SATÉLITE TRMM 3B43V7 NO PANTANAL DE MATO GROSSO DO SUL NOS ANOS DE 1998 A 2019

Remote sensing can assist in the acquisition of scarce surface data. The analyzes for validation of the precipitation product estimated by the TRMM satellite (Tropical Rainfall Measuring Mission) were carried out with the precipitation data observed on the surface during the period from 1998 to 2019. For this purpose, precipitation data from the Pantanal biome meteorological stations were used, located between the 16 and 22°S parallels and the 55 and 58°W meridians, and compared with the data from the TRMM 3B42 V7 product algorithms. Statistical analysis was performed based on the correlation coefficient, root mean square error (RMSE), and relative bias (BIAS) between the monthly precipitation data observed on the surface and the estimated precipitation data. The results found for product 3B43 V7 indicated that the precipitation estimates were representative when compared to the surface observations. However, when compared for the rainy and dry periods, there was underestimation and overestimation, respectively, of the product. The product 3B42 V7 satisfactorily represents the precipitation that occurs on the surface. Keywords: Tropical Rainfall, Remote Sensing, Conventional Meteorological Observation Precipitation Estimate.

Using SEEPS with a TRMM-Derived Climatology to Assess Global NWP Precipitation Forecast Skill

Abstract Monitoring precipitation forecast skill in global numerical weather prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products, on the other hand, provide near-global coverage at a resolution of ∼10–25 km, but limitations on data quality (e.g., biases) must be accommodated. In this paper the stable equitable error in probability space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology and then applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS are explored by comparing the gauge scores, based on extracting the nearest model grid point, with those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ∼6000 locations (dictated by the number of gauges available globally) with all land points within the TRMM region of 50°N and 50°S. The forecast performance over the oceanic areas is compared with performance over land. While the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way.

Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia

Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.

Evaluation of the Performance of Multi-Source Precipitation Data in Southwest China

The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion.

Evaluation gridded precipitation datasets in Indonesia

This preliminary study evaluates ten gridded precipitation datasets in Indonesia, namely APHRODITE, CMORPH, CHIRPS, GFD, SA-OBS, TMPA 3B42 v7, PERSIAN-CDR at 0.25°, moreover GSMaP_NRT V06, GPM-IMERG (Early-Run) V06, and MSWEP V2 at 0.1» in the period of 2003 to 2015. The evaluation focuses on time series bias using metrics such as Mean Error, Coefficient of Variation, Relative Change (Variability), and Kolmogorov-Smirnov test (KS-test) at daily, monthly, seasonal, and annual time scales. The statistical relationship between the precipitation datasets with reference observational data use Taylor diagrams for evaluating the relative skill of the precipitation dataset. The study aims to evaluate the uncertainty of the precipitation datasets compared to rain gauge datasets. Time series bias of SA-OBS and MSWEP have the nearest value to zero as the best score. The relative skill of monthly rainfall based on rainfall typical shows that MSWEP outperformed in regions A and B, GPM-IMERG in C region. GPM-IMERG's relative skill is outperformed than other datasets at annual time scale in Region A and B, while TMPA 3B42 in Region C. The application of existing precipitation datasets is essential to cope with the limitation of rain gauge observations. This study implicates the development of precipitation products in the Indonesia region.

Linkages of Active and Weakening MJO events to Seasonal Variations over the Maritime Continent

The Madden-Julian Oscillation (MJO) is a large-scale phenomenon of air-sea intra-seasonal variability in the equatorial area, particularly in the Maritime Continent (MC). This research focused on the analysis of the MJO propagation process in association with rainfall events and sea surface temperature anomaly (SSTA) during seasonal variations, i.e., November, December, January February, and March (NDJFM), and May, June, July, August September (MJJAS). MJO events from 2010 to 2019 were classified as MJO active or MJO weakening according to propagation characteristics and amplitude changes in the RMM index. This research uses a dataset of 10-year series of daily Tropical Rainfall Measuring Mission (TRMM) (3B42 V7 derived) measurements for detecting rain rates. Daily OLR data from the NOAA Physical Sciences Laboratory and SSTA daily data from Physical Oceanography Distributed Active Archive Centre (PODAAC) NOAA are considered for analysing MJO propagation. Composites of outgoing longwave radiation (OLR) were also identified differences between the two events; active MJO events had consistently higher negative OLR anomalies than weakening MJO events. Active MJO events during NDJFM had a higher rain rate and positive SSTA than weakening MJO events. Furthermore, composite rain rates distribution over MC during NDJFM are mainly located in the south of the equator, contrarily when MJJAS are north of the equator.

Evaluation and comparison of precipitation estimates and hydrologic utility of CHIRPS, TRMM 3B42 V7 and PERSIANN-CDR products in various climate regimes

Evaluation of satellite-based quantitative precipitation estimates (QPEs) with reliable and independent ground-based measurements is important for both product developers and users. Here, we present a comprehensive evaluation on 3 high-resolution QPEs, namely, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the latest non-real-time post-processing version of Tropical Rainfall Measuring Mission (TRMM 3B42 V7), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), in 3 basins with different climates in China. The accuracy of 3 QPEs in reproducing the spatial extent of daily and monthly precipitation (PR) as well as extreme PR indices was evaluated. Two simulation scenarios were utilized to evaluate the efficiency of hydrologic events forecasting of these 3 QPEs quantitatively. The results indicated that the 3 QPEs generally show high accuracy in estimating monthly PR in 3 basins, among which TRMM 3B42 V7 performs best (coefficient of determination R2 < 0.94) followed by CHIRPS (R2 < 0.91). However, all QPEs tend to overestimate daily PR of 3 basins, resulting in low accuracy at the daily scale (R2 < 0.35). For estimation of the extreme PR indices, the 3 QPEs show large differences in the spatio-temporal accuracy, but all with better performance in humid (R2 < 0.86) than arid (R2 < 0.7) basins. Similarly, all 3 QPEs show better performance in simulating streamflow in humid than arid basins. TRMM 3B42 V7 (Nash-Sutcliffe coefficient of efficiency, NSCE < 0.96) and CHIRPS (NSCE < 0.9) perform better in simulating streamflow in humid basins than PERSIANN-CDR (NSCE < 0.88), while PERSIANN-CDR performs best in arid basins (NSCE < 0.67). However, 3 QPEs mostly underestimate peak flow and overestimate soil moisture in all basins, suggesting that the necessity of improving hydrologic efficiency for all of them.

Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China

Quantifying the climatic and anthropogenic effects on hydrological processes has received considerable attention. However, diverse conclusions could be drawn when different models and forcing datasets are used. This is particularly uncertain and challenging in poorly gauged arid regions. Here we aim to tackle this issue in the poorly gauged Xiangride River Basin within the Qaidam Basin, one of the three prominent inland basins in China. We applied two distinct models (Budyko Mezentsev-Choudhurdy-Yang and process-based SWAT) to a poorly-gauged inland basin in West China. The model simulations were driven by four precipitation products including Tropical Rainfall Measuring Mission (TRMM) 3B42 V7, Global Precipitation Measurement (GPM) IMERG V6, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and China Meteorological Assimilation Driving Datasets (CMADS). Our results indicate that MSWEP performed best (NSE = 0.64 vs. 0.36–0.59 for other datasets) in the baseline period (2009–2012), whereas CMADS was more accurate during the impacted period (2013–2016); CMADS and GPM might underestimate the precipitation in the baseline and impacted period, respectively. Hydrological processes during the impacted period are presumed to be influenced by climate variation and/or human activities, compared to the relatively natural status in the baseline period. We conclude that runoff decline between the two periods was mainly affected by human activities (−66 to 94%), whereas the contribution of climate variation was more likely positive. A literature survey reveals that major anthropogenic effects in the study area includes reservoir, road construction and cropland expansion that could lead to runoff decrease. We recommend the use of process-based model (e.g., SWAT) in studies like this, as process-based models driven by high-quality remote-sensed or reanalysis climate datasets, better represents the spatiotemporal hydrological change under altered conditions, whereas the steady-state assumption of soil water for the Budyko model may not be fully satisfied during a short period.

Assessment of Satellite-based Precipitation Products Performance over the Hyper-arid Climate of Kuwait

AbstractPrecipitation is a complex natural parameter that is essential for water and environmental systems. Due to its variability on the spatial and temporal scales, satellite-based precipitation products (SPPs) have arisen interest in hydrology and meteorology applications. This study measures the performance of six high resolutions SPPs (GPM IMERG products (IMERG-E, IMERG-L, and IMERG-F), TMPA products (3B42 V7, 3B42RT V7), and PERSIANN product) in producing the observed precipitation over a hyper-arid climate, water scarce region for the period 2013-2018. It also evaluates their performance dependency on the aggregation time-step and topographic elevations. According to a number of continuous and categorical evaluation metrics: (a) SPPs overestimate the observed daily annual and seasonal precipitation, particularly with near real-time products, (b) all SPPs estimates depict correlation ranging from 0.68 to 0.84 with the annual and seasonal precipitation and weak correlations in dry season, and (c) their ability to detect rain/no-rain events is measured by Peirce Skill Score (PSS), ranging from 0.73 to 0.92 across annual and seasonal scales, whereas 3B42RT V7 reproduces lower PSSs. Furthermore, the study finds that aggregation to a monthly time-step improves only SPPs correlations. The performance of near real-time products shows significant dependency on elevations, especially with 3B42RT V7 that shows low skills at coastlands. The TMPA products ability to detect rain/no-rain events dramatically drops from highlands to coastlands, with low skills to generate observed no/tiny and light precipitation classes. The study addresses an adequate ability of IMERG-F and PERSIANN to be utilized in water and environmental studies over hyper-arid climate regions, with highlighting for the superiority of IMERG-F.

Application of Satellite-Based and Observed Precipitation Datasets for Hydrological Simulation in the Upper Mahi River Basin of Rajasthan, India

Hydrological modeling is an important tool used for basin management and studying the impacts of extreme events in a river basin. In streamflow simulations, precipitation plays an essential role in hydrological models. Meteorological satellite precipitation measurement techniques provide highly accurate rainfall information with high spatial and temporal resolution. In this analysis, the tropical rainfall monitoring mission (TRMM) 3B42 V7 precipitation products were employed for simulating streamflow by using the soil water assessment tool (SWAT) model. With India Metrological Department and TRMM data, the SWAT model can be used to predict streamflow discharge and identify sensitive parameters for the Mahi basin. The SWAT model was calibrated for 2 years and then independently validated for 2 years by comparing observed and simulated streamflow. A strong correlation was observed between the calibration and validation results for the Paderdibadi station, with a Nash–Sutcliffe efficiency of &gt;0.34 and coefficient of determination (R2) of &gt;0.77. The SWAT model was used to adequately simulate the streamflow for the Upper Mahi basin with a satisfactory R2 value. The analysis indicated that TRMM 3B42 V7 is useful in SWAT applications for predicting streamflow and performance and for sensitivity analysis. In addition, satellite data may require correction before its utilization in hydrological modeling. This study is helpful for stakeholders in monitoring and managing agricultural, climatic, and environmental changes.

Estimation of rainfall based on remote sensing and observation fields in Jaru Biological Reserve at the Brazilian Amazonian forest

The Amazon basin, with a drainage area of about 6 million km2, is the largest drainage basin in the world, consequently the accurate measuring the rainfall dynamics at high spatial and temporal resolution is essential for a better understanding of the hydrological cycle. So, we validated rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using surface precipitation data collected from 2004 to 2012. Rainfall data came from the Jaru Biological Reserve meteorological station, located to the east in the state of Rondônia, Brazil, and was compared with the estimates of the algorithms 3B42 V7 and 3B43 V7 of the product TRMM. Statistical analysis was based on indices of continuous variables such as the Spearman correlation coefficient (ρ), the square root mean square error normalized by the mean of the observed values ​​(NRMSE), the mean square error (RMSE), the error (ERV) and some categorical indices such as probability of detection (POD), False Alarm (FAR) and success rate (CSI) between the daily and monthly precipitation observed data and the estimated precipitation data. The 3B43 V7 precipitation estimates were broadly representative of surface observations, but underestimated precipitation in the wet season and overestimated precipitation in the dry season. The 3B42 V7 product performed poorly and does not generate a robust representation of surface precipitation.

Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan

The current study evaluates the potential of merged satellite precipitation datasets (MSPDs) against rain gauges (RGs) and satellite precipitation datasets (SPDs) in monitoring meteorological drought over Pakistan during 2000–2015. MSPDs evaluated in the current study include Regional Weighted Average Least Square (RWALS), Weighted Average Least Square (WALS), Dynamic Clustered Bayesian model Averaging (DCBA), and Dynamic Bayesian Model Averaging (DBMA) algorithms, while the set of SPDs is Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and ERA-Interim (re-analyses dataset). Several standardized precipitation indices (SPIs), including SPI-1, SPI-3, and SPI-12, are used to evaluate the performances of RGs, SPDs, and MSPDs across Pakistan as well as on a regional scale. The Mann–Kendall (MK) test is used to assess the trend of meteorological drought across different climate regions of Pakistan using these SPI indices. Results revealed higher performance of MSPDs than SPDs when compared against RGs for SPI estimates. The seasonal evaluation of SPIs from RGs, MSPDs, and SPDs in a representative drought year (2008) revealed mildly to moderate wetness in monsoon season while mild to moderate drought in winter season across Pakistan. However, the drought severity ranges from mild to severe drought in different years across different climate regions. MAPD (mean absolute percentage difference) shows high accuracy (MAPD &lt;10%) for RWALS-MSPD, good accuracy (10% &lt; MAPD &lt;20%) for WALS-MSPD and DCBA-MSPD, while good to reasonable accuracy (20% &lt; MAPD &lt; 50%) for DCBA in different climate regions. Furthermore, MSPDs show a consistent drought trend as compared with RGs, while SPDs show poor performance. Overall, this study demonstrated significantly improved performance of MSPDs in monitoring the meteorological drought.

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Evaluation of Precipitation Products by Using Multiple Hydrological Models over the Upper Yellow River Basin, China

In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models.