TRMM 3B42 V7 Research Articles

ASSESSING SATELLITE RAINFALL ACCURACY IN DENSE TROPICAL SABAH EAST COAST FOREST, MALAYSIA: A CROSS-VALIDATION OF DOWNSCALING TECHNIQUE

Rainfall is one of pivotal elements in the hydrological cycle that essential for ensuring the water balance in present and in the future. Traditional in-situ observations have been the conventional method for obtaining rainfall data, but their limitations arise from discrete point measurements that fail to represent the entire area. To overcome these limitations, this study utilizes satellite based TRMM data products for rainfall estimation. The research aims to cross-validate the TRMM 3B43-v7 product against corresponding in-situ measured rainfall, focusing on error localization in Sabah's east coast. The derivation of the rainfall rate from TRMM data is adequate with additional data from three manual gauges within the plots. The correlation between TRMM and ground was good (R2 = 0.78, RMSE = 65.65 mm). The Nash and Sutcliffe Error results closed to value 1 indicate that the accuracy of the TRMM data compared to the rain-gauge data in Danum has a good agreement. This is due to the IDW downscaling method for satellite rainfall data using additional data from three manual-gauges within the plots to increase the accuracy of the TRMM data. In summary, the downscaling method proves capable of providing fine spatial resolution and increasing the number of pixels in the study area. Future research endeavours may benefit from incorporating more station data to further improve the interpolation of satellite data and derive more precise results.

Evaluation of TRMM 3B43 V7 precipitation data in varied Moroccan climatic and topographic zones

Evaluation of TRMM 3B43 V7 precipitation data in varied Moroccan climatic and topographic zones

Akurasi Data Curah Hujan Satelit Terhadap Data Pengukuran di Daerah Tangkapan Air (DTA) Waduk Sutami

Sutami Reservoir that located in the Brantas River Basin is a multi-purpose reservoir, it’s used to provide of raw water, irrigation, flood control, and power plants, fish farm, and tourism. Rainfall data information is very important in hydrological analysis as the basis for determining operating patterns, water balances, and calculating sediment rates. Rainfall data that is recorded in a row can show us trends or the nature of rain, but in reality it is very difficult to obtain representative rainfall observation data, both in terms of quality and length of observation data, which is quite in accordance with what is required in several locations, it is very difficult due to the absence of rain stations or broken gauges. Therefore, by taking advantage of technological advances, it is necessary to analyze the accuracy of rainfall data via satellite (GPM V6 and TRMM 3B43 V7) as an alternative to using rainfall data to fill data shortages at certain locations. The results of the analysis of the two satellite rainfall data (GPM V6 and TRMM 3B43 V7) are based on the Nash Sutcliffe Efficiency (NSE) parameters, Root Mean Square Errror (RMSE), Real Error (KR), Correlation Coefficient (R) can be used as an alternative to rainfall data, with satellite rainfall data GPM V6 has better accuracy and performance with average value of NSE 0,8, RMSE 66,46, KR 21,63%, R 0,92.

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.

Applicabilty of TRMM Precipitation for Hydrological Modeling Using HEC-HMS in Karnali River Basin in Nepal

This study aims to validate the HEC-HMS model in the Karnali river basin of Nepal, while also physically verifying the model parameters for the area. The HEC-HMS model is a semi-distributed physically based model that requires site-specific information about the weather, as well as spatial information on soil and land use. Using Arc-GIS, HEC-HMS, and HEC DSS, a hydrological rainfall runoff model was developed for the Karnali River basin at Asaraghat (DHM station 240) for the period of 1998-2005. Calibration for the time period of 1998-2001 resulted in an NSE value of 73.2% and an R2 value of 84%. Validation for the time period of 2003-2005 resulted in an NSE value of 72.4% and an R2 value of 82.02%. To evaluate the performance of the TRMM 3B42V7 rainfall product, statistical indicators such as NSE and R2 were used. Results showed that the TRMM rainfall data was reliable and had good precision in application to the Karnali River Basin. This study concludes that the TRMM rainfall data can be used as an alternative data source for basins with sparse rain gauge stations in Nepal. The results also suggest that the HEC-HMS modeling tool is good enough to relate rainfall and runoff. The model parameters for each sub-basin and reaches were analyzed with the physical characteristics of the watershed. Minor variations were found within calibrated and calculated Initial abstraction and Muskingum K, x, leading to the verification of the model parameters physically.

Evaluation of the precipitation of the East Asia regional reanalysis system mainly over mainland China

AbstractIn this article, precipitation in a new regional reanalysis dataset, namely, the East Asia regional reanalysis system (EARS‐CMA) with a 12‐km resolution based on the Weather Research and Forecast (WRF) model, ERA‐Interim, ERA5, TRMM 3B42V7, Climate Prediction Center Morphing Technique (CMORPH) and CN05.1, is compared in 2008–2017. The results show that EARS‐CMA can capture the spatial features and temporal variation in precipitation over East Asia well. Focusing on mainland China, CMORPH performs worse in regard to winter precipitation and dryer areas than TRMM against CN05.1. Although ERA5 behaves better with integrated properties compared with ERA‐Interim and EARS‐CMA over East Asia, EARS‐CMA produces more reliable annual, summer and winter mean precipitation pattern than ERA5 over the subregions dominated by large scales in mainland China, including the Tibetan Plateau and northwestern China, similar to its driving fields, ERA‐Interim. In addition, the improvement from ERA‐Interim to EARS‐CMA can be obtained in reasonably reproducing precipitation seasonality in South China, Indo‐China and southern Japan. Thus, the compatibility of EARS is outstanding in maintaining the advantages of its corresponding driving fields, ERA‐Interim, as well as developing finer small scales. Although slightly behind ERA5 in the subregions affected by tropical systems and monsoons, EARS‐CMA is competitive in acting as reference data for daily, seasonal, annual and interannual precipitation estimations over mainland China.

Assessment of satellite precipitation products at different time scales over a cyclone prone coastal river basin in India

Abstract Precipitation is a fundamental input for many hydrological and water management studies. With the advancement in science, a variety of satellite precipitation products are available. In this study, the ability of three satellite precipitation products (TRMM-3B42v7, PERSIANN-CDR and GPM-IMERGv6) to capture rainfall were evaluated with ground-based Indian Meteorological Department (IMD) gridded data and also by driving the Soil Water Assessment Tool (SWAT) hydrological model for a cyclone prone coastal river basin in the southeast of India. Results indicate that among the three, GPM-IMERG v6 outperformed in all the statistical metrics, followed by TRMM-3B42 v7 at different temporal scales. GPM-IMERG v6 exhibited the highest correlation coefficient (0.48) and lowest root mean square error (9.52 mm/day), followed by TRMM-3B42 v7 (CC = 0.44, RMSE = 9.58 mm/day) at daily scale. In contrast to other studies in similar regions, IMERGV6 showed better performance than the PERSIANN CDR in detection skills of low, medium and high intensity rainfall events as well as False Alarm Ratio. Hydrological evaluation of the three products using the SWAT model over the study area showed satisfactory results at daily and monthly scale during the calibration and validation period. IMERGV6 is found to have better performance in hydrological evaluation as well.

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.

Evaluation of Eight High-Resolution Gridded Precipitation Products in the Heihe River Basin, Northwest China

The acquisition of the precise spatial distribution of precipitation is of great importance and necessity in many fields, such as hydrology, meteorology and ecological environments. However, in the arid and semiarid regions of Northwest China, especially over mountainous areas such as the Heihe River basin (HRB), the scarcity and uneven distribution of rainfall stations have created certain challenges in gathering information that accurately describes the spatial distribution of precipitation for use in applications. In this study, the accuracy of precipitation estimates from eight high-resolution gridded precipitation products (CMORPHv1-CRT, CRU TSv.4.05, ERA5, GSMaP_NRT, IMERG V06B-Final, MSWEPv2.0, PERSIANN-CDR and TRMM 3B42v7) are comprehensively evaluated by referring to the precipitation observations from 23 stations over the HRB using six indices (root mean square error, standard deviation, Pearson correlation coefficient, relative deviation, mean error and Kling–Gupta efficiency) from different spatial and temporal scales. The results show that at an annual scale, MSWEP has the highest accuracy over the entire basin, while PERSIANN, CRU and ERA5 show the most accurate results in the upper, middle and lower reaches of the HRB, respectively. At a seasonal scale, the performance of IMERG, CRU and ERA5 is superior to that of the other products in all seasons in the upper, middle and lower reaches, respectively. Over the entire HRB, PERSIANN displays the smallest deviation in all seasons except for spring. TRMM shows the highest accuracy in spring and autumn, while MSWEP and CRU show the highest accuracy in summer and winter, respectively. At a monthly scale, TRMM is superior to the other products, with a relatively stronger correlation almost every month, while GSMaP is inferior to the other products. Moreover, MSWEP and PERSIANN perform relatively best, with favorable statistical results around almost every station, while GSMaP shows the worse performance. In addition, ERA5 tends to overestimate higher values, while GSMaP tends to overestimate lower values over the entire basin. Moreover, the overestimation of ERA5 tends to appear in the upper reach area, while that of GSMaP tends to appear in the lower reach area. Only CRU and PERSIANN yield underestimations of precipitation, with the strongest tendency appearing in the upper reach area. The results of this study display some findings on the uncertainties of several frequently used precipitation datasets in the high mountains and poorly gauged regions in the HRB and will be helpful to researchers in various fields who need high-resolution gridded precipitation datasets over the HRB, as well as to data producers who want to improve their products.

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

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

Performance of the Trmm 3b43 V7 Rainfall Product Over Morocco's Regions (North Africa) in the Light of the Great Diversity of Climatic and Topographic of the Country

Performance of the Trmm 3b43 V7 Rainfall Product Over Morocco's Regions (North Africa) in the Light of the Great Diversity of Climatic and Topographic of the Country

Performance evaluation, error decomposition and Tree-based Machine Learning error correction of GPM IMERG and TRMM 3B42 products in the Three Gorges Reservoir Area

Satellite precipitation products (SPPs) have excellent development prospects in hydrometeorology research for their high spatial-temporal resolution. Understanding the performance of SPPs is essential before their application in estimating multiple processes. This paper evaluated the accuracy and error composition of GPM IMERG Final-run V06 and TRMM 3B42V7 in the Three Gorges Reservoir Area (TGRA) based on the China Gauge-based Daily Precipitation Analysis dataset (CGDPA) during 2001–2019. Afterwards, the precipitation correction by merging satellite and gauge observation precipitation was conducted with three Tree-based Machine Learning models, Decision Tree (DT), Adaptive Boosting Decision Trees (Adaboost), and Random Forest (RF). The results show that: (1) Two SPPs can well reflect the spatial pattern and in-year variation of precipitation in the TGRA. However, they are limited in estimating precipitation amount for seriously underestimated the light rain or overestimated torrential rain. Under the influence of climate and variety terrains, the SPPs accuracy in the middle-elevation relief mountains of downstream is significantly higher than that in the low-elevation relief mountains of upstream. Moreover, that on the monthly and annual scales are higher than on the daily scale. Comparing two SPPs, the area proportions of IMERG-F better than 3B42V7 are 64.0%, 86.0%, and 81.6% on daily, monthly and annual scales, respectively. (2) Both the systematic error components of two SPPs raise with the increase of precipitation amount, and the error spatial pattern of 3B42V7 is more affected by precipitation. The random error component of light rain and daily precipitation is higher than other grades and scales. Despite this, the random error component decreases significantly when daily precipitation is accumulated. (3) The three primary factors affecting error correction of SPPs are precipitation amount, longitude, and DEM in TGRA. Among the three Tree-based methods, the RF showed the best error correction effect, and it can significantly improve the monthly and annual precipitation accuracy and better reproduce probability density function (PDF). Cumulative precipitation can reduce the random error component of daily precipitation and have high correction accuracy when cumulative time exceeds 5-days.

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 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.

Evaluation of Three Gridded Precipitation Products to Quantify Water Inputs over Complex Mountainous Terrain of Western China

This study evaluates the capacity of three gridded precipitation products (MSWEP V2.2, TRMM-3B42 V7, and GPM-IMERG V6) to detect precipitation in the Min Jiang watershed, a data-scarce and mountainous region in western China. A set of statistical and contingency indices is calculated for the precipitation products and compared with rain gauge observations at 23 ground stations from July 2000 to May 2016. Consistency between gridded and ground precipitation datasets is examined at different temporal (i.e., daily, monthly, seasonally, and annually) and spatial (i.e., site level, sub-regional level, and watershed level) resolutions. We identify possible reasons for discrepancies among precipitation datasets. Our results indicate that: (1) the MSWEP product is best suited for the study of long-term mesoscale rainfall, rather than short-term light or extreme rainfall; (2) the IMERG product represents stable performance for the simulation of rainfall spatial variability and detection capability; and (3) Composition of the datasets, climatic systems, and regional topography are key factors influencing the consistency between gridded and ground precipitation datasets. Therefore, we suggest using MSWEP V2.2 and GPM-IMERG V6 as potential precipitation data sources for hydrometeorological studies over the Min Jiang watershed. The findings of this study inform future hydrometeorological and climate applications in data-scarce regions with complex terrain.

How suitable are satellite rainfall estimates in simulating high flows and actual evapotranspiration in MelkaKunitre catchment, Upper Awash Basin, Ethiopia?

Understanding the suitability of Satellite Rainfall Estimates (SREs) in simulating high flows and Actual Evapotranspiration (AET) is crucial for developing flood monitoring systems. Therefore, this study aims to assess i) the suitability of SREs in simulating both high flows and AET for different levels of model complexity, and ii) the effect of streamflow calibration on simulating AET for different rainfall inputs in Melkakunitre catchment, Upper Awash Basin, Ethiopia. Three state-of-the-art SREs (TRMM 3B42v7, IMERG v06B, and TAMSAT v3) were used and their usefulness in simulating high flows (Q5), daily streamflow, and wet season flows (from June to September) was assessed using the HBV-light model for the period 2003–2015. The model was set up for two levels of complexity: with and without considering the effect of orography on rainfall and temperature. Moreover, the water balance derived AET was compared against three remotely sensed AET products, MOD 16A2, GLEAM v3, and SSEBob, so as to examine the effect of streamflow calibration on AET simulation. Results show that rainfall inputs and model complexity have a strong impact on simulating streamflow and AET. For all rainfall forcing datasets, the performance of the hydrological model improves when we consider the effects of orography on rainfall and temperature. The IMERG v06B and TAMSAT v3 products showed the highest and least performances in simulating all the three flow conditions, respectively. Moreover, the MODIS-AET is the best remotely sensed AET product in reproducing the water balance-derived AET for all rainfall inputs except TAMSAT v3. The HBV-light model parameters calibrated with streamflow provided better results for simulating AET as well. On average, the usefulness of the IMERG v06B product for simulating high flows and AET is outstanding and can be thus used for developing flood monitoring and management systems in the study catchment.