TRMM) Multisatellite Precipitation Analysis Data Research Articles

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin

Precipitation is the main input for hydrological models. However, due to limitations of rain gauge stations, satellite precipitation estimates have become a good alternative to precipitation information. In this context, this study aimed to validate the precipitation data with Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data, in addition to assessing the uncertainty and performance of the Soil and Water Assessment Tool (SWAT) using observed precipitation (OP), TMPA, and IMERG data. Statistical coefficients were used to validate TMPA and IMERG precipitation data. P-factor and r-factor were considered for the uncertainty analysis, while the Nash-Sutcliffe efficiency (NSE), its logarithmic version (LNSE), and the percent bias (PBIAS) were analyzed to characterize the model performance analysis in monthly time steps. There was an overestimation by TMPA and IMERG in the precipitation estimation, especially in the dry period. OP, TMPA, and IMERG setups presented satisfactory results for uncertainty and performance analysis in hydrological modeling. The IMERG setup generally showed better results than the TMPA setup, being a good alternative for hydrological modeling, especially in regions with scarce precipitation datasets.

Comparison of Multi-Satellite Precipitation Data from the Global Precipitation Measurement Mission and Tropical Rainfall Measurement Mission Datasets: Seasonal and Diurnal Cycles

A comparison was made between seasonal and diurnal variations in the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) version 06B Final run and TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 products from April 2014 to March 2019. As for earlier IMERG versions, systematic differences between IMERG version 06B precipitation and TMPA precipitation data were larger over the oceans than over land. Systematic annual mean differences between the IMERG and TMPA data over the oceans were smaller for IMERG version 06B than for earlier IMERG versions, possibly because of updated calibration processes. The mean differences between the IMERG version 06B and TMPA data for tropical oceans were relatively smtropical Pacific for all four seasons were not. The diurnal amplitudes of the IMERG were smaller than those of the TMPA over most continents, and the differences increased with mean diurnal amplitudes. The diurnal amplitudes of the IMERG were larger than those of the TMPA data over the oceans. The differences between the phases of the precipitation diurnal harmonics in the IMERG and TMPA datasets varied widely in all four seasons, but the mean phases were almost the same over both the oceans and the land. The sources of the differences in diurnal precipitation amplitudes in the Bay of Bengal and along the west coast of Central America, which showed large diurnal ranges and rather different diurnal amplitudes, were assessed. Differences in seasonal means caused differences in diurnal amplitudes in the Bay of Bengal, but for Central America, differences in diurnal amplitudes were associated with seasonal mean diurnal amplitudes.

Very high resolution, altitude-corrected, TMPA-based monthly satellite precipitation product over the CONUS

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatology Network (GHCN), we compare the accuracy of TMPA 3B43 versus the new HRAC-Precip product. The results show an improvement of the mean absolute error of 12.98% on average.

A new approach for obtaining precipitation estimates with a finer spatial resolution on a daily scale based on TMPA V7 data over the Tibetan Plateau

ABSTRACTPrecipitation plays vital roles in the worldwide hydrological cycles. Gridded precipitation estimates with finer spatio-temporal resolutions are critical in various application fields. In this study, we focused on obtaining downscaled precipitation estimates (approximately1 km) at daily scale over the Tibetan Plateau (TP), which was considered as a great challenge in previous downscaling studies. To meet this challenge, a new approach, incorporating geographically ratio analysis (GRA) and spatially weighted moving window technique, was proposed. The performances of the downscaled results and those of TRMM Multisatellite Precipitation Analysis (TMPA) data were evaluated against point-based ground observations. The results indicated that: (1) the monthly downscaled results (R2 around 0.70, bias around 10%) outperformed TMPA 3B43 data (R2 around 0.55, bias around 25%) against ground observations; (2) the performances of the daily downscaled results (R2 around 0.65, bias around 10%) were better than those of the TMPA 3B42 data (R2 around 0.50, bias around 28%); and (3) the anomalies in the TMPA data did not exist in the downscaled results based on the proposed approach. Therefore, the proposed approach was suitable for obtaining both monthly and daily downscaled results based on TMPA data over the TP.

A Methodological Framework to Retrospectively Obtain Downscaled Precipitation Estimates over the Tibetan Plateau

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible.

Improving TMPA 3B43 V7 Data Sets Using Land-Surface Characteristics and Ground Observations on the Qinghai–Tibet Plateau

The accurate knowledge of precipitation information over the Qinghai–Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai–Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/ .

A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai–Tibet Plateau with the effects of systematic anomalies removed

Precipitation plays an important role in the water cycle and in matter and energy exchanges. Acquiring accurate information on precipitation over the Qinghai–Tibet Plateau, which has a limited rain gauge network, has been a great challenge. Downscaling the TRMM Multisatellite Precipitation Analysis (TMPA) 3B43 Version 7 dataset (0.25° resolution) provided an optimal approach to estimating precipitation at 1km resolution over this highland plateau. Our downscaling assumptions were that non-stationary relationships between precipitation and land surface characteristics occur and have varying two-dimensional scale effects, and that the relationships vary in different sub-regions having differing combinations of land surface characteristics, including vegetation index, topographical factors, and land surface temperatures. We used Cubist (a spatial data mining algorithm) to implement our assumption. Cubist separated the Qinghai–Tibet Plateau into sub-regions according to geographical similarities, and selected the most effective variables over each sub-region to build models. We found that: (1) the downscaled results using this algorithm were more accurate and precise than other commonly used algorithms (e.g., geographically weighted regression) and the original TMPA data at 0.25° resolution; (2) DEM showed limited correlation with precipitation over the Qinghai–Tibet Plateau; and (3) the effects of systematic anomalies in the original TMPA data were removed in the downscaled results based on Cubist. We conclude that Cubist is a promising algorithm able to take hundreds of variables into consideration, and in this study was used to retrieve precipitation estimates at approximately 1km resolution.

Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System

Abstract Current methods of assimilation of precipitation into numerical weather prediction models are able to make the model precipitation become similar to the observed precipitation during the assimilation, but the model forecasts tend to return to their original solution after a few hours. To facilitate the precipitation assimilation, a logarithm transformation has been used in several past studies. Lien et al. proposed instead to assimilate precipitation using the local ensemble transform Kalman filter (LETKF) with a Gaussian transformation technique and succeeded in improving the model forecasts in perfect-model observing system simulation experiments (OSSEs). In this study, the method of Lien et al. is tested within a more realistic configuration: the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecast System (GFS). With guidance from a statistical study comparing the GFS model background precipitation and the TMPA data, some modifications of the assimilation methods proposed in Lien et al. are made, including 1) applying separate Gaussian transformations to model and to observational precipitation based on their own cumulative distribution functions; 2) adopting a quality control criterion based on the correlation between the long-term model and observed precipitation data at the observation location; and 3) proposing a new method to define the transformation of zero precipitation that takes into account the zero precipitation probability in the background ensemble rather than the climatology. With these modifications, the assimilation of the TMPA precipitation data improves both the analysis and 5-day model forecasts when compared with a control experiment assimilating only rawinsonde data.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

Summary Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

Evaluating Satellite-Based Diurnal Cycles of Precipitation in the African Tropics

AbstractPrecipitation plays a major role in the energy and water cycles of the earth. Because of its variable nature, consistent observations of global precipitation are challenging. Satellite-based precipitation datasets present an alternative to in situ–based datasets in areas sparsely covered by ground stations. These datasets are a unique tool for model evaluations, but the value of satellite-based precipitation datasets depends on their application and scale. Numerous validation studies considered monthly or daily time scales, while less attention is given to subdaily scales. In this study subdaily satellite-based rainfall data are analyzed in West Africa, a region with strong diurnal variability. Several satellite-based precipitation datasets are validated, including Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), TRMM 3G68 products, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) morphing technique (CMORPH) data. As a reference, highly resolved in situ data from the African Monsoon Multidisciplinary Analysis–Couplage de l’Atmosphere Tropical et du Cycle Hydrologique (AMMA-CATCH) are used. As a result, overall the satellite products capture the diurnal cycles of precipitation and its variability as observed on the ground reasonably well. CMORPH and TMPA data show overall good results. For locally induced convective rainfall in the evening most satellite data show slight delays in peak precipitation of up to 2 h.

From TRMM to GPM: How well can heavy rainfall be detected from space?

In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998–2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations.

Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons

Summary A comprehensive error characterization of satellite-based precipitation products is vital for advancement of precipitation algorithms, evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are in high demand to users for numerous applications. The latest version 7 (V7) of TMPA products are assumed to be better than its previous versions due to substantial changes in input data sets and algorithm. The TMPA algorithm is also a benchmark algorithm for the Global Precipitation Measurement (GPM) mission multisatellite precipitation products. In this study, both near-real-time (TMPA-RT) and research-quality (TMPA-V7) TMPA data sets are extensively evaluated against gridded gauge-based data over India for pre-monsoon, monsoon, and post-monsoon seasons at daily scale for a 13-year (2001–2013) period. The systematic and random error components in TMPA data sets are decomposed for their wider usage in agricultural and hydrological applications. Although TMPA-V7 and RT data sets represent the mean seasonal rainfall characteristics reasonably well, both the satellite-based data sets show an overestimation of rainfall over most parts of the country except over the orographic regions. However, TMPA-V7 is better than TMPA-RT when compared with the reference observations. Both the TMPA products show larger systematic error over the mountainous regions of northeast India. TMPA-RT has larger random error than TMPA-V7 possibly due to difference in calibration methods for the development of these data sets. Both the satellite-based data sets show significant difference from the reference observations for light to moderate rainfall ranges especially in the monsoon season. Larger error associated with systematic component in both the data sets is found during the pre-monsoon season. Furthermore, bias and error in satellite-derived rainfall data show a considerable interannual variation. Overall results suggest that a suitable region and season dependent bias-correction is required in TMPA data sets before its integration in hydrological applications.

Spatial and Diurnal Variability of Monsoon Systems Assessed by TRMM Rain Rate Over Indus Basin

The spatiotemporal characteristics of the Asian monsoon over the Indus River basin are studied using the latest near real-time satellite precipitation estimates from the tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA). The TMPA data product (3B42RT V7) is used to analyze diurnal variability of the Asian monsoon over the study domain during January 2005 to December 2010. First, the spatiotemporal uncertainty of satellite estimates is systematically characterized by comparison to rain gauge observations using four standard error metrics, i.e., the Pearson correlation coefficient (CC), root-mean-square error (RMSE), mean absolute error (MAE), and relative bias (BIAS). Second, diurnal rainfall variability over selected regions is investigated by comparing rainfall patterns during premonsoon and monsoon seasons. The comparison and evaluation of satellite-based estimates and rain gauge data revealed significant correlation of 0.87 for the stations in the southwest and 0.63 in the northeast monsoon region. The results indicated TMPA underestimates over the intense monsoon region from $- \bf{8}\% $ to $- \bf{25}\% $ , while there is an overestimation over the southern region from 7% to 35%. This study improves understanding on the rainfall diurnal variations captured by the three hourly TMPA products during the April–June (premonsoon) and June–August (monsoon) over the extreme monsoon year 2010 versus the regular periods of 2005–2009 by investigating precipitation mean, frequency, and intensity, as well as the diurnal and semidiurnal cycles. A noticeable bimodal variation during the 2010 season showed an increase in rainfall associated with anomalous atmospheric conditions, causing catastrophic floods in Pakistan.

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

In this paper, Kalpana-1 derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates are compared with two multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP), and India Meteorological Department (IMD) surface rain gauge (SRG)-based rainfall at meteorological sub-divisional scale over India. The performance of the summer monsoon rainfall of 2013 over Indian meteorological sub-divisions is assessed at different temporal scales. Comparison of daily accumulated rainfall over India from IMSRA shows a linear correlation of 0.72 with TMPA-3B42 and 0.70 with GSMaP estimates. IMSRA is capable to pick up daily rainfall variability over the monsoon trough region as compared to TMPA-3B42 and GSMaP products, but underestimates moderate to heavy rainfall events. Satellite-derived rainfall maps at meteorological sub-divisional scales are in reasonably good agreement with IMD-SRG based rainfall maps with some exceptions. However, IMSRA performs better than GSMaP product at meteorological sub-divisional scale and comparable with TMPA data. All the satellite-derived rainfall products underestimate orographic rainfall along the west coast, the Himalayan foothills and over the northeast India and overestimate rainfall over the southeast peninsular India. Overall results suggest that IMSRA estimates have potential for monsoon rainfall monitoring over the Indian meteorological sub-divisions and can be used for various hydro-meteorological applications.

Spatio-temporal analysis of the accuracy of tropical multisatellite precipitation analysis 3B42 precipitation data in mid-high latitudes of China.

Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998–2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are important for the rational application of TMPA methods in climatic and hydrological research.

Application of satellite-derived rainfall for hydrological modelling in the data-scarce Black Volta trans-boundary basin

This study, conducted in the Black Volta basin of Ghana, determined how well TRMM Multi-Satellite Precipitation Analysis (TMPA) data compare with rain gauge measurements. The potential of using the TMPA data as inputs into a hydrological model for runoff simulation in a data-scarce basin was also assessed. Using a point-to-grid approach, accumulations of ground measured rainfall on daily, monthly and annual time scales were compared with accumulations derived from TMPA daily rainfall grids. The TMPA derived data together with other free global data were used as input into the soil and water assessment tool (SWAT) to set up a hydrological model for the basin. This model was calibrated and validated using streamflow data from a station located downstream of the basin. The study results showed a correlation from 0.85 to 0.92 for the monthly accumulated rainfall. Also, good Nash–Sutcliffe efficiencies of 0.94 and 0.67 were obtained for calibration and validation, respectively, on monthly scale. Moreover, simulation of streamflow was ‘satisfactory’ to ‘very good’ in terms of trends and residual variation. The study, therefore, shows that the use of satellite rainfall in the basin would be of great benefit considering the difficulties in accessing data across the basin.

Towards large-scale monitoring of soil erosion in Africa: Accounting for the dynamics of rainfall erosivity

Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff act on soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Joint accounting for the variability of these factors is required to effectively map and monitor soil erosion. However, most studies merely use average annual erosivity values, partly due to data paucity. This study analyses the variability of rainfall erosivity across Africa through the use of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) precipitation data. We obtained average annual erosivity estimates from 15yr of TMPA data (1998–2012) using intensity–erosivity relationships. Our estimates showed a positive correlation (r=0.84) with long-term annual erosivity values of 37 stations obtained from literature. Our TMPA-analysis confirmed and mapped the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. Seasonal variability of erosivity was investigated from TMPA-based average monthly erosivity estimates, which resulted in similar seasonal patterns as those reported in literature. We conclude that (1) spatial and temporal variability of erosivity is important and needs to be accounted for in combination with vegetation cover when monitoring soil erosion; and (2) 3-hourly TMPA data allow for a good first estimate of the variability of erosivity in Africa, which could be improved by upcoming techniques that provide more accurate rainfall information at higher spatial and temporal resolutions.

Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River

Satellite-based precipitation products with high spatial and temporal resolution and large areal coverage offer a potential alternative or supplement to ground-based rainfall estimates over sparsely gauged or ungauged basins. The original intention of the Tropical Rainfall Measuring Mission (TRMM) was to provide rainfall products over the global tropics. Although numerous efforts have been made to evaluate the applicability of TRMM precipitation in the low altitude of the tropics and the mid-latitudes, its applicability in high altitude is rare. The source region of Yellow River and its neighbor regions in the northeastern Tibetan Plateau provide a good example of high altitude and higher latitude for this purpose. In this study, we first provided a quantitative assessment of the TRMM Multisatellite Precipitation Analysis (TMPA) products (3B42V6) against the gauge rainfall from 1998 to 2008. In general, the TMPA rainfall captures the spatial and temporal rainfall characteristics less well over the region on daily time scales. However, the monthly TMPA data have a much better linear relationship with the gauge rainfall data with a high determination coefficient (R2) of 0.96. The distribution of the barycentre of the precipitation concentrates on the south-central of the study area, and the TMPA rainfall scatter in the west in comparison with the gauge rainfall. This study also concluded that the validation indices of TMPA were closely associated with latitude and annual precipitation. We subsequently evaluated the utility of the TMPA rainfall for daily and monthly hydrological process simulation by applying the rainfall data to a raster-based distributed hydrological model, Coupled Routing and Excess Storage (CREST). The results showed that the conventional areal rainfall based on gauge data produces an overall good fit, while the TMPA-driven simulations tend to yield an unsatisfied result in the daily discharge simulations. This analysis implies that TMPA cannot be acceptable to drive hydrological models for daily stream flow simulation in the study area. However, at the monthly time scale, the TMPA rainfall performs quite well. This study concluded that TMPA is more suitable for the long-term hydrological prediction.

Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins?

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001–2010) daily rain gauge dataset reveals that: (1) 3B42V7 generally improves upon 3B42V6’s underestimation both for the whole basin (bias from −41.15% to −8.38%) and for a 0.25° × 0.25° grid cell with high-density gauges (bias from −40.25% to 0.04%), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and (2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already-well-performing 3B42V7 except for a reduction in bias (from −26.98% to −4.81%). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.