Tropical Rainfall Measuring Mission Products 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).

Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran

Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling.

A New Downscaling-Calibration Procedure for TRMM Precipitation Data Over Yangtze River Economic Belt Region Based on a Multivariate Adaptive Regression Spline Model

High-resolution precipitation products are essential for accurate hydrological and meteorological applications. To improve the spatial resolution and accuracy of monthly satellite precipitation products, we developed a new downscaling-calibration framework with three key steps: 1) coarse-resolution satellite precipitation data are downscaled to 1-km resolution precipitation data with multivariate adaptive regression spline (MARS) model; 2) residual correction is applied to bridge the difference between the satellite precipitation data and downscaled precipitation data; and 3) the geographical differential analysis (GDA) calibration method is implemented to improve accuracy by merging the residual-corrected data with rain gauge data. In this study, geolocation variables (longitude and latitude), a digital elevation model (DEM) data, daytime and nighttime land surface temperatures, and four remote sensing indices were used to downscale monthly Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation datasets over the Yangtze River Economic Belt. The downscaled results showed that the MARS model can avoid “boxy artifact” and pixel-level anomalies, which are often found in geographically weighted regression (GWR) and random forest (RF) results. According to the validation, the step of residual correction is not necessary. With GDA calibration, the MARS-based estimated results were more accurate than the results of the other methods (i.e., GWR and RF) and original TRMM products. Therefore, the developed MARS-based downscaling-calibration procedure can improve not only the spatial resolution but also the quality of the TRMM 3B43 products.

A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes

Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.

Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

Uncertainties in satellite rainfall estimation may derive from both the local rainfall characteristics and its subpixel variability. To study this issue, Micro Rain Radars and a rain gauge network were deployed within a 9-km satellite pixel in the semi-arid Xilingol grassland of China in summer 2009. The authors characterized the subpixel variability with the coefficient of variation (CV) and evaluated the satellite rainfall estimation for this semi-arid area. The results showed that rainfall events with a high CV were mostly convective with a small amount of rainfall. Spatially inhomogeneous rainfall was most likely to occur at the edges of small clouds producing rain. The performance of the TRMM (Tropical Rainfall Measuring Mission) 3B42V7 product for daily rainfall was better than that of the CMORPH (Climate Prediction Center morphing technique) and PERSIANN (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks) products, although the TRMM product tended to overestimate rainfall in a lake area of the semi-arid grassland.摘要卫星估雨精度的不确定性受到当地降雨类型和像元内降雨非均匀性影响, 而结合这两个关键因素开展半干旱草原卫星估雨的研究有限. 2009年夏, 我们在中国锡林郭勒半干旱草原用多部微雨雷达和雨量计构建了9 km卫星像元降雨观测网, 观测了像元内降雨非均匀性(空间变异系数CV), 并评估了卫星估雨精度. 结果表明: (1) CV值受像元内平均降雨量, 降雨类型, 降雨云面积及移向等影响, 如高CV值的降雨过程大多为平均降雨量小, 对流性降雨过程, 降雨云边缘像元CV值较高; (2) TRMM 3B42V7 卫星估雨产品适用性较好, CMORPH和PERSIANN次之, 但TRMM 3B42V7易在半干旱草原湖泊处高估降雨.

Evaluation of the TRMM product for monitoring drought over Para\xedba State, northeastern Brazil: a trend analysis

Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.

Evaluation of the TRMM Product for Spatio-temporal Characteristics of Precipitation over Nepal (1998-2018)

Precipitation is a fundamental component of the water cycle and integral to the society and the ecosystem. Further, continuous monitoring of precipitation is essential for predicting severe weather, monitoring droughts, and high-intensity related extremes. The present study evaluated the spatio-temporal distribution of precipitation and trends between 1998– 2018 using Tropical Rainfall Measuring Mission (TRMM) (3B43-V7) with reference to 142-gauge observations over Nepal. TRMM moderately captured precipitation patterns' overall characteristics, although underestimated the mean annual precipitation during the study period. TRMM precipitation product well captured the seasonal variation of the observed precipitation with the highest correlation in the winter season. The decreasing seasonal and annual trend was found in both observed and TRMM products, with the highest (lowest) decreasing trend observed during the monsoon (winter) season. It was also noted that the TRMM product showed a smaller bias before 2007, while a large error was found after 2007, especially in the monsoon months. In general, the TRMM product is a good alternative to observe rain gauge measurement in Nepal. However, there is still space for further improvement in rainfall retrieval algorithms, especially in high-elevation areas during the winter season.

Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation

Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.

Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing

Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.

Evaluation of TRMM 3B42V7 and CHIRPS Satellite Precipitation Products as an Input for Hydrological Model over Eastern Nile Basin

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

Evaluation of the TRMM Product for Monitoring Drought over Paraíba State, Northeastern Brazil: A Statistical Analysis

Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor natural hazards in ungauged basins and complex areas of the world; however, the accuracies of these orbital products still need to be verified. Thus, this work aims to evaluate the performance of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal behavior of droughts at multiple temporal scales over Paraíba State based on the standardized precipitation index (SPI) over 20 years (1998–2017). For this purpose, rainfall data from 78 rain gauges and 187 equally spaced TRMM cell grids throughout the region are used, and accuracy analyses are performed at the single-gauge level and in four mesoregions at eight different time scales based on 11 statistical metrics calculations divided into three different categories. The results show that in the mesoregions close to the coast, the satellite-based product is less accurate in capturing the drought behavior regardless of the evaluated statistical metrics. At the temporal scale, the TRMM is more accurate in identifying the pattern of medium-term droughts; however, there is considerable spatial variation in the accuracy of the product depending on the performance index. Therefore, it is concluded that rainfall estimates from the TRMM satellite are a valuable source of data to identify drought behavior in a large part of Paraíba State at different time scales, and further multidisciplinary studies should be conducted to monitor these phenomena more accurately based on satellite data.

Hydrologic Assessment of TRMM and GPM-Based Precipitation Products in Transboundary River Catchment (Chenab River, Pakistan)

Water resources planning and management depend on the quality of climatic data, particularly rainfall data, for reliable hydrological modeling. This can be very problematic in transboundary rivers with limited disclosing of data among the riparian countries. Satellite precipitation products are recognized as a promising source to substitute the ground-based observations in these conditions. This research aims to assess the feasibility of using a satellite-based precipitation product for better hydrological modeling in an ungauged and riparian river in Pakistan, i.e., the Chenab River. A semidistributed hydrological model of The soil and water assessment tool (SWAT) was set up and two renowned satellite precipitation products, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were selected to assess the runoff pattern in Chenab River. The calibration was done from 2001–2006 with two years of a warmup period. The validation (2007–2010) results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations, IMERG-F (R2 = 0.89, NSE = 0.82), 3B42 (R2 = 0.85, NSE = 0.72), rather than daily timescale simulations, IMERG-F (R2 = 0.66, NSE = 0.61), 3B42 (R2 = 0.64, NSE = 0.54). Moreover, the comparison between IMERG-F and 3B42, shows that IMERG-F is superior to 3B42 by indicating higher R2, NSE and lower percent bias (PBIAS) at both monthly and daily timescale. The results are strengthened by Taylor diagram statistics, which represent a higher correlation (R) and less RMS error between observed and simulated values for IMERG-F. IMERG-F has great potential utility in the Chenab River catchment as it outperformed the 3B42 precipitation in this study. However, its poor skill of capturing peaks at daily timescale remains, leaving a room for IMERG-F to improve its algorithm in the upcoming release.

Assessment of the two satellite-based precipitation products TRMM and RFE rainfall records using ground based measurements

In several research domains, the precipitation products produced based on the Satellite images are used because of their advantage of high spatiotemporal resolution and near-global coverage. And still the products applications are limited according to the uncertainty. To facilitate using these products, it’s vital to identify and quantify their error characteristics. The objective of this study is to evaluate the two satellite-based precipitation products; RainFall Estimate (RFE) and Tropical Rainfall Measuring Mission (TRMM) using ground gauge-based rainfall measurements throughout the Blue Nile River sub-basin in the Sudan for the period (2001–2016). The importance of this study comes from the discontinuity of the ground data due to some difficulties such as the accessibility of the region’s topography, in addition to the poorly gauged areas. Therefore, estimating the amount of rain from verified satellites is useful to obtain the pattern of rainfall that could be used in hydrological models to provide river discharges forecasts and to specify areas of flood hazard. For a quantified evaluation of satellite-based precipitation products, continuous Mean Error (ME), and Correlation Coefficient (CC) were used. The results show that RFE and TRMM products performing well in estimating and observing rainfall over the study area.

Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China

The Tropical Rainfall Measurement Mission (TRMM) satellite is the first to be designed to measure precipitation, and its precipitation products have been assessed in a variety of ways. Data for its post-real-time level 2 product (3B42) performed well in terms of the precipitation amount at the monthly scale because they were corrected by a precipitation dataset that was gauged every month. However, the performance of this dataset in terms of precipitation frequency and intensity is still not ideal. To this end, TRMM 3B42 products were evaluated using precipitation data from 747 meteorological stations over mainland China in this study. The Pearson’s correlation coefficient (CC), relative bias (RB), and relative error (RE) were used to assess the capability of TRMM products in terms of estimating the frequency, intensity, and amount of precipitation for different categories of precipitation during nighttime and daytime in a multiscale analysis (including interannual variation, seasonal cycles, and spatial distribution). Our results showed the following: (1) The 3B42 products reproduced interannual trends of the frequency and amount of precipitation (except for trace precipitation) with an average correlation coefficient of 0.84. (2) 3B42 performed well at calculating the annual and monthly precipitation amount, but performed poorly for frequency and even worse for intensity. The biases in these two properties canceled out, however, which led to a better estimate of the amount. (3) 3B42 represented the distribution of the subdaily amount of precipitation over a majority of the regions in the east, but did not perform well on the Tibetan Plateau or in northwest China. The performance of 3B42, as detailed in this study, can serve as valuable guidance to data users and algorithm developers.

Assessment of TRMM product for precipitation extreme measurement over the Muda River Basin, Malaysia

The objective of this study is to evaluate the capability of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 product in estimating precipitation extremes over the Muda River Basin, Malaysia. Daily precipitation from 1998 to 2014 collected from eight climate stations that well distributed over the basin was used. Ten Expert Team on Climate Change Detection and Indices (ETCCDI) precipitation extreme indices were used to represent the precipitation extreme condition. The results of the correlation analysis show that TMPA performed well in the consecutive dry days (CDD), number of very heavy precipitation days (R20mm) and extremely wet days (R99p) indices calculations. Moreover, TMPA shows a similar increasing or decreasing trend as the gauge in most of the indices, indicating it can be used for precipitation extreme trend analysis over the basin.

Assessment of IMERG-V06 Precipitation Product over Different Hydro-Climatic Regimes in the Tianshan Mountains, North-Western China

This study presents an assessment of the version-6 (V06) of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product from June 2014 to December 2017 over different hydro-climatic regimes in the Tianshan Mountains. The performance of IMERG-V06 was compared with IMERG-V05 and the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 precipitation products. The precipitation products were assessed against gauge-based daily and monthly precipitation observations over the entire spatial domain and five hydro-climatologically distinct sub-regions. Results showed that: (1) The spatiotemporal variability of average daily precipitation over the study domain was well represented by all products. (2) All products showed better correlations with the monthly gauge-based observations than the daily data. Compared to 3B42V7, both IMERG products presented a better agreement with gauge-based observations. (3) The estimation skills of all precipitation products showed significant spatial variations. Overall performance of all precipitation products was better in the Eastern region compared to the Middle and Western regions. (4) Satellite products were able to detect tiny precipitation events, but they were uncertain in capturing light and moderate precipitation events. (5) No significant improvements in the precipitation estimation skill of IMERG-V06 were found as compared to IMERG-V05. We deduce that the IMERG-V06 precipitation detection capability could not outperform the efficiency of IMERG-V05. This comparative evaluation of the research products of Global Precipitation Measurement (GPM) and TRMM products in the Tianshan Mountains is useful for data users and algorithm developers.

Evaluation and Hydrological Application of TRMM and GPM Precipitation Products in a Tropical Monsoon Basin of Thailand

Watershed runoff is essential for water management. However, runoff materials are lacking in poorly gauged catchments and not always accessible. Microwave remote sensing offers emerging capabilities for hydrological simulation. In this study based on multi-satellite retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) products, and World Meteorological Organization (WMO) interpolated precipitation data, we simulated runoff using a variable infiltration capacity (VIC) model and studied the differences among the results. Then, we analyzed the impacts of the runoff on a moderate-resolution imaging spectroradiometer vegetation leaf area index (LAI) during dry seasons. The results showed that (1) IMERG V5 and TRMM products are capable of monitoring the night-day rainfall diurnal cycle and have higher correlations than the WMO daily observation interpolations. However, the WMO shows less overestimation of total precipitation than remote-sensing precipitation; (2) in the downstream, the TRMM shows better runoff simulation accuracy in the tributaries, and the WMO shows better results in the mainstreams. Therefore, at basin outlets in mainstreams, the Nash–Sutcliffe efficiency coefficients of monthly runoff by the WMO are higher than the simulations by the TRMM; (3) for the whole basin during dry seasons, the LAI variation is correlated with the outlet runoff, which is similar to the correlation with three- to six-month accumulated precipitation. TRMM products can be used to depict both precipitation deficit and runoff deficit, which cause vegetation variations. Our research suggests the potential of microwave precipitation products for detailed watershed runoff simulations and water management.

Hydrologic Evaluation of TRMM and GPM IMERG Satellite-Based Precipitation in a Humid Basin of China

Tropical Rainfall Measurement Mission (TRMM) is one of the most popular global high resolution satellite-based precipitation products with a goal of measuring precipitation over the oceans and tropics. However, in recent years, the TRMM mission has come to an end. Its successor, Global Precipitation Measurement (GPM) mission was launched to measure the earth’s precipitation structure, with an aim to improve upon the TRMM project. Both of the precipitation products have their own strengths and weaknesses in resolution, accuracy, and availability. The aim of this study is to evaluate the hydrologic utilization of the TRMM and GPM products in a humid basin of China. The main findings of this study can be summarized as follows: (1) 3B42V7 generally outperforms 3B42V6 in terms of hydrologic performance. Meanwhile, 3B42RTV7 significantly outperforms 3B42RTV6, and showed close performance with the bias-adjusted TRMM Multi-satellite Precipitation Analysis (TMPA) products. (2) The GPM showed better agreement with gauge observation than the TMPA products with lower RB and higher correlation coefficient (CC) values at different time scales. (3) The VIC hydrological model generally outperformed the XAJ hydrological model with lower RB, higher Nash–Sutcliffe Coefficient of Efficiency (NSCE) and CC values; though the 3B42RTV6 and 3B42RTV7 showed higher CC values in simulating the streamflow hydrograph by using the VIC and XAJ hydrological models. It can be found that the conceptual hydrological model was enough for the hydrologic evaluation of TRMM and GPM IMERG satellite-based precipitation in a humid basin of China. This study provides a reference for the comparison of multiple models on watershed scale.

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.

Constructing depth-area-duration curves using public domain satellite-based precipitation data

Dense network of rain gauges are used to accurately characterise the variation of rainfall over a less than ideal region such as Sarawak, Malaysia. This research aims to develop depth-area-duration (DAD) relationships of selected rainstorm event over Sungai Sarawak basin by using public domain satellite-based precipitation data from tropical rainfall measuring mission (TRMM) product. Geographic information system (GIS) was used to manipulate the three-hourly accumulated precipitation dataset from TRMM and subsequently used to investigate spatial and temporal pattern of rainstorm. The findings suggested that rainfall depth decreases with increasing area for a given duration. This also implies that the remotely sensed information from TRMM product can be used as an alternative source of dataset to envelop rainfall DAD curves. Future work suggested would be to use the plotted DAD curves to estimate the probable maximum precipitation for the purpose of hydraulic structure designs.