TRMM 3B42 V7 Research Articles

Evaluation and hydrological application of precipitation estimates derived from PERSIANN‐CDR, TRMM 3B42V7, and NCEP‐CFSR over humid regions in China

AbstractSatellite‐based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global high‐resolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi‐distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN‐CDR and NCEP‐CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN‐CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP‐CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN‐CDR‐driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP‐CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN‐CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP‐CFSR performs just the reverse, compared with PERSIANN‐CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of Satellite Retrieval Algorithm to Ground Rainfall Estimates Over Malaysia

Satellite precipitation data is an indirect measurement for land and water area over the globe. The information from this data is important especially to the area where direct rain gauge measurement is limited. However, the precipitation estimates require calibration and validation to assure the accuracy. Satellite rainfall estimations together with comprehensive ground validation and calibration have been found to offer good prospect for accurate and global rainfall database especially for remote areas and large bodies of water. This work presents performance evaluation of TRMM 3B43 V7 rainfall retrieval algorithms over Malaysia. Inter-comparison and validation of TRMM 3B43 V7 rainfall product with ground measurement is analysed statistically. The result of continuous statistical evaluation shows good agreement, in which, the best correlation for the algorithm 3B43 versus rain gauge is 0.9384. At lower percentage bias threshold, the 2 by 2 categorical statistic of rain or no rain occurrence for annual estimation reveals lower value of probability of detection and higher value of false alarm ratio. However, reverse results are shown at higher bias threshold. The accuracy of the algorithm for a threshold of 1–10 % which falls within International Telecommunication Union—Radio recommendation for radio propagation to discriminate between rain and no rain is 0.53, 0.49 and 0.48 for annual, monthly and wet season, respectively. From analysis, the categorical statistical approach has been able to reveal the level of accuracy of the algorithms as applicable to detection and estimation of rainfall.

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations.

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Evaluation of High-resolution Satellite Rainfall Data over Singapore

The uncertainties of two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations in Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of diurnal cycle and extreme precipitation for 10 years from Dec. 2000 to Nov. 2010. The satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the ITCZ is located in Singapore. However, they fail in estimating diurnal cycle in summer. The disagreement in summer can be accounted for by the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. According to analysis of extreme precipitation indices, both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spell over all stations. In particular, the uncertainty of extreme precipitation is higher in GSMaP than in TRMM, possibly due to the several effects such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and swath time of satellite. Such discrepancies between satellite-born and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results, this research on quantification of their uncertainty is useful in many respects, especially that the satellite products can stand scrutiny overplaces/stations where there are no good data to be compared against.

Correction of TRMM 3B42V7 Based on Linear Regression Models over China

High temporal-spatial precipitation is necessary for hydrological simulation and water resource management, and remotely sensed precipitation products (RSPPs) play a key role in supporting high temporal-spatial precipitation, especially in sparse gauge regions. TRMM 3B42V7 data (TRMM precipitation) is an essential RSPP outperforming other RSPPs. Yet the utilization of TRMM precipitation is still limited by the inaccuracy and low spatial resolution at regional scale. In this paper, linear regression models (LRMs) have been constructed to correct and downscale the TRMM precipitation based on the gauge precipitation at 2257 stations over China from 1998 to 2013. Then, the corrected TRMM precipitation was validated by gauge precipitation at 839 out of 2257 stations in 2014 at station and grid scales. The results show that both monthly and annual LRMs have obviously improved the accuracy of corrected TRMM precipitation with acceptable error, and monthly LRM performs slightly better than annual LRM in Mideastern China. Although the performance of corrected TRMM precipitation from the LRMs has been increased in Northwest China and Tibetan plateau, the error of corrected TRMM precipitation is still significant due to the large deviation between TRMM precipitation and low-density gauge precipitation.

Soil Erosion Modeling Using Satellite Rainfall Estimates

This study attempts to assess the soil erosion of a Himalayan River Basin, Karnali and Nepal using the rainfall erosivity (R- factor) derived from satellite rainfall estimates (TRMM-3B42 V7). Average annual sediment yield was estimated using the well- known Universal Soil Loss Equation (USLE). The annual average rainfall erosivity factor (R) for the Karnali River basin over eight years was found to be 2620.84 MJ mm ha−1 h−1 yr−1. Using intensity-erosivity relationships and a dataset consisting of eight years of TRMM daily rainfall (1998-2005), the average annual soil erosion was also estimated for the Karnali River Basin. Maximum and minimum values of rainfall erosivity varied between 1108.7 and 4868.49 MJ mm ha-1 h-1 yr-1 during the assessment period. The average annual soil loss in the Karnali River basin was found to be 38.17 t ha-1 yr-1. Finally, the total basin area was categorized into the following erosion classes: slight (0 to 5 t ha-1yr-1); moderate (5 to 10 t ha-1yr-1); high (10 to 20 t ha-1yr-1); very high (20 to 40 t ha-1yr-1); Severe (40 to 80 t ha-1yr-1); and Very Severe (>80 t ha-1yr-1). Approximately 30.86% of the river basin area was found to be in the slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones accounted for 13.09%, 6.36%, 11.09%, 22.02% and 16.64% of the study area, respectively. This study revealed that approximately 69% of the Karnali River basin needs immediate attention from soil conservation analysts.

Combining rainfall data from rain gauges and TRMM in hydrological modelling of Laotian data-sparse basins

At present, prediction of streamflow simulation in data-sparse basins of the South East Asia is a challenging task due to the absence of reliable ground-based rainfall information, while satellite-based rainfall estimates are immensely useful to improve our understanding of spatio-temporal variation of rainfall, particularly for data-sparse basins. In this study the TRMM 3B42 V7 and its bias-corrected data were, respectively, used to drive a physically based distributed hydrological model BTOPMC to perform daily streamflow simulations in Nam Khan River and Nam Like River basins during the years from 2000 to 2004 so as to investigate the potential use of the TRMM in complementing rain gauge data in hydrological modelling of data-sparse basins. The results show that although larger difference exists in the high streamflow process and the low streamflow process, the daily simulations fed with TRMM precipitation data could basically reflect the daily streamflow processes at the four stations and determine the time to peak. Furthermore, the calibrated parameters in the Nam Khan River basin are more suitable than that in the Nam Like River basin. By comparing the two precipitation data, it indicates that the integration of TRMM precipitation data and rain gauge data have a promising prospect on the hydrological process simulation in data-sparse basin.

Spatiotemporal characteristics and synchronization of extreme rainfall in South America with focus on the Andes Mountain range

The South American Andes are frequently exposed to intense rainfall events with varying moisture sources and precipitation-forming processes. In this study, we assess the spatiotemporal characteristics and geographical origins of rainfall over the South American continent. Using high-spatiotemporal resolution satellite data (TRMM 3B42 V7), we define four different types of rainfall events based on their (1) high magnitude, (2) long temporal extent, (3) large spatial extent, and (4) high magnitude, long temporal and large spatial extent combined. In a first step, we analyze the spatiotemporal characteristics of these events over the entire South American continent and integrate their impact for the main Andean hydrologic catchments. Our results indicate that events of type 1 make the overall highest contributions to total seasonal rainfall (up to $$50\,\%$$ ). However, each consecutive episode of the infrequent events of type 4 still accounts for up to $$20\,\%$$ of total seasonal rainfall in the subtropical Argentinean plains. In a second step, we employ complex network theory to unravel possibly non-linear and long-ranged climatic linkages for these four event types on the high-elevation Altiplano-Puna Plateau as well as in the main river catchments along the foothills of the Andes. Our results suggest that one to two particularly large squall lines per season, originating from northern Brazil, indirectly trigger large, long-lasting thunderstorms on the Altiplano Plateau. In general, we observe that extreme rainfall in the catchments north of approximately $$20^{\circ }$$ S typically originates from the Amazon Basin, while extreme rainfall at the eastern Andean foothills south of $$20^{\circ }$$ S and the Puna Plateau originates from southeastern South America.

Soil erosion assessment of a Himalayan river basin using TRMM data

Abstract. In this study, an attempt has been made to assess the soil erosion of a Himalayan river basin, the Karnali basin, Nepal, using rainfall erosivity (R-factor) derived from satellite-based rainfall estimates (TRMM-3B42 V7). Average annual sediment yield was estimated using the well-known Universal Soil Loss Equation (USLE). The eight-year annual average rainfall erosivity factor (R) for the Karnali River basin was found to be 2620.84 MJ mm ha−1 h−1 year−1. Using intensity–erosivity relationships and eight years of the TRMM daily rainfall dataset (1998–2005), average annual soil erosion was also estimated for Karnali River basin. The minimum and maximum values of the rainfall erosivity factor were 1108.7 and 4868.49 MJ mm ha−1 h−1 year−1, respectively, during the assessment period. The average annual soil loss of the Karnali River basin was found to be 38.17 t ha−1 year−1. Finally, the basin area was categorized according to the following scale of erosion severity classes: Slight (0 to 5 t ha−1 year−1), Moderate (5 to 10 t ha−1 year−1), High (10 to 20 t ha−1 year−1), Very High (20 to 40 t ha−1 year−1), Severe (40 to 80 t ha−1 year−1) and Very Severe (>80 t ha−1 year−1). About 30.86% of the river basin area was found to be in the slight erosion class. The areas covered by the moderate, high, very high, severe and very severe erosion potential zones were 13.09%, 6.36%, 11.09%, 22.02% and 16.64% respectively. The study revealed that approximately 69% of the Karnali River basin needs immediate attention from a soil conservation point of view.

Evaluation of remotely sensed data for estimating recharge to an outcrop zone of the Guarani Aquifer System (South America)

The Guarani Aquifer System (GAS) is the largest transboundary groundwater reservoir in South America, yet recharge in the GAS outcrop zones is one of the least known hydrological variables. The objective of this study was to assess the suitability of using remote sensing data in the water-budget equation for estimating recharge inter-annual patterns in a representative GAS outcropping area. Data were obtained from remotely sensed estimates of precipitation (P) and evapotranspiration (ET) using TRMM 3B42 V7 and MOD16, respectively, in the Onca Creek watershed in Brazil over the 2004–2012 period. This is an upland flat watershed (slope steepness <1 %) dominated by sandy soils and representative of the GAS outcrop zones. The remote sensing approach was compared to the water-table fluctuation (WTF) method and another water-budget equation using ground-based measurements. On a monthly basis, the TRMM P estimate showed significant agreement with the ground-based P data (r = 0.93 and RMSE = 41 mm). Mean(±SD) satellite-based recharge (R sat) was 537(±224) mm year−1. Mean ground-based recharge using the water-budget (R gr) and the WTF (R wtf) methods were 469 mm year−1 and 311(±75) mm year−1, respectively. Results show that 440 mm year−1 is a mean (between R sat, R gr and R wtf) recharge for the study area over the 2004–2012 period. The latter mean recharge estimate is about 29 % of the mean historical P (1,514 mm year−1). These results are useful for future studies on assessing recharge in the GAS outcrop zones where data are scarce or nonexistent.

A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics

Remote sensing is an important tool to monitor precipitation over regions with sparse rain gauge networks. To provide high-resolution precipitation estimates over un-gauged areas, great efforts have been taken to downscale low-resolution satellite precipitation datasets using the Normalized Difference Vegetation Index (NDVI) and the Digital Elevation Model (DEM) based on the assumption that precipitation can be simulated by vegetation and topography proxies at various spatial scales. However, the non-stationarity of the relationship between precipitation and vegetation or topography has not been appropriately considered when low-resolution satellite precipitation datasets are downscaled using NDVI and DEM in previous studies. To overcome this limitation, a new downscaling algorithm was proposed in this study by introducing a regional regression model termed as geographically weighted regression (GWR) to explore the spatial heterogeneity of the precipitation–NDVI and precipitation–DEM relationships. The performance of this new downscaling algorithm was assessed by downscaling the latest version of monthly TRMM precipitation datasets (referred to TRMM 3B43 V7) over the eastern Tibetan Plateau and the TianShan Mountains from 0.25° (about 25km) to 1km spatial resolution, and the downscaled precipitation datasets were validated against ground observations measured by rain gauges. The validation results indicate that the high-resolution precipitation datasets obtained through the new algorithm not only performed better than the traditional downscaling algorithms, but also had higher accuracy than the original TRMM 3B43 V7 dataset. Besides, we found that the performance of this new algorithm was largely dependent on the accuracy of the original TRMM 3B43 V7 data. We therefore recommend considering the non-stationarity of the precipitation–NDVI and precipitation–DEM relationships in the downscaling process, and demonstrate the possibility of downscaling satellite precipitation with NDVI and DEM at monthly temporal scale.

Extreme Rainfall of the South American Monsoon System: A Dataset Comparison Using Complex Networks

Abstract In this study, the authors compare six different rainfall datasets for South America with a focus on their representation of extreme rainfall during the monsoon season (December–February): the gauge-calibrated TRMM 3B42 V7 satellite product; the near-real-time TRMM 3B42 V7 RT, the GPCP 1° daily (1DD) V1.2 satellite–gauge combination product, the Interim ECMWF Re-Analysis (ERA-Interim) product; output of a high-spatial-resolution run of the ECHAM6 global circulation model; and output of the regional climate model Eta. For the latter three, this study can be understood as a model evaluation. In addition to statistical values of local rainfall distributions, the authors focus on the spatial characteristics of extreme rainfall covariability. Since traditional approaches based on principal component analysis are not applicable in the context of extreme events, they apply and further develop methods based on complex network theory. This way, the authors uncover substantial differences in extreme rainfall patterns between the different datasets: (i) The three model-derived datasets yield very different results than the satellite–gauge combinations regarding the main climatological propagation pathways of extreme events as well as the main convergence zones of the monsoon system. (ii) Large discrepancies are found for the development of mesoscale convective systems in southeastern South America. (iii) Both TRMM datasets and ECHAM6 indicate a linkage of extreme rainfall events between the central Amazon basin and the eastern slopes of the central Andes, but this pattern is not reproduced by the remaining datasets. The authors’ study suggests that none of the three model-derived datasets adequately captures extreme rainfall patterns in South America.

Prediction of heavy rainfall events over Rangamati, Bangladesh using high-resolution MM5 model

In this study, prediction of the heavy rainfall events over Rangamati, Bangladesh has been carried out using the Fifth-Generation PSU/NCAR Mesoscale Model (MM5) conducting two historical rainfall events. The model was run on two-way triple-nested domains at 45, 15, 5 km horizontal resolutions using Anthes-Kuo cumulus parameterization schemes (CPS) with MRF planetary boundary layer (PBL). Bangladesh is the main focus area in this study. Thus, Bangladesh is taken as inner most domain (D3) with 5 km horizontal resolution. The model-predicted rainfall was compared with TRMM 3B42V7 and BMD observed rainfall. Both subjective and objective evaluation methods have been followed. The MM5 model produces realistic prediction of heavy rainfall events in terms of intensity and structure. The results show that the model performed all the Day 1 (24 h), Day 2 (48 h) and Day 3 (72 h) predictions reasonably well. The predictions are more accurate for Day 2 (48 h) and worse for Day 4 (96 h) in both cases. The prediction deteriorates as the prediction time increases. Thus, the prediction may be updated in every 24 h which would provide more realistic prediction. The RMSE shows that the value for 24 h prediction lies within 10–20 mm range. The prediction error is minimal for 48 h prediction, the error ranging from 8 to 12 mm. The error increases thereafter for 72 and 96 h of predictions. The errors range from around 10–20 and 15–25 mm, respectively. The topography/terrain over the southeast hilly region of Bangladesh has not been resolved by USGS terrain data which was used in the MM5 model. Thus, accurate and high-resolution terrain data of this region is expected to improve the performance of the model over the southeast hilly regions of Bangladesh.

An Assessment on Geographically Weighted Regression-Based Merging Method of Satellite and Gauge Rainfall

Satellite-derived and gauge rainfall data are incompatible spatial data. Although great efforts have been devoted to their combination recently, it is still a complicated issue to be addressed. In this study, the performance of merging satellite and gauge rainfall analyses is examined over a humid region in Southeast China. Using satellite rainfall from TRMM 3B43V7 and ground rain gauge measurements, a geographically weighted regression (GWR) based statistical merging algorithm was proposed to continuously produce monthly rainfall fields at 1-km resolution during the period 2003-2009. Results indicate that the benefits of this rainfall merging approach were remarkable only when the gauge density is lower than one gauge per 1,500 km2, suggesting the information provided by TRMM 3B43V7 is more useful for estimating rainfall fields when the ground measurements are rather sparse.

Trends in water balance components across the Brazilian Cerrado

AbstractWe assess the water balance of the Brazilian Cerrado based on remotely sensed estimates of precipitation (TRMM), evapotranspiration (MOD16), and terrestrial water storage (GRACE) for the period from 2003 to 2010. Uncertainties for each remotely sensed data set were computed, the budget closure was evaluated using measured discharge data for the three largest river basins in the Cerrado, and the Mann‐Kendall test was used to evaluate temporal trends in the water balance components and measured river discharge. The results indicate an overestimation of discharge data, due mainly to the overestimation of rainfall by TRMM version 6. However, better results were obtained when the new release of TRMM 3B42 v7 was used instead. Our results suggest that there have been (a) significant increases in average annual evapotranspiration over the entire Cerrado of 51 ± 15 mm yr−1, (b) terrestrial water storage increases of 11 ± 6 mm yr−1 in the northeast region of the Brazilian Cerrado, and (c) runoff decreases of 72 ± 11 mm yr−1 in isolated spots and in the western part of the State of Mato Grosso. Although complete water budget closure from remote sensing remains a significant challenge due to uncertainties in the data, it provides a useful way to evaluate trends in major water balance components over large regions, identify dry periods, and assess changes in water balance due to land cover and land use change.

Vegetation-precipitation controls on Central Andean topography

Climatic controls on fluvial landscapes are commonly characterized in terms of mean annual precipitation. However, physical erosion processes are driven by extreme events and are therefore more directly related to the intensity, duration, and frequency of individual rainfall events. Climate also influences erosional processes indirectly by controlling vegetation. In this study, we explore how interdependent climate and vegetation properties affect landscape morphology at the scale of the Andean orogen. The mean intensity, duration, and frequency of precipitation events are derived from the TRMM 3B42v7 product. Relationships between mean hillslope gradients and precipitation event metrics, mean annual precipitation, vegetation, and bedrock lithology in the central Andes are examined by correlation analyses and multiple linear regression. Our results indicate that mean hillslope gradient correlates most strongly with percent vegetation cover (r = 0.56). Where vegetation cover is less than 95%, mean hillslope gradients increase with mean annual precipitation (r = 0.60) and vegetation cover (r = 0.69). Where vegetation cover is dense (>95%), mean hillslope gradients increase with increasing elevation (r = 0.74), decreasing inter-storm duration (r = −0.69), and decreasing precipitation intensity by ~0.5°/(mm d−1) (r = −0.56). Thus, we conclude that at the orogen scale, climate influences on topography are mediated by vegetation, which itself is dependent on mean annual precipitation (r = 0.77). Observations from the central Andes are consistent with landscape evolution models in which hillslope gradients are a balance between rock uplift, climatic erosional efficiency and erosional resistance of the landscape determined by bedrock lithology and vegetation.

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km2 watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze the precipitation products. Comparison with rain gauge observations reveals that there were significant biases in the satellite rainfall products and large variations in the estimated amounts. The radar basin average precipitation compared very well with the rain gauge product while the gauge-adjusted TRMM 3B42V7 precipitation compared best with observed rainfall among all satellite precipitation products. The NEXRAD MPE simulated streamflows matched the observed ones the best yielding the highest Nash-Sutcliffe Efficiency correlation coefficient values for both the July and August 2007 events. Simulations driven by TRMM 3B42V7 matched the observed streamflow better than other satellite products for both events. The PERSIANN coarse resolution product yielded better runoff results than the higher resolution product. The study reveals that satellite rainfall products are viable alternatives when rain gauge or ground radar observations are sparse or non-existent.

Assessing the drivers of biodiversity in Madagascar by quantifying its hydrologic properties at the watershed scale

Motivated by a theory developed by Wilmé et al. (2006) according to which, watersheds of Madagascar with headwaters at high altitude respond differently to drought from those with headwaters confined to relatively low elevations, with possibly profound effects on the biodiversity patterns of the island, we analyzed multi-year basin-specific observations of soil moisture and vegetation water content (derived from NRL's WindSat radiometer) and their response to precipitation departures (derived from TRMM 3B42 V7) from its local mean. These datasets were analyzed to investigate the hydrologic properties at the basin scale, including the speed with which vegetation and soil moisture respond to precipitation anomalies. We also looked at the basin-specific normalized radar surface-backscattering cross-sections from NASA's QuikSCAT Scatterometer, to obtain information on the vegetation regimes of the various Malagasy basins. Finally, we correlated the basin response to the precipitation forcing, and compared the amplitude and time lag of the correlations across watersheds with high elevation headwaters versus those with low elevation headwaters in the aim of evaluating the drought-response hypothesis of Wilmé et al. (2006). Our results indicate that the vegetation water content time series exhibit several features that are consistent with those of the majority of the bioclimatic zones of the island. Specifically, although the speed of the response of the vegetation water content varies significantly among the different basins, it is inter-annually consistent for each watershed, while the soil–moisture time series are less consistent than the vegetation water content time series. This study is a first step in the quantification of the hydrologic properties derived from microwave remote sensing, and which could potentially shed new light on the different intra-annual responses of watersheds to precipitation anomalies. Furthermore, this analysis offers important insights into the hydro-geomorphologic drivers associated with biodiversity patterns in Madagascar, contributing to a better understanding of the mechanisms that determine biotic diversification across the various bioclimatic regions of the island.

English

This paperwork describes a comprehensive statistical assessment of rain gauge and satellite- based monthly and annual rainfall measurements over Nigeria during the period of 2001 to 2010. Two statistical methods were empolyed for inter-comparison and validation of the Tropical Rainfall Measuring Mission - TRMM 3B43 V7 rainfall product. TRMM was selected because of the recently developed algorithms to estimate 3D rain distribution from the visual spectrum radiance, radar and microwave sensors. The results of the continuous statistical assessment of the rainfall algorithms show good agreement with rain gauge measurement in term of correlation coefficient and improved mean error tests. The geometric mean of correlation coefficient from all the locations for annual, monthly, wet and dry period are 0.43, 0.79, 0.64, and 0.41 respectively. The categorical analysis assessment is based on the International Telecommunication Union for Radio-communication (ITU-R) recommended threshold for radio propagation. The results of Accuracy and FBI for ITU-R recommendation threshold of 1 to 10% percentage error for radio propagation applications are 0.629 and 0.901 for annual while that of monthly are 0.558 and 0.416. The overall performance of the TRMM based rainfall assessment is encouraging but more improvement is still needed for accurate and sufficient global rainfall estimation. Key words: TRMM 3B43 V6, satellite data, cross-validation, categorical statistics, radio propagation, ITU-R.