Evaluation of climate reanalysis and space-borne precipitation products over Bangladesh

ABSTRACT

This study aims to quantify the spatial distribution of errors in two climate reanalysis (ERA5 and CFSR) and two satellite (TMPA-RT and TMPA-V7) precipitation products over Bangladesh. The datasets are assessed against ground-based rain gauge observations to capture the extreme rainfall accumulations at daily temporal scale over a 5-year period (January 2010–December 2014). The bias ratio scores indicate that CFSR and TMPA-RT seriously overestimate the rainfall values over much of the study area. Whilst TMPA-V7 performs better than the other precipitation products, all datasets lose their detection skills substantially for higher quantile thresholds (i.e. above 50th and 75th percentiles). With respect to rainfall detection metrics – probability of detection (POD) and volumetric hit index (VHI) – both ERA5 and CFSR show superior performance (in the range 0.9–1.0 for all the analysis grid boxes). All rainfall datasets are equally good in terms of false alarm ratio (FAR) and volumetric FAR (VFAR), even though the lowest values are associated with ERA5 for higher quantiles. All products demonstrate a decrease in skill to capture the amount of rainfall but show satisfactory results to detect the rainfall events when using higher quantile thresholds (i.e. rainfall above the 50th and 75th percentiles) to sample the data before computing product skill.     

Evaluating precipitation products for hydrologic modeling over a large river basin in the Midwestern USA

ABSTRACT

We evaluated precipitation estimates, TRMM (Tropical Rainfall Measuring Mission 3B42V7), CFSR (Climate Forecast System Reanalysis), GHCN-D (Global Historical Climatology Network-Daily Version 3.24), and Daymet, using the Soil and Water Assessment Tool (SWAT). The suitability and quality of TRMM, CFSR and Daymet in forcing the SWAT-based hydrological model was examined by means of model calibration. A calibrated TRMM-driven model slightly overestimated streamflow, while a calibrated CFSR-driven model performed worst. The Daymet-driven model performance was as good as the GHCN-D-driven model in reproducing observations. In addition, the temperature was far less sensitive compared with precipitation in driving SWAT. TRMM 3B42V7 showed great potential in streamflow simulation. The results and findings from this study provide new insights into the suitability of precipitation products for hydrological and climate impact studies in large basins, particularly those in typical climates and physiographic settings similar to the Midwestern USA.     

Correction of TRMM 3B43 monthly precipitation data over the mountainous areas of Peru during the period 1998–2007

In an attempt to estimate the spatial and temporal behaviour of rainfall over the mountainous areas of the Peruvian Andes, a new in situ monthly rainfall dataset has been collected (1998–2007) and compared with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data for regions located above 3000 m. The reliability of the TRMM 3B43 data varies depending on the root mean squared error ratio (%RMSE) and correlation coefficient. Because of the discrepancy between the two datasets, the use of additive and multiplicative correction models is proposed for the TRMM 3B43 data. In the Peruvian mountain ranges, these correction models better approximate TRMM rainfall monthly values, as already verified for annual values. Copyright © 2011 John Wiley & Sons, Ltd.     

2003—2012年新疆陆地水储量时空变化

利用2003年1月至2012年12月120个月的GRACE数据,基于高斯平滑和平滑滤波手段,辅以TRMM降水量、河网分布、蒸散发、径流等数据,反演了新疆陆地水储量,分析了10年间陆地水储量的时空变化情况.结果表明:2003—2012年新疆地区陆地水储量持续减少,但后半期水储量的减幅有所减缓;陆地水储量具有明显降低趋势的地区范围逐步向新疆东部迁移,水储量具有上升趋势的面积有逐渐增加的倾向,但是这种显著上升趋势所涵盖的面积没有占据研究区的主导地位;GRACE的年周期值和陆地水储量深度值的波动趋势一致;TRMM降水变化趋势与GRACE反演的陆地水变化趋势相辅相成,相互验证.该项研究可为新疆合理用水及缺水地区水资源合理的开发等提供理论依据.     

Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates

Rainfall measurements by conventional raingauges provide relatively accurate estimates at a few points of a region. The actual rainfield can be approximated by interpolating the available raingauge data to the remaining of the area of interest. In places with relatively low gauge density such interpolated rainfields will be very rough estimates of the actual events. This is especially true for tropical regions where most rainfall has a convective origin with high spatial variability at the daily level. Estimates of rainfall by remote sensing can be very useful in regions such as the Amazon basin, where raingauge density is very low and rainfall highly variable. This paper evaluates the rainfall estimates of the Tropical Rainfall Measuring Mission (TRMM) satellite over the Tapajós river basin, a major tributary of the Amazon. Three-hour TRMM rainfall estimates were aggregated to daily values and were compared with catch of ground-level precipitation gauges on a daily basis after interpolating both data to a regular grid. Both daily TRMM and raingauge-interpolated rainfields were then used as input to a large-scale hydrological model for the whole basin; the calculated hydrographs were then compared to observations at several streamgauges along the river Tapajos and its main tributaries. Results of the rainfield comparisons showed that satellite estimates can be a practical tool for identifying damaged or aberrant raingauges at a basin-wide scale. Results of the hydrological modeling showed that TRMM-based calculated hydrographs are comparable with those obtained using raingauge data.     

Relationships among remotely sensed soil moisture,precipitation and landslide events

Landslides are triggered by earthquakes, volcanoes, floods, and heavy continuous rainfall. For most types of slope failure, soil moisture plays a critical role because increased pore water pressure reduces the soil strength and increases stress. However, in-situ soil moisture profiles are rarely measured. To establish the soil moisture and landslide relationship, a qualitative comparison among soil moisture derived from AMSR-E, precipitation from TRMM and major landslide events was conducted. This study shows that it is possible to estimate antecedent soil moisture conditions using AMSR-E and TRMM satellite data in landslide prone areas. AMSR-E data show distinct annual patterns of soil moisture that reflect observed rainfall patterns from TRMM. Results also show enhanced AMSR-E soil moisture and TRMM rainfall prior to major landslide events in landslide prone regions of California, U.S.; Leyte, Philippines; and Dhading, Nepal.     

基于TRMM观测的亚洲——太平洋三个季风区的降水结构特征

The three-dimensional structure of precipitation on a seasonal scale in the Asian-Pacific＇s three monsoon regions is investigated based on the tropical rainfall measurement mission （TRMM） data. The results show that： （1） The maximum seasonal variation of the relative proportional difference of convective precipitation and stratiform rain occurs in the East Asian monsoon region, the second occurs in the Indian monsoon region, and the minimum is in the northwest Pacific monsoon region. In both the northwest Pacific mon soon region and the Indian monsoon region, the convective rain is proportionately larger than stratiform rain in all four seasons. （2） Cloud ice reaches its maximum at around 9 km. Cloud water＇s maximum range is between 3 and 4 km. The large value area of precipitation ice is mainly between 4 and 9 km. The precipi tation water particle is concentrated mostly below 4 km. The largest content is from the ground to 2 km. （3） The most remarkable variance of the content of cloud ice in the Indian monsoon region occurs from spring to winter, and the content of cloud water in the northwest Pacific is always higher than that in the other two regions. （4） The latent heat profile has a similar double-peak structure. The first peak is at 4 km and the second peak is at 2 km. In autumn and winter, the latent heat is higher in the northwest Pacific than in other two regions. In all three regions, the release of the latent heat is higher in summer and autumn than in spring and winter.     

Rainfall screening methodology using TRMM data over a river basin

ABSTRACT

A regionalized rain/no-rain classification (RNC) based on scattering index methodology is developed for detecting rainfall signatures over the land regions of the Mahanadi basin (India), using data products from the passive and active sensors onboard the Tropical Rainfall Measuring Mission (TRMM), namely the TRMM Microwave Imager (TMI) and Precipitation Radar (PR). The proposed model, developed using data for two years from the orbital database, was validated using PR and in-situ data for selected case study events in 2011 and 2012. Performance evaluation of the model is discussed using 10 metrics derived from the contingency table. Overall, the results show superior performance, with an average probability of detection of 0.83, bias of 1.10 and odds ratio skill score greater than 0.93. Accurate rainfall detection is obtained for 95% of case study events. The relative performance of the proposed model is dependent on rainfall type, but it should be useful in rainfall retrieval algorithms for current missions such as the Global Precipitation Measurement Mission.

Editor M.C. Acreman; Associate editor Y. Gyasi-Agyei     

Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area

The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be downscaled before being used. This study developed a statistical spatial downscaling scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The downscaled precipitation data were compared with ground observations and exhibited good agreement with r² values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.     

基于改进型TVDI在干旱区旱情监测中的应用研究

干旱是全球范围内影响最为广泛的自然灾害之一,其所导致的土壤沙漠化、荒漠化和盐碱化给生态环境造成不可逆的危害。通过对MODIS数据进行投影转换、去云等预处理的基础上,利用地形校正对TVDI模型进行改进,构建了改进型的温度植被干旱指数（mTVDI）用于新疆干旱区旱情监测。利用土壤实测数据对mTVDI及传统的TVDI模型进行对比验证。研究结果表明：（1） 利用EVI与校正后的LST构建的mTVDI_E对干旱区旱情的敏感度最高,与实测土壤水分数据的相关性R²为0.74。（2） 从空间上看,新疆2015年旱情分布以塔里木盆地和准噶尔盆地为两个干旱中心,旱情状况由严重逐步向周围山区递减至湿润状态。从时间上看,新疆6月、7月和8月旱情最为严重。（3） 研究利用TRMM降水数据对基于mTVDI_E反演的新疆旱情时空分布特征进行对比分析,结果表明二者所表现出的旱情时空分布较为一致,不同时间段内的降水量与mTVDIE之间具有一定的相关性,且均通过了P<0.01显著性检验。综上,基于TVDI所提出的mTVDI_E 能够有效开展新疆干旱区旱情监测,且精度较高,从而为今后定量化开展大区域尺度旱情监测研究提供参考。