Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments

Abstract

A novel approach is presented for combining spatial and temporal detail from newly available TRMM-based data sets to derive hourly rainfall intensities at 1-km spatial resolution for hydrological modelling applications. Time series of rainfall intensities derived from 3-hourly 0.25° TRMM 3B42 data are merged with a 1-km gridded rainfall climatology based on TRMM 2B31 data to account for the sub-grid spatial distribution of rainfall intensities within coarse-scale 0.25° grid cells. The method is implemented for two dryland catchments in Tunisia and Senegal, and validated against gauge data. The outcomes of the validation show that the spatially disaggregated and intensity corrected TRMM time series more closely approximate ground-based measurements than non-corrected data. The method introduced here enables the generation of rainfall intensity time series with realistic temporal and spatial detail for dynamic modelling of runoff and infiltration processes that are especially important to water resource management in arid regions.

Editor D. Koutsoyiannis

Citation Tarnavsky, E., Mulligan, M. and Husak, G., 2012. Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments. Hydrological Sciences Journal, 57 (2), 248–264.     

Relation between GRACE-derived surface mass variations and precipitation over Australia

Surface mass changes (SMCs) obtained from time-variable gravity observations of the Gravity Recovery and Climate Experiment (GRACE) satellite mission and precipitation data from the Australian Bureau of Metrology and the Tropical Rainfall Measurement Mission are analysed over the Australian continent to determine whether there is a statistically significant correlation between them. The multiple linear regression analysis and the principal-component analysis techniques are applied in order to reveal the spatial and temporal variability of each data set separately as well as their mutual relationships. The study provides results and their statistical significance for the whole of Australia including the Murray Darling Basin in the southeast. The results suggest a significant decrease in water storage in the southeast of Australia and a dominant annual cycle over the majority of the continent for the four year period considered (January 2003 to December 2006), both in the surface mass and rainfall time series. The study revealed a direct relation between the data sets over most parts of Australia as confirmed by visual comparison and correlation analysis. When compared with precipitation data GRACE-derived SMCs exhibit smoother spatial and temporal variations. The latter is better suited to detect long-term trends in the presence of strong annual signals, which can adversely affect long-term trend estimates. Results regarding the magnitude of the annual signal suggest that only about a fourth of the precipitation's water masses remain sufficiently long in an area to be detected as a gravity change. The respective phases of the annual signals show an average time lag of about 40 days between precipitation and SMCs, suggesting that it takes about one to two months until a temporal gravity observation can detect a precipitation event.     

Spatio-temporal Distribution of Drought in the Belt and Road Area During 1998-2015 Based on TRMM Precipitation Data

Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Based on the Tropical Rainfall Measuring Mission Satellite (TRMM) 3B43 precipitation data, we used the Precipitation Abnormity Percentage drought model to study the monthly spatio-temporal distribution of drought in south region of N50° of the Belt and Road area. It was observed that drought during winter was mainly distributed in Northeast Asia, Southeast Asia, and South Asia, while it was mainly distributed in Central Asia and West Asia during summer. The occurrence of historical droughts indicates an obvious seasonal cycle. The regional variations in drought were analyzed using the Breaks for Additive Season and Trend tool (BFAST) in six sub-regions according to the spatial distribution of six economic corridors in the Belt and Road area. The average drought conditions over the 18 years show a slight decreasing trend in Northeast Asia, West Asia, North Africa, South Asia, Central and Eastern Europe, and a slight increasing trend in Central Asia. However, it was a fluctuating pattern of first increasing and then decreasing in Southeast Asia. The results indicate that the total drought area in the Belt and Road region showed a general decreasing trend at a rate of 40,260 km2 per year from 1998 to 2015.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

The contribution of tropical cyclones to rainfall in Mexico

Investigating the contribution of tropical cyclones to the terrestrial water cycle can help quantify the benefits and hazards caused by the rainfall generated from this type of hydro-meteorological event. Rainfall induced by tropical cyclones can enhance both flood risk and groundwater recharge, and it is therefore important to characterise its minimum, mean and maximum contributions to a region or country’s water balance. This work evaluates the rainfall contribution of tropical depressions, storms and hurricanes across Mexico from 1998 to 2013 using the satellite-derived precipitation dataset TMPA 3B42. Additionally, the sensitivity of rainfall to other datasets was assessed: the national rain gauge observation network, real-time satellite rainfall and a merged product that combines rain gauges with non-calibrated space-borne rainfall measurements. The lower Baja California peninsula had the highest contribution from cyclonic rainfall in relative terms (∼40% of its total annual rainfall), whereas the contributions in the rest of the country showed a low-to-medium dependence on tropical cyclones, with mean values ranging from 0% to 20%. In quantitative terms, southern regions of Mexico can receive more than 2400 mm of cyclonic rainfall during years with significant TC activity. Moreover, (a) the number of tropical cyclones impacting Mexico has been significantly increasing since 1998, but cyclonic contributions in relative and quantitative terms have not been increasing, and (b) wind speed and rainfall intensity during cyclones are not highly correlated. Future work should evaluate the impacts of such contributions on surface and groundwater hydrological processes and connect the knowledge gaps between the magnitude of tropical cyclones, flood hazards, and economic losses.     

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China

The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission (TRMM) data with rain gauge data and further to use this TRMM data to drive a Distributed Time-Variant Gain Model (DTVGM) to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year (2001-2010) daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R² varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years (2001-2007) of data on daily hydrological processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamflow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.     

How do GPM and TRMM precipitation products perform in alpine regions? A case study in northwestern China’s Qilian Mountains

Journal of Geographical Sciences - Satellite technologies provide valuable areal precipitation datasets in alpine mountains. However, coarse resolution still limits the use of satellite...     

Comparison of TRMM and water district rain rates over New Mexico

This paper compares monthly and seasonal rain rates derived from the Version 5 (V5) and Version 6 (V6) TRMM Precipitation Radar (TPR, TSDIS reference 2A25), TRMM Microwave Imager (TMI, 2A12), TRMM Combined Instrument (TCI, 2B31), TRMM calibrated IR rain estimates (3B42) and TRMM merged gauge and satellite analysis (3B43) algorithms over New Mexico (NM) with rain gauge analyses provided by the New Mexico water districts (WD). The average rain rates over the NM region for 1998–2002 are 0.91mmd?1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07mmd?1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d?1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index (TPR and TMI) suggests that the low bias of V5 3B43 for the wet months (summer to early fall) may be due to the non-inclusion of some rain events in the operational gauge analyses that are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations (>0.8) exist between WD and the combined algorithms (3B42, 3B43 and TCA for both V5 and V6) while satellite instrument algorithms (PR, TMI and TCI) are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI (?7.4%) and TPR (?31%) rain rates (compared to V5) results in better agreement between WD estimates and TMI in winter and TPR during summer.     

利用TRMM卫星资料对青藏高原地区强对流天气特征分析

利用热带测雨卫星TRMM(Tropical Rainfall Measure Mission)多种探测结果,结合NCEP再分析资料,研究了发生在青藏高原地区的一次强对流天气特征,综合分析了高原地区对流云特殊的水平、垂直结构特征。结果表明:(1)该强对流降水系统由几个孤立、零散的块状降水云团组成,以深厚弱对流降水为主,微波亮温的低值区也呈孤立、零散的块状分布,并且整个对流系统的云顶高度一致偏高,深厚强对流降水的雨谱主要集中在1～20mm.h-1的范围内,90%以上的深厚弱对流降水样本数和降水量都集中在0～5mm.h-1范围内,在垂直方向上呈被"挤压"状态。除云冰粒子集中在6～18km高度外,可降冰、可降水和云水粒子都集中在低层8km以下,冰雹天气表现为可降冰粒子在低层含量偏高。(2)高原地区强对流天气的特征与其他地方的不同,表现为雨强较小,比平原地区明显偏弱,且对流云降雨样本在不同降雨率范围内分布不均匀,降水云团雨顶高度也远低于平原地区的对流云,地表降水率大值区与微波辐射亮温低值区呈不完全对称分布,潜热释放呈单峰型。(3)高原地区强对流系统发生时,垂直上升运动在400hPa达到最大,水汽主要集中在400hPa高度以下的范围内。     

TRMM和CMORPH卫星资料对三峡库区降水的评估分析

利用1998—2016年TRMM和CMORPH两种遥感卫星资料降水和同期三峡库区气象观测站数据,通过比较干、支流和远、近库区气象站点的降水变化和蓄水前后降水量、雨日、降水强度和频率等变化特征,分析评估了基于两种卫星遥感降水和测站降水的三峡库区局地降水变化。结果表明:库区TRMM和CMORPH卫星降水年际变化特征总体上与气象观测站相符,反演效果在日尺度TRMM略逊于CMORPH,在季尺度CMORPH略逊于TRMM;两种卫星资料对冬季降水反演效果都偏弱。三峡库区干流和支流站点的降水变化总体一致,干、支流各站点降水量均具有较强的年际变化特征。TRMM相比CMORPH更能重现干、支流测站降水的年际变化特征,CMORPH降水年际波动振幅总体上比测站偏大。蓄水前后时段(1998—2003年与2004—2016年)对比,从不同等级降水的强度和雨日、季节降水频率和总量等变化反演效果来看,CMORPH资料分布相比TRMM更接近测站的变化趋势,反演效果略优于TRMM;但两种卫星资料的降水频率和降水量分布与测站的误差在蓄水前后变化都不明显。此外,气象测站、TRMM、CMORPH资料都表现出蓄水后三峡远、近库区年降水量的比值呈平稳波动状态,表明三峡水库蓄水后附近地区降水没有明显变化。