Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China

2013 ◽  
Vol 726-731 ◽  
pp. 3385-3390
Author(s):  
Josephine Osei-Kwarteng ◽  
Qiong Fang Li ◽  
Kwaku Amaning Adjei
Keyword(s):  
Hydrological Modeling ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Station ◽  
Huaihe River ◽  
Resources Management ◽  
Sustainable Water Resources Management ◽  
Rain Gage ◽  
Gauge Station ◽  
Trmm 3B42

In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.

scholarly journals Intervention Analysis of Urbanization Effect on Rainfall Data at the Seoul Rain Gauge Station

2007 ◽  
Vol 40 (8) ◽  
pp. 629-641 ◽  
Author(s):  
Chul-Sang Yoo ◽  
Dae-Ha Kim ◽  
Sang-Hyoung Park ◽  
Byung-Su Kim ◽  
Chang-Yeol Park
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Intervention Analysis ◽  
Rain Gauge Station ◽  
Urbanization Effect ◽  
Gauge Station

Snow-Hydrological modeling using remote sensing data in Vilcanota basin, Peru

2020 ◽  
Author(s):  
Eber Risco ◽  
Waldo Lavado ◽  
Pedro Rau
Keyword(s):  
Hydrological Modeling ◽  
Remote Sensing Data ◽  
Time Consistency ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Direct Evaluation ◽  
Daily Discharge ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Trmm 3B42

<p>Water resources availability in the southern Andes of Peru is being affected by glacier and snow retreat. This problem is already perceived in the Vilcanota river basin, where hydro-climatological information is scarce. In this particular mountain context, any water plan represents a great challenge. To cope with these limitations, we propose to assess the space-time consistency of 10 satellite-based precipitation products (CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, GSMaP correction, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TRMM 3B42) with 25 rain gauge stations in order to select the best product that represents the variability in the Vilcanota basin. For this purpose, through a direct evaluation of sensitivity analysis via the GR4J parsimonious hydrological model over the basin. GSMap v.6, TRMM 3B42 and CHIRPS were selected to represent rainfall spatial variability according with different statistical criteria, such as correlation coefficient (CC), standard deviation (SD), percentage of bias (%B) and centered mean square error (CRMSE). To facilitate the interpretation of statistical results, Taylor's diagram was used to represent the CC statistics, normalized values of SD and CRMSE.</p><p>A distributed degree-day model was chosen to analyse the sensitivity of snow cover simulations and hydrological contribution. The GR4J rainfall-runoff model was calibrated (using global optimization) and applied to simulate the daily discharge and compared with the Distributed Hydrology and Vegetation Model with Glacier Dynamics (DHSVM-GDM) over the 2001-2018 period. Furthermore, the simulated streamflow was evaluated through comparisons with observations at the hydrological stations using Nash–Sutcliffe efficiency and Kling Gupta Efficiency (KGE). The results show that the snow-runoff have increased in recent years, so new water management and planning strategies should be developed in the basin. This research is part of the multidisciplinary collaboration between British and Peruvian scientists (Newton Fund, Newton-Paulet) through RAHU project.</p>

scholarly journals Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus

2020 ◽  
Vol 12 (19) ◽  
pp. 3212
Author(s):  
Adrianos Retalis ◽  
Dimitris Katsanos ◽  
Filippos Tymvios ◽  
Silas Michaelides
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Monthly Basis ◽  
Rain Gauges ◽  
Annual Basis ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Gauge Station ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.

scholarly journals Contamination Vulnerability Analysis of Watershed for Water Quality Monitoring

2016 ◽  
Vol 20 (1) ◽  
Author(s):  
Widyastuti Widyastuti ◽  
Slamet Suprayogi
Keyword(s):  
Water Quality ◽  
Land Surface ◽  
Water Quality Monitoring ◽  
Vulnerability Index ◽  
Rain Gauge ◽  
Vulnerability Analysis ◽  
Quality Monitoring ◽  
Neural Network Modeling ◽  
Rain Gauge Station ◽  
Gauge Station

This research is an early step to determine the location of rain gauge station for artificial neural network modeling. The implementation of this model is very useful for water quality monitoring. The objectives of this study are: 1) to study the distribution of watershed parameter, that are average annual precipitation, land use and land-surface slope, 2) to conduct vulnerability analysis of watershed contamination, 3) to determine the location of rain gauge station. The study was performed by weighing and rating method of watershed parameters. The vulnerability degree of watershedtocontaminationispresentedasvulnerabilityindex.Thisindexisdeterminedbyoverallsumofallmultiplication between score and weigh number of each parameter. All data manipulation and data analysis were performed by using Geographic Information System (ArcView version by 3.2). The vulnerability of watershed contamination map had been generated using overlay operation of parameters. The results show that vulnerability index are varies between 10 up to 40 intervals. Hence, the indexes were categorized into three levels of watershed vulnerability, namely low (10 – 20), moderate (20 – 30) and high (30 – 40). It is found that the study area covered more by high vulnerability of watershed to contamination. The zoning of watershed vulnerability meant to determine the rain gauge location. There are three rain gauge stations on the area that they are in a high vulnerability level, whereas the other vulnerability level area has one rain gauge station. Each level of vulnerability area is able to represent the source of contaminant that it maybe influence the water quality of Gajahwong river.

scholarly journals Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

2021 ◽  
Author(s):  
Bingru Tian ◽  
Hua Chen ◽  
Jialing Wang ◽  
Chong-Yu Xu
Keyword(s):  
Rain Gauge ◽  
Precipitation Intensity ◽  
Intensity Level ◽  
Total Precipitation ◽  
Rain Gauge Station ◽  
Xiangjiang River ◽  
River Catchment ◽  
Satellite Precipitation ◽  
Gauge Station ◽  
The Impact

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

scholarly journals Toward a practical approach for ergodicity analysis

2015 ◽  
Vol 2 (5) ◽  
pp. 1425-1446 ◽  
Author(s):  
H. Wang ◽  
C. Wang ◽  
Y. Zhao ◽  
X. Lin ◽  
C. Yu
Keyword(s):  
Time Series ◽  
Yellow River ◽  
Rain Gauge ◽  
Ergodic Property ◽  
Practical Approach ◽  
Function Evaluation ◽  
Rain Gauge Station ◽  
Gauge Station ◽  
Mean Ergodicity ◽  
The Mean

Abstract. It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of autocorrelation function evaluation and Augmented Dickey Fuller test, a radial basis function neural network, and the definition of mean ergodicity. The mean ergodicity of precipitation processes at the Lanzhou Rain Gauge Station in the Yellow River basin, the Ankang Rain Gauge Station in Han River, both in China, and at Newberry, MI, USA are analyzed using the proposed approach. The results indicate that the precipitations of March, July, and August in Lanzhou, and of May, June, and August in Ankang have mean ergodicity, whereas, the precipitation of any other calendar month in these two rain gauge stations do not have mean ergodicity. The precipitation of February, May, July, and December in Newberry show ergodic property, although the precipitation of each month shows a clear increasing or decreasing trend.

scholarly journals Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junzhi Liu ◽  
Zheng Duan ◽  
Jingchao Jiang ◽  
A-Xing Zhu
Keyword(s):  
Comprehensive Evaluation ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Lake Basin ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Using Data ◽  
Hydrological Applications ◽  
Trmm 3B42

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

scholarly journals Does More Moisture in the Atmosphere Lead to More Intense Rains?

2021 ◽  
Author(s):  
Agostino Manzato
Keyword(s):  
Precipitable Water ◽  
Rain Gauge ◽  
Rain Gauge Station ◽  
Scatter Plot ◽  
North East ◽  
Station Data ◽  
Gauge Station ◽  
Friuli Venezia Giulia

Abstract It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatter plot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moistures in the atmosphere is that there is a more chance to happen, rather than of an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm/6h to a lesser extent. The result is derived from an analysis of 33 local rain–gauge station data and a shared sounding over Friuli Venezia Giulia, North–East Italy.

Characterizing Congo Basin Rainfall and Climate Using Tropical Rainfall Measuring Mission (TRMM) Satellite Data and Limited Rain Gauge Ground Observations

2015 ◽  
Vol 54 (3) ◽  
pp. 541-555 ◽  
Author(s):  
Yolande A. Munzimi ◽  
Matthew C. Hansen ◽  
Bernard Adusei ◽  
Gabriel B. Senay
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Congo Basin ◽  
Annual Rainfall ◽  
Standard Errors ◽  
Bias Estimation ◽  
Tropical Regions ◽  
Tropical Rainfall ◽  
Mean Square Errors ◽  
Trmm 3B42

AbstractQuantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.

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