scholarly journals Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau

2013 ◽  
Vol 17 (2) ◽  
pp. 837-849 ◽  
Author(s):  
Y. C. Gao ◽  
M. F. Liu
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Data Sets ◽  
The Tibetan Plateau ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Mean Square Errors

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Four high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The four satellite-based precipitation data sets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 version 6 (TMPA) and its Real Time version (TMPART), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN and TMPART. TMPA has the lowest biases among the four precipitation data sets, which is likely due to the correction process against the monthly gauge observations from global precipitation climatology project (GPCP). TMPA also shows large improvement over TMPART, indicating the importance of gauge-based correction on accuracy of rainfall. The four products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the four precipitation products generally tend to overestimate light rainfall (0–10 mm) and underestimate moderate and heavy rainfall (>10 mm). Moreover, this study extracts 24 topographic variables from a DEM (digital elevation model) and uses a linear regression model to explore the bias–topography relationship. Results show that biases of TMPA and CMORPH present weak dependence on topography. However, biases of TMPART and PERSIANN present dependence on topography and variability of elevation and surface roughness plays important roles in explaining their biases.

scholarly journals Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau

2012 ◽  
Vol 9 (8) ◽  
pp. 9503-9532 ◽  
Author(s):  
Y. C. Gao ◽  
M. F. Liu
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Mean Square Errors

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Three high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The three satellite-based precipitation datasets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN. TMPA has the lowest biases among the three precipitation datasets, which is likely due to the correction process against monthly gauge observations from global precipitation climatology project (GPCP). The three products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the three precipitation products generally tend to overestimate light rainfall (0–10 mm) and underestimate moderate and heavy rainfall (>10 mm). PERSIANN produces obvious underestimation at low elevations and overestimation at high elevations. CMORPH and TMPA do not present strong bias-elevation relationships in most regions of TP.

scholarly journals Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

2018 ◽  
Vol 10 (8) ◽  
pp. 1316 ◽  
Author(s):  
Peng Bai ◽  
Xiaomang Liu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Distributed Hydrological Model ◽  
Satellite Precipitation ◽  
Rain Gauges ◽  
Upper Yangtze River ◽  
Precipitation Estimates

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

Evaluation of precipitation products based on rain gauge data and water budget in a mountainous river basin, Eastern Tibetan Plateau

2020 ◽  
Author(s):  
Yaozhi Jiang ◽  
Kun Yang ◽  
Xiaodong Li ◽  
Wenjiang Zhang ◽  
Yan Shen ◽  
...  
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
Tibetan Plateau ◽  
Water Budget ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Mountainous Areas ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
High Precipitation

<p>Precipitation in mountainous areas provides abundant water resources for downstream regions, and reliable precipitation data in these areas is of crucial importance for the management of water resources and water-related disasters. Because in-situ precipitation data are usually scarce in mountainous areas, satellite-based precipitation products are expected to play an important role; however, they should be carefully validated before application. This study evaluated the performance of three high-resolution precipitation products in the mountainous Qingyi River basin, by comparison with both rain gauge-based and water budget-based methods. The basin is located at the eastern margin of the Tibetan Plateau, and has high precipitation leading to high runoff (~1100 mm/year). The three precipitation products are CMPA (the China Merged Precipitation Analysis), IMERG (the Integrated Multi-satellitE Retrievals for GPM) and GSMaP (the Global Satellite Mapping of Precipitation). In general, both rain gauge-based and water budget-based methods showed that CMPA has the highest accuracy and IMERG has the poorest accuracy in this region. In two sub-basins with steep terrain and high precipitation, the rain gauge-based evaluation indicated negative or even positive basin-averaged biases of about 1 mm/day or less, but the water budget analysis indicated that all the products had much larger negative biases, of 2.4 ~ 3.8 mm/day. This difference likely arises because the evaluation based on rain gauge data cannot reflect errors in products at the basin-scale, due to the sparse spatial distribution of rain gauges. Finally, observed altitudinal gradients of precipitation were used to correct the precipitation products. Under this approach the water budget can be better closed but is not always satisfactory. Therefore, developing a high-quality precipitation data set for mountainous regions based only on satellite products and sparse ground observations remains challenging and other data sources (e.g. high-resolution meteorological modeling) should be taken into consideration in future.</p>

scholarly journals Evaluation of a Convection-Permitting Modeling of Precipitation over the Tibetan Plateau and Its Influences on the Simulation of Snow-Cover Fraction

2020 ◽  
Vol 21 (7) ◽  
pp. 1531-1548 ◽  
Author(s):  
Yanhong Gao ◽  
Fei Chen ◽  
Yingsha Jiang
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Snow Cover ◽  
Land Surface ◽  
Grand Canyon ◽  
Added Value ◽  
The Tibetan Plateau ◽  
Precipitation Data ◽  
Snow Cover Fraction

AbstractPrecipitation is a critical input to land surface and hydrology modeling and prediction. Dynamical downscale modeling has added value to representing precipitation, when compared with the performance of coarse-resolution reanalysis and global climate models, over the Tibetan Plateau (TP). Convection-permitting modeling (CPM) may even outperform dynamical downscale models (DDMs). In this study, 4-km CPM results were compared to 28-km DDM results for a snow season (1 October 2013–31 May 2014) over the TP. The CPM- and DDM-simulated precipitation, as well as three merged gridded precipitation datasets, were evaluated against in situ observations below 4800 m. The five precipitation datasets (CPM, DDM, CMFD, COPRPH, and TRMM) showed large differences over the TP with underestimation of TRMM and overestimation of CPM and DDM compared to observations. The most significant difference occurred in the Brahmaputra Grand Canyon. Given the substantial uncertainty in observed precipitation at high mountains, snow cover simulated by a high-resolution land data assimilation system was used to indirectly evaluate the above precipitation data using MODIS observations. Simulated snow-cover fraction was greatly underestimated using all the merged precipitation datasets. However, simulations using the DDM- and CPM-generated precipitation as input outperformed those using other gridded precipitation data, showing lower biases, higher pattern correlations, and closer probability distribution functions than runs driven by the merged precipitation. The findings of this study generally support the assumption that high-resolution CPM-produced precipitation has an added value for use in land surface and hydrology simulations in high-mountain regions without reliable in situ precipitation observations.

scholarly journals Long term variations of actual evapotranspiration over the Tibetan Plateau

2021 ◽  
Author(s):  
Cunbo Han ◽  
Yaoming Ma ◽  
Binbin Wang ◽  
Lei Zhong ◽  
Weiqiang Ma ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Tibetan Plateau ◽  
Water Cycle ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Actual Evapotranspiration ◽  
The Tibetan Plateau ◽  
Atmosphere Interaction ◽  
Mean Square Errors

Abstract. Terrestrial actual evapotranspiration (ETa) is a key parameter controlling the land-atmosphere interaction processes and the water cycle. However, the spatial distribution and temporal changes of ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Validation against data from six eddy-covariance monitoring sites yielded a root mean square errors ranging from 9.3 to 14.5 mm mo−1, and correlation coefficients exceeding 0.9. The domain mean of annual ETa on the TP decreased slightly (−1.45 mm yr−1, p 

scholarly journals Long-term variations in actual evapotranspiration over the Tibetan Plateau

2021 ◽  
Vol 13 (7) ◽  
pp. 3513-3524
Author(s):  
Cunbo Han ◽  
Yaoming Ma ◽  
Binbin Wang ◽  
Lei Zhong ◽  
Weiqiang Ma ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Tibetan Plateau ◽  
Water Cycle ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Data Bank ◽  
The Tibetan Plateau ◽  
Science Data ◽  
Autumn And Winter ◽  
Mean Square Errors

Abstract. Actual terrestrial evapotranspiration (ETa) is a key parameter controlling land–atmosphere interaction processes and water cycle. However, spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Validation against data from six eddy-covariance monitoring sites yielded root-mean-square errors ranging from 9.3 to 14.5 mm per month and correlation coefficients exceeding 0.9. The domain mean of annual ETa on the TP decreased slightly (−1.45 mm yr−1, p<0.05) from 2001 to 2018. The annual ETa increased significantly at a rate of 2.62 mm yr−1 (p<0.05) in the eastern sector of the TP (long >90∘ E) but decreased significantly at a rate of −5.52 mm yr−1 (p<0.05) in the western sector of the TP (long <90∘ E). In addition, the decreases in annual ETa were pronounced in the spring and summer seasons, while almost no trends were detected in the autumn and winter seasons. The mean annual ETa during 2001–2018 and over the whole TP was 496±23 mm. Thus, the total evapotranspiration from the terrestrial surface of the TP was 1238.3±57.6 km3 yr−1. The estimated ETa product presented in this study is useful for an improved understanding of changes in energy and water cycle on the TP. The dataset is freely available at the Science Data Bank (https://doi.org/10.11922/sciencedb.t00000.00010; Han et al., 2020b) and at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Hydro.tpdc.270995, Han et al., 2020a).

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

scholarly journals A New and Simplified Approach for Estimating the Daily River Discharge of the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the Upper Brahmaputra River

2020 ◽  
Vol 12 (13) ◽  
pp. 2103
Author(s):  
Tian Zeng ◽  
Lei Wang ◽  
Xiuping Li ◽  
Lei Song ◽  
Xiaotao Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Real Time ◽  
River Discharge ◽  
The Tibetan Plateau ◽  
Time Lags ◽  
Precipitation Data ◽  
Precipitation Time Series ◽  
Discharge Data ◽  
Satellite Precipitation

Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (>0.85) and Yangcun (>0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE > 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

scholarly journals Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

2018 ◽  
Vol 10 (12) ◽  
pp. 1883 ◽  
Author(s):  
Ziqiang Ma ◽  
Kang He ◽  
Xiao Tan ◽  
Jintao Xu ◽  
Weizhen Fang ◽  
...  
Keyword(s):  
Random Forest ◽  
Tibetan Plateau ◽  
Spatial Information ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Precipitation Data ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Improved Accuracy ◽  
Global Precipitation

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

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