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.

scholarly journals Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D

2020 ◽  
Vol 12 (13) ◽  
pp. 2114
Author(s):  
Christine Kolbe ◽  
Boris Thies ◽  
Nazli Turini ◽  
Zhiyu Liu ◽  
Jörg Bendix
Keyword(s):  
Tibetan Plateau ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Geo Satellites ◽  
Spatio Temporal ◽  
Model Training ◽  
Global Precipitation Measurement ◽  
Global Precipitation

We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MW precipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independent MW precipitation with IMERG’s IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found.

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 A flexible two-stage approach for blending multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

2020 ◽  
Author(s):  
Yingzhao Ma ◽  
Xun Sun ◽  
Haonan Chen ◽  
Yang Hong ◽  
Yinsheng Zhang
Keyword(s):  
Tibetan Plateau ◽  
Complex Terrain ◽  
Warm Season ◽  
Regression Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Satellite Precipitation ◽  
Northeastern Tibetan Plateau ◽  
Blending Method ◽  
Precipitation Estimates

Abstract. Substantial biases exist in the Satellite Precipitation Estimates (SPE) over complex terrain regions and it has always been a challenge to quantify and correct such biases. The combination of multiple SPE and ground observations would be beneficial to improve the precipitation estimates. In this study, a flexible two-step approach is proposed by firstly reducing the systematic errors of each SPE using rain gauge observations as references, and then merging the improved multi-SPE with a Bayesian weighting model. In the 1st stage, gauge references are assumed as a generalized regression function of SPE and terrain feature. In the 2nd stage, the weights assigned to the involved SPE are calculated according to the associated performance relative to gauge references. This blending method has the ability to exert benefits from multi-SPE in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying prior distributions on the model parameters, which enables to produce posterior ensembles associated with their predictive uncertainties. The performance of the two-step blending approach is assessed using independent rain gauge observations during the warm season of 2014 in the northeastern Tibetan Plateau. Results show that the blended multi-SPE are significantly improved compared to the original individuals, especially during heavy rainfall events. This study can also be expanded as a data fusion framework in the development of high-quality precipitation products in high-cold regions characterized by complex terrain.

scholarly journals A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

2021 ◽  
Vol 25 (1) ◽  
pp. 359-374
Author(s):  
Yingzhao Ma ◽  
Xun Sun ◽  
Haonan Chen ◽  
Yang Hong ◽  
Yinsheng Zhang
Keyword(s):  
Tibetan Plateau ◽  
Warm Season ◽  
Regression Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Two Stage ◽  
Validation Data ◽  
Satellite Precipitation ◽  
Northeastern Tibetan Plateau ◽  
Precipitation Estimates

Abstract. Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.

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 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.

scholarly journals Extension of the Upper Yellow River into the Tibet Plateau: Review and New Data

Quaternary ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 14
Author(s):  
Zhengchen Li ◽  
Xianyan Wang ◽  
Jef Vandenberghe ◽  
Huayu Lu
Keyword(s):  
Tibetan Plateau ◽  
Yellow River ◽  
Google Earth ◽  
Tibet Plateau ◽  
The Yellow River ◽  
Published Data ◽  
The Tibetan Plateau ◽  
Large Lakes ◽  
River Terraces ◽  
The Tibet Plateau

The Wufo Basin at the margin of the northeastern Tibet Plateau connects the upstream reaches of the Yellow River with the lowland catchment downstream, and the fluvial terrace sequence in this basin provides crucial clues to understand the evolution history of the Yellow River drainage system in relation to the uplift and outgrowth of the Tibetan Plateau. Using field survey and analysis of Digital Elevation Model/Google Earth imagery, we found at least eight Yellow River terraces in this area. The overlying loess of the highest terrace was dated at 1.2 Ma based on paleomagnetic stratigraphy (two normal and two reversal polarities) and the loess-paleosol sequence (12 loess-paleosol cycles). This terrace shows the connections of drainage parts in and outside the Tibetan Plateau through its NE margin. In addition, we review the previously published data on the Yellow River terraces and ancient large lakes in the basins. Based on our new data and previous researches, we conclude that the modern Yellow River, with headwaters in the Tibet Plateau and debouching in the Bohai Sea, should date from at least 1.2 Ma. Ancient large lakes (such as the Hetao and Sanmen Lakes) developed as exorheic systems and flowed through the modern Yellow River at that time.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

2016 ◽  
Author(s):  
Xiaomang Liu ◽  
Tiantian Yang ◽  
Koulin Hsu ◽  
Changming Liu ◽  
Soroosh Sorooshian
Keyword(s):  
Tibetan Plateau ◽  
River Basin ◽  
Yangtze River ◽  
Yellow River ◽  
Yangtze River Basin ◽  
Information Source ◽  
Hydrologic Model ◽  
The Tibetan Plateau ◽  
Climate Data ◽  
Streamflow Simulation

Abstract. On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable observation alternatives for investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), is used as input of a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River Basin on the Tibetan Plateau. The results show that the simulated streamflow using PERSIANN-CDR precipitation is closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River Basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River Basin, PERSIANN-CDR precipitation and gauge-based precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that PERSIANN-CDR rainfall product has good potentials to be a reliable dataset and an alternative information source besides the sparse gauge network for conducting long term hydrological and climate studies on the Tibetan Plateau.

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