Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Ye, Xiangyu; Guo, Yuhan; Wang, Zhonggen; Liang, Liaofeng; Tian, Jiayu

doi:10.3390/rs14143350

Cited by 10 publications

(4 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monthly and annual data were acquired from the respective platforms for the 2009-2019 period (validation period) and show record failures of less than 10% (Table 1). The data of the satellite rainfall products used are from the Tropical Rainfall Measuring Mission (TRMM) [42], Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) [17,23], Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN) [43], and Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) [44] (Table 2). TRMM (TRMM 3B42) data provided by the Goddard Earth Sciences Data and Information Services Center have a temporal resolution of 3 h and are available in a spatial resolution of 0.25 • (about 25 km) as of 1 January 1998 to 31 December 2019 [45,46].…”

Section: Acquisition Of Observed Data and Satellite Rainfall Product ...mentioning

confidence: 99%

“…For this research, it was also observed that in some months (May to October), the largest d-index values correspond to the highest correlation values. For the Yarlung Zangbo river basin (China) [43], different rainfall data such as TRMM, CHIRPS, CMORPH (Climate Prediction Center Morphing Method), and PERSIANN were highlighted. The results showed that TRMM presents the best performance (d-index), differing from this research.…”

Section: Monthly Scalementioning

confidence: 99%

See 1 more Smart Citation

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

Baratto,

de Bodas Terassi,

de Beserra de Lima

et al. 2024

Climate

View full text Add to dashboard Cite

The objective of this research is to select the best orbital sensor for rainfall estimates (monthly and annual scales) and to analyze the frequency and magnitude of extreme rainfall events and their trends and disruptions based on the use of satellite rainfall product data for the Cananeia–Iguape Coastal System (CICS). Data from four satellite rainfall products were used to identify the correspondence with seven points on the surface of the study area. Statistical metrics were used to identify the best satellite rainfall product. After identifying the sensor with the best performance in estimating orbital precipitation, extreme events were identified by the Standardized Precipitation Index (SPI) on a one-month (SPI-1), three-month (SPI-3), and twelve-month (SPI-12) scale. Trend and rupture detection in the time series were performed using different statistical techniques (Mann–Kendall, Pettitt, Standard Normal Homogeneity Test, or Buishand test). Among the satellite rainfall products, CHIRPS had the best measurements for the analyzed points on the surface. The year 1983 was characterized as very rainy, also marked by the occurrence of El Niño, and was marked by the rupture of the rains at all points (IDs 1, 2, 3, 4, 5, 6, and 7) analyzed in the month of June. The decrease in monthly rainfall was more significant in the months of February (at points IDs 1, 2, 3, 5, and 7) and April (IDs 1, 3, 5, and 7). Decreased rainfall may cause CICS mangrove shrinkage. These results showed the importance of studying rainfall in an area with mangroves in order to understand the dynamics of vegetation in the face of climate change.

show abstract

Section: Acquisition Of Observed Data and Satellite Rainfall Product ...mentioning

confidence: 99%

Section: Monthly Scalementioning

confidence: 99%

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

Baratto,

de Bodas Terassi,

de Beserra de Lima

et al. 2024

Climate

View full text Add to dashboard Cite

show abstract

“…According to those authors, the performance of satellite products considerably improves when adjusted with in situ observations. Ye et al [67] show a tendency to overestimate precipitation in the cold semi-arid climate (Bsk) zone in the southeast of the Qinghai-Tibet plateau. A possible explanation is that the precipitation detected by satellites in arid zones may evaporate before reaching the surface and thus not be recorded by the gauges.…”

Section: Case Study For North Brazil-r5mentioning

confidence: 99%

Analysis of Extreme Rainfall and Natural Disasters Events Using Satellite Precipitation Products in Different Regions of Brazil

Palharini

Vila²,

Rodrigues³

et al. 2022

Atmosphere

View full text Add to dashboard Cite

The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil’s south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events.

show abstract

“…However, other studies raised concerns regarding the performance of hydrological models at ungauged links [47]. With the rapid development of remote sensing, remotely sensed data, including forcing (e.g., precipitation) or underlying surface values (e.g., terrain and vegetation) have widely been applied in the hydrology community and achieved a considerable success [48][49][50][51]. Motivated by the capabilities of remote sensing data and ML in hydrology, the purpose of this paper was to fill the gap when using ML for hydrological model parameter estimation by investigating the routing parameter CS of the XAJ model, namely, the runoff recession coefficient in a stream network.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

Fang

Huang²,

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.

show abstract

Extensive Evaluation of Four Satellite Precipitation Products and Their Hydrologic Applications over the Yarlung Zangbo River

Cited by 10 publications

References 59 publications

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

Analysis of Extreme Rainfall and Natural Disasters Events Using Satellite Precipitation Products in Different Regions of Brazil

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

Contact Info

Product

Resources

About