scholarly journals Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1632
Author(s):  
Yufu Li ◽  
Xinxin Sui ◽  
Yunjun Yao ◽  
Haixia Cheng ◽  
Lilin Zhang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
River Basin ◽  
Latent Heat Flux ◽  
North China ◽  
Spatiotemporal Variability ◽  
Haihe River Basin ◽  
Haihe River ◽  
Basin Scale ◽  
Global Land

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p < 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

scholarly journals GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets

2021 ◽  
Vol 13 (6) ◽  
pp. 1054
Author(s):  
Lu Wang ◽  
Yuhu Zhang ◽  
Yunjun Yao ◽  
Zhiqiang Xiao ◽  
Ke Shang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
River Basin ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Gradient Boosting ◽  
Haihe River Basin ◽  
Haihe River ◽  
Global Land ◽  
The Haihe River Basin

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

scholarly journals The Spatiotemporal Pattern of Rainy-Season Precipitation in the Haihe River Basin, North China

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 73 ◽  
Author(s):  
Jun Guo ◽  
Guoyu Ren ◽  
Mingming Xiong ◽  
He Huang
Keyword(s):  
River Basin ◽  
Rainy Season ◽  
North China ◽  
Haihe River Basin ◽  
Spatiotemporal Pattern ◽  
The South ◽  
Haihe River ◽  
Continuous Precipitation ◽  
Flood Season ◽  
The Haihe River Basin

The Haihe River basin of North China is characterized by extremely low per capita water resources and a consistently long-term decreasing trend of precipitation and runoff over the last few decades. This study analyzes the climatological features of rainy season (May–September) precipitation in the Haihe River basin and its branch systems based on a high-density hourly observational dataset during 2007–2017. We show that there are two high-rainfall zones in the basin, with one along the south of the Yanshan Mountains to Taihang Mountains and another along the Tuma River in the south. Rainstorm centers exist amidst the two zones. July generally sees the highest precipitation, followed by August, and May has the lowest precipitation. The major flood season is reached between the third pentad of July and the fourth pentad of August. The precipitation is high at night but low in the daytime. In the pre-flood season before early July, rainfalls mostly arrive at 16:00–21:00 h. After entering the major flood season, the diurnal precipitation has two peaks, one at 17:00–22:00 h and the other at 0:00–7:00 h. In the post-flood season after mid-August, the most rain occurs at night, with the peak appearing at 0:00–8:00 h. The short-duration precipitation is mainly distributed in the mountainous areas, and the long-duration precipitation that contributes most to seasonal rainfalls appears in the plain areas, and the continuous precipitation mostly occurs in the windward slopes of the Taihang Mountains and the Yanshan Mountains. In addition, urbanization process around large city stations may have affected the rainy season precipitation to a certain extent in the Haihe River basin, with large and medium city stations experiencing around 10% higher precipitation than small city stations. However, this issue needs to be investigated exclusively.

scholarly journals Drought Analysis of the Haihe River Basin Based on GRACE Terrestrial Water Storage

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Wang ◽  
Dong Jiang ◽  
Yaohuan Huang ◽  
Hao Wang
Keyword(s):  
River Basin ◽  
Water Storage ◽  
Spatiotemporal Variability ◽  
Haihe River Basin ◽  
Terrestrial Water Storage ◽  
Haihe River ◽  
Drought Analysis ◽  
Terrestrial Water ◽  
The Haihe River Basin ◽  
Anomaly Index

The Haihe river basin (HRB) in the North China has been experiencing prolonged, severe droughts in recent years that are accompanied by precipitation deficits and vegetation wilting. This paper analyzed the water deficits related to spatiotemporal variability of three variables of the gravity recovery and climate experiment (GRACE) derived terrestrial water storage (TWS) data, precipitation, and EVI in the HRB from January 2003 to January 2013. The corresponding drought indices of TWS anomaly index (TWSI), precipitation anomaly index (PAI), and vegetation anomaly index (AVI) were also compared for drought analysis. Our observations showed that the GRACE-TWS was more suitable for detecting prolonged and severe droughts in the HRB because it can represent loss of deep soil water and ground water. The multiyear droughts, of which the HRB has sustained for more than 5 years, began in mid-2007. Extreme drought events were detected in four periods at the end of 2007, the end of 2009, the end of 2010, and in the middle of 2012. Spatial analysis of drought risk from the end of 2011 to the beginning of 2012 showed that human activities played an important role in the extent of drought hazards in the HRB.

The Influence of Precipitation Variability and Partial Irrigation within Grid Cells on a Hydrological Simulation

2007 ◽  
Vol 8 (3) ◽  
pp. 499-512 ◽  
Author(s):  
Qiuhong Tang ◽  
Taikan Oki ◽  
Shinjiro Kanae ◽  
Heping Hu
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
River Basin ◽  
Latent Heat Flux ◽  
Precipitation Variability ◽  
Mean Annual Precipitation ◽  
Constructive Model ◽  
Irrigation Season ◽  
Grid Cells ◽  
Hydrological Simulation

Abstract The effects of natural and anthropogenic heterogeneity on a hydrological simulation are evaluated using a distributed biosphere hydrological model (DBHM) system. The DBHM embeds a biosphere model into a distributed hydrological scheme, representing both topography and vegetation in a mesoscale hydrological simulation, and the model system includes an irrigation scheme. The authors investigated the effects of two kinds of variability, precipitation variability and the variability of irrigation redistributing runoff, representing natural and anthropogenic heterogeneity, respectively, on hydrological processes. Runoff was underestimated if rainfall was placed spatially uniformly over large grid cells. Accounting for precipitation heterogeneity improved the runoff simulation. However, the negative runoff contribution, namely, the situation that mean annual precipitation is less than evapotranspiration, cannot be simulated by only considering the natural heterogeneity. This constructive model shortcoming can be eliminated by accounting for anthropogenic heterogeneity caused by irrigation water withdrawals. Irrigation leads to increased evapotranspiration and decreased runoff, and surface soil moisture in irrigated areas increases because of irrigation. Simulations performed for the Yellow River basin of China indicated streamflow decreases of 41% due to irrigation effects. The latent heat flux in the peak irrigation season [June–August (JJA)] increased 3.3 W m−2 with a decrease in the ground surface temperature of 0.1 K for the river basin. The maximum simulated increase in the latent heat flux was 43 W m−2, and the ground temperature decrease was 1.6 K in the peak irrigation season.

scholarly journals Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China

2019 ◽  
Vol 11 (15) ◽  
pp. 1787
Author(s):  
Jia Xu ◽  
Yunjun Yao ◽  
Kanran Tan ◽  
Yufu Li ◽  
Shaomin Liu ◽  
...  
Keyword(s):  
Heat Flux ◽  
Kalman Filter ◽  
Latent Heat ◽  
River Basin ◽  
Latent Heat Flux ◽  
Northwest China ◽  
Heihe River Basin ◽  
Filter Method ◽  
Heihe River ◽  
Data Gaps

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions.

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