Evaluation of the WRF Physics Ensemble using Multivariable Integrated Evaluation Approach over Haihe river basin in north China

2020 ◽  
Author(s):  
Danqiong Dai
Keyword(s):  
Vector Field ◽  
Observational Data ◽  
River Basin ◽  
Land Surface ◽  
Regional Climate ◽  
Field Evaluation ◽  
Shortwave Radiation ◽  
Haihe River Basin ◽  
Spatial Average ◽  
Haihe River

<p>  A crucial step of the application of WRF in regional climate research is selection of the proper combinations of physical parameterizations. In this study, we performed experiments in WRF to assess the predict skill of various parametrization schemes sets in simulating precipitation, temperature over the Haihe river basin. The experiments driven by ERA-INTERIM reanalysis data are performed for a period of summer (1 June to 31 August, 2016) in this domain with 13 km grid spacing. Fifty-eight members of physics combinations thoroughly covering five types of physics options are assessed against the available observational data by utilizing the multivariable integrated evaluation (MVIE) method. It is deduced that the best performing setup consists of CAM5.1 microphysics, MRF PBL, BMJ Cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. To identify the robustness of the optimal scheme set, the vector field evaluation (VFE) diagram for displaying all simulations reveals that the optimal one is distinguished from others by higher vector field similarity coefficient(Rν), smaller root mean square vector deviation(RMSVD). The model deviations spatially for the precipitation show a promising tendency that a strong overestimation about 5 mm/day for the default configuration evolves small biases of the optimal setup with a range between -1 and 1 mm/day, and the surface temperature forecasts have improved to some extent although not significant as that of precipitation. The temporally analysis of the spatial average of all simulations exhibits that for temperature the optimal setup is more approaching to the observational data, but for precipitation no remarkable difference between all simulation and the observations. Further analysis of the sensitivities of model output to different types of physics option suggests that, microphysics, PBL, and Cumulus schemes have more significant impact on the model performances measured by a multivariable integrated evaluation index (MIEI) than radiation scheme and Land Surface schemes.</p>

scholarly journals Urbanization-Driven Changes in Land-Climate Dynamics: A Case Study of Haihe River Basin, China

2020 ◽  
Vol 12 (17) ◽  
pp. 2701
Author(s):  
Zhouyuan Li ◽  
Yanjie Xu ◽  
Yingbao Sun ◽  
Mengfan Wu ◽  
Bin Zhao
Keyword(s):  
Land Use ◽  
River Basin ◽  
Land Surface ◽  
Agricultural Land ◽  
Regional Climate ◽  
Climate Dynamics ◽  
Haihe River Basin ◽  
Haihe River ◽  
Land Use And Cover

Urbanization changes the land surface environment, which alters the regional climate system. In this study, we took the Haihe River Basin in China as a case study area, as it is highly populated and experienced rapid urbanization from 2000–2015. We investigated how land use and cover change (LUCC) was driven by urban land development affects land-climate dynamics. From 2000–2015, we collected data from the land use and cover database, the remote sensing database of the Moderate Resolution Imaging Spectroradiometer (MODIS) series, and the meteorological database to process and generate regional datasets for LUCC maps. We organized data by years aligned with the selected indicators of land surface, normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST), as well as of regional climate, cloud water content (CWC), and precipitation (P). The assembled datasets were processed to perform statistical analysis and conduct structural equation modelling (SEM). Based on eco-climatology principles and the biophysical process in the land-climate dynamics, we made assumptions on how the indicators connected to each other. Moreover, we testified and quantified them in SEM. LUCC results found that from 2000–2015 the urban area proportion increased by 214% (2.20–6.91%), while the agricultural land decreased by 7.2% (53.05–49.25%) and the forest increased by 4.3% (10.02–10.45%), respectively. This demonstrated how cropland intensification and afforestation happened in the urbanizing basin. SEM results showed that the forest had both positive and negative effects on the regional hydrological cycle. The agricultural land, grassland, and shrub had indirect effects on the P via different biophysical functions of LST. The overall effects of urbanization on regional precipitation was positive (pathway correlation coefficient = 0.25). The interpretation of how urbanization drives LUCC and alters regional climate were herein discussed in different aspects of socioeconomic development, biophysical processes, and urbanization-related atmospheric effects. We provided suggestions for further possible research on monitoring and assessment, putting forth recommendations to advance sustainability via land planning and management, including agricultural land conservation, paying more attention to the quality growth of forest rather than the merely area expansion, integrating the interdisciplinary approach, and assessing climatic risk for extreme precipitation and urban flooding.

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 Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

2019 ◽  
Vol 11 (24) ◽  
pp. 3050
Author(s):  
Yulong Zhong ◽  
Wei Feng ◽  
Vincent Humphrey ◽  
Min Zhong
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Human Impacts ◽  
Anthropogenic Impacts ◽  
Anthropogenic Activities ◽  
Water Storage ◽  
Haihe River Basin ◽  
Haihe River ◽  
The Haihe River Basin

Terrestrial water storage (TWS) can be influenced by both climate change and anthropogenic activities. While the Gravity Recovery and Climate Experiment (GRACE) satellites have provided a global view on long-term trends in TWS, our ability to disentangle human impacts from natural climate variability remains limited. Here we present a quantitative method to isolate these two contributions with reconstructed climate-driven TWS anomalies (TWSA) based on long-term precipitation data. Using the Haihe River Basin (HRB) as a case study, we find a higher human-induced water depletion rate (−12.87 ± 1.07 mm/yr) compared to the original negative trend observed by GRACE alone for the period of 2003–2013, accounting for a positive climate-driven TWSA trend (+4.31 ± 0.72 mm/yr). We show that previous approaches (e.g., relying on land surface models) provide lower estimates of the climate-driven trend, and thus likely underestimated the human-induced trend. The isolation method presented in this study will help to interpret observed long-term TWS changes and assess regional anthropogenic impacts on water resources.

scholarly journals Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jing Zhao ◽  
Xuelong Chen ◽  
Jing Zhang ◽  
Honggang Zhao ◽  
Yongyu Song
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Vegetation Index ◽  
Hydrologic Cycle ◽  
Haihe River Basin ◽  
High Temporal Resolution ◽  
Haihe River ◽  
Rate Of Increase ◽  
The Haihe River Basin ◽  
Sebs Model

Abstract Evapotranspiration (ET) is a key variable in hydrologic cycle that directly affects the redistribution of precipitation and surface balance. ET measurements with high temporal resolution are required for coupling with models of highly dynamic processes, e.g., hydrological and land surface processes. The Haihe River Basin is the focus of China’s industrial base and it is one of the three major grain-producing regions within the country. However, this area is facing serious water resource shortages and water pollution problems. The present study used geostationary satellite remote sensing data, in situ meteorological observations, and the surface energy balance system (SEBS) model with a new kB−1 parameterization to estimate 3-hourly and daily energy and water fluxes in the Haihe River Basin. The results of the SEBS model were validated with point-scale data from five observation flux towers. Validation showed that 3-hourly and daily ET derived from the SEBS model performed well (R2 = 0.67, mean bias = 0.027 mm/h, RMSE = 0.1 mm/h). Moreover, factors influencing ET were also identified based on the results of this study. ET varies with land cover type and physical and chemical properties of the underlying surface. Furthermore, ET is also controlled by water availability, radiation, and other atmospheric conditions. It was found that ET had strong correlation with the normalized difference vegetation index (NDVI). Specifically, daily ET fluctuated with the NDVI when the NDVI was <0.29, and ET increased rapidly as the NDVI increased from 0.29 to 0.81. For NDVI values >0.81, indicating a state of saturation, the rate of increase of ET slowed. This research produced reliable information that could assist in sustainable management of the water resources and in improved understanding of the hydrologic cycle of the Haihe River Basin.

Long-term variation of water vapor content and precipitation in the Haihe river basin

2014 ◽  
Vol 6 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Chun Chang ◽  
Ping Feng ◽  
Fawen Li ◽  
Yunming Gao
Keyword(s):  
Water Vapor ◽  
River Basin ◽  
Reanalysis Data ◽  
Water Vapor Content ◽  
Haihe River Basin ◽  
Haihe River ◽  
Annual Water ◽  
Environmental Prediction ◽  
High Consistency ◽  
The Haihe River Basin

Based on the Haihe river basin National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data from 1948 to 2010 and the precipitation data of 53 hydrological stations during 1957–2010, this study analyzed the variation of water vapor content and precipitation, and investigated the correlation between them using several statistical methods. The results showed that the annual water vapor content decreased drastically from 1948 to 2010. It was comparatively high from the late 1940s to the late 1960s and depreciated from the early 1970s. From the southeast to the northwest of the Haihe river basin, there was a decrease in water vapor content. For vertical distribution, water vapor content from the ground to 700 hPa pressure level accounted for 72.9% of the whole atmospheric layer, which indicated that the water vapor of the Haihe river basin was mainly in the air close to the ground. The precipitation in the Haihe river basin during 1957–2010 decreased very slightly. According to the correlation analysis, the precipitation and water vapor content changes showed statistically positive correlation, in addition, their break points were both in the 1970s. Furthermore, the high consistency between the precipitation efficiency and precipitation demonstrates that water vapor content is one of the important factors in the formation of precipitation.

scholarly journals Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1798
Author(s):  
Xu Wu ◽  
Su Li ◽  
Bin Liu ◽  
Dan Xu
Keyword(s):  
Temporal Variation ◽  
River Basin ◽  
Spatial And Temporal Variation ◽  
Haihe River Basin ◽  
Temperature Threshold ◽  
Threshold Method ◽  
Exponential Equation ◽  
Haihe River ◽  
The Haihe River Basin ◽  
Different Levels

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

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