Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Spatial and temporal effects of climate change and land use change on runoff, sediment, nitrogen and phosphorus loss in the Haihe River Basin over the past 40 years

Investigating the impacts of climate and land use changes on basin’s hydrological cycle and environment is important to provide scientific evidence to manage the trade-off and synergies among water resource, agricultural production and environment protection. In this study, we quantified the contributions of climate and land-use changes to runoff, sediment, nitrogen and phosphorus losses in the Haihe River basin since the 1980s. The results showed that (1) climate and land-use changes significantly increased evapotranspiration (ET), transport loss (TL), sediment input (SI) and output (SO), and organic nitrogen (ON) and phosphorus production (OP), with ET, SI, and ON affected most. (2) The runoff, sediment and ammonia nitrogen were affected most by climate and land use changes in the Daqing River Basin (217.3 mm), Nanyun River Basin (3917.3 ton) and Chaobai River Basin (87.6 kg/ha), respectively. (3) The impacts of climate and land-use changes had explicit spatial-temporal patterns. In the Daqing River, Yongding River and Nanyun River, the contribution of climate change to runoff and sediment kept increasing and reached 88.6%~98.2% and 63%~77.2%, respectively. In the Ziya River and Chaobai River Basin, the contribution of land use was larger, reaching 88.6%~92.8% and 59.8%~92.7%, respectively. In the Yongding River Basin, Chaobai River Basin, Ziya River Basin and Daqing River Basin, the contribution of land use to nitrogen and phosphorus loss showed an increasing trend in the past 40 years (maximum: 89.7%). By contrast, in Nanyun River and Luanhe River, the contribution of climate change to nitrogen and phosphorus loss increased more obviously (maximum: 92.1%). We quantitatively evaluated the spatial and temporal impacts of climate and land-use changes on runoff, sediment, and nitrogen and phosphorus loss, which are useful to support the optimizations of land and water resources in the River Basin.

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

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.

Temporal and Spatial Variation Characteristics of Precipitation in the Haihe River Basin under the Influence of Climate Change

The impact of global climate change on the temporal and spatial variations of precipitation is significant. In this study, daily temperature and precipitation data from 258 meteorological stations in the Haihe River Basin, for the period 1960–2020, were used to determine the trend and significance of temperature and precipitation changes at interannual and interseasonal scales. The Mann–Kendall test and Spearman’s correlation analysis were employed, and significant change trends and correlations were determined. At more than 90% of the selected stations, the results showed a significant increase in temperature, at both interannual and interseasonal scales, and the increasing trend was more significant in spring than in other seasons. Precipitation predominantly showed a decreasing trend at an interannual scale; however, the change trend was not significant. In terms of the interseasonal scale, the precipitation changes in spring and autumn showed an overall increasing trend, those in summer showed a 1:1 distribution ratio of increasing and decreasing trends, and those in winter showed an overall decreasing trend. Furthermore, the Spearman’s correlation analysis showed a negative correlation between temperature and precipitation in the entire Haihe River Basin, at both interannual and interseasonal scales; however, most of the correlations were weak.

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

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.