Binghao Jia

Effects of anthropogenic water regulation and groundwater lateral flow on land processes

Both anthropogenic water regulation and groundwater lateral flow essentially affect groundwater table patterns. Their relationship is close because lateral flow recharges the groundwater depletion cone, which is induced by over-exploitation. In this study, schemes describing groundwater lateral flow and human water regulation were developed and incorporated into the Community Land Model 4.5. To investigate the effects of human water regulation and groundwater lateral flow on land processes as well as the relationship between the two processes, three simulations using the model were conducted for the years 2003–2013 over the Heihe River Basin in northwestern China. Simulations showed that groundwater lateral flow driven by changes in water heads can essentially change the groundwater table pattern with the deeper water table appearing in the hillslope regions and shallower water table appearing in valley bottom regions and plains. Over the last decade, anthropogenic groundwater exploitation deepened the water table by approximately 2 m in the middle reaches of the Heihe River Basin and rapidly reduced the terrestrial water storage, while irrigation increased soil moisture by approximately 0.1 m3 m−3. The water stored in the mainstream of the Heihe River was also reduced by human surface water withdrawal. The latent heat flux was increased by 30 W m−2 over the irrigated region, with an identical decrease in sensible heat flux. The simulated groundwater lateral flow was shown to effectively recharge the groundwater depletion cone caused by over-exploitation. The offset rate is higher in plains than mountainous regions.

Measuring and modeling the impact of a severe drought on terrestrial ecosystem CO2 and water fluxes in a subtropical forest

A severe drought occurred in central and southern China in the summer of 2013. The precipitation dropped to less than 25% of the long-term average, and temperatures were abnormally high for more than two months with return periods of 125-year and 301-year respectively, which induced significant changes in the terrestrial eco-hydrological cycle. In this study, the impact of the severe drought on a subtropical forest ecosystem was investigated using measurements from a newly established flux tower, and simulations performed by the Community Land Model version 4.5 (CLM4.5). Based on in situ observations, we found that both gross primary production (GPP) and evapotranspiration experienced strong reductions of 76% and 40% respectively during the prolonged dry and hot spell. There was an exponential relationship between ecosystem respiration (Reco) and temperatures when soil moisture was not too dry, but Reco decreased along with GPP when the temperature exceeded 32 °C and soil moisture was below 0.14 m3 m–3. The ecosystem even switched to a net source of carbon in late August. The model captured the variations in water vapor fluxes well, with correlation coefficients r > 0.88, but overestimated net ecosystem CO2 exchange (NEE) because it did not adequately represent carbon fluxes responses to water stress and failed to capture the nonlinear relationship between GPP and Reco during the drought period. The long-term simulation suggested that water availability severely limited carbon sequestration, and both the underlying water use efficiency (uWUE) and inherent WUE (IWUE) reached their maximum values.

Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

In order to reduce the uncertainty of offline land surface model (LSM) simulations of land evapotranspiration (ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs (BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982–2007. The results showed that various simulations of each member and their arithmetic mean (Ens Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates (Obs MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens Mean was closer to Obs MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs MTE and Ens Mean exhibited a significant increasing trend during 1982–98; whereas after 1998, when the last big EI Ni˜no event occurred, the Ens Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

Diurnal and Seasonal Variations of CO2 Fluxes and Their Climate Controlling Factors for a Subtropical Forest in Ningxiang

In this study, the diurnal and seasonal variations of CO2 fluxes in a subtropical mixed evergreen forest in Ningxiang of Hunan Province, part of the East Asian monsoon region, were quantified for the first time. The fluxes were based on eddy covariance measurements from a newly initiated flux tower. The relationship between the CO2 fluxes and climate factors was also analyzed. The results showed that the target ecosystem appeared to be a clear carbon sink in 2013, with integrated net ecosystem CO2 exchange (NEE), ecosystem respiration (RE), and gross ecosystem productivity (GEP) of −428.8, 1534.8 and 1963.6 g C m−2yr−1, respectively. The net carbon uptake (i.e. the -NEE), RE and GEP showed obvious seasonal variability, and were lower in winter and under drought conditions and higher in the growing season. The minimum NEE occurred on 12 June (−7.4 g C m−2 d−1), due mainly to strong radiation, adequate moisture, and moderate temperature; while a very low net CO2 uptake occurred in August (9 g C m−2 month−1), attributable to extreme summer drought. In addition, the NEE and GEP showed obvious diurnal variability that changed with the seasons. In winter, solar radiation and temperature were the main controlling factors for GEP, while the soil water content and vapor pressure deficit were the controlling factors in summer. Furthermore, the daytime NEE was mainly limited by the water-stress effect under dry and warm atmospheric conditions, rather than by the direct temperature-stress effect.

Incorporation of a Dynamic Root Distribution into CLM4.5: Evaluation of Carbon and Water Fluxes over the Amazon

Roots are responsible for the uptake of water and nutrients by plants and have the plasticity to dynamically respond to different environmental conditions. However, most land surface models currently prescribe rooting profiles as a function only of vegetation type, with no consideration of the surroundings. In this study, a dynamic rooting scheme, which describes root growth as a compromise between water and nitrogen availability, was incorporated into CLM4.5 with carbon–nitrogen (CN) interactions (CLM4.5-CN) to investigate the effects of a dynamic root distribution on eco-hydrological modeling. Two paired numerical simulations were conducted for the Tapajos National Forest km83 (BRSa3) site and the Amazon, one using CLM4.5-CN without the dynamic rooting scheme and the other including the proposed scheme. Simulations for the BRSa3 site showed that inclusion of the dynamic rooting scheme increased the amplitudes and peak values of diurnal gross primary production (GPP) and latent heat flux (LE) for the dry season, and improved the carbon (C) and water cycle modeling by reducing the RMSE of GPP by 0.4 g C m-2 d-1, net ecosystem exchange by 1.96 g C m-2 d-1, LE by 5.0 W m-2, and soil moisture by 0.03 m3 m-3, at the seasonal scale, compared with eddy flux measurements, while having little impact during the wet season. For the Amazon, regional analysis also revealed that vegetation responses (including GPP and LE) to seasonal drought and the severe drought of 2005 were better captured with the dynamic rooting scheme incorporated.

Impacts of hydraulic redistribution on eco-hydrological cycles: A case study over the Amazon basin

Hydraulic redistribution (HR) refers to the process of soil water transport through the low-resistance pathway provided by plant roots. It has been observed in field studies and proposed to be one of the processes that enable plants to resist water limitations. However, most land-surface models (LSMs) currently do not include this underground root process. In this study, a HR scheme was incorporated into the Community Land Model version 4.5 (CLM4.5) to investigate the effect of HR on the eco-hydrological cycle. Two paired numerical simulations (with and without the new HR scheme) were conducted for the Tapajos National Forest km83 (BRSa3) site and the Amazon. Simulations for the BRSa3 site in the Amazon showed that HR during the wet season was small, <0.1 mm day–1, transferring water from shallow wet layers to deep dry layers at night; however, HR in the dry season was more obvious, up to 0.3 mm day–1, transferring water from deep wet layers to shallow dry layers at night. By incorporating HR into CLM4.5, the new model increased gross primary production (GPP) and evapotranspiration (ET) by 10% and 15%, respectively, at the BRSa3 site, partly overcoming the underestimation. For the Amazon, regional analysis also revealed that vegetation responses (including GPP and ET) to seasonal drought and the severe drought of 2005 were better captured with the HR scheme incorporated.

Interannual Variations and Trends in Remotely Sensed and Modeled Soil Moisture in China

AbstractIn this study, a microwave-based multisatellite merged product released from the European Space Agency’s Climate Change Initiative (ESA CCI) and two model-based simulations from the Communi...

An inverse method to estimate emission rates based on nonlinear least-squares-based ensemble four-dimensional variational data assimilation with local air concentration measurements

An inverse source estimation method is proposed to reconstruct emission rates using local air concentration sampling data. It involves the nonlinear least squares-based ensemble four-dimensional variational data assimilation (NLS-4DVar) algorithm and a transfer coefficient matrix (TCM) created using FLEXPART, a Lagrangian atmospheric dispersion model. The method was tested by twin experiments and experiments with actual Cs-137 concentrations measured around the Fukushima Daiichi Nuclear Power Plant (FDNPP). Emission rates can be reconstructed sequentially with the progression of a nuclear accident, which is important in the response to a nuclear emergency. With pseudo observations generated continuously, most of the emission rates were estimated accurately, except under conditions when the wind blew off land toward the sea and at extremely slow wind speeds near the FDNPP. Because of the long duration of accidents and variability in meteorological fields, monitoring networks composed of land stations only in a local area are unable to provide enough information to support an emergency response. The errors in the estimation compared to the real observations from the FDNPP nuclear accident stemmed from a shortage of observations, lack of data control, and an inadequate atmospheric dispersion model without improvement and appropriate meteorological data. The proposed method should be developed further to meet the requirements of a nuclear emergency response.

Improving the simulation of terrestrial water storage anomalies over China using a Bayesian model averaging ensemble approach

ABSTRACT The ability to estimate terrestrial water storage (TWS) is essential for monitoring hydrological extremes (e.g., droughts and floods) and predicting future changes in the hydrological cycle. However, inadequacies in model physics and parameters, as well as uncertainties in meteorological forcing data, commonly limit the ability of land surface models (LSMs) to accurately simulate TWS. In this study, the authors show how simulations of TWS anomalies (TWSAs) from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging (BMA) ensemble approach to improve monitoring and predictions. Simulations using three forcing datasets and two LSMs were conducted over mainland China for the period 1979–2008. All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08. The correlation coefficient ranged between 0.5 and 0.8 in the humid regions (e.g., the Yangtze river basin, Huaihe basin, and Zhujiang basin), but was much lower in the arid regions (e.g., the Heihe basin and Tarim river basin). The BMA ensemble approach performed better than all individual member simulations. It captured the spatial distribution and temporal variations of TWSAs over mainland China and the eight major river basins very well; plus, it showed the highest R value (> 0.5) over most basins and the lowest root-mean-square error value (< 40 mm) in all basins of China. The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term, high-resolution spatial and temporal TWSA data. Graphical Abstract

Land Surface Improvements

Land surface processes play an important role in the climate system. In this chapter, some improvements to parameterizations of land surface processes are introduced, including representation of water table dynamics, inclusion of anthropogenic groundwater exploitation, development of a frozen soil model considering the freezing–thawing interface, representation of crop growth, and development of a land surface model. Furthermore, the development of our land data assimilation system will be also described in this chapter.