Yibo Liu

Water use efficiency of China’s terrestrial ecosystems and responses to drought

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss of terrestrial ecosystems, and better understanding its dynamics and controlling factors is essential for predicting ecosystem responses to climate change. We assessed the magnitude, spatial patterns, and trends of WUE of China’s terrestrial ecosystems and its responses to drought using a process-based ecosystem model. During the period from 2000 to 2011, the national average annual WUE (net primary productivity (NPP)/evapotranspiration (ET)) of China was 0.79 g C kg−1 H2O. Annual WUE decreased in the southern regions because of the decrease in NPP and the increase in ET and increased in most northern regions mainly because of the increase in NPP. Droughts usually increased annual WUE in Northeast China and central Inner Mongolia but decreased annual WUE in central China. “Turning-points” were observed for southern China where moderate and extreme droughts reduced annual WUE and severe drought slightly increased annual WUE. The cumulative lagged effect of drought on monthly WUE varied by region. Our findings have implications for ecosystem management and climate policy making. WUE is expected to continue to change under future climate change particularly as drought is projected to increase in both frequency and severity.

Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield

There has been growing evidence that vegetation greenness has been increasing inmany parts of the northernmiddle and high latitudes includingChina during the last three to four decades. However, the effects of increasing vegetation greenness particularly afforestation on the hydrological cycle have been controversial.We used a process-based ecosystemmodel and a satellite-derived leaf area index (LAI) dataset to examine how the changes in vegetation greenness affected annual evapotranspiration (ET) andwater yield for China over the period from2000 to 2014. Significant trends in vegetation greenness were observed in 26.1%ofChina’s land area.We used twomodel simulations drivenwith original and detrended LAI, respectively, to assess the effects of vegetation ‘greening’ and ‘browning’ on terrestrial ET andwater yield. On a per-pixel basis, vegetation greening increased annual ET and decreasedwater yield, while vegetation browning reduced ET and increasedwater yield. At the large river basin and national scales, the greening trends also had positive effects on annual ET and had negative effects onwater yield. Our results showed that the effects of the changes in vegetation greenness on the hydrological cycle variedwith spatial scale. Afforestation efforts perhaps should focus on southernChinawith larger water supply given thewater crisis in northernChina and the negative effects of vegetation greening onwater yield. Future studies on the effects of the greenness changes on the hydrological cycle are needed to account for the feedbacks to the climate.

Evaluation and improvement of MODIS gross primary productivity in typical forest ecosystems of East Asia based on eddy covariance measurements

Gross primary productivity (GPP) is a major component of carbon exchange between the atmosphere and terrestrial ecosystems and a key component of the terrestrial carbon cycle. Because of the large spatial heterogeneity and temporal dynamics of ecosystems, it is a challenge to estimate GPP accurately at global or regional scales. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) GPP product provides a near real time estimate of global GPP. However, previous studies indicated that MODIS GPP has large uncertainties, partly caused by biases in parameterization and forcing data. In this study, MODIS GPP was validated using GPP derived from the eddy covariance flux measurements at five typical forest sites in East Asia. The validation indicated that MODIS GPP was seriously underestimated in these forest ecosystems of East Asia, especially at northern sites. With observed meteorological data, fraction of photosynthetically active radiation absorbed by the plant canopy (fPAR) calculated using smoothed MODIS leaf area index, and optimized maximum light use efficiency (εmax) to force the MOD17 algorithm, the agreement between predicted GPP and tower-based GPP was significantly improved. The errors of MODIS GPP in these forest ecosystems of East Asia were mainly caused by uncertainties in εmax, followed by those in fPAR and meteorological data. The separation of canopy into sunlit and shaded leaves, for which GPP is individually calculated, can improve GPP simulation significantly.

Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale.

Applicability of spectral and spatial information from IKONOS-2 imagery in retrieving leaf area index of forests in the urban area of Nanjing, China

Abstract. Remote sensing is currently an indispensable tool for retrieving the leaf area index (LAI) of forests. However, the applicability of remote sensing in retrieving LAI of forests in urban areas has not been thoroughly investigated. The ability of spectral and spatial information from IKONOS-2 imagery to retrieve LAI of forests was studied through analyzing the correlations of four commonly used vegetation indices (VIs) and four texture measures (TEXs) with LAI measured at different types of plots in the urban area of Nanjing, China and comparing the ability of models based on these parameters to estimate LAI of forests. The results show that VIs and TEXs calculated from the high-resolution remote sensing data are both applicable in retrieving LAI of forests in urban areas. The relative advantages of VIs and TEXs are related to the density and spatial regularity of forests. TEX exceeds VI for regularly planted low broad-leaf forests with low density owing to the deterioration of the linkage of VIs with canopy LAI caused by strong soil noise. For forests with moderate and high density, VI exceeds TEX in the retrieval of LAI. As to natural broad-leaf forests with high density and spatial complexity, combining VI and TEX can improve the accuracy of the retrieved LAI by 8.9% to 27.0%. VIs and TEXs are exclusive in retrieving LAI due to the intrinsic linkages of these parameters. The atmospherically resistant vegetation index over-perform other VIs in retrieving LAI of forests owing to its ability to constrain atmospheric disturbance on remote sensing data, which is serious and exhibits great spatial variability in the study area.

A novel moisture adjusted vegetation index (MAVI) to reduce background reflectance and topographical effects on LAI retrieval.

A new moisture adjusted vegetation index (MAVI) is proposed using the red, near infrared, and shortwave infrared (SWIR) reflectance in band-ratio form in this paper. The effectiveness of MAVI in retrieving leaf area index (LAI) is investigated using Landsat-5 data and field LAI measurements in two forest and two grassland areas. The ability of MAVI to retrieve forest LAI under different background conditions is further evaluated using canopy reflectance of Jack Pine and Black Spruce forests simulated by the 4-Scale model. Compared with several commonly used two-band vegetation index, such as normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, optimized soil adjusted vegetation index, MAVI is a better predictor of LAI, on average, which can explain 70% of variations of LAI in the four study areas. Similar to other SWIR-related three-band vegetation index, such as modified normalized difference vegetation index (MNDVI) and reduced simple ratio (RSR), MAVI is able to reduce the background reflectance effects on forest canopy LAI retrieval. MAVI is more suitable for retrieving LAI than RSR and MNDVI, because it avoids the difficulty in properly determining the maximum and minimum SWIR values required in RSR and MNDVI, which improves the robustness of MAVI in retrieving LAI of different land cover types. Moreover, MAVI is expressed as ratios between different spectral bands, greatly reducing the noise caused by topographical variations, which makes it more suitable for applications in mountainous area.

Response of evapotranspiration to changes in land use and land cover and climate in China during 2001–2013

Land surface evapotranspiration (ET) is a central component of the Earths global energy balance and water cycle. Understanding ET is important in quantifying the impacts of human influences on the hydrological cycle and thus helps improving water use efficiency and strengthening water use planning and watershed management. China has experienced tremendous land use and land cover changes (LUCC) as a result of urbanization and ecological restoration under a broad background of climate change. This study used MODIS data products to analyze how LUCC and climate change affected ET in China in the period 2001-2013. We examined the separate contribution to the estimated ET changes by combining LUCC and climate data. Results showed that the average annual ET in China decreased at a rate of -0.6mm/yr from 2001 to 2013. Areas in which ET decreased significantly were mainly distributed in the northwest China, the central of southwest China, and most regions of south central and east China. The trends of four climatic factors including air temperature, wind speed, sunshine duration, and relative humidity were determined, while the contributions of these four factors to ET were quantified by combining the ET and climate datasets. Among the four climatic factors, sunshine duration and wind speed had the greatest influence on ET. LUCC data from 2001 to 2013 showed that forests, grasslands and croplands in China mutually replaced each other. The reduction of forests had much greater effects on ET than change by other land cover types. Finally, through quantitative separation of the distinct effects of climate change and LUCC on ET, we conclude that climate change was the more significant than LULC change in influencing ET in China during the period 2001-2013. Effective water resource management and vegetation-based ecological restoration efforts in China must consider the effects of climate change on ET and water availability.

Performance of a two-leaf light use efficiency model for mapping gross primary productivity against remotely sensed sun-induced chlorophyll fluorescence data

Estimating terrestrial gross primary production is an important task when studying the carbon cycle. In this study, the ability of a two-leaf light use efficiency model to simulate regional gross primary production in China was validated using satellite Global Ozone Monitoring Instrument - 2 sun-induced chlorophyll fluorescence data. The two-leaf light use efficiency model was used to estimate daily gross primary production in Chinas terrestrial ecosystems with 500-m resolution for the period from 2007 to 2014. Gross primary production simulated with the two-leaf light use efficiency model was resampled to a spatial resolution of 0.5° and then compared with sun-induced chlorophyll fluorescence. During the study period, sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model exhibited similar spatial and temporal patterns in China. The correlation coefficient between sun-induced chlorophyll fluorescence and monthly gross primary production simulated by the two-leaf light use efficiency model was significant (p<0.05, n=96) in 88.9% of vegetated areas in China (average value 0.78) and varied among vegetation types. The interannual variations in monthly sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model were similar in spring and autumn in most vegetated regions, but dissimilar in winter and summer. The spatial variability of sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model was similar in spring, summer, and autumn. The proportion of spatial variations of sun-induced chlorophyll fluorescence and annual gross primary production simulated by the two-leaf light use efficiency model explained by ranged from 0.76 (2011) to 0.80 (2013) during the study period. Overall, the two-leaf light use efficiency model was capable of capturing spatial and temporal variations in gross primary production in China. However, the model needs further improvement to better simulate gross primary production in summer.

The comparison of different methods to measure leaf area index of forests in Maoershan Mountain, Northeastern China

Different optical instruments are currently available for measuring LAI such as LAI 2000 Plant Canopy Analyser (LAI-2000), Tracing Radiation and Architecture of Canopies (TRAC) and Digital Hemispherical Photography (DHP). Their applicability varies in different ecosystems. This study was devoted to compare LAI measured using four different methods (LAI measured by DHP, LAI measured by TRAC, LAI calculated using effective LAI measured by LAI-2000 and clumping index measured by DHP, and LAI calculated using effective LAI measured by LAI-2000 and clumping index measured by TRAC) in the Maoershan experimental forest farm of Northeast Forestry University located in Shangzhi city of Heilongjiang province. Methods used to measure LAI have considerable effects on observed LAI. The means of LAI measured by four different methods are 3.15, 4.73, 3.97, and 4.24 and corresponding standard deviations are 1.54, 2.39, 1.82, and 1.75, respectively. According to previous studies, the combination of LAI-2000 with TRAC can give the most reliable measurements of LAI. Therefore, DHP tends to underestimate LAI at this area, especially for dense canopies while TRAC tends to overestimate slightly LAI for dense canopies. The fitting of LAI measured using four different methods with normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) calculated from TM data acquired on June 24, 2009 indicated that RSR is a better predictor of LAI than NDVI in this study area. The agreements between measured and estimated LAI using the best fitted models are 58%, 70%, 57% and 68% for these four different methods. Corresponding root mean square errors (RMSE) are 0.80, 0.85, 0.88, and 0.75, respectively. The regional means of LAI retrieved using the empirical models established on the basis of RSR and LAI measured with four different methods are 3.47, 5.26, 4.31, and 4.68, respectively, indicating that if DHP is used as a surrogate of TRAC and LAI-2000, LAI might be underestimated by about 25.7% in this area.

Decrease of net primary productivity in China's terrestrial ecosystems caused by severe droughts in 2009

In 2009, terrestrial ecosystems in China were hit by a series of droughts in different seasons. However, the degree at which net primary productivity (NPP) of terrestrial ecosystems was affected in China is not clear yet. In this study, the remote sensing driven process-based Boreal Ecosystem Productivity Simulator (BEPS) model was used to estimate NPP decrease in Chinas terrestrial ecosystems caused by the abnormal droughts in 2009. The results show that the BEPS model is able to estimate gross primary productivity (GPP) and NPP of Chinas terrestrial ecosystems reliably. Estimated GPP and NPP show similar spatial patterns, decreasing from east to west and from south to north. In 2009, annual NPP was lower than the averages over the period from 2000 to 2010 in most regions of China, especially in areas of southern China. The decrease of annual NPP in 2009 over southeast Tibet and southeast coastal areas was even more than 100 g C m−2 yr−1. The annual total NPP of Hunan and Yunnan provinces, Guangxi and Tibet autonomous regions in 2009 decreased by 4% to 6% of multi-year means, owing to the impact of consecutive drought from summer to winter in these areas. The national total of NPP in this year decreased about 35.5 Tg C yr−1, approximately equivalent to 1% of annual total NPP in Chinas terrestrial ecosystems averaged during the period from 2000 to 2010.