Primary Productivity and Precipitation-Use Efficiency in Temperate Grassland in the Loess Plateau of China

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA-GPP ) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA-GPP relationship (RWA-GPP ) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near-infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance-light-use efficiency GPP. We found that the 3- and 7-year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17- and 40-year time scales (7.28% and 8.23%). The long-term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA-GPP and RWA-GPP with different source products: In high-latitude regions, RWA-GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid-low latitudes. Forests had weak RWA-GPP at all time scales, shrublands showed negative RWA-GPP at long time scales, and grassland (GL) showed a positive RWA-GPP at short time scales. Globally, the EWA-GPP , whether positive or negative, enhanced significantly at 3-, 7-, and 17-year time scales. For arid and humid zones, the semi-arid and sub-humid zones experienced a faster increase in the positive EWA-GPP , whereas the humid zones experienced a faster increase in the negative EWA-GPP . At the ecosystem types, the positive EWA-GPP at a 3-year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA-GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA-GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Plant Community Traits Respond to Grazing Exclusion Duration in Alpine Meadow and Alpine Steppe on the Tibetan Plateau

Grazing exclusion has been a primary ecological restoration practice since the implement of "Returning Grazing Land to Grassland" program in China. However, the debates on the effectiveness of grazing exclusion have kept for decades. To date, there has been still a poor understand of vegetation restoration with grazing exclusion duration in alpine meadows and alpine steppes, limiting the sustainable management of grasslands on the Tibetan Plateau. We collected data from previous studies and field surveys and conducted a meta-analysis to explore vegetation restoration with grazing exclusion durations in alpine meadows and alpine steppes. Our results showed that aboveground biomass significantly increased with short-term grazing exclusion (1-4 years) in alpine meadows, while medium-term grazing exclusion (5-8 years) in alpine steppes (P < 0.05). By contrast, belowground biomass significantly increased with medium-term grazing exclusion in alpine meadows, while short-term grazing exclusion in alpine steppes (P < 0.05). Long-term grazing exclusion significantly increased belowground biomass in both alpine meadows and alpine steppes. medium-tern, and long-term grazing exclusion (> 8 years) significantly increased species richness in alpine meadows (P < 0.05). Only long-term GE significantly increased Shannon-Wiener index in plant communities of alpine steppes. The efficiency of vegetation restoration in terms of productivity and diversity gradually decreased with increasing grazing exclusion duration. Precipitation significantly positively affected plant productivity restoration, suggesting that precipitation may be an important factor driving the differential responses of vegetation to grazing exclusion duration in alpine meadows and alpine steppes. Considering the effectiveness and efficiency of grazing exclusion for vegetation restoration, medium-term grazing exclusion are recommended for alpine meadows and alpine steppes.

Spatiotemporal Change of Net Primary Productivity and Its Response to Climate Change in Temperate Grasslands of China

The temperate grasslands in China play a vital part in regulating regional carbon cycle and climate change. Net primary productivity (NPP) is a crucial index that reflects ecological function of plants and the carbon sequestration capacity of grassland ecosystem. Climate change can affect NPP by changing vegetation growth, but the effects of climate change on the NPP of China's temperate grasslands remain unclear. Based on MODIS data and monthly climate data during 2000-2020, this study explored the spatiotemporal changes in grassland NPP and its response to climate change in temperate grasslands of China. We found that the annual NPP over the entire China's temperate grasslands increased significantly by 4.0 gC/m2/year from 2000 to 2020. The annual NPP showed increasing trends for all the different grassland vegetation types, with the smallest increase for temperate desert steppe (2.2 gC/m2/year) and the largest increase for temperate meadow (5.4 gC/m2/year). The correlation results showed that increased annual precipitation had a positive relationship with the NPP of temperate grasslands. Increased summer and autumn precipitation could increase grassland NPP, particularly for the temperate meadow. With regard to the effects of temperatures, increased temperature, particularly the summer maximum temperature, could decrease annual NPP. However, increased spring minimum temperature could increase the NPP of temperate desert steppe. In addition, this study found, for the first time, an asymmetric relationship between summer nighttime and daytime warming and the NPP of temperate meadow. Specifically, nighttime warming can increase NPP, while daytime warming can reduce NPP in temperate meadow. Our results highlight the importance of including seasonal climate conditions in assessing the vegetation productivity for different grassland types of temperate grasslands and predicting the influences of future climate change on temperate grassland ecosystems.

Evaluation of NPP using three models compared with MODIS-NPP data over China

Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

Frameworks on Patterns of Grasslands’ Sensitivity to Forecast Extreme Drought

Climate models have predicted the future occurrence of extreme drought (ED). The management, conservation, or restoration of grasslands following ED requires a robust prior knowledge of the patterns and mechanisms of sensitivity—declining rate of ecosystem functions due to ED. Yet, the global-scale pattern of grasslands’ sensitivity to any ED event remains unresolved. Here, frameworks were built to predict the sensitivity patterns of above-ground net primary productivity (ANPP) spanning the global precipitation gradient under ED. The frameworks particularly present three sensitivity patterns that could manipulate (weaken, strengthen, or erode) the orthodox positive precipitation–productivity relationship which exists under non-drought (ambient) condition. First, the slope of the relationship could become steeper via higher sensitivity at xeric sites than mesic and hydric ones. Second, if the sensitivity emerges highest in hydric, followed by mesic, then xeric, a weakened slope, flat line, or negative slope would emerge. Lastly, if the sensitivity emerges unexpectedly similar across the precipitation gradient, the slope of the relationship would remain similar to that of the ambient condition. Overall, the frameworks provide background knowledge on possible differences or similarities in responses of grasslands to forecast ED, and could stimulate increase in conduct of experiments to unravel the impacts of ED on grasslands. More importantly, the frameworks indicate the need for reconciliation of conflicting hypotheses of grasslands’ sensitivity to ED through global-scale experiments.

Remote Sensing of Grassland Production and Management—A Review

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Permanent dry soil layer a critical control on soil desiccation on China's Loess Plateau

The wide spread of dry soil layers (DSL) in China’s Loess Plateau region has negative effects on the ecosystem, including soil degradation and vegetation failure. To understand the temporal persistence of DSL, a ca. 860 km south-north transect was established and soil water content of the 0–5 m depth soil layer repeatedly measured for a period of four years. The results indicated that DSL varied with time and had a distribution area over 21.5–47.0% in the 860 km transect during the study period. The DSL could be divided into temporary and permanent types based on the length of period for which the soil remains dry. While temporary DSL is recoverable, permanent DSL (which existed in 47 out of 86 sites) was apparently unrecoverable as it persisted throughout the observation period. Permanent DSL was characterized by high temporal persistence, severe soil desiccation and thick dry layers; all of which suggested severe negative effect on the ecosystem. Non-climatic factors, rather than climate factors, contributed more to the formation of permanent DSL in the study area. Thus, it was suggested that policies and measures should be enacted to control especially permanent DSL formation in the region.

Deep soil water storage varies with vegetation type and rainfall amount in the Loess Plateau of China

Soil-water storage in a deep soil layer (SWSD), defined as the layer where soil water is not sensitive to daily evapotranspiration and regular rainfall events, functions as a soil reservoir in China’s Loess Plateau (LP). We investigated spatial variations and factors that influence the SWSD in the 100–500 cm layers across the entire plateau. SWSD generally decreased from southeast to northwest following precipitation gradient, with a mean value of 587 mm. The spatial variation in the SWSD in grassland was the highest, followed by protection forests, production forests and cropland. Variation in the >550 mm rainfall zone was much lower than that in the <550 mm zone. The significant influencing variables explained 22.3–65.2% of the spatial variation in SWSD. The joint effect of local and climatic variables accounted for most of the explained spatial variation of SWSD for each vegetation type and the <450 mm rainfall zone. Spatial variation of SWSD, however, was dominantly controlled by the local variables in the 450–550 and the >550 mm rainfall zones. Therefore, regional models of SWSD for a specific vegetation need to incorporate climatic, soil and topographic variables, while for a rainfall zone, land use should not be ignored.

Relative importance of climate changes at different time scales on net primary productivity-a case study of the Karst area of northwest Guangxi, China

Climate changes are considered to significantly impact net primary productivity (NPP). However, there are few studies on how climate changes at multiple time scales impact NPP. With MODIS NPP product and station-based observations of sunshine duration, annual average temperature and annual precipitation, impacts of climate changes at different time scales on annual NPP, have been studied with EEMD (ensemble empirical mode decomposition) method in the Karst area of northwest Guangxi, China, during 2000-2013. Moreover, with partial least squares regression (PLSR) model, the relative importance of climatic variables for annual NPP has been explored. The results show that (1) only at quasi 3-year time scale do sunshine duration and temperature have significantly positive relations with NPP. (2) Annual precipitation has no significant relation to NPP by direct comparison, but significantly positive relation at 5-year time scale, which is because 5-year time scale is not the dominant scale of precipitation; (3) the changes of NPP may be dominated by inter-annual variabilities. (4) Multiple time scales analysis will greatly improve the performance of PLSR model for estimating NPP. The variable importance in projection (VIP) scores of sunshine duration and temperature at quasi 3-year time scale, and precipitation at quasi 5-year time scale are greater than 0.8, indicating important for NPP during 2000-2013. However, sunshine duration and temperature at quasi 3-year time scale are much more important. Our results underscore the importance of multiple time scales analysis for revealing the relations of NPP to changing climate.

Driving forces of changes in the water and sediment relationship in the Yellow River

The world is composed of various river basins. Within a specific river basin, water and sediment dynamics, and the relationship between them, can be assessed to reflect the basin's functions and services. Due to its changing nature, understanding and balancing the relationship between water and sediment is a global concern and is crucial for the sustainable management of river basins, especially for the Yellow River (YR), which is one of the most sediment-laden rivers in the world. Here, we used the past 60years of runoff and sediment load observations to investigate the middle reach of the YR, i.e., the Loess Plateau (LP), the source of nearly 90% of the sediment load of the river. We found that a sharp (58%) reduction of sediment after 1979 was mainly (59%) caused by a water yield decrease. Engineering and vegetation measures have induced land surface modifications, which are responsible for 76% of the water reduction. These measures have been implemented as part of a coordinated set of soil and water conservation, and sediment control polices. We propose the cessation of such construction and the maintenance of a sustainable (i.e., minimal water consumption) vegetated ecosystem on the LP for soil conservation, and the establishment of an integrated basin-wide ecosystem and land use management regime for sustainable water use and sediment regulation.