Uncertainties in classification system conversion and an analysis of inconsistencies in global land cover products

In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future

Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia

Vegetation changes play a vital role in modifying local temperatures although, until now, the climate feedback effects of vegetation changes are still poorly known and large uncertainties exist, especially over Central Asia. In this study, using remote sensing and re-analysis of existing data, we evaluated the impact of vegetation changes on local temperatures. Our results indicate that vegetation changes have a significant unidirectional causality relationship with regard to local temperature changes. We found that vegetation greening over Central Asia as a whole induced a cooling effect on the local temperatures. We also found that evapotranspiration (ET) exhibits greater sensitivity to the increases of the Normalized Difference Vegetation Index (NDVI) as compared to albedo in arid/semi-arid/semi-humid regions, potentially leading to a cooling effect. However, in humid regions, albedo warming completely surpasses ET cooling, causing a pronounced warming. Our findings suggest that using appropriate strategies to protect vulnerable dryland ecosystems from degradation, should lead to future benefits related to greening ecosystems and mitigation for rising temperatures

Response of hydrological processes to input data in high alpine catchment : an assessment of the Yarkant River basin in China

Most studies of input data used in hydrological models have focused on flow; however, point discharge data negligibly reflect deviations in spatial input data. To study the effects of different input data sources on hydrological processes at the catchment scale, eight MIKE SHE models driven by station-based data (SBD) and remote sensing data (RSD) were implemented. The significant influences of input variables on water components were examined using an analysis of the variance model (ANOVA) with the hydrologic catchment response quantified based on different water components. The results suggest that compared with SBD, RSD precipitation resulted in greater differences in snow storage in the different elevation bands and RSD temperatures led to more snowpack areas with thinner depths. These changes in snowpack provided an appropriate interpretation of precipitation and temperature distinctions between RSD and SBD. For potential evapotranspiration (PET), the larger RSD value caused less plant transpiration because parameters were adjusted to satisfy the outflow. At the catchment scale, the spatiotemporal distributions of sensitive water components, which can be defined by the ANOVA model, indicate that this approach is rational for assessing the impacts of input data on hydrological processes

Numerical simulations of the impacts of mountain on oasis effects in arid Central Asia

The oases in the mountain-basin systems of Central Asia are extremely fragile. Investigating oasis effects and oasis-desert interactions is important for understanding the ecological stability of oases. However, previous studies have been performed only in oasis-desert environments and have not considered the impacts of mountains. In this study, oasis effects were explored in the context of mountain effects in the northern Tianshan Mountains (NTM) using the Weather Research and Forecasting (WRF) model. Four numerical simulations are performed. The def simulation uses the default terrestrial datasets provided by the WRF model. The mod simulation uses actual terrestrial datasets from satellite products. The non-oasis simulation is a scenario simulation in which oasis areas are replaced by desert conditions, while all other conditions are the same as the mod simulation. Finally, the non-mountain simulation is a scenario simulation in which the elevation values of all grids are set to a constant value of 300 m, while all other conditions are the same as in the mod simulation. The mod simulation agrees well with near-surface measurements of temperature, relative humidity and latent heat flux. The Tianshan Mountains exert a cooling and wetting effects in the NTM region. The oasis breeze circulation (OBC) between oases and the deserts is counteracted by the stronger background circulation. Thus, the self-supporting mechanism of oases originating from the OBC plays a limited role in maintaining the ecological stability of oases in this mountain-basin system. However, the mountain wind causes the cold-wet'' island effects of the oases to extend into the oasis-desert transition zone at night, which is beneficial for plants in the transition region

Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product

Lower Mekong Basin (LMB) experiences a recurrent drought phenomenon. However, few studies have focused on drought monitoring in this region due to lack of ground observations. The newly released Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) with a long-term record and high resolution has a great potential for drought monitoring. Based on the assessment of CHIRPS for capturing precipitation and monitoring drought, this study aims to evaluate the drought condition in LMB by using satellite-based CHIRPS from January 1981 to July 2016. The Standardized Precipitation Index (SPI) at various time scales (1-12-month) is computed to identify and describe drought events. Results suggest that CHIRPS can properly capture the drought characteristics at various time scales with the best performance at three-month time scale. Based on high-resolution long-term CHIRPS, it is found that LMB experienced four severe droughts during the last three decades with the longest one in 1991-1994 for 38 months and the driest one in 2015-2016 with drought affected area up to 75.6%. Droughts tend to occur over the north and south part of LMB with higher frequency, and Mekong Delta seems to experience more long-term and extreme drought events. Severe droughts have significant impacts on vegetation condition

Improved atmospheric modelling of the oasis-desert system in Central Asia using WRF with actual satellite products

Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1-31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations

Partitioning global surface energy and their controlling factors based on machine learning

As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R-2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m(2) to 21.94 W/m(2) for the LE and from 12.05 W/m(2) to 22.34 W/m(2) for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982-2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies