The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature-dynamic simulations of autoregressive distributed lag (ARDL) models-to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change.

Spectral characteristics of CDOM (Chromophoric dissolved organic matter) in water columns are a key parameter for bio-optical modeling. Knowledge of CDOM optical properties and spatial discrepancy based on the relationship between water quality and spectral parameters in the Yinma River watershed with in situ data collected from highly polluted waters are exhibited in this study. Based on the comprehensive index method, the riverine waters showed serious contamination; especially the chemical oxygen demand (COD), iron (Fe), manganese (Mn), mercury (Hg) and dissolved oxygen (DO) were out of range of the contamination warning. Dissolved organic carbon (DOC) and total suspended matter (TSM) with prominent non-homogenizing were significantly high in the riverine waters, but chlorophyll-a (Chl-a) was the opposite. The ternary phase diagram showed that non-algal particle absorption played an important role in total non-water light absorption (>50%) in most sampling locations, and mean contributions of CDOM were 13% and 22% in the summer and autumn, respectively. The analysis of the ratio of absorption at 250-365 nm (E 250:365 ) and the spectral slope (S 275-295 ) indicated that CDOM had higher aromaticity and molecular weight in autumn than in summer, which is consistent with the results of water quality and the CDOM relative contribution rate. Redundancy analysis (RDA) indicated that the environmental variables OSM (Organic suspended matter) had a strong correlation with CDOM absorption, followed by heavy metals, e.g., Mn, Hg and Cr 6+ . However, for the specific UV absorbance (SUVA 254 ), the seasonal values showed opposite results compared with the reported literature. The potential reasons were that more UDOM (uncolored dissolved organic matter) from human sources (wastewater effluent) existed in the waters. Terrigenous inputs simultaneously are in relation to the a CDOM (440)-DOC relationship with the correlation coefficient of 0.90 in the summer (two-tailed, p < 0.01), and 0.58 in the autumn (two-tailed, p < 0.05). Spatial distribution of the CDOM parameters exhibited that the downstream regions focused on dry land have high CDOM molecular weight and aromatic hydrocarbon. Partial sampling locations around the cities or countries generally showed abnormal values due to terrigenous inputs. As a bio-optical model parameter, the spectral characteristic of CDOM is helpful in adjusting the derived algorithms in highly polluted environments. The study on organic carbon and pollutants in highly polluted waters had an important contribution to global carbon balance estimation and water environment protection.

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.

Notice of republicationThis article was republished on December 30, 2020, to remove Fig 5 because this figure was derived from Fig 1 of [2], which was published in 2017 by the Midwest Political Science Association and is not offered under a CC-BY license. The PLOS ONE article [1] now cites [2] as a reference for the ARDL model used in the study, and Figs 6-8 have been renumbered as Figs 5-7.In the revised article, citations have also been added to the Stationarity test subsection of the Methods and to the Discussion to address the following text overlap concerns.