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Grassland resilience is influenced by climate change and human activities. However, little is known about how grassland resilience has changed, driven by climate change and human activities, on the Mongolian Plateau. We calculated grassland resilience on the Mongolian Plateau from 2000 to 2021 using the kernel normalized difference vegetation index (kNDVI), quantified the trends of grassland resilience using a newly proposed "critical slowing down" indicator with a machine learning algorithm, and compared the driving forces for these changes between Inner Mongolia and Mongolia. The findings of this study demonstrate that heightened levels of precipitation and reduced temperature contribute to the enhanced resilience of grassland ecosystems on the Mongolian Plateau. Conversely, the presence of grazing activities exhibits a detrimental effect on such resilience. In semi-arid regions, approximately 43% of grassland areas are undergoing a discernible decline in resilience. This decline is particularly pronounced in regions characterized by heightened levels of grazing intensity. In addition, resilience declined in 54% of areas with population growth compared with 32% in areas with population decline. Inner Mongolia, with its higher intensity of human activities, has a more serious decline in ecological resilience than Mongolia, indicating that further ecological restoration measures are needed. [ABSTRACT FROM AUTHOR]

Aims: This study investigates how precipitation, temperature and seasonality (as a proxy of plant productivity) affect the temporal and spatial variability of soil CO2 efflux in two dry semiarid grasslands with different degrees of land degradation. Methods: We measured soil CO2 efflux over four years under plant, biological soil crust and bare soil patches and estimated annual soil carbon losses in both, a natural and a degraded grassland, by means of generalised additive mixed models considering temporal autocorrelation in the data. Results: Soil CO2 efflux ranged from 0.08 to 3.70 and from 0.10 to 3.01 μmol CΟ2 m−2 s−1 in the natural and degraded grasslands, respectively. Daily soil CO2 efflux was mostly affected by moisture in the degraded grassland (25.4%), while in the natural grassland was affected jointly by seasonality, temperature and moisture (27.5%). Overall, the highest soil carbon fluxes were measured in soils covered by biological soil crusts (1.24 ± 0.02 and 1.10 ± 0.02) and the lowest in bare soils (1.11 ± 0.02 and 0.82 ± 0.02 μmol CΟ2−2 s−1) in the natural and degraded sites, respectively. Cumulative soil carbon fluxes were mainly driven by temperature and previous precipitation (over three months). The highest soil carbon losses were estimated in the driest year (2009) and the lowest in the wettest (2010) with almost twice the amount of rainfall. The main difference between these years was the timing of the events that mostly occurred in the moments of maximum plant activity with optimum temperatures in spring in the dry year. Conclusions: Changes in precipitation patterns will affect soil carbon fluxes more than rainfall amount, particularly in degraded grasslands. Therefore, considering all climate drivers together with plant activity is essential to predict how climate change will affect soil biological processes in drylands. [ABSTRACT FROM AUTHOR]

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms--which probably act in many species--can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought. [ABSTRACT FROM AUTHOR]

Summary: A pan-Nordic dataset of 151 streamflow records was analysed to detect spatial and temporal changes in streamflow. Prior to undertaking analyses, all streamflow records with significant levels of autocorrelation were pre-whitened to remove the adverse effect of temporal autocorrelation on the test results. The Mann–Kendall trend test was applied to study changes in annual and seasonal streamflow as well as floods and droughts for three periods: 1920–2005, 1941–2005 and 1961–2000. Field significance was evaluated to determine the percentage of stations that are expected to show a trend due to the effect of cross-correlation. The period analysed and the selection of stations influenced the regional patterns found, but the overall picture was that trends of increased streamflow dominate annual values and the winter and spring seasons. Trends identified in summer flows differed between the three periods analysed, whereas no trend was found for the autumn season. In all three periods, a signal towards earlier snowmelt floods was clear, as was the tendency towards more severe summer droughts in southern and eastern Norway. These trends in streamflow result from changes in both temperature and precipitation, but the temperature induced signal is stronger than precipitation influences. This is evident because the observed trends in winter and spring, where snowmelt is the dominant process, are greater than the annual trends. A qualitative comparison of the findings with available streamflow projections for the region showed that the strongest trends found are generally consistent with future changes expected in the projection periods, for example increased winter discharge and earlier snowmelt floods. However, there are predicted changes that are not reflected in past trends, such as the expected increase in autumn discharge in Norway. Hence, the changes expected because of increased temperatures are reflected in the observed trends, whereas changes anticipated due to increases in precipitation are not. [Copyright &y& Elsevier]

Computer models of flow and transport processes in critical or vulnerable reaches of rivers have become essential tools in assessing the long-term sustainable management of river basins. A generic long-standing source of uncertainty in the output from these models arises from the difficulty in estimating flow rates at the upstream boundary of the reach. When the process being modeled relies on an accurate description of the autocorrelation in the flow, such as in sediment transport, and predictions are required for future changed climatic conditions, this difficulty becomes acute. One currently popular solution is to combine stochastic weather generators with catchment hydrology models to simulate hydrographs. This paper offers an alternative solution, which bypasses the complexities of this approach and the extra uncertainties that arise from imposing restrictive predefined model structures. The proposed method uses an autoregressive multinomial logit model to link temperature and precipitation catchment data to flow data of the River Tees in the northeast United Kingdom. This model accounts for temporal autocorrelation by using the previous day's observation and other variables as explanatory variables. Incorporating basin area average temperature and precipitation allows climate change predictions from regional climate models to be used when predicting flows. The successful application of the method is demonstrated for the River Tees in northeast United Kingdom. [ABSTRACT FROM AUTHOR]