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We assessed how climate change may impact net primary production (NPP) and reference evapotranspiration (ET0) from the Zagros region of western Iran covered extensively by oak, -Quercus branti Lindl. forests. The daily meteorological parameters of temperature (T), precipitation (P), relative humidity (RH), wind speed (WS), and sunshine hours were obtained from 20 meteorological stations throughout the region over a 30-year period (1988-2017). Net primary production and ET0 were estimated by the synthetic and the FAO Penman-Monteith models, respectively. A non-dimensional relative sensitivity coefficient was used to examine the sensitivity of NPP and ET0 to changes in the meteorological parameters. The sensitivity analyses were carried out for T, P, and WS within a possible range of ± 10%, ± 30%, and ± 10%, respectively, from the long-term mean. Except for the P with no notable trend, other meteorological parameters exhibited upward or downward trends. The mean NPP and ET0 values were estimated to be 6.5 t ha-1 y-1 and 3.9 mm d-1 across the Zagros region, respectively. Net primary production was found to be more sensitive to precipitation with a sensitivity coefficient of 0.66 and less sensitive to temperature with a sensitivity coefficient of 0.40. The ET0 sensitivity coefficients in response to T changes doubled relative to WS (0.33 against 0.63). Identifying the contributing factors in NPP and ET0 trends is important for understanding the relative impacts of climate change and human activities in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Predicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management. Plain Language Summary: The prediction of flood event classes is more effective and informative than that of individual events for obtaining comprehensive dynamic characteristics of flood events for early warning and development of emergency plans. However, this is challenging owing to massive flood events with remarkable spatiotemporal heterogeneity and unclear class membership functions. We propose a class prediction approach for flood events using the class membership functions of flood behavior metrics based on frequency analysis and catchment hydrological modeling. This approach deeply mines the behavior characteristics of historical flood events with a strong mathematical basis and cooperates with hydrological models to predict flood classes. Over thousand unimpacted flood events in 68 headstream catchments across China were selected to validate the robustness of the prediction scheme. Our results show that the proposed approach well predicts the flood class with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Our study has strong implications for scientific flood warning research and flood mitigation management. Key Points: A prediction approach for flood event classes is developed using class membership functions of flood regime metrics with hydrological modelClass membership degree is estimated using the frequency distribution function, which fits well with statistical significanceAverage class hit rates are 68.3% ± 0.4% for all the events and predictions of dumpy flood class are better than those of spike flood classes [ABSTRACT FROM AUTHOR]

Classification is beneficial for understanding flood variabilities and their formation mechanisms from massive flood event samples for both flood scientific research and management purposes. Our study investigates spatial and temporal variabilities of 1446 unregulated flood events in 68 headstream catchments in China at class scale using hierarchical and partitional clustering methods. Control mechanisms of meteorological and physio-geographical factors (e.g., meteorology, land cover and catchment attributes) are explored for individual flood event classes using constrained rank analysis and Monte Carlo permutation test. Results show that we identify five robust flood event classes, i.e., moderately, highly, and slightly fast floods, as well as moderately and highly slow floods, which accounts for 24.0 %, 21.2 %, 25.9 %, 13.5 % and 15.4 % of total events, respectively. All the classes are evenly distributed in the whole period, but the spatial distributions are quite distinct. The fast flood classes are mainly in the southern China, and the slow flood classes are mainly in the northern China and the transition region between southern and northern China. The meteorological category plays a dominant role in flood event variabilities, followed by catchment attributes and land covers. Precipitation factors, such as volume and intensity, and aridity index are the significant control factors. Our study provides insights into flood event variabilities and aids in flood prediction and control. [ABSTRACT FROM AUTHOR]

Beyond traditional meteorological and (paleo)climatological analyses, numerical moisture tracking provides a quantitative diagnosis of moisture sources to the Tibetan Plateau (TP). While existing studies predominantly employ either the Eulerian or Lagrangian method, the potential differences in their simulations and the underlying causes of these discrepancies remain unexplored. In this study, we compare the applications of the most widely used Eulerian (WAM-2layers) and Lagrangian (FLEXPART-WaterSip) models in the TP, specifically in an Indian Summer Monsoon (ISM)-dominated basin (Yarlung Zangbo River Basin, YB) and a westerlies-dominated basin (upper Tarim River Basin, UTB). Compared to FLEXPART-WaterSip, WAM-2layers generally estimates higher moisture contributions from westerlies-dominated and distant source regions but lower contributions from local recycling. However, WAM-2layers simulations can be improved by using higher spatial-temporal resolution forcing data. The inherent ability in WAM-2layers to distinguish between evaporation and precipitation makes it more effectively in identifying varying moisture contributions arising from distinct surface evaporation sources. In contrast, in regions heavily influenced by smaller-scale convective systems with high spatial heterogeneity, such as the UTB when compared to the YB, simulations from FLEXPART-WaterSip tend to be more reliable. However, FLEXPART-WaterSip is prone to introducing additional errors when using specific humidity information in particles to infer moisture uptake and loss, although it accurately depicts the three-dimensional movement of air particles. [ABSTRACT FROM AUTHOR]

The Zoige peatlands are the largest peatland area in China, and the largest high‐altitude peatland in the world. As with many peatlands worldwide, degradation from land management and climate change mean that the intact Zoige peatland area has decreased, potentially reducing the carbon (C) sink function and ecosystem services. This review summarizes current knowledge of the impacts of land‐use and climate change on the Zoige peatland C cycle in a global perspective and identifies future research and management directions. The existing literature suggests that artificial drainage carried out to lower water tables and improve grazing has a significant impact on the peatland C cycle. Drained and degraded areas may act as a net C source, through increased CO2 emissions, although the overall C balance of the Zoige peatlands is likely still a net C sink. Future climate change may also impact upon the peatland C cycle. Warming of 2°C may significantly reduce the strength of the C sink of intact peatland areas, which may shift the overall Zoige peatland C cycle balance to a net C source. The effect of warming on degraded Zoige peatlands is a major uncertainty, although the global literature suggests warming effects may be greater in degraded peatlands. Restoration of degraded peatlands (by blocking drains) may help reverse some of the impacts of degradation and gradually recover C sink function. However, there are fewer studies in Zoige peatlands than elsewhere. We conclude with several specific suggestions for future research on the peatland C cycle. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangeAssessing Impacts of Climate Change > Observed Impacts of Climate ChangeClimate, Ecology, and Conservation > Observed Ecological Changes [ABSTRACT FROM AUTHOR]