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A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies. [ABSTRACT FROM AUTHOR]

Climate change directly impacts the hydrological cycle via increasing temperatures and seasonal precipitation shifts, which are variable at local scales. The water resources of the Upper Yangtze River Basin (UYRB) account for almost 40% and 15% of all water resources used in the Yangtze Basin and China, respectively. Future climate change and the possible responses of surface runoff in this region are urgent issues for China's water security and sustainable socioeconomic development. This study evaluated the potential impacts of future climate change on the hydrological regimes (high flow (Q5), low flow (Q95), and mean annual runoff (MAR)) of the UYRB using global climate models (GCMs) and a variable infiltration capacity (VIC) model. We used the eight bias-corrected GCM outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) to examine the effects of climate change under two future representative concentration pathways (RCP4.5 and RCP8.5). The direct variance method was adopted to analyze the contributions of precipitation and temperature to future Q5, Q95, and MAR. The results showed that the equidistant cumulative distribution function (EDCDF) can considerably reduce biases in the temperature and precipitation fields of CMIP5 models and that the EDCDF captured the extreme values and spatial pattern of the climate fields. Relative to the baseline period (1961–1990), precipitation is projected to slightly increase in the future, while temperature is projected to considerably increase. Furthermore, Q5, Q95, and MAR are projected to decrease. The projected decreases in the median value of Q95 were 21.08% to 24.88% and 16.05% to 26.70% under RCP4.5 and RCP8.5, respectively; these decreases were larger than those of MAR and Q5. Temperature increases accounted for more than 99% of the projected changes, whereas precipitation had limited projected effects on Q95 and MAR. These results indicate the drought risk over the UYRB will increase considerably in the future. [ABSTRACT FROM AUTHOR]

The upper region of the Yangtze River, as the freshwater source for the middle and lower Yangtze River, is highly vulnerable to climate change. Understanding and projecting the spatiotemporal variability of extreme flows in this region are key to creating appropriate adaptation policies and strategies. In this study, climate change signals were scaled from seven bias-corrected CMIP5 models and were used to force a variable infiltration capacity (VIC) model to quantify and assess the impact of climate change on the hydrology of the region. The respective contribution of precipitation (P) and temperature (T) to evapotranspiration (ET) and hydrological regimes (annual mean runoff, high flow, and low flow) were also quantified using the analysis of variance method (ANOVA). For the scenario period (2031–2060), the projected runoff is projected to increase in vary between 11.33% and 64.08% in winter, while increases in summer range from 8.64% to 40.97%. The changes in ET are different depending on the climate mode used; these increase during winter and decrease in summer under the influence of temperature. In addition, we found that low flows are projected to decrease while the mean annual runoff will increase in the future period. These projected changes demonstrated that temperature effects dominate in the study area for the future period under the representative concentration pathway 8.5 scenario. • We quantify how temperature and precipitation affect evapotranspiration and runoff. • Our method plus VIC model effectively projected climate change impacts on hydrology. • Droughts and floods are projected to increase in the upper region of Yangtze River. [ABSTRACT FROM AUTHOR]

Background and aims: Plant interactions with soil microbes are important drivers of biodiversity and dynamics of alpine plant communities. However, little is known about the effects of these interactions on seed germination and plant establishment of alpine plants under global climate change. We investigated the individual and interactive effects of warming, soil microbes and shrubs on seed germination, survival, and growth of herbaceous species. Methods: We simulated warming and manipulated live and sterile soil microbes and microhabitat in the alpine meadow ecosystem dominated by the shrub (Dasiphora fruticosa) on the Qinghai-Tibet Plateau. We tested the interactive effects of warming, soil microbes, and shrubs on the germination, early life survival, and growth of herbaceous species. Soil microbial communities were determined by high-throughput sequencing. Results: Our results showed that seed germination was significantly reduced under warming; seed germination was higher in live soils than in sterile soils; early life survival was lower under shrub canopies. Additionally, in the warming treatment, the positive effect of soil microbes on seed germination shifted to negative on the early survival of herbaceous species. Furthermore, in the warming treatment, shrubs enhanced the positive effects of soil microbes on seed germination and reduced the negative effects of soil microbes on early survival. The effects of these individual and interactive factors on herbaceous species also depended on species identity and life stages. Conclusion: Overall, our work demonstrates that soil microbes were particularly critical at seed germination stage and plant interactions with soil microbes were significantly altered with experimental warming. [ABSTRACT FROM AUTHOR]

[Display omitted] • A cascade model chain suitable for the risk of future DWAA events is proposed. • Evolution characteristics of DWAA in the future period of YRB are revealed. • Severity of DWAA events in far-future is greater than that in the near-future. • Extreme precipitation events have strong synchronization with DWAA events. At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1. [ABSTRACT FROM AUTHOR]

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July–September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed. [ABSTRACT FROM AUTHOR]

Summary Reasonable input data selection is of great significance for accurate computation of drought indices. In this study, a comprehensive comparison is conducted on the sensitivity of two commonly used standardization procedures (SP) in drought indices to datasets, namely the probability distribution based SP and the self-calibrating Palmer SP. The standardized Palmer drought index (SPDI) and the self-calibrating Palmer drought severity index (SC-PDSI) are selected as representatives of the two SPs, respectively. Using meteorological observations (1961–2012) in the Yellow River basin, 23 sub-datasets with a length of 30 years are firstly generated with the moving window method. Then we use the whole time series and 23 sub-datasets to compute two indices separately, and compare their spatiotemporal differences, as well as performances in capturing drought areas. Finally, a systematic investigation in term of changing climatic conditions and varied parameters in each SP is conducted. Results show that SPDI is less sensitive to data selection than SC-PDSI. SPDI series derived from different datasets are highly correlated, and consistent in drought area characterization. Sensitivity analysis shows that among the three parameters in the generalized extreme value (GEV) distribution, SPDI is most sensitive to changes in the scale parameter, followed by location and shape parameters. For SC-PDSI, its inconsistent behaviors among different datasets are primarily induced by the self-calibrated duration factors ( p and q ). In addition, it is found that the introduction of the self-calibrating procedure for duration factors further aggravates the dependence of drought index on input datasets compared with original empirical algorithm that Palmer uses, making SC-PDSI more sensitive to variations in data sample. This study clearly demonstrate the impacts of dataset selection on sensitivity of drought index computation, which has significant implications for proper usage of drought indices and related assessments, and potentially provide some valuable references for future researches on drought indices improvements. [ABSTRACT FROM AUTHOR]

Considering the drawbacks of the original Palmer drought severity index (PDSI) in terms of its simplified hydrologic algorithm and spatio-temporal inconsistency, we compare six variants of PDSI derived from different combinations of two hydrologic algorithms and three standard processes so as to provide deep insights into the individual impacts of hydrological processing and standardization on final PDSI values as well as their combined effects. Investigations are conducted in whole Yellow River basin. On basis of 52 years' (1961-2012) hydro-meteorological data, comprehensive analysis on multiple drought characteristics are carried out for each PDSI variant, combined with comparison of three crucial intermediate variables of PDSI. Results show that variable infiltration capacity (VIC) model based modification in the hydrologic accounting section significantly improve drought trends with more reasonable spatial distributions presented. For the statistical characteristics of drought areas and frequency, comparable performance is found between VIC-based modification and self-calibrating standard procedure-based modification, though they are derived from different mechanisms. However, in case of the coupling of these two modifications, indices derived from combined modifications perform poorly than single modification-based indices with unexpected high frequency of extreme events detected in certain regions. This reflects the complicated mechanism of PDSI and it is essential to propose an appropriate standardization to match the hydrological algorithm and further improve the performance of relevant drought index. With the crucial findings mentioned above, this study is promising to provide some theoretical supports and serve as a competent reference for future PDSI based researches. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of future climate change impacts on hydrological drought characteristics is one of important measures for implementing sustainable water resources management and effective disaster mitigation in drought-prone regions under the changing environment. In this study, a modeling system for projecting the potential future climate change impacts on hydrological droughts in the Weihe River basin (WRB) in North China is presented. This system consists of a large-scale hydrological model driven by climate outputs from three climate models (CMs) for future streamflow projections, a probabilistic model for univariate drought assessment, and a copula-based bivariate model for joint drought frequency analysis under historical and future climates. With the observed historical climate data as the inputs, the Variable Infiltration Capacity hydrological model projects an overall runoff reduction in the WRB under the Intergovernmental Panel on Climate Change A1B scenario. The univariate drought assessment found that although fewer hydrological drought events would occur under A1B scenario, drought duration and severity tend to increase remarkably. Moreover, the bivariate drought assessment reveals that future droughts in the same return period as the baseline droughts would become more serious. With these trends in the future, the hydrological drought situation in the WRB would be further deteriorated. [ABSTRACT FROM AUTHOR]

• An observation-based natural and human-impacted catchment comparison method is proposed. • Human influence are quantified based on gridded socio-economic and land use data. • Climatic and anthropogenic influences on drought propagation are investigated. • Climate change accelerates the response of hydrological drought to meteorological drought. • Human activities delay the propagation from meteorological to hydrological drought. It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management. [ABSTRACT FROM AUTHOR]

The impact of global changes on food security is of serious concern. Breeding novel crop cultivars adaptable to climate change is one potential solution, but this approach requires an understanding of complex adaptive traits for climate-change conditions. In this study, plant growth, nitrogen (N) uptake, and yield in relation to climatic resource use efficiency of nine representative maize cultivars released between 1973 and 2000 in China were investigated in a 2-year field experiment under three N applications. The Hybrid- Maize model was used to simulate maize yield potential in the period from 1973 to 2011. During the past four decades, the total thermal time (growing degree days) increased whereas the total precipitation and sunshine hours decreased. This climate change led to a reduction of maize potential yield by an average of 12.9% across different hybrids. However, the potential yield of individual hybrids increased by 118.5 kg ha−1 yr−1 with increasing year of release. From 1973 to 2000, the use efficiency of sunshine hours, thermal time, and precipitation resources increased by 37%, 40%, and 41%, respectively. The late developed hybrids showed less reduction in yield potential in current climate conditions than old cultivars, indicating some adaptation to new conditions. Since the mid-1990s, however, the yield impact of climate change exhibited little change, and even a slight worsening for new cultivars. Modern breeding increased ear fertility and grain-filling rate, and delayed leaf senescence without modification in net photosynthetic rate. The trade-off associated with delayed leaf senescence was decreased grain N concentration rather than increased plant N uptake, therefore N agronomic efficiency increased simultaneously. It is concluded that modern maize hybrids tolerate the climatic changes mainly by constitutively optimizing plant productivity. Maize breeding programs in the future should pay more attention to cope with the limiting climate factors specifically. [ABSTRACT FROM AUTHOR]

• A comprehensive framework for quantifying the climatic and anthropogenic influences on river hydrological health is proposed. • Human activities significantly aggravate the degradation of river hydrological health for the studied catchment. • Widespread artificial water withdrawal and reservoir operation are the two crucial human activities. • Adequate rainfall maintains the river hydrological health at a good status in the 1990 s. Climate change and human activities have together altered the river hydrological regime and consequently threatened the health of river ecosystems. Quantifying the impact of climate change and human activities on river hydrological health regimes is essential for water resource management and river ecology protection. Although previous studies have analysed the hydrologic alterations using some indicators, separating effects of climate change and human activities on river hydrological health is needed for developing adaptive measures to protect the ecosystem of river basins. In this study, a comprehensive assessment framework for quantifying climatic and anthropogenic influences on river hydrological health variation was proposed. The framework consists of the following steps: (1) the reconstruction of natural river streamflow using the variable infiltration capacity (VIC) hydrological model, (2) calculation of river hydrological health through the ecological flow threshold method, and (3) quantification of the impacts of climate change and human activities on river hydrological health using the 'observed–simulated' comparison approach. The semi-arid Laohahe Basin in northern China, which consists of three human-influenced catchments (Taipingzhuang, Chifeng, and Xinglongpo) and one natural catchment (Xiquan), was selected as the case study area. The case study demonstrated that the proposed procedure is efficient in quantifying climatic and anthropogenic influences on river hydrological health. The results revealed that the hydrological health level has significantly declined in the three human-influenced catchments for the human-influenced period (1980–2016), particularly in the 2000 s and 2010 s, where it degraded much more severely. Whereas, the relatively adequate rainfall in the 1990 s maintained the river hydrological health at a good status. The quantitative evaluation showed that human activities were the main driving factors for the hydrological health degradation during the whole human-influenced period, with contributions of 80.8%, 91.9%, and 86.0% for the Taipingzhuang, Chifeng, and Xinglongpo catchments, respectively. Widespread artificial water withdrawal and reservoir operation were the two crucial human activities that caused the degradation of river hydrological health for the studied catchment. The proposed procedure and findings of this study not only help in deeper understanding of the evolutionary characteristics and driving mechanisms of river hydrological health in a changing environment in general, but also provide scientific basis for local water resources management and river ecosystems protection. [ABSTRACT FROM AUTHOR]

Extreme flow projections are of considerable necessity for flood and drought disaster prevention and sustainable water resources management to adapt to future climate change. However, these projections are usually associated with large uncertainties arising from different sources. A numerical modeling system including three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distribution (PD) functions was established in this study to project future extreme flow changes at basin scales. This modeling system was employed in the following three watersheds with different climatic and topographical characteristics in China: The Upper Ganjiang River basin (UGRB) in the humid region, the Laoha River basin (LRB) in the semi-arid region, and the Yellow River source region (YRSR) in the semi-humid region. Subsequently, the analysis of variance approach (ANOVA) was used to quantify the contribution of different uncertainty sources in extreme flow projections. Results show that extreme high flow was projected to increase in all basins. By contrast, extreme low flow would decrease in UGRB, and inconsistent change signals were found in LRB and YRSR. The ANOVA-based assessment reveals that CM is generally the dominant uncertainty source for mean monthly streamflow and extreme low flow projections. ES explains a large proportion of total uncertainty for extreme low flow projection in the 2060s in UGRB because of the large difference in non-precipitation frequency projected under different ESs. SD is the main uncertainty source in extreme high flow projection in UGRB where extreme precipitation events frequently occur. Compared with other basins, HM produces relatively high uncertainty in LRB due to its inferior performance in historical streamflow simulations. PD contributes a low percentage of uncertainty in extreme flow projections. Interactions among uncertainty sources even produce larger uncertainties than most individual sources. Although future extreme flow projections are associated with non-negligible uncertainties, the projected remarkable changes in extreme high and low flows indicate that stakeholders should take certain measures to mitigate flood risks in the three basins and alleviate drought risks in UGRB • Extreme high flow would increase in the three basins with different climate features. • Extreme low flow would decrease in the watershed in humid regions. • Uncertainty of extreme flow projection is generally dominated by climate models. • Uncertainty sources have different impacts associated with watershed characteristics. [ABSTRACT FROM AUTHOR]