Search

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr-1 decade-1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016.

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr -1 decade -1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia.

Organizations are increasingly adopting new technologies, such as enterprise social software (ESS), which afford employees a repertoire of uses. They not simply focused on work but also on socialization and entertainment. Previous research suggested that the implementation of ESS in an organization had an impact on their employee’s creative behavior. However, how does it impact on is still not clear. In the present study, the generative mechanism of the impact on their employee’s innovation behavior is investigated. Based on the practice theory of Bourdieu and theory of critical realism, a large number of interviews within four small medium enterprises was conducted by the grounded theory methodology. The results indicated that the impact on their employee’s creative behavior was accomplished by four steps, i.e., ESS usage, capitals reproduced, being recognized and practice of creation. The managerial implications from our study are discussed finally. Keywords: ESS, Symbolic capital, Innovation behavior, Generative mechanism

Aim of study: To understand throughfall (TF) sensitivity to variability in rainfall amount (Pg) for typical forest sites across the main climate types of Iran.Area of study: Nine forest stands of several common native and introduced tree species situated in all common Iranian climate types, but located primarily in northern Iran.Material and methods: A nondimensional relative sensitivity coefficient was employed to predict responses of TF to Pg changes. Projected Pg changes over the measurement sites for the period 2020-50 were estimated using one of the Coupled Model Intercomparison Project phase 5 (CMIP5) known as HadGEM2-ES under low and high emission scenarios (RCP 2.6 and 8.5).Main results: TF displayed strong positive linear relationships with Pg at all sites [TF=0.66 Pg -0.16; R2=0.91]. The sensitivity coefficient ranged from 0.96-2.35 across the nine forest sites and large sensitivity coefficientdifferences were found between small (< mean annual Pg) and large (> mean annual Pg) storms for arid and Mediterranean plantations. Shifts in Pg and increased small storm frequency are predicted for these regions (2020-50) under low and high emission scenarios.Research highlights: TF sensitivity may be a useful variable when selecting tree species for afforestation to buffer expected shifts in Pg due to climate change.Keywords: climate change; forest ecosystems; precipitation projection; throughfall sensitivity.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (−20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from −20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o, National Youth Top-notch Talent Support Program in China, Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Key Research Program of the Chinese Academy of Sciences, National Natural Science Foundation of China 10.13039/501100001809, Peer Reviewed