Susceptible-infectious-removed (SIR) epidemic models are proposed to consider the impact of available resources of the public health care system in terms of the number of hospital beds. Both the incidence rate and the recovery rate are considered as nonlinear functions of the number of infectious individuals, and the recovery rate incorporates the influence of the number of hospital beds. It is shown that backward bifurcation and saddle-node bifurcation may occur when the number of hospital beds is insufficient. In such cases, it is critical to prepare an appropriate amount of hospital beds because only reducing the basic reproduction number less than unity is not enough to eradicate the disease. When the basic reproduction number is larger than unity, the model may undergo forward bifurcation and Hopf bifurcation. The increasing hospital beds can decrease the infectious individuals. However, it is useless to eliminate the disease. Therefore, maintaining enough hospital beds is important for the prevention and control of the infectious disease. Numerical simulations are presented to illustrate and complement the theoretical analysis.

Climate change and land use/cover change are altering the global hydrological cycle, leading to intensified water scarcity issues and rising risks of water-related natural hazards (e.g., floods and droughts) in many regions of the world (e.g., Cook et al., 2018;Konapala et al., 2020;Padrón et al., 2020). As a vital component of the terrestrial hydrological cycle, river streamflow is not only the most direct freshwater resource for various kinds of human needs but also an (if not the most) representative indicator of hydrological response to environmental changes at the catchment scale, since streamflow represents an integrated consequence of multiple hydrological processes and their interactions with climate and surface characteristics in a catchment (Zaitchik et al., 2010). Many studies have assessed the hydrological responses to global environmental changes using observed streamflow at the catchment and/or regional scales (e.g.,

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Global hydrological stations are unevenly distributed, with sparse hydrometeorological observation networks in developing countries and dense ones in developed countries (Ma et al., 2021). Moreover, observations over some regions are not publicly available for policy reasons (Feng et al., 2021). Thus, PUB is challenging for the hydrological sciences (Tsai et al., 2021). World Bank statistics show that river monitoring networks cannot meet current needs in 78% of low-and middle-income countries and 86% of least-developed countries. Precise streamflow PUB is essential for both water management and policy decision-makers (Cho & Kim, 2022;Merz et al., 2021;Tellman et al., 2021).Traditional PUB primarily based on catchment hydrological similarity, whereby parameters are migrated from adjacent or similar catchments a regression function is constructed between physical descriptors of the catchment and model parameters for streamflow PUB (Guo et al., 2021). Bao et al. (2012) found that a similarity-based approach outperformed a regression-based approach in 55 catchments in China. Gou et al. (2021) successfully constructed a high-quality natural runoff dataset in China using a variable infiltration capacity (VIC) macroscale hydrologic model and multiscale parameter regionalization techniques. In recent years, the geophysical community has shown great interest in deep learning, with relevant research exploding exponentially in the society of exploration geophysics and the American Geophysical Union (X X Zhu et al., 2017). Long Short-Term Memory (LSTM) neural networks are widely used in hydrology, because of their excellent simulation performance and suitability for streamflow forecasting (

<p>Recent advances in global hydrological modeling yield many global runoff datasets that are extensively used in global hydrological analyses. Here, we provide a comprehensive evaluation of simulated runoff from 21 global models, including 12 climate models from CMIP6, six global hydrological models from the Inter-Sectoral Impact Model Inter-Comparison Project (ISMIP2a) and three land surface models from the Global Land Data Assimilation System (GLDAS), against observed streamflow in 840 unimpaired catchments globally. Our results show that (i) no model performs consistently better in estimating runoff from all aspects, and all models tend to perform better in more humid regions and non-cold areas; (ii) the interannual runoff variability is well represented in ISIMIP2a and GLDAS models, and no model performs satisfactorily in capturing the annual runoff trend; (iii) the runoff intra-annual cycle is reasonably captured by all models yet an overestimation of intra-annual variability and an early bias in peak flow timing are commonly found; and (iv) model uncertainty leads to a larger uncertainty in runoff estimates than that induced by forcing uncertainty in ISIMIP2a, and model uncertainty in GLDAS is larger than that in ISIMIP2a. Finally, we confirm that the multi-model ensemble is an effective way to reduce uncertainty in individual models except for CMIP6 regarding mean annual magnitude and annual runoff trend. Overall, our findings suggest that assessments/projections of runoff changes based on these global outputs contain great uncertainties and should be interpreted with caution, and call for more advanced, observation-guided ensemble techniques for better large-scale hydrological applications.</p>

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A comprehensive understanding of the patterns of drought and flood alternation in adjacent months is essential for enabling climate adaptation and mitigation strategies. However, there is a paucity of knowledge regarding short‐cycle drought–flood abrupt alteration (s‐DFAA) and its responses to multiple environmental factors at various timescales. This is because of the inadequate formula construction of the existing short‐cycle drought–flood abrupt alteration index (SDFAI), such as the inability to introduce drought standards. To accurately capture the s‐DFAA's characteristics in the Qinling‐Daba Mountains (Qinba Mountains), we proposed a revised SDFAI (R‐SDFAI), which incorporates the standardized precipitation index (SPI) and allows for the customization of drought standards. Next, we used wavelet transform coherence and multiple wavelet coherence to investigate the timescale relationships between s‐DFAA events and their influence factors such as relative humidity, sunshine duration, temperature, evaporation, Arctic Oscillation (AO), Niño3.4 SSTA Index (Niño3.4), Total Sunspot Number Index (TSNI) and Pacific Decadal Oscillation Index (PDO). Our results showed that the R‐SDFAI outperformed traditional SDFAI in capturing s‐DFAA events and characterizing their severity. Furthermore, s‐DFAA events identified by R‐SDFAI at different levels (i.e., mild, moderate, severe, extreme and total) displayed insignificant downward trends. Spatially, there were more s‐DFAA events in the east than the west. Wavelet analysis indicated that meteorological factors and teleconnections significantly impact s‐DFAA events at large timescales, though their driving mechanisms differed substantially. Among meteorological factors, single relative humidity and its related combinations exhibited relatively high percent area of significant coherence (PASC, ranging from 17.47 to 29.46). Each teleconnection and its combinations are irreplaceable, with PASC values always increasing with the number of variables. The PASC ranges from 8.3 to 10.29 for one factor, 12.38 to 18.65 for two factors, 24.02 to 30.58 for three factors and 40.88 for four factors, respectively.