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Vegetation is an important component of the terrestrial ecosystem that plays an essential role in the exchange of water and energy in climate and biogeochemical cycles. This study investigated the spatiotemporal variation of normalized difference vegetation index (NDVI) in northern China using the GIMMS-MODIS NDVI during 1982–2018. We explored the dominant drivers of NDVI change using regression analyses. Results show that the regional average NDVI for northern China increased at a rate of 0.001 year−1. NDVI improved and degraded area corresponded to 36.1% and 9.7% of the total investigated area, respectively. Climate drivers were responsible for NDVI change in 46.2% of the study area, and the regional average NDVI trend in the region where the dominant drivers were temperature (T), precipitation (P), and the combination of precipitation and temperature (P&T), increased at a rate of 0.0028, 0.0027, and 0.0056 year−1, respectively. We conclude that P has positive dominant effects on NDVI in the subregion VIAiia, VIAiic, VIAiib, VIAib of temperate grassland region, and VIIBiia of temperate desert region in northern China. T has positive dominant effects on NDVI in the alpine vegetation region of Qinghai Tibet Plateau. NDVI is negatively dominated by T in the subregion VIIBiib, VIIBib, VIIAi, and VIIBi of temperate desert regions. Human activities affect NDVI directly by reforestation, especially in Shaanxi, Shanxi, and Hebei provinces.

In the Asia–Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990–2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June–August (JJA), and by precipitation frequency in December–February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March–May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imperfect character of individual models. In this paper, a genetic algorithm together with a Bayesian model averaging method are employed to provide a multi-model ensemble (MME) and combined runoff prediction under climate change scenarios produced from eight rainfall–runoff models for the Yellow River Basin. The results show that the multi-model ensemble method, especially the genetic algorithm method, can produce more reliable predictions than the other considered rainfall–runoff models. These results show that it is possible to reduce the uncertainty and thus improve the accuracy for future projections using different models because an MME approach evens out the bias involved in the individual model. For the study area, the final combined predictions reveal that less runoff is expected under most climatic scenarios, which will threaten water security of the basin.

Climate change is closely monitored and numerous studies reports increasing air temperature and weather extremes across the globe. As a direct consequence of the increase of global temperature, the increased heat stress is becoming a global threat to public health. While most climate change and epidemiological studies focus on air temperature to explain the increasing risks, heat strain can be predicted using comprehensive indices such as Universal Thermal Climate Index (UTCI). The Asia–Pacific region is prone to thermal stress and the high population densities in the region impose high health risk. This study evaluated the air temperature and UTCI trends between 1990 and 2019 and found significant increasing trends for air temperature for the whole region while the increases of UTCI are not as pronounced and mainly found in the northern part of the region. These results indicate that even though air temperature is increasing, the risks of heat stress when assessed using UTCI may be alleviated by other factors. The associations between El Niño Southern Oscillation (ENSO) and heat stress was evaluated on a seasonal level and the strongest regional responses were found during December-January (DJF) and March–May (MAM).

The use of organic mulch is important for urban green applications. For urban areas in arid and semiarid regions receiving short high-intensive rainfall, rainfall characteristics, and soil slope play an important role for mulch functioning. These properties of mulch were studied. For this purpose, rainfall simulation experiments using organic mulching were conducted in Jiufeng National Forestry Park to analyze the influence of organic mulch under different slope and heavy rainfall events. The results showed that soil water content displayed a decreasing tendency with increasing mulch application. Compared to bare soil, a mulch application of 0.25 kg/m 2 and 0.50 kg/m 2 led to maximum soil water content and maximum runoff decrease occurred for 0.50 kg/m 2 mulch. Higher application rate of mulch displayed less soil water content and greater runoff. The runoff amount and runoff generation rate decreased by 28–83% and 21–83%, respectively, as compared to bare soil. With a mulch application of 0.25–1.00 kg/m 2 , soil drainage accounted for 56–60% of total rainfall. Overall, an efficient mulch application was found to be 0.25–0.50 kg/m 2 . The results of this study are relevant for arid and semiarid urban regions that experience heavy rainfall.

Ongoing climate variability and change are increasing the burden of diarrhoeal disease worldwide. Meaningful early warning systems with adequate lead times (weeks to months) are needed to guide public health decision–making and enhance community resilience against health threats posed by climate change. Toward this goal, we trained various machine-learning models to predict diarrhoeal disease rates in Nepal (2002–2014), Taiwan (2008–2019), and Vietnam (2000–2015) using temperature, precipitation, previous disease rates, and El Niño Southern Oscillation phases. We also compared the performance of shallow time-series neural network (NN), Random Forest Regressor, artificial nn, gradient boosting regressor, and long short-term memory–based methods for their effectiveness in predicting diarrhoeal disease burden across multiple countries. We evaluated model performance using a test dataset and assessed the accuracy of predicted diarrhoeal disease incidence rates for the last year of available data in each district. Our results suggest that even in the absence of the most recent disease surveillance data, a likely scenario in most low- and middle-income countries, our NN-based early warning system using historical data performs reasonably well. However, future studies are needed to perform prospective evaluations of such early warning systems in real-world settings.

Temperature and precipitation extremes are the dominant causes of natural disasters. In this study, seven indices of extreme temperature and precipitation events in Gansu Province, China, were analysed for the period 1961–2017. An abrupt climate shift was recorded during 1980–1981. Thus, the study period was divided into a preshift (before the climate shift) period 1961–1980 and an aftshift (after the climate shift) period 1981–2017. Comparison of mean extreme indices for preshift and aftshift periods was performed for the purpose of exploring possible increasing/decreasing patterns. Generalized extreme value (GEV) distribution was applied spatially to fit the extreme indices with return periods up to 100 years for preshift/aftshift periods. Singular value decomposition (SVD) was adopted to investigate possible correlation between the extreme climate events and indices of large-scale atmospheric circulation. The results indicate that changes in mean and return levels between the preshift and aftshift periods vary significantly in time and space for different extreme indices. Increase in extreme temperature regarding magnitude and frequency for the aftshift period as compared with the preshift period suggests a change to a warmer and more extreme climate during recent years. Changes in precipitation extremes were different in southern and northern parts of Gansu. The precipitation extremes in the north have increased that can result in more serious floods and droughts in the future. SVD analyses revealed a complex pattern of correlation between climate extremes and indices of large-scale atmospheric circulation. Strengthening of westerlies and weakening of the south summer monsoon contribute to the complex changing patterns of precipitation extremes. Results in this study will contribute to disaster risk prevention and better water management in this area.

Root-induced channels are the primary controlling factors for rapid movement of water and solute in forest soils. To explore the effects of root distribution on preferential flow during rainfall events, deciduous (Quercus variabilis BI.) and coniferous forest (Platycladus orientalis (L.) Franco) sites were selected to conduct dual-tracer experiments (Brilliant Blue FCF and Bromide [Br−]). Each plot (1.30 × 1.30 m) was divided into two subplots (0.65 × 1.30 m), and two rainfall simulations (40 mm, large rainfall and 70 mm, extreme rainfall) were conducted in these. Vertical soil profiles (1.00 m × 0.40 m) were excavated, and preferential flow path features were quantified based on digital image analysis. Root (fine and coarse) abundance and Br− concentration were investigated for each soil profile. In deciduous forest, accumulated roots in the upper soil layer induce larger lateral preferential flow as compared to the coniferous forest soil during large rainfall events. Compared with deciduous forest, coniferous forest soil, with higher (horizontal and vertical) spatial variability of preferential flow paths, promotes higher percolation and solute leaching to deeper soil layers during extreme rainfall events. Fine roots, accounting for a larger proportion of total roots (compared to coarse roots), facilitate preferential flow in the 0–40 cm forest soil layer. Overall, our results indicate that the root distribution pattern of different tree species can exert diverse effects on preferential flow in forest soils.

Construction of subway line intersection stations will become more and more popular in the future. Research on the deformation characteristics in the construction process is the best way to ensure the safety of the foundation pit itself, the surrounding buildings, and adjacent subway stations. In this paper, the deformation characteristics of a T-shaped subway foundation pit during construction are studied by an integrated dewatering-excavation three-dimensional numerical simulation method. The results show that the main causation of the surface settlement around the foundation pit is the stratum compression caused by dewatering, and the main causation of the deformation of the diaphragm wall and the existing subway station is the soil deformation caused by excavation unloading. It is suggested that constructing a high-quality diaphragm wall, controlling the water level within the excavation foundation pit, strengthening the monitoring, and timely cement pouring of the bottom plate are key countermeasures to reduce the deformation. The research results of this paper have reference significance for similar projects.

The Nordic Association for Hydrology – NHF1 – turned 50 years in 2020. The anniversary is celebrated by this special issue and was also marked at the Nordic Hydrological Conference (NHC) in Tallin in 2022. Since 1970, NHF has been an important hub for the hydrological community in the Nordic countries and from 1996, the three Baltic countries were included in the association. The roots of NHF can be traced back to the Baltic Sea conferences starting in 1926 and the Nordic Conferences of Hydrologists starting in 1955. In 2020, the association had more than 220 members including 13 institutional members.