Streamflow seasonality in a snow-dwindling world.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

Source-sink relationships of anthropogenic metal(loid)s from urban catchment to waterway in relation to spatial pattern of urban green infrastructures.

Surface sediment in urban waterways originates from fine topsoil particles within catchments via surface erosion, often bonded with non-degradable metal(loid)s. This study posited that urban green infrastructures (UGIs) can influence anthropogenic metal(loid) transport from catchment topsoil to waterway sediment by retaining moveable particles. In multiply channeled downtown Suzhou, China, UGIs' spatial patterns were examined in relations to metal(loid)s source (catchment topsoil) - sink (waterway surface sediment) dynamics. Anthropogenic metal(loid)s - As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn - were spatially quantified in sediment at 144 waterway points and in topsoil at 154 UGIs' points across 7 subwatersheds. Integrated metal(loid) loads revealed significantly higher sediment loads (except for As) than topsoil, varying with element specificity and spatial unmatching across the subwatersheds. Loads of metal(loid)s in topsoil showed no significant differences among UGI types, but sediment loads of As, Cr, and Ni correlated positively with topsoil loads in roadside and public facility UGIs within 100 m- and 200 m-wide riparian buffer zones. However, waterfront UGIs negatively impacted on these correlations for Cr, Hg, and Ni loads within the riparian buffer zones. These findings highlight metal(loid) specificity and UGIs' spatial pattern effects on anthropogenic metal(loid) loads between catchment topsoil (source) and waterway surface sediment (sink), offering valuable guidelines for UGIs' design and implementation.

The cumulative effects of cascade reservoirs control nitrogen and phosphorus flux: Base on biogeochemical processes.

The reservoir serves as a water source, a flood control structure, a navigational aid, and also impacts the downstream ecosystem as well as the reservoir zone. However, debate exists about effectiveness of cascade reservoirs in controlling the transportation of nutrients, particularly in the Yangtze River basin, which has been significantly affected by reservoir development. This research develops a new model X-NPSEM (X with Nitrogen and Phosphorus Steady-state Reservoir Model) based on biogeochemical processes of nitrogen and phosphorus reaction for investigating the dynamic storage capacity of cascade reservoirs at both reservoir- and watershed scales. Then the cumulative effects of cascade reservoirs and the related mechanism were investigated in Fujiang watershed, China. Based on the results, cascade reservoirs retained 16.3 % of nitrogen fluxes and 37.6 % of phosphorus fluxes annually. Downstream reservoirs have higher retention rates of phosphorus (0.48/d) compared to upstream reservoirs (0.10/d), mainly due to inflow sediment. Nitrogen retention rates show seasonal variations: wet season (0.21/d) and dry season (0.17/d). These fluctuations in nitrogen retention are primarily influenced by changes in temperature rather than other factors such as operation period, nitrogen and phosphorus concentration, or the nitrogen/phosphorus ratio. In upstream, the concentration of sediment entering the reservoir plays a decisive role in the transformation of P retention from sink to source. The X-NPSRM coupler model could be used for global reservoir operation and watershed management.

Impacts of vegetation restoration on water resources and carbon sequestration in the mountainous area of Haihe River basin, China.

The mountainous region of the Haihe River basin (MHRB) plays an important role in the water resource supply of its nearby mega-cities, including Beijing and Tianjin, and large areas of cropland. With the implementation of afforestation projects in recent decades, vegetation and carbon (C) uptake have greatly increased in the MHRB. In addition, the annual runoff has significantly declined, threatening regional water security. The trade-off relationship between water yield and C uptake in the MHRB remains unknown. This study employed a biogeochemical model (Biome-BGC) to simulate the natural vegetation dynamics and gross primary productivity (GPP) during 1982-2019 driven by climate forcing. A distributed hydrological model (geomorphology-based hydrological model, GBHM) was adopted to assess the impact of vegetation restoration on the hydrological processes. The results indicated that the leaf area index in the MHRB increased significantly (P < 0.01) during 1982-2019, which led to evapotranspiration increase and runoff (R) reduction. Under the influence of vegetation restoration, both the GPP and the water use efficiency (WUE) increased significantly in the MHRB during 2000-2019, however, the improvement of WUE decreased with the aridity index increasing. Our results showed that vegetation restoration can improve C sequestration efficiency in the MHRB and that the trade-off between water yield and C sequestration should be considered in planning ecological projects to achieve C neutrality.

Spatial effects of urban green infrastructure on instream water quality assessed by chemical and sensory indicators.

Urban green infrastructure has been simulated effectively and economically to reduce volume and pollutants of stormwater runoffs but its spatial effects remain unclear. A snap sampling campaign was carried out for surface water quality in the downtown waterway network of a pilot sponge city (Suzhou) in China, dividing into 7 subwatersheds according to the digital elevation map. In total, 144 sampling points were investigated and measured for chemical quality of surface water while 68 out of the sampling points had a sensory evaluation questionnaire interview for water quality with 321 respondents, in whom the native residents scored a significant spatiality of water quality. The downtown waterway network had phosphorus-limited eutrophic surface water with total nitrogen worse than Class V of the national guidelines. Chemical and sensory evaluation indexes of surface water quality had significant spatial consistency (p < 0.001). All types of green spaces (%) in subwatershed, especially along the urban waterway network (waterfront) and roadside, and in the 100 m riparian buffer zone, significantly influenced nutrient loads in surface water. Findings of the present study suggest that the 100 m riparian buffer zone would be priority areas and the waterfront and roadside should be the highly efficient spots for planning strategy on urban green infrastructure implementation to reduce nutrient loads in surface water and to improve urban landscape aesthetics.

Spatial-temporal variations of reference evapotranspiration and its driving factors in cold regions, northeast China.

Reference evapotranspiration ([Formula: see text]) is an important indicator for hydrometeorological change, which integrates atmospheric and surface conditions, and its downward trends have been reported in many regions over the past several decades. Cold regions constitute an important ecological barrier in China; however, few studies focus on change in [Formula: see text] in cold regions. Especially in the cold region of northeast China (CRNEC), as one of the national strategic grain bases, understanding spatial-temporal variations of [Formula: see text] is important for agriculture water management and ecological protection. This study selected the observations at 113 national meteorological stations located in CRNEC and evaluated the trends of [Formula: see text] and their driving factors from 1961 to 2017. Results indicate that annual [Formula: see text] increases from the northeast to the southwest of CRNEC and has an insignificant decreasing trend in the whole study period, in which 33 stations (29.2%) show significant decreasing trends and only 19 stations (16.8%) show significant increasing trends at the 95% confidence level. An abrupt change in [Formula: see text] data is detected from 1994. Reasons for this abrupt change in [Formula: see text] vary largely over the study areas. Analysis shows that wind speed and minimum air temperature are the two major factors that control the change of [Formula: see text] before 1994. It also shows that wind speed and actual vapor pressure are the two major controlling factors after 1994. We also found that [Formula: see text] shows a certain correlation with Pacific Decadal Oscillation and Western Pacific Index, but there is a significant correlation between meteorological factors and teleconnection factors related to [Formula: see text]. These findings will promote agricultural water management and improve water ecological protection in the CRNEC. We investigated changes in reference evapotranspiration relationships with atmospheric circulation and its attributions over the cold regions in northeast China during 1961 ~ 2017. The results indicate that the wind speed and minimum air temperature are the two major factors that control the change of ET0 before 1994, and wind speed and actual vapor pressure are the two major controlling factors after 1994. We also found that ET0 shows a certain correlation with Western Pacific Index in the whole period.

Long-term leaf C:N ratio change under elevated CO2 and nitrogen deposition in China: Evidence from observations and process-based modeling.

Climate change, elevating atmosphere CO2 (eCO2) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon­nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO2 and ΔMAT rather than ΔMAP and ecosystem types. Process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau.

Soil organic carbon (SOC) stored in permafrost across the high-latitude/altitude Northern Hemisphere represents an important potential carbon source under future warming. Here, we provide a comprehensive investigation on the spatiotemporal dynamics of SOC over the high-altitude Tibetan Plateau (TP), which has received less attention compared with the circum-Arctic region. The permafrost region covers ~42% of the entire TP and contains ~37.21 Pg perennially frozen SOC at the baseline period (2006-2015). With continuous warming, the active layer is projected to further deepen, resulting in ~1.86 ± 0.49 Pg and ~3.80 ± 0.76 Pg permafrost carbon thawing by 2100 under moderate and high representative concentration pathways (RCP4.5 and RCP8.5), respectively. This could largely offset the regional carbon sink and even potentially turn the region into a net carbon source. Our findings also highlight the importance of deep permafrost thawing that is generally ignored in current Earth system models.

Efficient nanointerface hybridization in a nickel/cobalt oxide nanorod bundle structure for urea electrolysis.

Urea electrolysis has received great attention for the energy-relevant applications, and efficient nanostructured catalysts are required to overcome the sluggish urea oxidation kinetics. Herein, we noticed that the valence state of Ni in the hybrid Ni/Co oxide nanorods can be correlated to the catalytic capability for urea oxidation. Crystal lattice hybridization was found in the interface of Ni/Co oxide nanoparticles that assembled as a nanorod bundle structure. The more or the less of Ni2+/Ni3+ generated lower catalytic ability, and Ni/Co oxide with the optimum content of Ni2+/Ni3+ exhibited the highest catalytic ability for urea oxidation because of the efficient synergism, resulting from the formation of high valence state of Ni species and improved kinetics. A low onset potential of 1.29 V was required for the urea oxidation compared with the high onset potential of 1.52 V for water oxidation; high selectivity for urea oxidation was found in the potential below 1.50 V, and as a promising application for urea-assisted water electrolysis about 190 mV less was required to provide 10 mA cm-2 in the two-electrode system, indicating the energy-efficient nature for hydrogen evolution. The study provides some novel insights into the Ni/Co catalyst design and fabrication with efficient catalytic synergism for electrocatalysis.

Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau.

Frozen ground degradation profoundly impacts the hydrology, ecology and human society on the Tibetan Plateau (TP) and the downstream regions. The spatial distribution and potential changes of permafrost and maximum thickness of seasonally frozen ground (MTSFG) on the TP is of great importance and needs more in-depth studies. This study maps the permafrost and MTSFG distribution in the baseline period (2003-2010) and in the future (2040s and 2090s) with 1-km resolution. Logistic regression (LR), support vector machine (SVM) and random forest (RF) are validated using 106 borehole observations and proved to be applicable in estimating permafrost distribution. According to the majority voting results of the three algorithms, 45.9% area of the TP is underlain by permafrost in the baseline period, and respectively 25.9% and 43.9% of the current permafrost will disappear by the 2040s and the 2090s projected by mean of the projections from the five General Circulation Models under the Representative Concentration Pathway 4.5 scenario. SVM performs better in spatial generalization than RF based on the results of nested cross validation. According to the MTSFG results derived from SVM, the most dramatic decrease in MTSFG will occur in the southwestern TP, which is projected to exceed 50 cm in the 2090s compared with the baseline period. This study introduces the statistics and machine learning algorithms to frozen ground estimation on the TP, and the high resolution permafrost and MTSFG maps produced by this study can provide useful information for future studies on the third pole region.

Correlation between vegetation and environment at different levels in an arid, mountainous region of China.

Vegetation patterns and spatial organization are influenced by the changing environmental conditions and human activities. However, the effect of environment on vegetation at different vegetation classification levels has been unclear. We conducted an analysis to explore the relationship between environment and vegetation in the land use/land cover (LULC), vegetation group, vegetation type, and formation and subformation levels using redundancy analysis with seven landscape metrics and 33 environmental factors in the upper reaches of the Heihe River basin in an arid area of China to clarify this uncertainty. Atmospheric counter radiation was the most important factor at the four levels. The effect of soil was the second determinant factor at three levels (except in vegetation formation and subformation level). The number of variables whose relationship to vegetation reached significant levels varied from 26 to 28, and 20 variables were the same at all four levels. The factors affecting vegetation were basically the same at vegetation group level and vegetation-type level. It was sufficient to analyze the relationship between environmental and vegetation patterns only in LULC, vegetation group and vegetation formation and subformation level in mountainous regions; different factors should be considered at different vegetation levels.

Impacts of climate warming on the frozen ground and eco-hydrology in the Yellow River source region, China.

The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.

Alpine vegetation phenology dynamic over 16years and its covariation with climate in a semi-arid region of China.

Vegetation phenology is a sensitive indicator of ecosystem response to climate change, and plays an important role in the terrestrial biosphere. Improving our understanding of alpine vegetation phenology dynamics and the correlation with climate and grazing is crucial for high mountains in arid areas subject to climatic warming. Using a time series of SPOT Normalized Difference Vegetation Index (NDVI) data from 1998 to 2013, the start of the growing season (SOS), end of the growing season (EOS), growing season length (GSL), and maximum NDVI (MNDVI) were extracted using a threshold-based method for six vegetation groups in the Heihe River headwaters. Spatial and temporal patterns of SOS, EOS, GSL, MNDVI, and correlations with climatic factors and livestock production were analyzed. The MNDVI increased significantly in 58% of the study region, whereas SOS, EOS, and GSL changed significantly in <5% of the region. The MNDVI in five vegetation groups increased significantly by a range from 0.045 to 0.075. No significant correlation between SOS and EOS was observed in any vegetation group. The SOS and GSL were highly correlated with temperature in May and April-May, whereas MNDVI was correlated with temperature in August and July-August. The EOS of different vegetation groups was correlated with different climatic variables. Maximum and minimum temperature, accumulated temperature, and effective accumulated temperature showed stronger correlations with phenological metrics compared with those of mean temperature, and should receive greater attention in phenology modeling in the future. Meat and milk production were significantly correlated with the MNDVI of scrub, steppe, and meadow. Although the MNDVI increased in recent years, ongoing monitoring for rangeland degradation is recommended.

Comparison modeling for alpine vegetation distribution in an arid area.

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations.

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998-2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002-2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

[Characteristics of surface energy fluxes over a sparse shrubland ecosystem in the farming-pastoral zone of the Loess Plateau, Northwest China].

Based on the energy flux and meteorological data during 2011-2012 over a sparse shrubland ecosystem in the farming-pastoral zone of the Loess Plateau, this study investigated the diurnal and seasonal variations of the energy balance components, and discussed the responses of the latent and sensible heat fluxes to different intensities of rainfall events. In addition, we identified the major environmental controlling factors on latent and sensible heat fluxes via correlation analysis. The results showed that the diurnal and seasonal variations of net radiation (Rn), sensible heat flux (H), latent heat flux (LE) and soil heat flux (G) all showed single-peak curves. The annual mean values of Rn, H, LE and G were 78.19, 33.32, 24.91 and 2.65 W · m(-2), respectively. The ratios of energy budget components to net radiation were 43% (H/Rn), 32% (LE/Rn), and 3% (G/Rn), which indicated that sensible heat flux was the major form of energy loss at this site. In the growing season, the ratios of sensible heat flux and latent heat flux to net radiation were nearly the same (36%); while in the non-growing season, sensible heat flux accounted for 54% of net radiation. Latent heat flux increased sharply after heavy and weak rainfall events, while sensible heat flux decreased sharply at the same time. Continuous rainfall events would lead to fluctuations in latent and sensible heat fluxes. There were significant correlations between latent heat flux and net radiation, vapor pressure deficit and vegetation parameter, while remarkable correlations were found between sensible heat flux and net radiation, and air temperature gradient.