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Annual average wind speed and sand-dust storm frequency trends in China changed synchronously.

Material fluxes at the land–ocean interface impact seawater composition and global cycling of elements. However, most attention has been focused on the fluvial dissolved fluxes. For elements like lead (Pb), whose fluvial particulate flux into the ocean is two orders of magnitude higher than the dissolved counterpart, the role of particulates in elemental cycling is potentially important but currently less appreciated. Using both chemical analyses on samples collected from around equatorial Southeast Asia and model simulations, we show that particulate-dissolved exchange is an important mechanism controlling the concentration and isotopic composition of dissolved Pb in the ocean. Our model indicates that Pb contributed from particulate-dissolved exchange at ocean boundaries is larger than, or at least comparable to, other major Pb sources to the seawater before the Anthropocene, when the anthropogenic Pb was absent. Our work highlights the importance of boundary exchange in understanding marine element cycling and weathering-climate feedback.

As mountain tourism rapidly expands in remote landscapes, there is a critical need for improved disaster risk management to ensure the safety of tourists and industry workers, safeguard infrastruct...

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

The Indus River, originating from Manasarovar Lake in Tibet, runs along the Indus Tsangpo suture zone in Ladakh which separates the Tethyan Himalaya in the south from the Karakoram zone to the north. Due to the barriers created by the Pir-Panjal ranges and the High Himalaya, Ladakh is located in a rain shadow zone of the Indian summer monsoon (ISM) making it a high-altitude desert. Occasional catastrophic hydrological events are known to endanger lives and properties of people residing there. Evidence of such events in the recent geologic past that are larger in magnitude than modern occurrences is preserved along the channels. Detailed investigation of these archives is imperative to expand our knowledge of extreme floods that rarely occur on the human timescale. Understanding the frequency, distribution, and forcing mechanisms of past extreme floods of this region is crucial to examine whether the causal agents are regional, global, or both on long timescales. We studied the Holocene extreme flood history of the Upper Indus catchment in Ladakh using slackwater deposits (SWDs) preserved along the Indus and Zanskar Rivers. SWDs here are composed of stacks of sand-silt couplets deposited rapidly during large flooding events in areas where a sharp reduction of flow velocity is caused by local geomorphic conditions. Each couplet represents a flood, the age of which is constrained using optically stimulated luminescence for sand and accelerator mass spectrometry and liquid scintillation counter 14C for charcoal specks from hearths. The study suggests occurrence of large floods during phases of strengthened ISM when the monsoon penetrated into arid Ladakh. Comparison with flood records of rivers draining other regions of the Himalaya and those influenced by the East Asian summer monsoon (EASM) indicates asynchronicity with the Western Himalaya that confirms the existing anti-phase relationship of the ISM-EASM that occurred in the Holocene. Detrital zircon provenance analysis indicates that sediment transportation along the Zanskar River is more efficient than the main Indus channel during extreme floods. Post–Last Glacial Maximum human migration, during warm and wet climatic conditions, into the arid upper Indus catchment is revealed from hearths found within the SWDs.

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

The capacity factor (CF) is a vital parameter used to quantify the performance and efficiency of a wind turbine. An increase in generation efficiency leads to higher wind power production, improving the economics within the growing global wind market. In this research, we use a data-driven statistical method to explore the contributions of the three main drivers of CF change: turbine aging, changes in wind speed, and technological improvements. We find that for the group of old turbines (operated before 2008) with an unchanging technical condition, wind increases contributed ∼10% to the increasing CF on average from 2010 to 2020. For new turbines (built from 2008 to 2020), technological improvements had a strong positive effect on CF from 2015 to 2020, exceeding the effect of wind increases and offsetting the effects of aging. On average, rising wind speeds increased CF by ∼5% per year, while technological improvements increased it by ∼12%. As the installed capacity of wind turbines grew, technological progress became the dominant driver in CF increase. However, poor site selection potentially compromised the positive effect on CF afforded by technology changes early in the decade.

High-intensity monsoon rainfall in the Indian Himalaya generates multiple environmental hazards. This study examines the variability in long-term trends (1901–2013) in the intensity and frequency of high-intensity monsoon rainfall events of varying depths (high, very high and extreme) in the Upper Ganges Catchment in the Indian Himalaya. Using trend analysis on the Indian Meteorological Department (IMD) rainfall dataset, we find statistically significant positive trends in all categories of monsoon rainfall intensity and frequency over the 113-year period. The majority of the trends for both intensity and frequency are spatially located in the Higher Himalayan region encompassing upstream sections of the Mandakini, Alaknanda and Bhagirathi River systems. The extreme rainfall trends for both intensity and frequency are found to be only located in the vicinity of the upstream section of the Mandakini Catchment. Further, we explored the relationship between the Arctic Oscillation (AO) climate system and the frequency of occurrence of high-intensity rainfall events. Results indicate that AO is more likely to influence the occurrence of extreme monsoon events when it has a higher magnitude of negative AO phase. This study will help in better understanding of the influence of climate change at higher latitudes on mid-latitude rainfall extremes, particularly in the Himalayas. The implications of the findings are that statistically significant increasing rainfall depths and frequency in the Higher Himalayan region support the notion of higher frequency of rainfall-induced hazards in the future.

During a university class project related to climate change mitigation strategies, we utilized a university green space as a “living laboratory” for collaborative learning exercise to estimate landscape-level carbon biomass storage. The key objective of the exercise was to foster sustainability awareness with regard to the effectiveness of tree-planting initiatives to offset carbon emissions. Collaborative learning is a process by which students work together in small groups to accomplish a common goal. As experiences are active, social and student-owned, the process leads to the development of a variety of cognitive and transferable skills that are beneficial in academia and the workplace. Through data collection/analysis, the carbon biomass exercise not only allowed students to assess critically the efficacy of a tree-planting initiative as a means to sequester carbon, but they became aware of the difficulties in performing research on complex environmental issues. The intention of the research was to give students an opportunity to practice data collection, data analysis, problem solving, teamwork, communication and scientific literacy skills, meanwhile utilizing the campus open green space to enhance the knowledge discovery process. Informal assessment and discussions with students demonstrated that the activity was successful in reaching a wide range of students with varying backgrounds and initial attitudes about climate change mitigating strategies, which was our objective. Our case study demonstrates how learning objectives can be integrated with university sustainability initiatives to improve learning and student engagement. Finally, we see green spaces as dynamic settings for learning about physical processes and issues related to environmental management and sustainability.

This study examined the effects of two substrates (SOIL and COMMERCIAL) and grass on the green roof runoff quality in Singapore. Ten events were sampled over a 9-month period. Rainfall and green roof runoff from grass and bare experimental configurations were tested for total organic carbon (TOC), nitrogen and phosphorus nutrients (NO3−-N and PO43−-P), cations/anions and trace metals (Fe, Cu, Zn, Cd and Pb). All configuration units neutralised acid rainfall and removed metals except Fe despite their proximity to an industrial area. Concentrations decrease over the monitoring period for most water quality variables. The COMMERCIAL (COM) configurations elevated Cl− (3.8–10.8 ppm), SO42− (1.5–32.4 ppm), NO3−-N (7.8–75.6 ppm) and NH4+-N (22.0–53.1 ppm) concentrations in the runoff. Concentrations of NO3−-N (4.5–67.7 ppm) and NH4+-N (14.7–53.0 ppm) remained high at the end of the monitoring period for the COMgrass configuration, even with dilution from monsoon rainfall, making it suitable as an irrigation water source and a fertiliser substitute. The SOIL substrate retained N-nutrients, TOC and trace metals with concentrations comparable or below rainfall inputs. This substrate is suitable for widespread green roof applications in Singapore and other tropical cities. We recommend substrate testing before their approval for use on green roofs and encourage the long-term monitoring of these systems. HIGHLIGHTS All configurations removed trace metals and neutralised acid rainfall despite close proximity to an industrial area.; Substrates low in organic matter content were found to be suitable for widespread implementation in Singapore.; High nutrient concentrations in green roof runoff make them suitable for irrigating urban greenery.

The immense reduction in aerosol levels during the COVID-19 pandemic provides an opportunity to reveal how atmospheric chemistry is regulating our climate, among which the effect of aerosols on climate is a phenomenon of great interest but still in hot debate. The Intergovernmental Panel on Climate Change (IPCC) has continually identified the effect of aerosols on climate to have the largest uncertainty among the factors contributing to global climate change. Several studies indicate an inverse relationship between aerosol presence in the atmosphere and the diurnal surface air temperature range (DTR). Herein, we test this relationship by analyzing the DTR values from in situ weather station records for periods before and during the COVID-19 epidemic in China where aerosol levels have substantially reduced, compared with the climatological mean levels for a 19 year period. Our analyses find that DTRs from February to June during the COVID-19 pandemic are greater than 3 standard deviations above the climatological mean DTR. This anomaly has never occurred before in the 21st century and is at least in part associated with the observed reduction in aerosols.

Previous estimates of tropical forest carbon loss in the twenty-first century using satellite data typically focus on its magnitude, whereas regional loss trajectories and associated drivers are rarely reported. Here we used different high-resolution satellite datasets to show a doubling of gross tropical forest carbon loss worldwide from 0.97 ± 0.16 PgC yr−1 in 2001–2005 to 1.99 ± 0.13 PgC yr−1 in 2015–2019. This increase in carbon loss from forest conversion is higher than in bookkeeping models forced by land-use statistical data, which show no trend or a slight decline in land-use emissions in the early twenty-first century. Most (82%) of the forest carbon loss is at some stages associated with large-scale commodity or small-scale agriculture activities, particularly in Africa and Southeast Asia. We find that ~70% of former forest lands converted to agriculture in 2001–2019 remained so in 2020, confirming a dominant role of agriculture in long-term pan-tropical carbon reductions on formerly forested landscapes. The acceleration and high rate of forest carbon loss in the twenty-first century suggest that existing strategies to reduce forest loss are not successful; and this failure underscores the importance of monitoring deforestation trends following the new pledges made in Glasgow.

International audience

Gridded wind speed data products with global coverage and continuous long-term time series are widely used in many applications, such as evaluating wind energy potential [1] and drought processes [2]. However, some available products do not accurately reproduce observed wind speed trends on land [3], [4], leading to biased or inaccurate conclusions in studies on wind-related phenomena. In-situ weather stations involve direct measurements that accurately preserve wind speed trends. Still, the uneven distribution and incomplete time series have constrained their widespread applications in regional and global analyses. These limitations, which we have encountered firsthand in investigating the global wind stilling and reversal phenomena [4], have inspired us to create a new global gridded surface wind product that preserves observed wind patterns and trends., This work was supported by the National Natural Science Foundation of China (42071022), the start-up fund provided by Southern University of Science and Technology (29/Y01296122), and Highlight Project on Water Security and Global Change of Southern University of Science and Technology (G02296302). Cesar Azorin-Molina was supported by Evaluación y atribución de la variabilidad de la velocidad media y las rachas máximas de viento: causas del fenómeno stilling (RTI2018-095749-A-100), Cambios observados, proyecciones futuras e índices de la velocidad del viento y sus extremos en la Comunidad Valenciana (AICO/2021/023), and the Spanish National Research Centre Interdisciplinary Thematic Platform PTI-CLIMA. We thank Met Office (HadISD), ECMWF (ERA5), and CMIP6 for providing wind speed data used in this work.

To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980-2018) for 4,722 meteorological stations. Through examining monthly-averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (-19%), South America (-16%), Australia (-14%), and Asia (-13%), with corresponding rate reductions of -0.13, -0.08, -0.09 and -0.06 m s-1 decade-1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean-atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including West Hemisphere warm pool, East Atlantic Patten, Pacific Decadal Oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind energy generations, now and in the future., This research was supported by the National Natural Science Foundation of China (grants no. 42071022), the start-up fund provided by Southern University of Science and Technology (no. 29/Y01296122) and Highlight Project on Water Security and Global Change of the Southern University of Science and Technology (SUSTech, Grant No. G02296302), as well as Ramon y Cajal fellowship (RYC-2017-22830) and the grants no. VR-2017-03780 and RTI2018-095749-A-I00 (MCIU/AEI/FEDER, UE).

Wood‐based mulches are a preferred erosion control material for rehabilitation of degraded lands because they can be made from native wood materials; however, research is needed to verify the effectiveness of new products prior to application. The present study aims to assess the effectiveness of two types of native wood strand mulches from Iran (waste byproducts of Alnus glutinosa and Fagus orientalis working) in reducing runoff, soil loss, and sediment concentration under laboratory conditions. Toward this goal, rainfall simulations were conducted 27 times using a 50 mm hr⁻¹ rainfall during 20‐min experiments on erosion plots with treatments of different cover percentages (bare, 30 and 70%) and strand dimensions (16 and 4 cm in length; 1.5 cm wide). The results showed that all strand applications reduced runoff (>12%), soil loss (>47%), and sediment concentration (>44%) compared to the bare plots. The most effective application treatment was a 70% coverage of the 4‐cm strands of either material (p value < .002). The shorter strands were most effective for all hydrological and erosion variables reductions because they maintained more contact with the soil surface during simulated rainfall and then increased the soil retention potential, whereas the longer strands realigned in the direction of flow, thereby limiting their ability to retard flowing water. The small strands also facilitated the creation of microdams that blocked flow and promoted infiltration. Both materials were deemed effective native erosion control products in degraded sites in the Hyrcanian forests of Iran, particularly when small strands were applied at high cover rates.

In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Landslides triggered by monsoon rainfall are a recurring hazard that lead to loss of life and cause enormous property and infrastructure damage in the Indian Himalaya. This study is focused on understanding the role of extreme rainfall and physical factors in causing landslides in the Indian Himalaya, particularly in the Mandakini Catchment where an enormous landslide and flood disaster occurred in June 2013 following a two-day extreme rainfall event. Results indicate that sub-daily extreme rainfall depths causing landslides vary with elevation across the catchment. Antecedent rainfall six days prior to the extreme rainfall event was found to have substantial depths that could have primed the area for landslides. Except for aspect of slopes, the causative factors including land use/land cover, lithology, elevation, slope, river network, distance to roads, and total extreme rainfall as a triggering factor were found to be statistically significant in causing landslides in the catchment. The final product of the study is a new landslide susceptibility map that better delineates the landslide prone regions in the disaster-prone Mandakini Catchment after the June 2013 extreme rainfall event. The Map was prepared using logistic regression that shows medium and high susceptibility zones at upper sections of the catchment as well as along the Mandakini River and its tributaries where major sacred shrines, tourist spots and human establishments are located.

To understand the distribution of soil elemental concentrations and their potential sources of trace metal contamination in the high-conservation Nee Soon freshwater swamp forest in Singapore, we analyzed samples from 227 surface and 35 subsurface (auger profiles) locations. Our assessment involved distribution maps, principle component analysis, cluster analysis, and correlation analysis of element concentrations determined from a mixed acid digestion and measurement on an ICP-MS. We found a distinct zonation in the distribution of several elements (Ba, Cr, Cu, Fe, Mn, Pb, Sr, Ti, V, and Zn) between the upper and lower catchment that gives an erroneous notion of widespread contamination in the lower catchment. We believe this zonation is natural, likely related to differences in the underlying geology. However, Cu, Pb, and Sb concentrations were greatly enriched by anthropogenic activities on military training lands in the lower catchment, firing ranges in particular. Barium, Sr, and Zn also appear to be enriched in the lower part of the catchment, possibly from anthropogenic activities including military activity and roads. Although soils in the catchment are not highly contaminated, isolated areas with high concentrations of Cu, Pb and Sb may warrant management attention given the sensitive nature of the urban forest, which includes the last remaining fresh water swamp forest in Singapore.

Volatile rivers that involve floodplain stripping and subsequent floodplain reconstruction by vertical accretion are poorly known worldwide. This paper aims to partially fill this knowledge gap by a review of existing information and the provision of the currently most detailed account of such a river, namely the Ping River of northern Thailand. Recognition of this river type depends upon stratigraphic and morphostratigraphic analysis and so does not come within the ambit of those who focus only on river form and modern flood hydrology, and also not within the ambit of ‘traditional’ palaeoflood hydrology. A morphostratigraphic analysis of the Ping River adds to the small but valuable corpus of existing studies and provides an indication of the level of detail that is required for an in-depth understanding. A global review shows that extreme rainfall is always involved in floodplain stripping, although the sensitivity to rainfall extremes of catchments is not understood, including the contributions of land use, topography and alluvial resistance to erosion. The Ping River has the same characteristics as other volatile rivers, with an average interarrival time for stripping events of about three centuries probably caused by different combinations of wet periods, tropical lows, typhoons, and atmospheric rivers. As the intensity of extreme rainfall increases as Earth warms, the frequency of floodplain stripping events may increase with implications for both the pace of change in some fluvial landscapes and flood mitigation strategies.

Mountain forests, widely distributed around the world, are hotspots of biodiversity and provide important environmental services by conserving water and soil, regulating river flow and storing carbon. The upper altitudinal limits of trees is defined as the treeline. Some field investigations indicate that treelines around the world are moving upward as a response to global climate change. However, to date, a high-resolution spatial map of global mountain treeline position is still lacking. In this study, we develop an algorithm to detect the present-day tree line positions in mountain regions globally, via integrating a high-resolution tree distribution dataset with a high-resolution digital elevation model. The results are validated with even finer resolution remote sensing images in Google Earth. We analyse a range of climate datasets to understand important climate drivers of the present-day tree line position. Further, we explore the change in Normalized Difference Vegetation Index (NDVI) within the buffer zone of the treeline to determine how the treeline position has shifted in the last three decades. By providing the first global mountain treeline distribution, our analysis will help to reveal how mountain forests are responding to climate change globally, and to detect how the responses vary regionally.

Agriculture is expanding in tropical mountainous areas, yet its climatic effect is poorly understood. Here, we investigate how elevation regulates the biophysical climate impacts of deforestation over tropical mountainous areas by integrating satellite-observed forest cover changes into a high-resolution land–atmosphere coupled model. We show that recent forest conversion between 2000 and 2014 increased the regional warming by 0.022 ± 0.002 °C in the Southeast Asian Massif, 0.010 ± 0.007 °C in the Barisan Mountains (Maritime Southeast Asia), 0.042 ± 0.010 °C in the Serra da Espinhaco (South America) and 0.047 ± 0.008 °C in the Albertine Rift mountains (Africa) during the local dry season. The deforestation-driven local temperature anomaly can reach up to 2 °C where forest conversion is extensive. The warming from mountain deforestation depends on elevation, through the intertwined and opposing effects of increased albedo causing cooling and decreased evapotranspiration causing warming. As the elevation increases, the albedo effect increases in importance and the warming effect decreases, analogous to previously highlighted decreases of deforestation-induced warming with increasing latitude. As most new croplands are encroaching lands at low to moderate elevations, deforestation produces higher warming from suppressed evapotranspiration. Impacts of this additional warming on crop yields, land degradation and biodiversity of nearby intact ecosystems should be incorporated into future assessments. Deforestation causes elevation-dependent warming over tropical mountain regions, according to high-resolution climate simulations.

[Excerpt] Asia is one of the world's most flood-prone regions by many metrics: high flood magnitudes, frequency, severity; the number countries affected, the area of inundation; the number of people at risk; and importantly, flood-related fatalities (AIR, 2014; Luo, Maddoks, Iceland,Ward, & Winsemius, 2015; Table 1). With respect to mortality, nearly all the countries with more than 5,000 flood-related deaths since 1985 are from Asia (11 of 13; Table 1; Figure 1). As we write this commentary, flooding associated with tropical storm Nangka has caused more than 40 deaths in Laos, Cambodia, and Vietnam(Floodlist, 2020).

To address the lack of adequate measures for flood risk reduction in Nepal, where recurrent flood‐related hazards have had grave consequences for many people over the past decades, we develop a flood risk assessment model for a study area in the Sindhupalchok District. The model considers direct and indirect damages that are assigned to four asset categories (hydropower plants, roads, houses, and farmlands) and two scenarios (low‐exposure‐low‐flood and high‐exposure‐high‐flood scenarios). Model results indicate the following: (a) the planned expansion of hydropower plants reflected in the high exposure scenario is responsible for a substantial increase of economic damage compared with the low scenario and (b) for both scenarios, flood damage is largely related to road closures, which result in the loss of income for villagers and the loss of customs revenues. As this study aims both to provide methods for the assessment of flood risk and to demonstrate them in a case study, we discuss future work related to model‐based flood risk assessments that are needed towards flood risk reduction in Nepal.

To improve management and conservation in ungauged catchments in northern Iran, we developed regional models to predict annual runoff and discharge using multi-year time series from 20 gauged catchments in Ardabil, Province. We employed correlation, Ward cluster analysis, and principal component analysis to reduce the total parameter set (69 parameters represent catchment geometry, geology, soil, rainfall, and climate) to robust sets for regression analysis. The reduction method based on PCA using non-log-transformed data produced the best results, based on several error metrics (modified Nash Sutcliffe efficiency, R2, root mean squared error, and bias). Important parameters included area and mean annual precipitation, as well as those indicative of catchment shape and geology. Despite excellent ability to simulate mean annual discharge and sediment yield in the 20 catchments, the models performed poorly when applied to estimating year-to-year discharge and sediment yield in individual catchments. Poor prediction likely resulted from (a) rainfall data of insufficient spatial resolution in the large area with greatly varying elevation including mountains; and (b) inability to include data reflecting yearly changes in land-cover/land-use. Nevertheless, the regional models are likely useful for estimating mean runoff and sediment yields in other catchments in the region with similar geology, geomorphology, and climate.

High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones. The world keeps urbanizing. This study finds that since 1985 global urban lands have expanded four times faster than previously recognized and faster than population is growing.

The impact of rapid urbanization on the spatiotemporal pattern of short-term extreme precipitation in China remains unclear at the subnational scale. In this study, we present a general framework that measures urbanization-induced variation in hourly extreme wet season precipitation (April–October) from 1985 to 2012, with reference to a dynamic urban–rural station classification based on annual changes in urban extent. We found that urbanization in south China (−1 in Guangzhou) and July–September in the central and north (1.16 mm h−1 in August of Beijing). Urbanization also increases hourly extreme precipitation at peak times in diurnal cycles. The results indicate that urbanization has caused overall more and more heterogeneous spatial patterns over China and concentrated distributions during the rainy season and peak time. These patterns warrant attention when assessing the risk of increased waterlogging and flash flooding in urban areas.

Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation. China, with one of the world’s most considerable afforestation rates, has increased its forest cover from 16.6% 20 years ago to 23.0% by 2020. However, the maximum potential forest coverage achieved via tree planting and restoration is uncertain. To map potential tree coverage across China, we developed a random forest regression model relating environmental factors and appropriate forest types. We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas, urban areas, and agriculture land covers/uses, which is 50% higher than the current understanding. Converting these lands to the forest would generate 3.99 gigatons of new above- and belowground carbon stocks, representing an important contribution to achieving carbon neutrality. This potential is spatially imbalanced, with the largest restorable carbon potential being located in the southwest (29.5%), followed by the northeast (17.2%) and northwest (16.8%). Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential. In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning, reforestation should provide other environmental services such as the restoration of degraded soils, conservation of biological diversity, revitalization of hydrological integrity, localized cooling, and improvement in air quality. Because of the collective benefits of forest restoration, we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.

Recent flood disasters in Nepal have been the result of a combination of a fast-growing vulnerability of the local population and variable, possibly changing climatic conditions. In the future, flood disasters are likely to occur if vulnerability does not decrease. Nonetheless, the current state of flood disaster governance in Nepal, including flood risk assessment, is limited. To better inform policy and flood disaster governance, we created a flood risk assessment model that maps and quantifies the vulnerability of populations in flood-prone areas in Sindhupalchok District, Nepal. Three scenarios were considered in the model: a baseline-flood scenario, a medium-extreme-flood scenario, and a high-extreme-flood scenario. Overall, the model demonstrates that the local population is indeed vulnerable to flood risk, and increasingly for extreme floods. The model results indicate the following: (1) vulnerability to flood risk increases significantly from the baseline-flood scenario to the two extreme-flood scenarios; (2) houses are especially vulnerable to flood risk in the Village Development Committees (VDCs) of Gloche, Pagretar, Tatopani, Kadambas, and Bahrabise; and (3) income and food from crop production are most vulnerable to flood risk in the VDCs of Gloche, Fulpingdanda, Kadambas, Tatopani, and Batase. Based on these results, moving towards improved flood disaster governance, we make recommendations to improve measures for flood risk reduction with a focus on implementing flood early warning systems, clarifying the assignment of roles and responsibilities to stakeholders in flood risk governance, and increasing the development/use of flood risk assessment models.

Constraining the contribution of mangrove-derived carbon in tidal creeks is fundamental to understanding the fate of mangrove primary production and the role of mangroves as coastal carbon sinks. Porewater measurements and 24-h time series in a mangrove tidal creek were conducted during the dry and wet season, and over contrasting tidal ranges at the Can Gio Biosphere Reserve, Vietnam. Surface water carbon concentrations (dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), partial pressure of carbon dioxide (pCO2)) and their respective δ13C values were correlated with radon, suggesting that porewater input drives mangrove-derived carbon in the tidal creek. Based on three complementary mixing models, porewater input contributed to about 30% of the water volume and 46% to 100% of DOC and DIC pools in the tidal creek at low tide, with variabilities between seasons and tidal amplitudes. The creek carbon pool was 88% DIC, 6% DOC, and 6% particulate organic carbon (POC). The pCO2 values during the wet season (2973–16,495 μatm) were on average 5-fold higher than during the dry season (584–2946 μatm). This was explained by a potential greater mineralization attributed to higher organic matter availability and residual humidity that stimulate bacterial activity, and by a potential tidal dilution changing the pCO2/DIC ratio as suggested by the Revelle factor. Consequently, average CO2 evasion from the creek was estimated at 327–427 mmolC m−2 d−1 during the wet season and 92–213 mmolC m−2 d−1 during the dry season, using two independent approaches. Tidal amplitude seemed to influence porewater input and its carbon loads, with a higher contribution during frequent and high tidal amplitudes (symmetric). However, the highest input occurred in a tidal cycle which was preceded by tidal cycle of low amplitude (asymmetric). We explain this ambiguity by the influence of both, rapid water turnover intensifying porewater exchange, and long water residence time enhancing carbon load in porewater.

Southeast Asia is a hotspot of tropical deforestation for agriculture. Most of the deforestation is thought to occur in lowland forests, whereas the region’s mountainous highlands undergo very limited deforestation. However, regional reports of cropland expansion in some highland areas suggest that this assumption is inaccurate. Here we investigate patterns of forest change and cropland expansion in the region for the twenty-first century, based on multiple streams of state-of-the-art satellite imagery. We find large increases in cultivated areas that have not been documented or projected. Many of these cultivated areas have evolved from forests that vary in health and status, including primary and protected forests, or from recovering lands that were on a trajectory to become secondary forests. These areas all have different biophysical features than croplands. We estimate that an area of 82 billion m2 has been developed into croplands in the Southeast Asian highlands. Some portion of this land-use change is probably attributable to agricultural intensification on formerly swidden agriculture lands; however, a substantial proportion is from new forest loss. Our findings are in marked contrast with projections of land-cover trends that currently inform the prediction of future climate change, terrestrial carbon storage, biomass, biodiversity, and land degradation.