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In this paper, a novel single image dehazing method based on pyramid multi-scale transposed convolutional network (MST-Net) is proposed. Conventional haze removal algorithms based on the atmospheric scattering model may lead to some problems such as incomplete dehazing and colour distortion due to the inaccurate parameter approximations or indirect image optimization and reconstruction. Therefore, we design a real end-to-end image dehazing network to directly learn the mapping relationship between hazy images and the corresponding clear images. In this network, the cascaded feature extraction blocks extract the diversified feature information of the input images by multi-channel concatenation structure, which can effectively fuse the local features of the first convolution layer into the semantic features of subsequent layers in the block. In order to reconstruct high-quality dehazed images relieving the colour distortion, we design a multi-scale transposed convolution block to gradually expand the resolution of the obtained feature maps, and introduce skip connections from the feature extraction module to supplement the detailed information of the feature map pyramid. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze completely and achieve superior performance in subjective and objective evaluation over the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion results. The method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework, which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for training. The model is trained by feeding rain-contaminated and clean radar images in an unpaired manner, and the atmospheric scattering model parameters are not required as a prior condition. Additionally, a self-attention mechanism is introduced into the model, allowing it to focus on rain clutter when processing radar images. This combines global and local rain clutter context information to output more accurate and clear de-raining radar images. The proposed method is validated by applying it to actual field test data, which shows that compared with the wave height derived from the original rain-contaminated data, the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14% using the de-raining method. These results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology. [ABSTRACT FROM AUTHOR]

If left unmodeled, the delay suffered by electromagnetic waves while crossing the neutral atmosphere negatively affects Global Navigation Satellite System positioning. The modeling of the delay has been carried out by means of empirical models formulated based on climatological information or using information extracted from numerical weather prediction (NWP) models. This paper explores the potential use of meteorological information of several types that will become available with the increasing number of sensors (e.g. a cell phone, or the thermometer of a nearby smart home) in cyberspace. How can we make use of these potentially huge data-sets, which may help to provide the best possible representation of the neutral atmosphere at any given time, as readily and as accurately as possible? This situation falls in the realm of Big Data. A few potential scenarios, a sequential improvement of Marini mapping function coefficients, a self-feeding NWP, and near real-time empirical model updates, are discussed in this paper. The pros and cons of each approach are discussed in comparison with what is done today. Experiments indicate that they have potential for a positive contribution. [ABSTRACT FROM AUTHOR]

Rainfall is considered a major input in designing stormwater management measures, especially for any low-impact development (LID) control design. With the impact of climate change, rainfall frequency and its patterns are changing continuously. Quantification of these changes and their impact on the performance of LID design becomes crucial. This paper presents a methodology to quantify the change in rainfall patterns using the Coupled Model Intercomparison Project 5 (CMIP5) climate model and to select the most feasible LID for a catchment with haphazard development. Interconnected decentralization-based LID controls are evaluated with the objective of emulating a pre-urbanized scenario. The overall analyses indicated that green roof (GR) followed by infiltration trenches (IT), rooftop disconnection (RTD), and permeable pavement (PP) showed better performance. Furthermore, a combination of IT, PP, and RTD accomplishes better efficiency for extreme rainfall events. Implementation of the most feasible combination will provide the additional benefit of water recycle and reuse. [ABSTRACT FROM AUTHOR]

This paper examines the performance of three climate re-analysis datasets in Angola, namely the ERA-40, NCEP-r1 and JRA-55, by comparing annual and seasonal estimates of precipitation, surface air temperature and evaporation with ground observation measurements. The observational dataset describes a region poorly covered by international climate databases and it is believed that most of its data have not been used in the data assimilation procedures of the climate models. This paper therefore provides a fresh look at the performance of these climate re-analysis datasets in a vast area where distance and civil war have hindered ground monitoring efforts. The re-analysis exercises offer better temperature estimates than precipitation estimates. When offered, the evaporation estimates from all three products are very poor. The three products are able to describe the main features of the spatial distribution of average annual precipitation and temperature, but struggle to reproduce the temporal changes of these variables. The results from a set of performance criteria show that the correlation between the observed ground measurements and re-analysis estimates is poor overall and the NSE values indicate that the average measured value at each location is usually a better estimate than the re-analysis estimate.EDITOR A. CastellarinASSOCIATE EDITOR S. Kanae [ABSTRACT FROM AUTHOR]

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ETo), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ETo amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ETo. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ETo amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation. [ABSTRACT FROM AUTHOR]

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Southwestern Amazonia has low spatial coverage of hydro-meteorological data, making climate and hydrological models essential tools. Thus, it is necessary to evaluate these models to verify their suitability for future climate change assessments. We evaluate the ability of a regional climate model (Eta) forced with three Earth system models and a large-scale hydrological model (MGB) to represent hydroclimatic variables for the current climate in the Madeira River basin. Different hydro-meteorological databases are used to evaluate the systematic errors of the coupled model system. The simulation results show that the Eta model underestimates (overestimates) precipitation over the basin during the rainy (dry) season. The hydrological processes simulated by the MGB are considered satisfactory, although the model has some limitations with respect to the timing of floods and their removal in different discharge regimes. [ABSTRACT FROM AUTHOR]

Analysis of the uncertainty propagation along a hydroclimatic modelling chain has been performed by few studies to date on subsurface drainage hydrology. We performed such an analysis in a representative French drainage site. A set of 30 climate projections provided future climatic conditions for three representative concentration pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. Three hydrological models for drainage systems, MACRO, DRAINMOD for DRAINage MODel, and SIDRA-RU for "SImulation du DRAinage - Réserve Utile" in French, on the three different parameter sets were used to quantify uncertainties from hydrological components. Results showed that the RCP contribution to total uncertainty reaches almost 40% for air temperature, does not exceed 15% for precipitation, and is almost negligible for hydrological indicators (HIs). The main source of uncertainty comes from the climate models, representing 50–90% of the total uncertainty. The contribution of the hydrological components (models and parameter sets) to the HI uncertainty is almost negligible too, not exceeding 5%. [ABSTRACT FROM AUTHOR]

Drought is often monitored through standardized indices. However, while enabling comparisons across different climatic regions, standardization poses an issue when using indices to assess future climate change impacts on drought, since they have a null average by definition. To address this issue, in this study we introduce a dynamic approach where future changes are assessed by computing climate normals using moving time windows. The approach is applied to Sicily and Calabria (Southern Italy) using the standardized precipitation index (SPI) and the standardized precipitation–evapotranspiration index (SPEI), and considering climate change scenarios RCP4.5 and RCP8.5. An optimized ensemble weighted average (OEWA) of climate models is introduced to reduce model biases. The results indicate that the region is likely to experience an increase in drought events due to climate change. The findings highlight the need for revised drought identification strategies that account for non-stationarity in climate. [ABSTRACT FROM AUTHOR]

Predicting the annual frequency of tropical storms is of interest because it can provide basic information towards improved preparation against these storms. Sea surface temperatures (SSTs) averaged over the hurricane season can predict annual tropical cyclone activity well. But predictions need to be made before the hurricane season when the predictors are not yet observed. Several climate models issue forecasts of the SSTs, which can be used instead. Such models use the forecasts of SSTs as surrogates for the true SSTs. We develop a Bayesian negative binomial regression model, which makes a distinction between the true SSTs and their forecasts, both of which are included in the model. For prediction, the true SSTs may be regarded as unobserved predictors and sampled from their posterior predictive distribution. We also have a small fraction of missing data for the SST forecasts from the climate models. Thus, we propose a model that can simultaneously handle missing predictors and variable selection uncertainty. If the main goal is prediction, an interesting question is should we include predictors in the model that are missing at the time of prediction? We attempt to answer this question and demonstrate that our model can provide gains in prediction. [ABSTRACT FROM AUTHOR]

The assessment of future extremes is hindered by the lack of long time series. Weather generators can alleviate this problem, but easily become complex. In this study, a weather generator combining Bartlett-Lewis models and vine copulas is presented. This combination allows for the generation of time series with statistics similar to those of the input. This model chain has never been assessed on the basis of future simulations. However, it could have value for extending climate simulations. The model chain was applied to historical observations and one climate model time series. The statistical moments and the correlation on the basis of the future simulations were comparable to those on basis of the historical observations. The results for the extremes were ambiguous, but still provided valuable information. The adequate performance for the statistical moments and the correlation indicates that the weather generator might be useful for the characterization of future extremes. [ABSTRACT FROM AUTHOR]

Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited 'slow rise, slow fall, sharp fall, and sharp rise.' Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact. [ABSTRACT FROM AUTHOR]

There is a high demand for openly accessible hydroclimatic data for climate change adaptation. Different data sources are available, however, discrepancy between the data can confuse users and should be evaluated and explained. This study, investigates how climate impact indicators (CIIs) developed for global users in the Copernicus Climate Change Service (C3S) are comparable to other openly available global data for water and climate. We found that, for temperature, datasets are comparable and climate impacts are thus considered robust. Important discrepancies arise in the precipitation indicators. Of the CIIs analysed in this study, the hydrological CIIs differ most so they should be used with care. These differences are probably caused by model uncertainty (hydrological model, HM; global climate model, GCM), ensemble size and model selection. A HM ensemble, as well as a GCM ensemble combined with improved model performance and selection criteria, should be used to ensure high-quality global water and climate services. [ABSTRACT FROM AUTHOR]

This study evaluates the uncertainty of four components of the hydroclimatic modelling chain on flood projections over 96 basins covering contrasting hydrometeorological regimes located in Canada and Mexico. Two ensembles of climate simulations are considered, a large ensemble of 22 global climate model simulations and a smaller ensemble of three high-resolution regional climate model simulations. The other components are two post-processing techniques, three lumped hydrological models and six probability distributions. These four sources are assessed through a method of variance decomposition applied to six flood indicators over a reference period and two future periods: 1976–2005, 2041–2070 and 2070–2099. Systematic differences are observed between basins with contrasting flood-generating processes. Snow-dominated basins consistently show larger variance contributions from hydrological models, while rain-dominated basins show climate simulations as their dominant source. These results underline the need to consider the variability of each component's uncertainty contribution and its link to hydroclimatic conditions and dominant processes. [ABSTRACT FROM AUTHOR]

When the ambient temperature is low, a large amount of water mist and dust will inevitably appear around the zinc flotation cell, forming haze, which seriously affects the extraction of flotation froth image features. General defogging methods of natural image are difficult to obtain satisfactory results for such industrial haze image. Therefore, we propose a real-time defogging method based on ASM-VoFD (Atmospheric Scattering Model and the Variable-order Fractional Differential). First, the dark pixel ratio is used to detect fog in froth image, which solves the redundant calculation caused by unnecessary defogging operations. Second, the linear transformation of the atmospheric scattering model is used to calculate the initial transmission map, and the gaussian filter is used to optimize the initial transmittance, and the haze-free image is restored with atmospheric light estimation. Finally, a variable order fractional differential operator is used to enhance the edges and texture details of the restored froth image, which solves the problems of blurred edges and low contrast. The experiments show that the algorithm has a good defogging effect on the industrial images, enhance the edges of the image, and can be effectively implemented in O(N) time to meet the application requirements of real-time flotation monitoring. [ABSTRACT FROM AUTHOR]

In this study the capability of two advanced hybrid artificial intelligence-based methods is investigated in modelling meteorological droughts based on the standardized precipitation index (SPI) for various time windows. The outcomes are compared with adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA). The best-fitted distribution functions are found to vary with respect to stations and time windows. For Canakkale, Istanbul and Tekirdag stations, the correlation coefficients (CC) of hybridized method of ANFIS with gray wolf optimization (ANFIS-GWO) and ARIMA are in the range of 0.88–0.94, 0.88–0.96 and 0.86–0.94, respectively. The performance index also showed that the ANFIS-GWO provides superior accuracy in modelling droughts, with the minimum value (PI = 0.78) for SPI12 of Canakkale station. Forward-chaining cross-validation and the P value of the Chi-squared test also confirm that ANFIS-GWO is the superior model, in which there is not a significant difference between the trend of the predicted categories and that of the real categories for all stations and SPIs. [ABSTRACT FROM AUTHOR]

The principal objective of this article is to calculate rate of below-cloud scavenging of dust by rain along the Libyan coastline during an extraordinary dust storm that occurred early in March, 2004. A numerical investigation of wet deposition of four dust particles including clay, small silt, large silt and sand was performed using a regional meteorological model (Eta) coupled with the module describing desert dust cycle. For correct estimation of below-cloud wet deposition, explicit information about cloud and precipitation water at all model levels was used. An amount of 1764 mgr/m2, which was simulated at El Khoms, Ajdabia and Benina stations, was the maximum calculated rate of clay in the study area. An amount estimated at 7848 mgr/m2 of small silt particles and 3058 mgr/m2 of large silt particles were found in Masrata region. Compared to other sediment types, deposited silts were found to be much bigger across the affected regions during the studied event, with small silt quantities being the highest. The sand component seems to be the least deposited aerosol through the entire studied region. The highest quantity of 1187 mgr/m2 of sand materials was simulated in Zawara and Azzawia regions. The obtained results show that Libyan coastal areas have considerable dust wet deposition during the study period; however, the concentration rates are highly variable in space. Copyright © 2022 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

This study investigates the potential differences between spatial and temporal transferability of the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model parameters in 576 Iranian catchments. Our goal is to determine how parameter transferability is affected by (a) parameter transfer modes (temporal or spatial) and (b) different climate conditions. In the temporal mode, we make decisions in each catchment based on a benchmark baseline that accounts for the seasonality of flows. In the spatial mode, we examine physical similarity and spatial proximity methods. The main conclusions are that: (1) the HBV model struggles to beat the benchmark in lowland catchments; (2) under stationary conditions, rainfall-runoff models have greater temporal transferability than spatial transferability; (3) under non-stationary climate conditions, the number of catchments that perform best in terms of temporal transferability is smaller than in stationary climate conditions; and (4) the rainfall-runoff model has better transferability from drier to wetter conditions. [ABSTRACT FROM AUTHOR]

Changes in climate are altering the historical characteristics of the streamflow regime and affecting the performance of water systems. Here, the role of representing natural streamflow conditions in quantification of water system vulnerability under changing climate is evaluated in the Oldman River Basin, Canada. Four hydrological models are developed using point- and grid-based climate data and considering lumped and semi-distributed representations of the watershed. These hydrological models are then coupled with a reservoir water allocation model. Using an ensemble of climate model projections fed into these integrated models, changes in the water system's behaviour are evaluated. Although intensified and earlier peak flows and more critical water deficits are projected, the estimated risks of failure strongly depend on the considered hydrological model configuration. The divergence among models' projections for water deficit can be as high as 300%. Therefore, usage of all configurations is recommended to revise the reservoir operational policies. [ABSTRACT FROM AUTHOR]

Mediterranean countries are strongly affected by reduced surface water resources. This study aims to assess the climate change impacts on runoff supply to the Bin El Ouidane dam in Morocco, the largest in the country in terms of hydropower production. A water balance model coupled with a stochastic calibration method to provide confidence intervals on simulated discharge is used to reproduce discharge using observed and bias-corrected ECMWF Reanalysis 5th Generation (ERA5) rainfall (WFDE5). Five regional climate models from the EURO-CORDEX initiative are considered to estimate the climate change impacts on surface runoff for the Representative Concentration Pathway (RCP4.5) and RCP8.5 scenarios. Results indicate a future increase in evapotranspiration (+15 to +36%) associated with a decrease in precipitation (−16 to −34%) in both climate scenarios. The hydrological projections indicated a significant decrease in runoff (−57 to −86%), more severe when the model is driven by WFDE5 compared to observed precipitation. Consequently, the strong decline of surface water resources imply a change in water ressource management for hydropower production and local agriculture to adapt to the future climatic conditions. [ABSTRACT FROM AUTHOR]

Society is facing increasingly rapid and frequent changes, which are transforming the decision making paradigm from a static, value-based perspective to a dynamic, scenario-based perspective. Flood risk assessment (FRA) is moving towards an approach that encompasses social, climatic, and environmental changes. Advances in climate and hydraulic modelling allow the development of long-term scenarios with greater reliability. However, the coupling of human behaviour in the process of flood risk assessment is still in an embryonic phase. Here we attempt to fill this gap by (1) coupling a socio-hydrological model with an urban flood model using a cellular automata approach, and (2) using climate change projections to estimate flood risk in the coming decades. We illustrate our analytical framework with an application in a Brazilian catchment, showing its potential in developing long-term scenarios that can ultimately improve the process of flood risk assessment. [ABSTRACT FROM AUTHOR]

Abstract–Climate field reconstructions (CFRs) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. We apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states. for this article are available online. [ABSTRACT FROM AUTHOR]

Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state. [ABSTRACT FROM AUTHOR]

This study investigated the impacts of climate and socio-economic changes on water resources in the Beas-Sutlej river basin, Himalaya. Multiple-model ensembles of five climate models forced with the Representative Concentration Pathway (RCP) 8.5 scenario and the Shared Socio-economic Pathway SSP 1 were used. A validated Water Evaluation And Planning (WEAP) model of the basin was used for assessing the resulting sectoral water allocations. The results showed increasing runoff during the pre-monsoon and monsoon seasons, principally as the respective consequences of increased glaciers melting and higher rainfall. The finding also indicates that ensemble mean annual irrigation water demand will decrease by between 8 and 13% in Punjab due to the conversion of agricultural land to urban centres but will increase in Rajasthan by about 14%. These outcomes will be useful for the Bhakra and Beas Management Board in developing adaptation strategies to cope with any future water shortages that may occur in the basin. [ABSTRACT FROM AUTHOR]

Abutilon theophrasti (velvetleaf, butterprint), a problematic weed of crops in some temperate countries, was introduced into New Zealand in 1948 as a potential fibre crop. It has naturalised in the North Island in the Waikato and Auckland regions but its status at the many sites where it was inadvertently sown in 2015 as a contaminant of Beta vulgaris seed lines remains unknown. To determine the potential distribution of the species in New Zealand we used an existing and a newly developed climate niche model, both constructed using the modelling software CLIMEX and a current global climate surface dataset. The Holt & Boose model, published in 2000, fitted only 54% of the known 3825 global occurrences of the species. It predicts that most of the North Island is climatically optimal and that most of the South Island is unsuitable. By contrast, the new model, parameterised using a geographically wider set of global occurrences, fitted 98% of the global occurrences. This more robust model predicts that almost all New Zealand, including all agricultural land, is currently climatically suitable for the species. We conclude that velvetleaf could become a weed throughout the country. [ABSTRACT FROM AUTHOR]

This study investigates the uncertainty in hydroclimatic modelling chain components on future changes in potential evapotranspiration, specially on low flows, at the watershed scale. A large domain covering 2080 watersheds located in Canada, the United States and Mexico is studied. The outputs from eight global climate models are post-processed using two methods to produce climate scenarios that drive 10 temperature- and radiation-based potential evapotranspiration (PET) formulas and three lumped conceptual hydrological models, under representative concentration pathways (RCP) 4.5 and 8.5 and for the future periods 2041–2070 and 2071–2100. All simulated results show an increase in PET and a decrease in low flows, computed through three statistical metrics. Based on the results, we advise the use of multiple hydrological model–PET formula combinations when investigating the impact of climate change on low flows. This study provides an assessment of the contribution of potential evapotranspiration formulas to uncertainty throughout the modelling chain. [ABSTRACT FROM AUTHOR]

A spatio-temporal analysis of representative concentration pathways (RCPs)-based projections of surface air temperatures under different combinations of global circulation models (GCMs) and RCPs for Satluj River Basin has been presented. The projections of temperature anomalies have been obtained for several meteorological stations in the basin using 16 different combinations of emission scenarios and global climate models (GCMs). For each combination, the projections of temperature anomalies have been analysed for two future time periods centred at 2030 and 2050. The results of the analysis conducted herein clearly indicate that the temperature will rise for all the future time scales with the maximum increase being projected under RCP8.5 compared to the baseline 1986–2005. However, there is large inter-model variability in the projections of temperature anomalies under different RCPs. Under RCP 8.5, the average temperature in Satluj basin is projected to rise by around 4°C by mid of the twenty-first century. The projections of temperature anomalies analysed herein could be potentially used for the evaluation of hydrological impacts of climate change in Satluj River Basin—a key basin in the Himalayan region from the view point of global warming. [ABSTRACT FROM AUTHOR]

Pacific Lamprey (Entosphenus tridentatus) are a native anadromous species that, like salmon, historically returned to spawn in large numbers in watersheds along the west coast of the United States (U.S.). Lamprey play a vital role in river ecosystems and are one of the oldest vertebrates that have persisted over time likely influencing the evolution of many aquatic species. Pacific Lamprey have declined in abundance and are restricted in distribution throughout Washington, Oregon, Idaho and California. A key uncertainty influencing Pacific Lamprey status is the impact of climate change. We modified the NatureServe Climate Change Vulnerability Index (CCVI) to accommodate climate predictions from the International Panel on Climate Change. Using downscaled information, we characterized changes in 15 rivers occupied by Pacific Lamprey in the western U.S. We evaluated this risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for two time periods (mid-century 2040–2069 and end-century 2070–2099). The CCVI scores generally increased when going from RCP 4.5 to RCP 8.5 in three Global Climate Models for both mid-century and end-century, which our analyses forecasts degraded stream temperature and hydrologic conditions under increasing greenhouse gas emissions. The geographically assessed results suggest that climate change impacts to Pacific Lamprey vulnerability are magnified in highly altered rivers. If we continue to observe greenhouse gas emission levels associated with the RCP 8.5, Pacific Lamprey will be at greater risk to climate change impacts. In order to mitigate the risk from climate change toward the end of the century, additional actions will need to be prioritized to rapidly reduce the impact of these threats such as increasing flow, creating backwater habitat, restoring riparian vegetation and reducing stream disturbances. The findings revealed the patterns of vulnerability for Pacific Lamprey across their U.S. range are informative for prioritizing river restoration actions when paired with regional implementation plans. [ABSTRACT FROM AUTHOR]

The Devon Ice Cap (DIC) is one of the largest ice masses in the Canadian Arctic. Each summer, extensive supraglacial river networks develop on the DIC surface and route large volumes of meltwater from ice caps to the ocean. Mapping their extent and understanding their temporal evolution are important for validating runoff routing and melt volumes predicted by regional climate models (RCMs). We use 10 m Sentinel-2 images captured on 28 July and 10/11 August 2016 to map supraglacial rivers across the entire DIC (12,100 km2). Both dendritic and parallel supraglacial drainage patterns are found, with a total length of 44,941 km and a mean drainage density (Dd) of 3.71 km−1. As the melt season progresses, Dd increases and supraglacial rivers form at progressively higher elevations. There is a positive correlation between RCM-derived surface runoff and satellite-mapped Dd, suggesting that supraglacial drainage density is primarily controlled by surface runoff. [ABSTRACT FROM AUTHOR]

Based on a future temperature increase of 0.5°C and precipitation decrease of 25%, the climate elasticity of streamflow to precipitation and temperature changes in 12 Andean watersheds of the Coquimbo Region, north-central Chile, was assessed. Also, the possible relationships between this elasticity and specific physiographic characteristics of the watersheds (area, average elevation, slope distribution, terrain roughness, slope orientation, vegetation cover) were studied. Climate elasticity of streamflow ranged between 0 and 2.8. Watersheds presenting higher elevations, with a fairly well-balanced distribution of slope exposure tend to exhibit lower elasticity, which could be explained by the contribution of snowfall to the hydrological regime, more significant in those watersheds. Results should be considered when downscaling climate model projections at the basin scale in mountain settings. Finally, uncertainties in the approach, given by factors such as streamflow seasonality, data availability and representativeness and watershed characteristics, and therefore the scope of the results, are discussed. [ABSTRACT FROM AUTHOR]

Rainfall events largely control hydrological processes occurring on and in the ground, but the performance of climate models in reproducing rainfall events has not been investigated enough to guide selection among the models when making hydrological projections. We proposed to compare the durations, intensities, and pause periods, as well as depths of rainfall events when assessing the accuracy of general circulation models (GCMs) in reproducing the hydrological characteristics of observed rainfall. We also compared the sizes of design storm events and the frequency and severity of drought to demonstrate the consequences of GCM selection. The results show that rainfall and extreme hydrological event projections could significantly vary depending on climate model selection and weather stations, suggesting the need for a careful and comprehensive evaluation of GCM in the hydrological analysis of climate change. The proposed methods are expected to help to improve the accuracy of future hydrological projections for water resources planning. [ABSTRACT FROM AUTHOR]

Using regionally downscaled and adjusted outputs of three global climate models (GCMs), meteorological drought analysis was accomplished across Ankara, the capital city of Turkey. To this end, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were projected under (representative concentration pathway) RCP4.5 and RCP8.5 greenhouse gas scenarios. In general, our results show that Ankara experienced six severe and two extreme drought events during the reference period, 1971–2000. However, the projections indicate fewer drought events for the near-future period of 2016–2040, with no potential extreme drought events. While the RCP4.5 scenario showed that dry spells will be dominant in the second half of the near-future period, the RCP8.5 scenario projected that dry spells will be evenly distributed during the entire near-future period. [ABSTRACT FROM AUTHOR]

The groundwater contamination risk in future climates was investigated at three locations in Sweden. Solute transport penetration depths were simulated using the HYDRUS-1D model using historical data and an ensemble of climate projections including two global climate models (GCMs), three emission scenarios and one regional climate model. Most projections indicated increasing precipitation and evapotranspiration until mid-century with a further increase at end-century. Results showed both increasing and decreasing groundwater contamination risks depending on emission scenario and GCM. Generally, the groundwater contamination risk is likely to be unchanged until mid-century, but higher at the end of the century. Soil and site specific relationships between Δ(P – PET) (i.e. change in the difference between precipitation, P, and potential evapotranspiration, PET) and changes in solute transport depths were determined. Using this, changes in solute transport depths for other climate projections can be assessed. [ABSTRACT FROM AUTHOR]

Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure. [ABSTRACT FROM AUTHOR]

The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false "discoveries." A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this article, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

The responses of aerosol particles to heating are important for measurements of their chemical, physical, and optical properties, classification, and determination of origin. However, the thermal behavior of organic aerosol particles is largely unknown. We provide a method to analyze such thermal behavior through heating from room temperature to >600°C by using a heating holder within a transmission electron microscope (TEM). Here we describe in-situ shape and size changes and variations in the compositions of individual particles before and after heating. We use ambient samples from wildland and agricultural biomass fires in North America collected during the 2013 Biomass Burning Observation Project (BBOP). The results indicate that individual tar balls (TB; spherical organic material) from biomass burning retained, on average, up to 30% of their volume when heated to 600°C. Chemical analysis reveals that K and Na remain in the residues, whereas S and O were lost. In contrast to bulk sample measurements of carbonaceous particles using thermal/optical carbon analyzers, our single-particle results imply that many individual organic particles consist of multiple types of organic matter having different thermal stabilities. Beyond TBs, our results suggest that because of their thermal stability some organic particles may not be detectable by using aerosol mass spectrometry or thermal/optical carbon analyzers. This result can lead to an underestimate of the abundance of TBs and other organic particles, and therefore biomass burning may have more influence than currently recognized in regional and global climate models. Copyright © 2018 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies. [ABSTRACT FROM AUTHOR]

Assessments of hydrological response to climatic changes are characterized by different types of uncertainties. Here, the uncertainty caused by weather noise associated with the chaotic character of atmospheric processes is considered. A technique for estimating such uncertainty in simulated water balance components based on application of the land surface model SWAP and the climate model ECHAM5 is described. The technique is applied for estimating the uncertainties in the simulated water balance components (precipitation, river runoff and evapotranspiration) of some northern river basins of Russia. It is shown that the larger the area of a basin the less the uncertainty. This dependency is smoothed by differences in natural conditions of the basins. Analysis of the spectral densities of water balance components shows that a river basin filters out high-frequency harmonics of spectral density of precipitation (corresponding to synoptic or sub-seasonal scale) during its transformation into evapotranspiration and especially into runoff.EDITOR D. KoutsoyiannisASSOCIATE EDITOR H. Kreibich [ABSTRACT FROM AUTHOR]

This study aimed to quantify possible climate change impacts on runoff for the Rheraya catchment (225 km2) located in the High Atlas Mountains of Morocco, south of Marrakech city. Two monthly water balance models, including a snow module, were considered to reproduce the monthly surface runoff for the period 1989‒2009. Additionally, an ensemble of five regional climate models from the Med-CORDEX initiative was considered to evaluate future changes in precipitation and temperature, according to the two emissions scenarios RCP4.5 and RCP8.5. The future projections for the period 2049‒2065 under the two scenarios indicate higher temperatures (+1.4°C to +2.6°C) and a decrease in total precipitation (−22% to −31%). The hydrological projections under these climate scenarios indicate a significant decrease in surface runoff (−19% to −63%, depending on the scenario and hydrological model) mainly caused by a significant decline in snow amounts, related to reduced precipitation and increased temperature. Changes in potential evapotranspiration were not considered here, since its estimation over long periods remains a challenge in such data-sparse mountainous catchments. Further work is required to compare the results obtained with different downscaling methods and different hydrological model structures, to better reproduce the hydro-climatic behaviour of the catchment.EDITOR M.C. AcremanASSOCIATE EDITOR R. Hirsch [ABSTRACT FROM PUBLISHER]

The low density of meteorological stations in parts of Canada necessitates using numerical weather prediction (NWP)/assimilation output for hydrological modelling. In this study, comparisons are made of simulated land surface variables when using field observationsversusNWP output as forcing for two well-instrumented sites: the mountainous and forested Marmot Creek Basin (MCRB) in the Canadian Rocky Mountains, and a prairie cropland/grassland site (Kenaston). The Canadian Land Surface Scheme 3.6 (CLASS) was used for modelling. The Global Environmental Multiscale (GEM) model with Canadian Precipitation Analysis (CaPA) was also used as forcing. There was good agreement between observed meteorology and GEM/CaPA, though some deficiencies in GEM/CaPA were identified: the effects of sub-grid topography on incoming radiation and wind speed were not accounted for at MCRB, and CaPA did not capture some convective rainfall events at Kenaston. CLASS simulations using both sets of forcing showed difficulties in simulating snow depth, soil moisture and evapotranspiration; certain difficulties were linked to GEM/CaPA deficiencies and/or CLASS. Both sets of forcing tended to overestimate the duration of snow cover at MCRB, but during different years. With GEM/CaPA as forcing, CLASS overestimated the duration of frozen soils. The GEM/CaPA precipitation difficulties at Kenaston degraded soil moisture simulations.EDITOR A. Castellarin; ASSOCIATE EDITOR E. Volpi [ABSTRACT FROM PUBLISHER]

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.Editor M.C. Acreman; Associate editor S. Kanae [ABSTRACT FROM PUBLISHER]

A semi-distributed hydrological model is developed, calibrated and validated against unregulated river discharge from the Tocantins-Araguaia River Basin, northern Brazil. Climate change impacts are simulated using projections from the 41 Coupled Model Intercomparison Project Phase 5 climate models for the period 2071–2100 under the RCP4.5 scenario. Scenario results are compared to a 1971–2000 base line. Most climate models suggest declines in mean annual discharge although some predict increases. A large proportion suggest that the dry season experiences large declines in discharge, especially during the transition to the rising water period. Most models (>75%) suggest declines in annual minimum flows. This may have major implications for both current and planned hydropower schemes. There is greater uncertainty in projected changes in wet season and annual maximum discharges. Two techniques are investigated to reduce uncertainty in projections, but neither is able to provide more confidence in the simulated changes in discharge.Editor D. Koutsoyiannis Associate editor F. Hattermann [ABSTRACT FROM PUBLISHER]

Global climate variations are expected to cause serious challenges to water resources planning and management, including an increase in sea level, abrupt changes in rainfall patterns and changes in ecosystems. This study evaluates impacts of mid-century climate variability as projected by climate models in the Haw River watershed, which contributes significantly to Jordan Lake, a major source of drinking water supply in central North Carolina, USA. The watershed-based hydrological model, Soil and Water Assessment Tool (SWAT), was successfully calibrated with very good to excellent performance. Projected precipitation and temperature information for 2040–2069 from four dynamically downscaled regional climate models (RCMs) was used to force the SWAT modeling set-up of the watershed. On a long-term basis, a 38% decrease in the precipitation in early fall is expected while spring months are expected to receive 30% higher precipitation compared to the baseline condition (1980–2009). Water yield was found to increase in spring months, with a maximum of 74% increase on average. Summer months are expected to have on average 8% higher evapotranspiration (ET) than the baseline. Analysis of the change in average monthly streamflow at the watershed outlet (which leads to Lake Jordan) shows that there might be, on average, an 80% increase in streamflow in spring months (February, March, April and May), with the greatest increase (107%) in May. In general, simulation results indicated that the hydrological response of the watershed is very sensitive to the potential variation in climate (precipitation and temperature), with precipitation being one of the decisive factors in water yield increase.Editor Z.W. Kundzewicz Associate editor N. Verhoest [ABSTRACT FROM PUBLISHER]

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM)-simulated local climate variables as the input data for hydrological models due to systematic errors of RCMs. Most of the present bias-correction methods adjust statistical properties between observed and simulated data based on a predefined duration (e.g. a month or a season). However, there is a lack of analysis of the optimal period for bias correction. This study attempted to address the question whether there is an optimal number for bias-correction groups (i.e. optimal bias-correction period). To explore this we used a catchment in southwest England with the regional climate model HadRM3 precipitation data. The proposed methodology used only one grid of RCM in the Exe catchment, one emissions scenario (A1B) and one member (Q0) among 11 members of HadRM3. We tried 13 different bias-correction periods from 3-day to 360-day (i.e. the whole of one year) correction using the quantile mapping method. After the bias correction a low pass filter was used to remove the high frequencies (i.e. noise) followed by estimating Akaike’s information criterion. For the case study catchment with the regional climate model HadRM3 precipitation, the results showed that a bias-correction period of about 8 days is the best. We hope this preliminary study on the optimum number bias-correction period for daily RCM precipitation will stimulate more research to improve the methodology with different climatic conditions. Future efforts on several unsolved problems have been suggested, such as how strong the filter should be and the impact of the number of bias correction groups on river flow simulations.Editor M.C. Acreman Associate editor S. Kanae [ABSTRACT FROM PUBLISHER]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The climate-change and environmental literature, including that on aerosols, is replete with mention of black carbon (BC) and soot. The terms are used interchangeably in much of the literature, although BC and soot commonly have operational and source-based definitions, respectively, and reliable reference samples and aerosol standards do not exist for either one. The uncertainty about their exact chemical nature and properties can be decreased by materials-based measurement techniques and terminology. Here, we discuss ambiguities in common uses of BC and soot and propose the term ns-soot, where “ns” refers to carbon nanospheres, for a characteristic constituent of BC and soot. Based on its composition, morphology, and structure, we define ns-soot as particles that consist of nanospheres, typically with diameters <100 nm, that possess distinct structures of concentrically wrapped, graphene-like layers of carbon and with grape-like (aciniform) morphologies. We additionally propose that, because of their importance for climate modeling and health issues, distinctions are made among bare, coated, and embedded ns-soot particles. Copyright 2014 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)