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Chair 3D Co(II) phosphite frameworks have been prepared by the ionothermal method. It belongs to chiral space group P3221, and the whole framework can be topologically represented as a chiral 4-connected qtz net. It shows a multistep dielectric response arising from the reorientation of Me2-DABCO in the chiral cavities. It can also serve as a pron conductor with high conductivity, 1.71 × 10−3 S cm−1, at room temperature, which is attributed to the formation of denser hydrogen-bonding networks providing efficient proton-transfer pathways.

The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

Abstract All species of big cats, including tigers, cheetahs, leopards, lions, snow leopards, and jaguars, are protected under the Convention on the International Trade in Endangered Species (CITES). This is due in large part to population declines resulting from anthropogenic factors, especially poaching and the unregulated and illegal trade in pelts, bones, teeth and other products that are derived from these iconic species. To enhance and scale up monitoring for big cat products in this trade, we created a rapid multiplex qPCR test that can identify and differentiate DNA from tiger (Panthera tigris), cheetah (Acinonyx jubatus), leopard (Panthera pardus), lion (Panthera leo), snow leopard (Panthera uncia), and jaguar (Panthera onca) in wildlife products using melt curve analysis to identify each species by its unique melt peak temperature. Our results showed high PCR efficiency (> 90%), sensitivity (detection limit of 5 copies of DNA per PCR reaction) and specificity (no cross amplification between each of the 6 big cat species). When paired with a rapid (

Documenting year-to-year variations in carbon storage potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO2) emissions. However, the magnitude, pattern, and inner biomass partitioning of carbon storage potential and the effect of the changes in climate and CO2 on inner carbon stocks remain poorly quantified. Herein, we use a spatially explicit individual-based dynamic global vegetation model to investigate the influences of the changes in climate and CO2 on the enhanced carbon storage potential of vegetation. The modelling included a series of factorial simulations using the Climatic Research Unit (CRU) dataset from 1916 to 2015. The results show that CO2 predominantly leads to a persistent and widespread increase in light-gathering vegetation biomass carbon stocks (LVBC) and water-gathering vegetation biomass carbon stocks (WVBC). Climate change appears to play a secondary role in carbon storage potential. Importantly, with the intensification of water stress, the magnitude of the light- and water-gathering responses in vegetation carbon stocks gradually decreases. Plants adjust carbon allocation to decrease the ratio between LVBC and WVBC for capturing more water. Changes in the pattern of vegetation carbon storage were linked to zonal limitations in water, which directly weaken and indirectly regulate the response of potential vegetation carbon stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrate that the long-term trend in increased vegetation biomass carbon stocks is driven by CO2 fertilization and temperature effects that are controlled by water limitations.

Climate change impact assessment related to floods, infrastructure networks, and water resource management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on a historical observation-based gridded data product, with coarser-scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.

Previous studies have shown that the seasonal dynamics of model parameters can compensate for structural defects of hydrological models and improve the accuracy and robustness of the streamflow forecast to some extent. However, some fundamental issues for improving model performance with seasonal dynamic parameters still need to be addressed. In this regard, this study is dedicated to (1) proposing a novel framework for seasonal variations of hydrological model parameters to improve model performance and (2) expanding the discussion on model results and the response of seasonal dynamic parameters to dynamic characteristics of catchments. The procedure of the framework is developed with (1) extraction of the dynamic catchment characteristics using current data-mining techniques, (2) subperiod calibration operations for seasonal dynamic parameters, considering the effects of the significant correlation between the parameters, the number of multiplying parameters, and the temporal memory in the model states in two adjacent subperiods on calibration operations, and (3) multi-metric assessment of model performance designed for various flow phases. The main finding is that (1) the proposed framework significantly improved the accuracy and robustness of the model; (2) however, there was a generally poor response of the seasonal dynamic parameter set to catchment dynamics. Namely, the dynamic changes in parameters did not follow the dynamics of catchment characteristics. Hence, we deepen the discussion on the poor response in terms of (1) the evolutionary processes of seasonal dynamic parameters optimized by global optimization, considering that the possible failure in finding the global optimum might lead to unreasonable seasonal dynamic parameter values. Moreover, a practical tool for visualizing the evolutionary processes of seasonal dynamic parameters was designed using geometry visualization techniques. (2) We also discuss the strong correlation between parameters considering that dynamic changes in one parameter might be interfered with by other parameters due to their interdependence. Consequently, the poor response of the seasonal dynamic parameter set to dynamic catchment characteristics may be attributed in part to the possible failure in finding the global optimum and strong correlation between parameters. Further analysis also revealed that even though individual parameters cannot respond well to dynamic catchment characteristics, a dynamic parameter set could carry the information extracted from dynamic catchment characteristics and improve the model performance.

Understanding the propagation of prolonged meteorological drought helps solve the problem of intensified water scarcity around the world. Most of the existing literature studied the propagation of drought from one type to another (e.g., from meteorological to hydrological drought) with statistical approaches; there remains difficulty in revealing the causality between meteorological drought and potential changes in the catchment water storage capacity (CWSC). This study aims to identify the response of the CWSC to the meteorological drought by examining the changes of hydrological-model parameters after drought events. Firstly, the temporal variation of a model parameter that denotes that the CWSC is estimated to reflect the potential changes in the real CWSC. Next, the change points of the CWSC parameter were determined based on the Bayesian change point analysis. Finally, the possible association and linkage between the shift in the CWSC and the time lag of the catchment (i.e., time lag between the onset of the drought and the change point) with multiple catchment properties and climate characteristics were identified. A total of 83 catchments from southeastern Australia were selected as the study areas. Results indicated that (1) significant shifts in the CWSC can be observed in 62.7 % of the catchments, which can be divided into two subgroups with the opposite response, i.e., 48.2 % of catchments had lower runoff generation rates, while 14.5 % of catchments had higher runoff generation rate; (2) the increase in the CWSC during a chronic drought can be observed in smaller catchments with lower elevation, slope and forest coverage of evergreen broadleaf forest, while the decrease in the CWSC can be observed in larger catchments with higher elevation and larger coverage of evergreen broadleaf forest; (3) catchments with a lower proportion of evergreen broadleaf forest usually have a longer time lag and are more resilient. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.

The Himalayan Mountains are the source region of one of the world's largest supplies of freshwater. The changes in glacier melt may lead to droughts as well as floods in the Himalayan basins, which are vulnerable to hydrological changes. This study used an integrated glacio-hydrological model, the Glacier and Snow Melt – WASMOD model (GSM-WASMOD), for hydrological projections under 21st century climate change by two ensembles of four global climate models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and two bias-correction methods (i.e., the daily bias correction (DBC) and the local intensity scaling (LOCI)) in order to assess the future hydrological changes in the Himalayan Beas basin up to Pandoh Dam (upper Beas basin). Besides, the glacier extent loss during the 21st century was also investigated as part of the glacio-hydrological modeling as an ensemble simulation. In addition, a high-resolution WRF precipitation dataset suggested much heavier winter precipitation over the high-altitude ungauged area, which was used for precipitation correction in the study. The glacio-hydrological modeling shows that the glacier ablation accounted for about 5 % of the annual total runoff during 1986–2004 in this area. Under climate change, the temperature will increase by 1.8–2.8 ∘C at the middle of the century (2046–2065), and by 2.3–5.4 ∘C until the end of the century (2080–2099). It is very likely that the upper Beas basin will get warmer and wetter compared to the historical period. In this study, the glacier extent in the upper Beas basin is projected to decrease over the range of 63 %–87 % by the middle of the century and 89 %–100 % at the end of the century compared to the glacier extent in 2005. This loss in glacier area will in general result in a reduction in glacier discharge in the future, while the future streamflow is most likely to have a slight increase because of the increase in both precipitation and temperature under all the scenarios. However, there is widespread uncertainty regarding the changes in total discharge in the future, including the seasonality and magnitude. In general, the largest increase in river total discharge also has the largest spread. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling. A decrease in discharge is found in July from DBC, while it is opposite for LOCI. Besides, there is a decrease in evaporation in September from DBC, which cannot be seen from LOCI. The study helps to understand the hydrological impacts of climate change in northern India and contributes to stakeholder and policymaker engagement in the management of future water resources in northern India.

Soil acidity has become a principal constraint in dry land crop production systems of acidic Ultisols in tropical and subtropical regions of southern China, where winter wheat and canola are cultivated as important rotational crops. There is little information on the determination of critical soil pH as well as aluminium (Al) concentration for wheat and canola crops. The objective of this study is to determine the critical soil pH and exchangeable aluminium concentration (AlKCl) for wheat and canola production. Two pot cultures with two Ultisols from Hunan and Anhui (SE China) were conducted for wheat and canola crops in a controlled growth chamber. Aluminium sulfate (Al2(SO4)3) and hydrated lime (Ca(OH)2) were used to obtain the target soil pH levels from 3.7 (Hunan) and 3.97 (Anhui) to 6.5. Plant height, shoot dry weight, root dry weight, and chlorophyll content (SPAD value) of wheat and canola were adversely affected by soil acidity in both locations. The critical soil pH and AlKCl of the Ultisol from Hunan for wheat were 5.29 and 0.56 cmol kg−1, respectively. At Anhui, the threshold soil pH and AlKCl for wheat were 4.66 and 1.72 cmol kg−1, respectively. On the other hand, the critical soil pH for canola was 5.65 and 4.87 for the Ultisols from Hunan and Anhui, respectively. The critical soil exchangeable Al for canola cannot be determined from the experiment of this study. The results suggested that the critical soil pH and AlKCl varied between different locations for the same variety of crop, due to the different soil types and their other soil chemical properties. The critical soil pH for canola was higher than that for wheat for both Ultisols, and thus canola was more sensitive to soil acidity. Therefore, we recommend that liming should be undertaken to increase soil pH if it falls below these critical soil pH levels for wheat and canola production.

A need for multi-functional assessment tools evaluating trade-offs and co-benefits for various types of Nature-Based Solutions (NBS) has been increasingly identified in recent years. Methodologically, concepts for a tool are presented which include quantifying the demand and potential for NBS to enhance ecosystem service (ES) provision, and linking ESs to readily quantifiable and legislatively-relevant environmental quality indicators (EQIs). The objective of tool application is to identify optimal NBS placement across a diverse set of socio-environmental indicators, whilst also incorporating issues of relative location of areas of implementation and benefit accrual. Embedded within the tool is the importance of evaluating outcomes in terms of economic benefits and of sustainable development goals. The concepts are illustrated with simplified examples, relating to the case of implementing urban forestry as an exemplar NBS. By summarising the knowledge base it is demonstrated that benefits of NBS are substantially scale-dependent in two main respects; those of extent and proximity to receptors. Evaluation tools should be capable of quantifying scale-dependence. The substantive importance of these considerations and how their dynamics vary between indicators and services is illustrated graphically through schematic functions. When developed, the tool should be used as a focus for consultation and co-design to pinpoint the size of NBS necessary to achieve a sufficient level of benefit for a particular receptor. This could be measured against target levels of benefit for each indicator, distinguishing between primary intended outcomes and those co-benefits or trade-offs that are secondary or unintended.

Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency – with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally calibrated simulations and often encompassed them. The regionalised FDCs were found to be useful on their own as a basic signature constraint; however, additional regionalised signatures could further constrain the uncertainty in the predictions and may increase the robustness to severe data inconsistencies, which are difficult to detect for ungauged basins.

Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i) basin areas for different hydrographic datasets, and (ii) between climate data (precipitation and potential evaporation) and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i) most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii) basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii) the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering sub-grid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent model simulations.

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested – based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

The area of the Upper Blue Nile in Eastern Sudan is considered prone to soil erosion which is an important indicator of the land degradation process. In this study, an erosion identification and mapping approach is developed based on adaptations to the regional characteristics of the study area and the availability of data. This approach is derived from fusion between remote sensing data and geographical information systems (GIS). The developed model is used to map the spatial distribution of soil erosion caused by the rains of 2006 using automatic classification of multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Shuttle Radar Topography Mission (SRTM) digital elevation model is used to orthoproject ASTER data. A maximum likelihood classifier is trained with four classes, Gully, Flat_land, Mountain and Water and applied to images from March and December 2006. Validation is done with field data from December and January 2006/2007. The results allow the identification of erosion gullies and subsequent estimation of eroded area. Consequently, the results are up-scaled using Moderate Resolution Imaging Spectroradiometer (MODIS) products of the same dates. Because the selected study site is representative of the wider Blue Nile region, it is expected that the approach presented could be applied to larger areas.

Maize yield is severely affected by soil salinity. In an effort to engineer maize for improved salt tolerance, embryogenic calli of maize were co-bombarded with plasmids containing Oryza sativa Na+/H+ antiporter gene (OsNHX1) and bar genes. For the molecular analysis of putative transgenic samples, PCR, Southern and Northern blots were carried out. The maize plants over-expressing OsNHX1 accumulated more biomass when grown in the presence of 200mM NaCl in greenhouse conditions. Higher Na+ and K+ content was observed in transgenic leaves than in wildtype leaves when treated with 100~200mM NaCl, while the osmotic potential and the proline content in transgenic leaves was lower than in wild-type maize. A field trial revealed that the transgenic maize plants produced higher grain yields than the wild-type plants at the vegetative growth stage. These results demonstrate that the OsNHX1 gene was successfully transferred into Zea mays, and the salt-tolerance of transgenic maize was improved by overexpression of the OsNHX1 gene.

Noninvasive genetic sampling enables biomonitoring without the need to directly observe or disturb target organisms. This paper describes a novel and promising source of noninvasive spider and insect DNA from spider webs. Using black widow spiders (Latrodectus spp.) fed with house crickets (Acheta domesticus), we successfully extracted, amplified, and sequenced mitochondrial DNA from spider web samples that identified both spider and prey to species. Detectability of spider DNA did not differ between assays with amplicon sizes from 135 to 497 base pairs. Spider and prey DNA remained detectable at least 88 days after living organisms were no longer present on the web. Spider web DNA as a proof-of-concept may open doors to other practical applications in conservation research, pest management, biogeography studies, and biodiversity assessments.

Nickel alloys are burgeoning as interesting non-precious electrocatalysts for their excellent catalytic activity toward hydrogen evolution reaction (HER). In this work, we report a facile electrochemical strategy for fabricating porous nanoparticle-packed Ni-La alloy films grown on copper substrate from N-butyl-N-methyl pyrrolidinium dicyanamide ionic liquid (BMP-DCA IL) containing anhydrous NiCl2 and LaCl3, which can be acted as efficient and stable catalysts for HER in alkaline solution. The relevant voltammetric behaviors of Ni(II), La(III) and their mixtures in the BMP-DCA IL, potentiostatic electrodeposition processes, as well as the HER electrocatalytic performances of the resultant deposits were investigated. The electrocrystalization of Ni nuclei was found to promote the electrochemical reduction of La, and induce the co-deposition of amorphous Ni-La alloy. The prepared Ni-La alloy films coated on a copper wire (Ni-La/Cu) obtained at optimal preparing conditions exhibit high-performance HER catalytic activity with a Tafel slope of 75.6 mV dec-1 and an exchange current density of 0.115 mA cm-2. Compared with other Ni-RE (rare earth) based HER electrocatalysts in alkaline media, the electrode needs low overpotentials of -190, -218 and -270 mV to drive cathodic current densities of 10, 20 and 100 mA cm-2, respectively, and its catalytic activity can be maintained for at least 27 h. [ABSTRACT FROM AUTHOR]

Large-scale hydrological modelling has become an important tool for the study of global and regional water resources, climate impacts, and water-resources management. However, modelling efforts over large spatial domains are fraught with problems of data scarcity, uncertainties and inconsistencies between model forcing and evaluation data. Model-independent methods to screen and analyse data for such problems are needed. This study aimed at identifying data inconsistencies in global datasets using a pre-modelling analysis, inconsistencies that can be disinformative for subsequent modelling. The consistency between (i) basin areas for different hydrographic datasets, and (ii) between climate data (precipitation and potential evaporation) and discharge data, was examined in terms of how well basin areas were represented in the flow networks and the possibility of water-balance closure. It was found that (i) most basins could be well represented in both gridded basin delineations and polygon-based ones, but some basins exhibited large area discrepancies between flow-network datasets and archived basin areas, (ii) basins exhibiting too-high runoff coefficients were abundant in areas where precipitation data were likely affected by snow undercatch, and (iii) the occurrence of basins exhibiting losses exceeding the potential-evaporation limit was strongly dependent on the potential-evaporation data, both in terms of numbers and geographical distribution. Some inconsistencies may be resolved by considering sub-grid variability in climate data, surface-dependent potential-evaporation estimates, etc., but further studies are needed to determine the reasons for the inconsistencies found. Our results emphasise the need for pre-modelling data analysis to identify dataset inconsistencies as an important first step in any large-scale study. Applying data-screening methods before modelling should also increase our chances to draw robust conclusions from subsequent model simulations. [ABSTRACT FROM AUTHOR]

Neutral ytterbium atoms embedded in solid neon qualitatively retain the structure of free atoms. Despite the atom-solid interaction, the 6s6p 3P0 level is found to remain metastable with its lifetimes determined to be in the range of ten to hundreds of seconds. The atomic population can be almost completely transferred between the ground level and the metastable level via optical excitation and spontaneous decay. The dynamics of this process is examined and is used to explicitly demonstrate that the transition broadening mechanism is homogeneous. [ABSTRACT FROM AUTHOR]

The degree of belief we have in predictions from hydrologic models depends on how well they can reproduce observations. Calibrations with traditional performance measures such as the Nash-Sutcliffe model efficiency are challenged by problems including: (1) uncertain discharge data, (2) variable importance of the performance with flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. A new calibration method using flow-duration curves (FDCs) was developed which addresses these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) of the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested -- based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments without resulting in overpredicted simulated uncertainty. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application e.g. using more/less EPs at high/low flows. While the new method is less sensitive to epistemic input/output errors than the normal use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow. The results suggest that the new calibration method can be useful when observation time periods for discharge and model input data do not overlap. The new method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account. [ABSTRACT FROM AUTHOR]

Nearly monodispersed FeNi3 submicrometre spheres with an average diameter of 220 nm were synthesized by a simple low temperature reduction method. SiO2@FeNi3 core-shell structured submicrometre spheres with 25 nm thick SiO2 shell were then fabricated by a sol-gel process. A significant enhancement of electromagnetic absorption (EMA) performance was achieved by the silica coating over the 2-18 GHz. The reflection loss (RL) exceeding [?]20 dB of the composite was obtained over 6.7-15.1 GHz by choosing an appropriate sample thickness between 2.1 and 3.3 mm, and an optimal RL of [?]61.3 dB was obtained at 8.7 GHz with a thin absorber thickness of 2.9 mm. The coating of the dielectric silica shell significantly enhanced the EMA performance due to the enhancement of interface polarization at the alloys and dielectric interfaces. [ABSTRACT FROM AUTHOR]

This study evaluates seven evapotranspiration models and their performance in water balance studies by using lysimeter measurement data at the Mönchengladbach hydrological and meteorological station in Germany. Of the seven evapotranspiration models evaluated, three models calculate actual evapotranspiration directly using the complementary relationship approach, i.e. the CRAE model of Morton, the advection–aridity (AA) model of Brutsaert and Stricker, and the GG model of Granger and Gray, and four models calculate first potential evapotranspiration and then actual evapotranspiration by considering the soil moisture condition. Two of the four potential evapotranspiration models belong to the temperature‐based category, i.e. the Thornthwaite model and the Hargreaves model, and the other two belong to the radiation‐based category, i.e. the Makkink model and the Priestley–Taylor model. The evapotranspiration calculated by the above seven models, together with precipitation, is used in the water balance model to calculate other water balance components. The results show that, for the calculation of actual evapotranspiration, the GG model and the Makkink model performed better than the other models; for the calculation of groundwater recharge using the water balance approach, the GG model and the AA models performed better; for the simulation of soil moisture content using the water balance approach, four models (GG, Thornthwaite, Makkink and Priestley–Taylor) out of the seven give equally good results. It can be concluded that the lysimeter‐measured water balance components, i.e. actual evapotranspiration, groundwater recharge, soil moisture, etc., can be predicted by the GG model and the Makkink model with good accuracy. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Sugarcane is a perennial crop that contributes to nearly 80% of the global sugar-based products. Therefore, sugarcane growers and food companies are seeking ways to address the concerns related to sugarcane crop yield and health. In this study, a spatial and spectral analysis on the peak growth stage of the sugarcane fields in Bundaberg, Queensland, Australia is performed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) derived from high-resolution WorldView-2 (WV2) images and multispectral Unmanned Aerial Vehicle (UAV) images. Two topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index, a common crop parameter for the assessment of crop yield and health stages; 2) the impact of spatial resolution on the systematic difference in the abovementioned two Vegetation Indices (VIs). The statistical correlation analysis between the WV2 and UAV images produced correlation coefficients of 0.68 and 0.71 for NDVI and NDRE, respectively. In addition, an overall comparison of the WV2 and UAV-derived VIs indicated that the UAV images produced a better accuracy than the WV2 images because UAV can effectively distinguish various status of vegetation owing to its high spatial resolution. The results illustrated a strong positive correlation between NDVI and NDRE, each derived from the WV2 and UAV images, and the correlation coefficients were 0.81 and 0.90, respectively, i.e. the correlation between NDVI and NDRE is higher in the UAV images than the WV2 images.

We present an updated, spatially resolved estimate of 2003–2008 glacier surface elevation changes for the entire region of High Mountain Asia (HMA) from ICESat laser altimetry data. The results reveal a diverse pattern that is caused by spatially greatly varying glacier sensitivity, in particular to precipitation availability and changes. We introduce a spatially resolved zonation where ICESat samples are grouped into units of similar glacier behaviour, glacier type and topographic settings. In several regions, our new zonation reveals local differences and anomalies that have not been described previously. Glaciers in the Eastern Pamirs, Kunlun Shan and central TP were thickening by 0.1–0.7 m a−1, and the thickening anomaly has a crisp boundary in the Eastern Pamirs that continues just north of the central Karakoram. Glaciers in the south and east of the TP were thinning, with increasing rates towards southeast. We attribute the glacier thickening signal to a stepwise increase in precipitation around ∼1997–2000 on the Tibetan Plateau (TP). The precipitation change is reflected by growth of endorheic lakes in particular in the northern and eastern TP. We estimate lake volume changes through a combination of repeat lake extents from Landsat data and shoreline elevations from ICESat and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for over 1300 lakes. The rise in water volume contained in the lakes corresponds to 4–25 mm a−1, when distributed over entire catchments, for the areas where we see glacier thickening. The precipitation increase is also visible in sparse in situ measurements and MERRA-2 climate reanalysis data but less visible in ERA-Interim reanalysis data. Taking into account evaporation loss, the difference between average annual precipitation during the 1990s and 2000s suggested by these datasets is 34–100 mm a−1, depending on region, which can fully explain both lake growth and glacier thickening (Kunlun Shan) or glacier geometry changes such as thinning tongues while upper glacier areas were thickening or stable (eastern TP). The precipitation increase reflected in these glacier changes possibly extended to the northern slopes of the Tarim Basin, where glaciers were nearly in balance in 2003–2008. Along the entire Himalaya, glaciers on the first orographic ridge, which are exposed to abundant precipitation, were thinning less than glaciers in the dryer climate of the inner ranges. Thinning rates in the Tien Shan vary spatially but are rather stronger than in other parts of HMA.

Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

With the increase in the number of available global climate models (GCMs), pragmatic questions come up in using them to quantify climate change impacts on hydrology: is it necessary to unequally weight GCM outputs in the impact studies, and if so, how should they be weighted? Some weighting methods have been proposed based on the performances of GCM simulations with respect to reproducing the observed climate. However, the process from climate variables to hydrological responses is nonlinear, and thus the assigned weights based on performances of GCMs in climate simulations may not be correctly translated to hydrological responses. Assigning weights to GCM outputs based on their ability to represent hydrological simulations is more straightforward. Accordingly, the present study assigns weights to GCM simulations based on their ability to reproduce hydrological characteristics and investigates their influences on the quantification of hydrological impacts. Specifically, eight weighting schemes are used to determine the weights of GCM simulations based on streamflow series simulated by a lumped hydrological model using raw or bias-corrected GCM outputs. The impacts of weighting GCM simulations are investigated in terms of reproducing the observed hydrological regimes for the reference period (1970–1999) and quantifying the uncertainty of hydrological changes for the future period (2070–2099). The results show that when using raw GCM outputs to simulate streamflows, streamflow-based weights have a better performance in reproducing observed mean hydrograph than climate-variable-based weights. However, when bias correction is applied to GCM simulations before driving the hydrological model, the streamflow-based unequal weights do not bring significant differences in the multi-model ensemble mean and uncertainty of hydrological impacts, since bias-corrected climate simulations become rather close to observations. Thus, it is likely that using bias correction and equal weighting is viable and sufficient for hydrological impact studies.

Multivariate hydrologic design under stationary conditions is traditionally performed through the use of the design criterion of the return period, which is theoretically equal to the average inter-arrival time of flood events divided by the exceedance probability of the design flood event. Under nonstationary conditions, the exceedance probability of a given multivariate flood event varies over time. This suggests that the traditional return-period concept cannot apply to engineering practice under nonstationary conditions, since by such a definition, a given multivariate flood event would correspond to a time-varying return period. In this paper, average annual reliability (AAR) was employed as the criterion for multivariate design rather than the return period to ensure that a given multivariate flood event corresponded to a unique design level under nonstationary conditions. The multivariate hydrologic design conditioned on the given AAR was estimated from the nonstationary multivariate flood distribution constructed by a dynamic C-vine copula, allowing for time-varying marginal distributions and a time-varying dependence structure. Both the most-likely design event and confidence interval for the multivariate hydrologic design conditioned on the given AAR were identified to provide supporting information for designers. The multivariate flood series from the Xijiang River, China, were chosen as a case study. The results indicated that both the marginal distributions and dependence structure of the multivariate flood series were nonstationary due to the driving forces of urbanization and reservoir regulation. The nonstationarities of both the marginal distributions and dependence structure were found to affect the outcome of the multivariate hydrologic design.

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union (EGU) in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals.

We demonstrate that simple, non-invasive environmental DNA (eDNA) methods can detect transgenes of genetically modified (GM) animals from terrestrial and aquatic sources in invertebrate and vertebrate systems. We detected transgenic fragments between 82-234 bp through targeted PCR amplification of environmental DNA extracted from food media of GM fruit flies (Drosophila melanogaster), feces, urine, and saliva of GM laboratory mice (Mus musculus), and aquarium water of GM tetra fish (Gymnocorymbus ternetzi). With rapidly growing accessibility of genome-editing technologies such as CRISPR, the prevalence and diversity of GM animals will increase dramatically. GM animals have already been released into the wild with more releases planned in the future. eDNA methods have the potential to address the critical need for sensitive, accurate, and cost-effective detection and monitoring of GM animals and their transgenes in nature.

Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the Great Himalayas region. High-quality precipitation data are difficult to obtain due to a sparse network, cold climate and high heterogeneity in topography. In this paper, we examine four datasets in northern India of the Western Himalayas: interpolated gridded data based on gauge observations (IMD, 1° × 1°, and APHRODITE, 0.25° × 0.25°), reanalysis data (ERA-Interim, 0.75° × 0.75°) and high-resolution simulation by a regional climate model (WRF, 0.15° × 0.15°). The four datasets show a similar spatial pattern and temporal variation during the period 1981–2007, though the absolute values vary significantly (497–819 mm year−1). The differences are particularly large in July and August at the windward slopes and high-elevation areas. Overall, the datasets show that the summer is getting wetter and the winter is getting drier, though most of the trends in monthly precipitation are not significant. Trend analysis of summer and winter precipitation at every grids confirms the changes. Wetter summers will result in more and bigger floods in the downstream areas. Warmer and drier winters will result in less glacier accumulation. All the datasets show consistency in the period 1981–2007 and can give a spatial overview of the precipitation in the region. Comparing with the Bhuntar gauge data, the WRF dataset gives the best estimates of extreme precipitation. To conclude, we recommend the APHRODITE dataset and the WRF dataset for hydrological studies for their improved spatial variation which match the scale of hydrological processes as well as accuracy in extreme precipitation for flood simulation.

Considering rapid increases in the number of climate model simulations being produced by modelling centres, it is often infeasible to use all of them in climate change impact studies. In order to thoughtfully select subsets of climate simulations from a large ensemble, several envelope-based methods have been proposed. The subsets are expected to cover a similar uncertainty envelope to the full ensemble in terms of climate variables. However, it is not a given that the uncertainty in hydrological impacts will be similarly well represented. Therefore, this study investigates the transferability of climate uncertainty related to the choice of climate simulations to hydrological impacts. Two envelope-based selection methods, K means clustering and the Katsavounidis–Kuo–Zhang (KKZ) method, are used to select subsets from an ensemble of 50 climate simulations over two watersheds with very different climates using 31 precipitation and temperature variables. Transferability is evaluated by comparing uncertainty coverage between climate variables and 17 hydrological variables simulated by a hydrological model. The importance of choosing climate variables properly when selecting subsets is investigated by including and excluding temperature variables. Results show that KKZ performs better than K means at selecting subsets of climate simulations for hydrological impacts, and the uncertainty coverage of climate variables is similar to that of hydrological variables. The subset of the first 10 simulations covers over 85 % of total uncertainty. As expected, temperature variables are important for the snow-related watershed, but less important for the rainfall-driven watershed. Overall, envelope-based selection of around 10 climate simulations, based on climate variables that characterize the physical processes controlling the hydrology of the watershed, is recommended for hydrological impact studies.

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Studies for the prevention and mitigation of floods require information on discharge and extent of inundation, commonly unavailable or uncertain, especially during extreme events. This study was initiated by the devastating flood in Tegucigalpa, the capital of Honduras, when Hurricane Mitch struck the city. In this study we hypothesized that it is possible to estimate, in a trustworthy way considering large data uncertainties, this extreme 1998 flood discharge and the extent of the inundations that followed from a combination of models and post-event measured data. Post-event data collected in 2000 and 2001 were used to estimate discharge peaks, times of peak, and high-water marks. These data were used in combination with rain data from two gauges to drive and constrain a combination of well-known modelling tools: TOPMODEL, Muskingum–Cunge–Todini routing, and the LISFLOOD-FP hydraulic model. Simulations were performed within the generalized likelihood uncertainty estimation (GLUE) uncertainty-analysis framework. The model combination predicted peak discharge, times of peaks, and more than 90 % of the observed high-water marks within the uncertainty bounds of the evaluation data. This allowed an inundation likelihood map to be produced. Observed high-water marks could not be reproduced at a few locations on the floodplain. Identifications of these locations are useful to improve model set-up, model structure, or post-event data-estimation methods. Rainfall data were of central importance in simulating the times of peak and results would be improved by a better spatial assessment of rainfall, e.g. from radar data or a denser rain-gauge network. Our study demonstrated that it was possible, considering the uncertainty in the post-event data, to reasonably reproduce the extreme Mitch flood in Tegucigalpa in spite of no hydrometric gauging during the event. The method proposed here can be part of a Bayesian framework in which more events can be added into the analysis as they become available.

World water resources have primarily been analysed by global-scale hydrological models in the last decades. Runoff generation in many of these models are based on process formulations developed at catchments scales. The division between slow runoff (baseflow) and fast runoff is primarily governed by slope and spatial distribution of effective water storage capacity, both acting at very small scales. Many hydrological models, e.g. VIC, account for the spatial storage variability in terms of statistical distributions; such models are generally proven to perform well. The statistical approaches, however, use the same runoff-generation parameters everywhere in a basin. The TOPMODEL concept, on the other hand, links the effective maximum storage capacity with real-world topography. Recent availability of global high-quality, high-resolution topographic data makes TOPMODEL attractive as a basis for a physically-based runoff-generation algorithm at large scales, even if its assumptions are not valid in flat terrain or for deep groundwater systems. We present a new runoff-generation algorithm for large-scale hydrology based on TOPMODEL concepts intended to overcome these problems. The TRG (topography-derived runoff generation) algorithm relaxes the TOPMODEL equilibrium assumption so baseflow generation is not tied to topography. TRG only uses the topographic index to distribute average storage to each topographic index class. The maximum storage capacity is proportional to the range of topographic index and is scaled by one parameter. The distribution of storage capacity within large-scale grid cells is obtained numerically through topographic analysis. The new topography-derived distribution function is then inserted into a runoff-generation framework similar VIC's. Different basin parts are parameterised by different storage capacities, and different shapes of the storage-distribution curves depend on their topographic characteristics. The TRG algorithm is driven by the HydroSHEDS dataset with a resolution of 3" (around 90 m at the equator). The TRG algorithm was validated against the VIC algorithm in a common model framework in 3 river basins in different climates. The TRG algorithm performed equally well or marginally better than the VIC algorithm with one less parameter to be calibrated. The TRG algorithm also lacked equifinality problems and offered a realistic spatial pattern for runoff generation and evaporation.

In this study, the reliability of different versions of autoregressive error models as post-processors for probabilistic streamflow forecasts is evaluated. Rank histograms and reliability indices are used as performance measures. An algorithm for the construction of confidence intervals to indicate ranges of reliable forecasts within the rank histograms is presented. To analyse differences in performance of the post-processors, scatter plots of the standardized residuals of the error models are generated to assess the homoscedacity of the residuals with respect to streamflow. A problem of distorted impressions may appear when such plots are generated with a regular x-scale. The problem is analysed with both synthetic and real data, and a rank scaled x-axis is proposed to remedy the problem. The results of the study reveal large differences in the reliability of the post-processors. Versions with empirical distribution functions are clearly superior to those with standard normal distribution, but for validations with independent data their rank histograms still lie outside of the confidence bands for reliable forecasts.