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Spatially explicit mathematical models are key to a mechanistic understanding of environmental processes in rivers. Such models necessitate extended information on networks' morphology, which is often retrieved from geographic information system (GIS) software, thus hindering the establishment of replicable script-based workflows. Here I present rivnet, an R package for GIS-free extraction and analysis of river networks based on digital elevation models (DEMs). The package exploits TauDEM's flow direction algorithm in user-provided or online accessible DEMs, and allows for computing covariate values and assigning hydraulic variables across any network node. The package is designed so as to require minimal user input while allowing for customization for experienced users. It is specifically intended for application in models of ecohydrological, ecological or biogeochemical processes in rivers. As such, rivnet aims to make river network analysis accessible to users unfamiliar with GIS-based and geomorphological methods and therefore enhance the use of spatially explicit models in rivers. [ABSTRACT FROM AUTHOR]

Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) has launched a new initiative, the Open-Source Science Initiative (OSSI), to enable and support science towards openness. The OSSI supports open-source software development and dissemination. In this work, we present NASAaccess, which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the NASAaccess platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The NASAaccess platform is available as software packages (i.e., the R and conda packages) as well as an interactive-format web-based environmental modeling application for earth observation data developed with Tethys Platform. NASAaccess has been envisioned as lowering the technical barriers and simplifying the process of accessing scalable distributed computing resources and leveraging additional software for data and computationally intensive modeling frameworks. Specifically, NASAaccess has been developed to meet the need for seamless earth observation remote-sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, NASAaccess is also contributing to keeping interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote-sensing products. NASAaccess' current capabilities cover various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products. [ABSTRACT FROM AUTHOR]

This study investigates the contamination potential of herbicides to groundwater with the help of numerical modeling (HYDRUS‐1D) and stable carbon isotopes for characterizing biodegradation. Four herbicides, metolachlor, terbuthylazine, prosulfuron, and nicosulfuron, were applied over a period of 4.5 years on two lysimeters located in Wielenbach, Germany, and monitored by lysimeter drainage. These lysimeters contained soil cores dominated by sandy gravel (Ly1) and clayey sandy silt (Ly2) and were both cropped with maize (Zea mays). In the preceding study, we characterized flow within the lysimeters by using stable water isotopes and unsaturated flow models. Building up on these findings, models were extended for describing reactive transport of the herbicides and investigating process contributions. At the end of the experiment, 0.9%–15.9% of the applied herbicides (up to 20.9% if including metabolites) were recovered by lysimeter drainage. Metabolite formation and accumulation was observed, and biodegradation was also indicated by small changes in carbon isotope signals (δ13C) between applied and leached herbicides. Model setups could describe the dynamics of herbicide concentrations in lysimeter drainage well. Concentration peaks in drainage were partly also linked with strong precipitation events, indicating preferential flow influence. The soil core with the coarser texture (Ly1) showed less herbicide leaching than the finer texture (Ly2), which can be explained by a larger mobile phase in Ly1. Overall, our approaches and findings contribute to the understanding of multi‐process herbicide transport in the vadose zone and leaching potentials to groundwater, where δ13C can provide valuable hints for microbial degradation. Core Ideas: Leached herbicide concentrations depend on the soil texture.Concentration peaks of herbicides in lysimeter drainage can be linked with rain events.Solute transport modeling allows us to identify dominant processes affecting herbicide leaching.Stable carbon isotopes measured in drainage can identify biodegradation processes in the unsaturated zone. [ABSTRACT FROM AUTHOR]

Understanding transport and fate processes in the subsurface is of fundamental importance to identify the leaching potentials of herbicides or other compounds to groundwater resources. HYDRUS‐1D was used to simulate water flow and solute transport in arable land lysimeters. Simulations were compared to observed drainage rates and stable water isotopes (δ18O) in the drainage. Four different model setups were investigated and statistically evaluated for their model performance to identify dominant processes for water flow characterization in the vadose zone under similar cultivation management and climatic conditions. The studied lysimeters contain soil cores dominated by sandy gravel (Ly1) and clayey sandy silt (Ly2), both cropped with maize located in Wielenbach, Germany. First, a single‐porosity setup was chosen. For Ly1, modeling results were satisfactory, but for Ly2, the damping observed in the isotope signature of the drainage could not be fully covered. By considering immobile water with a dual‐porosity setup for Ly2, model performance improved. This could be due to a higher fraction of fine pores in Ly2 available for water storage, leading to mixing processes of isotopically enriched summer precipitation and lighter winter water. Accounting for isotopic evaporation fractionation processes in both model setups did not lead to improved model performance. Consequentially, the difference in soil hydraulic properties between the two lysimeters seems to impact water flow processes. Knowledge of such differences is crucial to prevent contamination and mitigate potential risks to soil and groundwater. Core Ideas: For effective model description, multi‐process modeling must be explored.Mixing processes between immobile and mobile water are of greater importance for finer soils.Evaporation fractionation effects have no clear impact on simulated stable water isotopes in lysimeter drainage. [ABSTRACT FROM AUTHOR]

Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions. However, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated. This study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions. For each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE > 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs. We found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 % confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation. We discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies. [ABSTRACT FROM AUTHOR]

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world. [ABSTRACT FROM AUTHOR]

In this study, I argue that existing theories of organizational adaptation are inadequate in the face of the growing frequency of life-threatening events such as natural disasters, terrorist attacks, and pandemic diseases. An alternative is to build on the research on resilience. To that end, I highlight (a) the key differences between life-threatening events and traditional environmental challenges such as economic and technological change, (b) what makes resilience a more suitable lens for the discussion of life-threatening events, (c) the various modes and approaches that are associated with resilience, and (d) a synthesis of mechanisms that contribute to organizational resilience. This perspective advances a theoretically grounded understanding of the changing nature of environmental challenges and identifies the models of adaptation that are more suited to these challenges. It offers a renewed understanding of organizational adaptation, a guideline for practicing managers, and a forum for policy discussions that can more effectively address emerging environmental challenges. [ABSTRACT FROM AUTHOR]

Water quality impacts of stream water nitrate (NO 3-) on downstream ecosystems are largely determined by the load of NO 3- from the watershed to surface waters. The largest NO 3- loads often occur during storm events, but it is unclear how loads of different NO 3- sources change during storm events relative to baseflow or how watershed attributes might affect source export. To assess the role of storm flow and baseflow in NO 3- source export and how these roles are modulated by hydrologic effects of land-use practices, we measured nitrogen (δ15 N) and oxygen (Δ17 O) isotopes of NO 3- and oxygen isotopes (δ18 O) of water in rainfall and stream water samples from before, during, and after eight storm events across 14 months in two Chesapeake Bay watersheds of contrasting land use. Storms had a disproportionately large influence on the export of unprocessed atmospheric NO 3- (NO 3Atm-) and a disproportionately small influence on the export of terrestrial NO 3- (NO 3Terr-) relative to baseflow in the developed urban watershed. In contrast, baseflow and storm flow had similar influences on NO 3Atm- and NO 3Terr- export in the mixed agricultural–forested watershed. An equivalent relationship between NO 3Atm- deposition on impervious surfaces and event NO 3Atm- stream water export in the urban watershed suggests that impervious surfaces that hydrologically connect runoff to channels likely facilitate the export of NO 3Atm- during rainfall events. Additionally, larger rainfall events were more effective at exporting NO 3Atm- in the urban watershed, with increased rainfall depth resulting in a greater fraction of event NO 3Atm- deposition exported. Considering both projected increases in precipitation amounts and intensity and urban/suburban sprawl in many regions of the world, best management practices that reduce the hydrologic connectivity of impervious surfaces will likely help to mitigate the impact of storm events on NO 3Atm- export from developed watersheds. [ABSTRACT FROM AUTHOR]

Low impact development (LID) is a common approach to storm water management aimed at mitigating the impact of flooding in urban areas. Most previous studies on this topic focused solely on the qualitative assessment of urban resilience. The lack of operational or quantitative resilience indicators has made it difficult to elucidate the degree to which LID affects resilience to flooding. This study established a quantitative flood resilience index (FRI) to assess the resilience of specific locations to flooding. We then used a 3Di hydrodynamic model to simulate time‐variant inundation conditions under an extreme weather event (799 mm in 72 h) in Tainan, Taiwan. Physical and socioeconomic factors were identified as the important factors in calculating the FRI in areas affected by LID. Our results revealed that LID can reduce inundation area and depth during short‐duration (sub‐daily) rainfall events; however, its effectiveness in reducing the effects of flooding during long‐duration (>1 day) events is negligible. Nonetheless, LID could enhance urban resilience to flooding by reducing the duration of flooding events and thereby making it possible to initiate recovery efforts earlier. [ABSTRACT FROM AUTHOR]

Intensified extreme precipitation and corresponding floods are the most relevant consequences of climate change over the northeastern US (NEUS). To evaluate the impacts of climate change or certain climate perturbations on future extreme weather events which are dynamically similar to historic analogs, the pseudo-global warming (PGW) method has been frequently employed; however, this method lacks precise definition and guidelines, thus limiting its application. More specifically, three key questions related to the application of the PGW method remain unanswered: at what spatial scale should climate perturbations be applied? Among the different meteorological variables available, which ones should be perturbed? And will PGW projections vary significantly when different perturbations are applied? To address these questions, we examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments with varied perturbation spatial scales and choice of perturbed meteorological variables. The results show that the projections of precipitation and other essential variables at the regional mean scale are consistent across the PGW simulations, with a relative difference of much less than 10 %; however, different perturbation modifications can cause significant displacements of the storm events being simulated. Several previously assumed advantages of modifying only the temperature at regional mean scale, such as the preservation of geostrophic balance, do not appear to hold. Also, for these experiments, we find the regional mean perturbation produces a positive precipitation bias because it ignores the land–ocean warming contrast, which is a robust regional response to global warming. Overall, PGW experiments with perturbations from temperature or the combination of temperature and wind at the grid point scale are both recommended, depending on the research questions. The first approach can isolate the spatially dependent thermodynamic impact, and the latter incorporates both the thermodynamic and dynamic impacts. [ABSTRACT FROM AUTHOR]

Per‐ and polyfluoroalkyl substances (PFAS) are surface‐active contaminants experiencing strong retention in vadose zones due to adsorption at air–water and solid–water interfaces. Leaching of PFAS through vadose zones poses great risks of groundwater contamination. Prior PFAS transport studies have focused on homogenous or layered vadose zones that significantly underrepresented the impact of preferential flow caused by soil heterogeneities—a primary factor known to dominantly control the subsurface transport of many contaminants. We conduct numerical simulations to investigate the impact of preferential flow on PFAS leaching in stochastically generated heterogeneous vadose zones. The simulations show that while shorter‐chain PFAS experience accelerated leaching similar to non‐surfactant solutes, the accelerated leaching of more surface‐active longer‐chain PFAS is uniquely amplified by 1.1–4.5 times due to reduced accessible air–water interfacial areas along preferential flow pathways. Our study highlights the criticality of characterizing soil heterogeneities for accurately predicting the leaching of long‐chain PFAS in vadose zones. Plain Language Summary: Per‐ and polyfluoroalkyl substances (PFAS) are a group of emerging contaminants used in many industrial and domestic applications. A growing body of field investigations have shown that PFAS are widespread in the environment. Notably, soils above the groundwater table at many contamination sites serve as significant PFAS reservoirs. PFAS are active at interfaces and tend to accumulate at air–water and solid–water interfaces. This surface activity has led to the strong retention of PFAS in shallow soils (especially the more surface‐active PFAS with longer carbon chains). However, field observations have shown that a small fraction of longer‐chain PFAS has migrated to deep soils at many contamination sites—in contrast to prior model predictions that underrepresented subsurface heterogeneities. We conduct systematic numerical experiments to test the hypothesis that subsurface heterogeneities accelerate the migration of PFAS in soils. We demonstrate that the heterogeneity‐generated preferential flow reduces the air–water interfacial area accessible by PFAS. The accelerated migration is unique to PFAS and is much stronger for longer‐chain than that for shorter‐chain PFAS. Overall, the present study suggests that characterizing and representing soil heterogeneities are critical for determining the accessible air–water interfacial area and accurately quantifying PFAS contamination risks of groundwater. Key Points: The presence of heterogeneity significantly reduces the accessible air–water interface area in vadose zonesReduction of accessible air–water interfacial area amplifies the preferential‐flow‐induced acceleration of Per‐ and polyfluoroalkyl substances (PFAS) leachingThe amplified acceleration is unique for PFAS and is greater for longer chains, drier climates, and more heterogeneous vadose zones [ABSTRACT FROM AUTHOR]

Excess nutrient inputs from agricultural and urban sources have accelerated eutrophication and increased the incidence of algal blooms in the Great Lakes Basin (GLB). Lake basin management to address these threats relies on understanding the key drivers of pollution. Here, we use a random forest machine learning model to leverage information from 202 monitored streams in the GLB to predict seasonal and annual flow‐weighted concentrations of nitrogen and phosphorus, as well as nutrient ratios across the GLB. Land use (agricultural and urban land) and land management (tile drainage and wetland density) emerge as the two most important predictors for dissolved inorganic nitrogen (DIN; NO3− + NO2−) and soluble reactive phosphorus (SRP; PO43), while soil type and wetland density are more important for particulate P (PP). Partial dependence plots demonstrate increasing nutrient concentrations with increasing tile density and decreasing wetland density. In addition, increasing tile and livestock densities and decreasing forest cover correspond to higher SRP:Total Phosphorus (TP) ratios. Seasonally, the highest proportions of SRP occur in summer and fall. Higher livestock densities are also correlated with increasing N:P (DIN:TP) ratios. Livestock operations can contribute to the buildup of soil nutrients from excess manure application, while increasing subsurface drainage can provide transport pathways for dissolved nutrients. Given that both SRP:TP and the N:P ratios are strong predictors of harmful algal blooms, our study highlights the importance of livestock management, drainage management, and wetland restoration in efforts to address eutrophication in intensively managed landscapes. Plain Language Summary: While attempts have been made to improve water quality and reduce algal blooms in the Great Lakes Basin, we still have a limited understanding of where the greatest inputs of nutrients to the lakes are coming come from and why. In the current study, we have used nitrogen and phosphorus concentration data from over 200 monitoring stations around the Great Lakes to model daily, seasonal, and annual concentrations and to link these concentration magnitudes to a variety of watershed characteristics. Our results show that land use and land management are important predictors of nitrogen and phosphorus concentrations, with tile drainage emerging as a key driver of higher nitrate and soluble phosphorus concentrations. For particulate phosphorus, however, our results show that soil type and wetland density are more important predictors. Higher tile drainage densities and livestock densities were found to be associated with higher N:P ratios and higher ratios of soluble to total phosphorus. We also found that concentrations of soluble reactive phosphorus are highest during the summer and fall months in watersheds dominated by agriculture, which is in contrast to seasonal patterns observed in less‐impacted watersheds. Understanding watershed drivers of nutrient concentrations is critical for managing and improving water quality Key Points: Random forest model, developed using data from 202 streams, identifies various land use and management controls on nutrientsHigh livestock densities correspond with greater proportions of bioavailable P and higher N:P ratios, and thus greater risk of bloomsThe highest proportions of bioavailable P occur seasonally in summer and fall, and in the Lake Erie Basin [ABSTRACT FROM AUTHOR]

Copyright of Revista em Agronegócio e Meio Ambiente is the property of Revista em Agronegocio e Meio Ambiente 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.)

Soluble reactive phosphorus (SRP) concentrations in agricultural headwaters can display pronounced seasonal variability at low flow, often with the highest concentrations occurring in summer. These SRP concentrations often exceed eutrophication levels, but their main sources, spatial distribution, and temporal dynamics are often unknown. The purpose of this study is therefore to differentiate between potential SRP losses and releases from soil drainage, anoxic riparian wetlands, and stream sediments in an agricultural headwater catchment. To identify the dominant SRP sources, we carried out three longitudinal stream sampling campaigns for SRP concentrations and fluxes. We used salt dilution tests and natural 222 Rn to determine water fluxes in different sections of the stream, and we sampled for SRP, Fe, and 14 C dissolved organic carbon (DOC) to examine possible redox-mediated mobilization from riparian wetlands and stream sediments. The results indicate that a single short section in the upper headwater reach was responsible for most of the SRP fluxes to the stream. Analysis of samples taken under summer low-flow conditions revealed that the stream water SRP concentrations, the fraction of SRP within total dissolved P (TDP), and DOC radiocarbon ages matched those in the groundwater entering the gaining section. Pore water from the stream sediment showed evidence of reductive mobilization of SRP, but the exchange fluxes were probably too small to contribute substantially to SRP stream concentrations. We also found no evidence that shallow flow paths from riparian wetlands contributed to the observed SRP loads in the stream. Combined, the results of this campaign and previous monitoring suggest that groundwater is the main long-term contributor of SRP at low flow, and agricultural phosphorus is largely buffered in the soil zone. We argue that the seasonal variation of SRP concentrations was mainly caused by variations in the proportion of groundwater present in the streamflow, which was highest during summer low-flow periods. Accurate knowledge of the various input pathways is important for choosing effective management measures in a given catchment, as it is also possible that observations of seasonal SRP dilution patterns stem from increased mobilization in riparian zones or from point sources. [ABSTRACT FROM AUTHOR]

This contribution summarizes the significant progress in a variety of topic areas related to internal tropical cyclone (TC) intensity change processes over 2018-2022 from the WMO Tenth International Workshop on Tropical Cyclones (IWTC-10). These topic areas include surface and boundary layer processes; TC internal structure and microphysical processes; and, radiation interactions with TCs. Recent studies better frame the uncertainty in the surface drag and enthalpy coefficients at high wind speeds. These parameters greatly impact TC intensity and it is therefore important that more direct measurements of these boundary layer parameters are made. Particularly significant scientific strides have been made in TC boundary layers. These advancements have been achieved through improved coupled models, large-eddy simulations, theoretical advancements, and detailed observations. It is now clear that the research field needs to better represent the eddy viscosity throughout the depth of the boundary layer. Furthermore, detailed study of coherent structures in TC boundary layers will likely be a propitious direction for the research community. Meanwhile, in-depth observational field campaigns and assiduous data analysis have made significant headway into verifying theory and modeling studies of intensification processes related to TC vortex alignment, efficient latent heating distributions, and overall 3D structure. Substantial efforts have also been made to better understand the intricate roles radiative processes play in TC evolution and intensity change. Finally, some promising progress has been made in the development of time-dependent theories of TC intensification and the predictability of internal TC intensity change. Overall, there have been well-earned gains in the understanding of intensity change processes intrinsic to the TC system, but the journey is not complete. This paper highlights some of the most relevant and important research areas that are still shedding new light into internal factors governing TC intensity change. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to provide a conceptual framework with the scope to assist in establishing sustainable supply chain clusters (SCCs) by providing an instrument for organisations to enhance the three sustainability dimensions in a dynamic environment. Design/methodology/approach: This research proposes a conceptual framework to enhance sustainability and organisational performance through three theoretical lenses: the system theory, extended resource-based view and the dynamic capabilities (DCs) theory. This approach is carried out through a comprehensive review of the existing literature on SCCs. Findings: Four main propositions are formulated and demonstrated using the developed framework, which expands the discussion about SCCs and their key characteristics in a dynamic environment. This is particularly relevant as it allows empirical testing of the theories in an SCC context. Research limitations/implications: It can be noted that more extensive research is needed to further understand the issues faced in establishing sustainable clusters. Drawing on the theoretical lenses to establish the framework helps to enhance the understanding and operational capabilities of sustainable SCCs during and after disruptions, such as the global disruption created by COVID-19. Practical implications: This research paves the way to help organisations improve their adaptability to the dynamic business environment by emphasizing the importance of clustering and linking it to sustainability through DCs to establish a sustainable cluster. Originality/value: This research aims to guide organisations' use of SCCs as tools to enhance sustainability in a dynamic environment, given that the relationship amongst supply chain cluster design characteristics (SCCDCs), DCs and sustainability remains unexplored. The combination of the three theoretical lenses in developing the proposed framework will assist in further understanding the applicability of these theories when they are considered together. [ABSTRACT FROM AUTHOR]

The present work quantifies metal-contaminant interactions between palladium substrates and three salient chlorinated organic contaminants, namely trichloroethylene 1,3,5-trichlorobenzene (TCB), and 3,3',4,4'-tetrachlorobiphenyl (PCB77). Given that Pd is one of the conventional catalytically active materials known for contaminant removal, maximizing catalytic efficiency through optimal adsorption dynamics reduces the cost of remediation of contaminants that are persistent water pollutants chronically affecting public health. Adsorption efficiency analyses from all-atom molecular dynamics (MD) simulations advance the understanding of reaction mechanisms available from density functional theory (DFT) calculations to an extractable feature scale that can fit the parametric design of supported metal catalytic systems and feed into high throughput catalyst selection. Data on residence time, site-specific adsorption, binding energies, packing geometries, orientation profiles, and the effect of adsorbate size show the anomalous behaviour of organic contaminant adsorption on the undercoordinated {110} surface as compared to the {111} and {100} surfaces. The intermolecular interaction within contaminants frommolecular dynamics simulation exhibits refreshing results than ordinary single molecule density functional theory calculation. Since complete adsorption and dechlorination is an essential step for chlorinated organic contaminant remediation pathways, the presented profiles provide essential information for designing efficient remediation systems through facet-controlled palladium nanoparticles. [ABSTRACT FROM AUTHOR]

Water utilities' supply systems are vulnerable to several climate‐related hazards, including droughts, floods and cyclones. Here we propose a generally applicable framework for conducting multi‐hazard risk assessments of water supply systems and use it to quantify the impact of present and future climate extremes on the national water supply network in Jamaica. The proposed framework involves stress‐testing a model of the system with a large set of spatially coherent drought, cyclone and pluvial and fluvial flood events to calculate the number of water users whose supplies would be disrupted during an event, that is, the Customer disruption days (CDD). We estimate the total multi‐hazard annual expected disruption to be approximately 5 days per year per utility customer under present conditions. This is increased by a factor of between 2 and 2.5 when end‐of‐century climate scenarios are propagated through the model. Our analysis shows that more high probability drought events lead to greater CDD compared with asset damage events. However, extreme asset damage events, despite manifesting over shorter timescales (days) compared to drought events (months), can lead to more widespread CDD. This quantified risk framework would allow utility managers to compare the risk of both asset damage‐ and water shortage‐induced disruptions via a common, decision‐relevant metric. However, applications to other utilities would require tailored hazard modeling approaches. The proposed risk assessment is intended to inform prioritization of infrastructure investments, ranging from asset protection to drought mitigation projects, with the goal of enhancing water supply resilience in the face of a changing climate. Key Points: We propose a spatial multi‐hazard risk framework for analyzing present‐day and future climate risks to water usersWe propose the use of customer disruption days as a common metric for comparing different hazards impactsThe framework can be used by decision makers to prioritize investments across asset protection against flooding and cyclones, and drought mitigation options [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office 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.)

In 2018–2019, Central Europe experienced an unprecedented 2-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997–2017) nitrate export with 2018–2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modeling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface transport. We found a drought-induced shift in concentration–discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 73 % higher compared to the long-term load–discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (<2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to drought. In contrast, longer transit times downstream (>20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality. [ABSTRACT FROM AUTHOR]

Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability generally leads to effective behaviors and emerging phenomena that cannot be predicted from conventional approaches based on homogeneous assumptions and models. However, it is not always clear when, why, how, and at what scale the 4D (3D + time) nature of the subsurface needs to be considered in hydrogeological monitoring, modeling, and applications. In this paper, we discuss the interest and potential for the monitoring and characterization of spatial and temporal variability, including 4D imaging, in a series of hydrogeological processes: (1) groundwater fluxes, (2) solute transport and reaction, (3) vadose zone dynamics, and (4) surface–subsurface water interactions. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. We then highlight recent innovations that have led to significant breakthroughs in high-resolution space–time imaging and modeling the characterization, monitoring, and modeling of these spatial and temporal fluctuations. We finally propose a classification of processes and applications at different scales according to their need and potential for high-resolution space–time imaging. We thus advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications. This calls for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components. [ABSTRACT FROM AUTHOR]

Large agricultural streams receive excessive inputs of nitrogen. However, quantifying the role of these streams in nitrogen processing remains limited because continuous direct measurements of the interacting and highly time-varying nitrogen processing pathways in larger streams and rivers are very complex. Therefore, we employed a monitoring-driven modelling approach with high-frequency in situ data and the river water quality model Water Quality Analysis Simulation Program (WASP) 7.5.2 in the 27.4 km reach of the sixth-order agricultural stream called Lower Bode (central Germany) for a 5-year period (2014–2018). Paired high-frequency sensor data (15 min interval) of discharge, nitrate, dissolved oxygen, and chlorophyll a at upstream and downstream stations were used as model boundaries and for setting model constraints. The WASP model simulated 15 min intervals of discharge, nitrate, and dissolved oxygen with Nash–Sutcliffe efficiency values higher than 0.9 for calibration and validation, enabling the calculation of gross and net dissolved inorganic nitrogen uptake and pathway rates on a daily, seasonal, and multiannual scale. Results showed daily net uptake rate of dissolved inorganic nitrogen ranged from - 17.4 to 553.9 mgNm-2d-1. The highest daily net uptake could reach almost 30 % of the total input loading, which occurred at extreme low flow in summer 2018. The growing season (spring and summer) accounted for 91 % of the average net annual uptake of dissolved inorganic nitrogen in the measured period. In spring, both the DIN gross and net uptake were dominated by the phytoplankton uptake pathway. In summer, benthic algae assimilation dominated the gross uptake of dissolved inorganic nitrogen. Conversely, the reach became a net source of dissolved inorganic nitrogen with negative daily net uptake values in autumn and winter, mainly because the release from benthic algae surpassed uptake processes. Over the 5 years, average gross and net uptake rates of dissolved inorganic nitrogen were 124.1 and 56.8 mgNm-2d-1 , which accounted for only 2.7 % and 1.2 % of the total loadings in the Lower Bode, respectively. The 5-year average gross DIN uptake decreased from assimilation by benthic algae through assimilation by phytoplankton to denitrification. Our study highlights the value of combining river water quality modelling with high-frequency data to obtain a reliable budget of instream dissolved inorganic nitrogen processing which facilitates our ability to manage nitrogen in aquatic systems. This study provides a methodology that can be applied to any large stream to quantify nitrogen processing pathway dynamics and complete our understanding of nitrogen cycling. [ABSTRACT FROM AUTHOR]

Excess export of nitrate to streams affects ecosystem structure and functions and has been an environmental issue attracting worldwide attention. The dynamics of catchment-scale solute export from diffuse nitrogen sources can be explained by the changes of dominant flow paths, as solute attenuation (including the degradation of nitrate) is linked to the age composition of outflow. Previous data-driven studies suggested that catchment topographic slope has strong impacts on the age composition of streamflow and consequently on in-stream solute concentrations. However, the impacts have not been systematically assessed in terms of solute mass fluxes and solute concentration levels, particularly in humid catchments with strong seasonality in meteorological forcing. To fill this gap, we modeled the groundwater flow and nitrate transport for a small agricultural catchment in Central Germany. We used the fully coupled surface and subsurface numerical simulator HydroGeoSphere (HGS) to model groundwater and overland flow and nitrate transport. We computed the water ages using numerical tracer experiments. To represent various topographic slopes, we additionally simulated 10 synthetic catchments generated by modifying the topographic slope from the real-world scenario. Results suggest a negative correlation between the young streamflow fraction and the topographic slope. This correlation is more pronounced in flat landscapes with slopes <1:60. Flatter landscapes tend to retain more N mass in the soil (including mass degraded in soil) and export less N mass to the stream, due to reduced leaching and increased degradation. The mean in-stream nitrate concentration shows a decreasing trend in response to a decreasing topographic slope, suggesting that a large young streamflow fraction is not sufficient for high in-stream concentrations. Our results improve the understanding of nitrate export in response to topographic slope in a temperate humid climate, with important implications for the management of stream water quality. [ABSTRACT FROM AUTHOR]

Long‐term nitrogen (N) transport and retention dynamics across catchments are not well understood. Using a process‐based model for 89 German catchments, results across study catchments reveal that most N surplus (during 1950–2014) was removed by denitrification (mean ± standard deviation: 58 ± 15%) while the remaining fraction was mostly stored in the soil (14% ± 11%). The mean groundwater transit times in these catchments varied from 3.2 to 20.3 years. These results indicate that past N inputs could continue to affect surface and groundwater quality in the coming years. We identified four catchment groups with distinct archetypal N transport and retention dynamics, which are linked to the catchments' climate, topographic, and geological conditions. Overall, our results shed light on long‐term N dynamics in German catchments and how they are linked to catchment characteristics, emphasizing the role of long‐term N accumulation and transport for water quality management and evaluation programs. Plain Language Summary: High nitrate concentrations in German water bodies are quite common. It is unclear to what degree current nitrogen levels in water bodies are due to current or past nitrogen (N) application on agricultural fields. What happened to excess N (N was not taken up by plants) in catchments has not been fully understood. In this study, we modeled the fate of excess N in 89 German catchments during the period 1950–2014. Our results suggest that most of the excess N was removed from the catchment in gaseous form (denitrification), and a substantial portion of the remaining excess N was stored in the soil zone. This soil N can leach into the deeper zone (groundwater) where it may travel for 3–20 years to reach the catchment outlet. We also identified four distinct groups of catchments with different behavior in terms of N transport and storage, which further exhibited different climatic, topographic, and subsurface conditions, suggesting that these factors could play a role in catchment N transport and retention. Overall, our results show that there could be a substantial delay between implemented management practices and resulting changes in surface or groundwater quality, which should be considered in water quality management. Key Points: We provided insights into the long‐term (1950–2014) nitrogen (N) transport and retention across various German catchmentsLarge‐sample assessment shows that 57% of N surplus was removed by denitrification and 15% of N surplus was accumulated in the soil zoneFour catchment clusters with distinct nitrogen transport and retention dynamics were linked to climatic, topographic, and geological factors [ABSTRACT FROM AUTHOR]

The mechanisms underlying observed global patterns of partitioning precipitation (P $P$) to evapotranspiration (E $E$) and runoff (Q $Q$) are controversially debated. We test the hypothesis that asynchrony between climatic water supply and demand is sufficient to explain spatio‐temporal variability of water availability. We developed a simple analytical model for Q $Q$ that is determined by four dimensionless characteristics of intra‐annual water supply and demand asynchrony. The analytical model, populated with gridded climate data, accurately predicted global runoff patterns within 2%–4% of independent estimates from global climate models, with spatial patterns closely correlated to observations (R2=0.93 ${R}^{2}=0.93$). The supply‐demand asynchrony hypothesis provides a physically based explanation for variability of water availability using easily measurable characteristics of climate. The model revealed widespread responsiveness of water budgets to changes in climate asynchrony in almost every global region. Furthermore, the analytical model using global averages independently reproduced the Budyko curve (R2=0.99 ${R}^{2}=0.99$) providing theoretical foundation for this widely used empirical relationship. Plain Language Summary: Local water availability is ultimately controlled by the amount of precipitation that is evaporated from a landscape. Empirical measurements have long shown a coherent global pattern in precipitation partitioning, but a consensus theoretical explanation is surprisingly lacking. We test the hypothesis that intra‐annual asynchrony between climatic water supply and demand drives the observed global patterns. We developed a simple analytical framework to predict runoff (available water) based on deficits between sinusoidal representations of precipitation (supply) and potential evapotranspiration (demand). Our framework accurately predicted global runoff patterns and reproduced a widely applied empirical precipitation partitioning relationship. Thus, supply and demand asynchrony provides both a mechanistic explanation for observed variation in global water budgets and the capability to predict water availability under future climates. Key Points: We developed a simple analytical runoff model based on four dimensionless characteristics of intra‐annual water supply and demand asynchronyThe climate asynchrony framework accurately predicted global runoff patterns and explains the widely applied empirical Budyko curveThe model revealed local responsiveness of water budgets to changes in climate asynchrony in almost every global region [ABSTRACT FROM AUTHOR]

Climate change may significantly increase flood risk globally, but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is analysed for Great Britain (GB) to provide the first spatially consistent GB projections to include both climate ensembles and hydrological model parameter uncertainties. We use the latest high-resolution (12 km) regional climate model ensemble from the UK Climate Projections (UKCP18). These projections are based on a perturbed-physics ensemble of 12 regional climate model simulations and allow exploration of climate model uncertainty beyond the variability caused by the use of different models. We model 346 larger (>144 km 2) catchments across GB using the DECIPHeR hydrological modelling framework. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1, and annual maximum) along the western coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for western Scotland. In contrast, median flows (Q50) were projected to decrease across GB. Even when using an ensemble based on a single regional climate model (RCM) structure, all flow projections contained large uncertainties. While the RCM parameters were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in eastern and south-eastern England. Regional variations in flow projections were found to relate to (i) differences in climatic change and (ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, and highlight the impact of hydrological model parameter uncertainties when modelling climate change impact on high flows. [ABSTRACT FROM AUTHOR]

Relationships between nitrate concentrations and discharge rates (C–Q) at the catchment outlet can provide insights into sources, mobilization and biogeochemical transformations of nitrate within the catchment. Nitrate C–Q relationships often exhibit considerable scatter that might be related to variable hydrologic conditions during runoff events at sampling time, corresponding to variable sources and flow paths despite similar discharge (Q) rates. Although previous studies investigated the origins of this scatter in individual or in a few catchments, the role of different runoff event types across a large set of catchments is not yet fully understood. This study combines a hydrological runoff event classification framework with low-frequency nitrate samples in 184 catchments to explore the role of different runoff events in shaping long-term C–Q relationships and their variability across contrasting catchments. In most of the catchments, snow-impacted events produce positive deviations of concentrations, indicating an increased nitrate mobilization compared to the long-term pattern. In contrast, negative deviations occur mostly for rainfall-induced events with dry antecedent conditions, indicating the occurrence of lower nitrate concentrations (C) in river flows than their long-term pattern values during this type of event. Pronounced differences in event runoff coefficients among different event types indicate their contrasting levels of hydrologic connectivity that in turn might play a key role in controlling nitrate transport due to the activation of faster flow paths between sources and streams. Using long-term, low-frequency nitrate data, we demonstrate that runoff event types shape observed scatter in long-term C–Q relationships according to their level of hydrologic connectivity. In addition, we hypothesize that the level of biogeochemical attenuation of catchments can partially explain the spatial variability of the scatter during different event types. [ABSTRACT FROM AUTHOR]

The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change. [ABSTRACT FROM AUTHOR]

In forest headwater streams, metabolic processes are predominately heterotrophic and depend on both the availability of carbon (C) and nitrogen (N) and a favourable C:N stoichiometry. In this context, hydrological conditions and the presence of riparian forests adjacent to streams can play an important, yet understudied role in determining dissolved organic carbon (DOC) and nitrate (NO 3-) concentrations and DOC:NO3- molar ratios. Here, we aimed to investigate how the interplay between hydrological conditions and riparian forest coverage drives DOC and NO 3- supply and DOC:NO3- stoichiometry in an oligotrophic headwater Mediterranean stream. We analysed DOC and NO 3- concentrations and DOC:NO3- molar ratios during both base flow and storm flow conditions at three stream locations along a longitudinal gradient of increased riparian forest coverage. Further, we performed an event analysis to examine the hydroclimatic conditions that favour the transfer of DOC and NO 3- from riparian soils to the stream during storms. Stream DOC and NO 3- concentrations were generally low (overall averages ± SD were 1.0±0.6 mg C L -1 and 0.20±0.09 mg N L -1), although significantly higher during storm flow compared to base flow conditions in all three stream sites. Optimal DOC:NO3- stoichiometry for stream heterotrophic microorganisms (corresponding to DOC:NO3- molar ratios between 4.8 and 11.7) was prevalent at the midstream and downstream sites under both flow conditions, whereas C-limited conditions were prevalent at the upstream site, which had no surrounding riparian forest. The hydroclimatic analysis of storms suggested that large and medium storm events display a distinct mechanism of DOC and NO 3- mobilization. In comparison to large storms, medium storm events showed limited hydrological responses that led to significantly lower increases in stream DOC and NO 3- concentrations. During large storm events, different patterns of DOC and NO 3- mobilization arise, depending on antecedent soil moisture conditions: drier antecedent conditions promoted rapid elevations of the riparian groundwater table, hydrologically activating a wider and shallower soil layer, and leading to relatively higher increases in stream DOC and NO 3- concentrations compared to large storm events preceded by wet conditions. Our results suggest that (i) increased supply of limited resources during storms can potentially sustain in-stream heterotrophic activity during high flows, especially during large storm events preceded by dry conditions, and (ii) C-limited conditions upstream were overcome downstream, likely due to higher C inputs from riparian forests present at lower elevations. The contrasting spatiotemporal patterns in DOC and NO 3- availability and DOC:NO3- stoichiometry observed at the studied stream suggest that groundwater inputs from riparian forests are essential for maintaining in-stream heterotrophic activity in oligotrophic, forest headwater catchments. [ABSTRACT FROM AUTHOR]

This paper exploits triggered lightning as a point source for the basin‐scale electromagnetic tomographic survey to image 3‐D subsurface electrical properties in basins. This paper further develops a new temporal moment approach, overcoming the difficulties in forward and inverse modeling of 3‐D Maxwell's equations with heterogeneous parameter fields. Using this approach, we find that the influence of a single triggered lightning strike covers a radius of 20–70 km with detectable signals. The cross‐correlation analysis between the moment difference of the electric and electric/magnetic property field indicates that the approach is suitable for mapping subsurface electric conductivity (σ $\sigma $) heterogeneity. A numerical experiment with 3‐D spatially random parameter fields demonstrates that the method captures the spatial distribution of electric conductivity over large areas with a sparse monitoring network. It reveals the potential of using triggered lightning as a basin‐scale electric/magnetic tomography survey. Plain Language Summary: Triggered lightning experiments traditionally aim at adverse impacts of lightning phenomena on near‐surface structures (such as buildings, power, communication, and transportation networks). Magnetotellurics surveys have exploited electromagnetic (EM) waves from thunderstorm activities and the interaction of solar wind with the Earth's magnetosphere to map the subsurface structure, assuming that electromagnetic waves are planar and propagate vertically into the Earth. This paper, in contrast, explores the EM waves generated by flashes of lightning triggered by a lightning rocket at designated locations as EM point sources and their measurements at different depths and distances in the subsurface. Such experiments are tantamount to an EM tomographic survey, viewing the subsurface from different perspectives. This paper further develops a new stochastic methodology to analyze the propagation of EM waves in heterogeneous geologic media over hundreds of kilometers. These accomplishments permit harvesting the lightning signals to image the subsurface over greater depths and areas and address the image's uncertainty. Numerical experiments confirm the robustness of this proposed concept, which could be a new technology to explore subsurface processes and natural resources in basins and mountain terrains. Key Points: An efficient 3‐D forward and inverse model based on the temporal moment is developed to solve Maxwell's equations in random parameter fieldsTriggered lightning tomography yields an excellent subsurface electric conductivity spatial distribution in a basin of several kilometersDeep lightning rods in the subsurface improve vertical resolution and depth of investigation [ABSTRACT FROM AUTHOR]

In order to discover the fractal and multifractal nature of land surface temperature (LST) spatiotemporal patterns, this study computed the LST in thecoastal city, Xiamen, southeastern China using Landsat TM/OLI/TIRS images from 1994 to 2015, and introduced two-dimensional box-counting and two-dimensional multifractal box-counting methods to quantitatively characterize the LST pattern. Results suggest that increasing the binarization threshold decreased the box-counting dimensions with values from 1.86 to 1.65 in 1994, 1.67 in 2000, 1.56 in 2004, 1.57 in 2010, and 1.56 in 2015, respectively. The two-dimensional multifractal approach employs a set of intertwined nonfractal subsets over different spatial scales to describe the LST patterns. LST pattern possesses a increased multifractality and the similar multifractal spectra of left-hook shapes throughout the study period, suggesting the increasing trends of the spatial heterogeneity in LST distribution. The probability of a given pixel having a high LST value is consistently high in the study area, as indicated by the positive ratio between the regions in which the probability measure appears most concentrated and those in which it is most sparse. Rapid urbanization and the large-scale urban land surface changes in the coastal city determine the variation in the multifractal parameters. [ABSTRACT FROM AUTHOR]

There is evidence that freshwater resources available for agriculture are decreasing with an unprecedented record. New irrigation strategies are developed and tested on crops that feed the world, such as maize, to improve water productivity. Deficit irrigation is one of these strategies that can improve water productivity without a significant impact on crop production. Here, the authors review the factors that affect the response of maize yield and irrigation water productivity to water stress induced by deficit irrigation using a quantitative approach. Data were collected from peer-reviewed publications worldwide that respond to predefined criteria. The authors defined grain yield variation (GYv) and variation of irrigation water use efficiency (IWUEv) as response variables and used simple and multiple linear regression models for data analysis. Overall, maize response to deficit irrigation is significantly correlated to the water stress level (WD). Mild stress below 20% of optimal irrigation led to 0.5% to 17.45% of yield loss in the vegetative stage (VS) but resulted in 46% yield loss at the reproductive stage (RS). Water stress (20–40%) applied at both vegetative and reproductive stages (VRSs) can reduce yield by 44%. The maximum yield loss was 90% in the RS. The multiple linear regression revealed that 62% of the grain yield variability was explained by both WD and nitrogen rates in the VS, while 54% and 13% of this variability was explained in the RS and VRS, respectively. The authors also found that the WD and the climate explained the best the GYv and the IWUEv under deficit irrigation. These results suggest that regarding the climatic characteristics of site location and the complexity of maize production systems, site-specific levels of deficit irrigation should be recommended to limit yield loss and increase water productivity. [ABSTRACT FROM AUTHOR]

Over the last decades, treatment of domestic wastewater promoted by environmental regulations have reduced human health risks and improved water quality. However, ecological risks caused by effluents of wastewater treatment plants (WWTPs) discharged into rivers still persist. Moreover, the evolution of these ecological risks in the future is intimately related to effects of changing climate, especially regarding streamflow in receiving rivers. Here, we present an analytical and transferable framework for assessing the ecological risks posed by WWTP‐effluents at the catchment scale. The framework combines the size‐class k of WWTPs, which is a load‐proxy, with their outflows' location in river networks, represented by stream‐order ω. We identify ecological risks by using three proxy indicators: the urban discharge fraction and the local‐scale concentrations of each total phosphorous and ammonium‐nitrogen discharged from WWTPs. About 3,200 WWTPs over three large catchments (Rhine, Elbe, and Weser) in Central Europe were analyzed by incorporating simulated streamflow for the most extreme projected climate change scenario. We found that WWTPs causing ecological risks in the future prevail in lower ω, across almost all k. Distinct patterns of ecological risks are identified in the k‐ω framework for different indicators and catchments. We show, as climate changes, intensified risks are especially expected in lower ω receiving effluents of intermediate‐k WWTPs. We discuss the implications of our findings for prioritizing WWTPs upgrading and urging updates on environmental regulations. Further discussions underline the feasibility of applying the framework to any geographical regions and highlight its potentials to help in achieving global long‐term commitments on freshwater security. Key Points: An analytical, generic framework was developed to assess wastewater treatment plants causing ecological risks in rivers under climate changeSmaller streams will face higher ecological risks for almost all load classes of wastewater treatment plants in future climateOf the legally regulated effluent parameters for treated wastewater, ammonium‐nitrogen concentration will pose the greatest ecological risk [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to nitrate pollution of groundwater which in many cases serves as a source of drinking water. Therefore, targeted reduction of nitrogen leaching losses is fully justified to address this issue. The aim of the study was to define the areas of utilized agricultural land (UAL) in Slovakia, where a nitrogen surplus needs to be reduced. Using the average values of leachable nitrogen in the period 2015-2018 and the long-term amount of percolated water, the nitrate concentration in leachate was calculated. To ensure that agricultural activities will contribute to the gradual reduction of nitrate concentration in groundwater, the nitrate concentration in leachate of 40 mg L-1 was chosen as the target value. This concentration was exceeded at 11.7% of the UAL area. The average indicative amount of nitrogen in industrial fertilizers that needs to be reduced to achieve a stricter nitrate concentration in the leachate in these hot-spots is 16 kg ha-1 with the proviso that in two districts this value exceeds 30 kg ha-1. [ABSTRACT FROM AUTHOR]

As the centerpiece of ecosystems and human societies, river basins are complex social–ecological systems (SESs) that depend on the natural flow regime and the hydrologic variability to adapt to changes and absorb disturbances. Anthropogenic and climate change disturbances destabilize river systems. Therefore, a resilience question arises: What is the carrying capacity of a river basin, i.e., how much disturbance can a river basin take until the system undergoes a regime shift? To answer this question, this study aims to identify regime shifts, thresholds, and the carrying capacity of the transboundary Rio Grande–Rio Bravo (RGB) basin using 110 years of monthly streamflow data. To address this research question, first, gauged (regulated) and naturalized streamflow data is collected; if naturalized flows are not available, they are calculated through streamflow naturalization. Second, streamflow standardization is estimated using the streamflow drought index. Third, a regime shift assessment is performed using Fisher Index, and fourth, the nonparametric Mann-Kendall test is used to assess the Sustainable Regime Hypothesis which evaluates regime shifts and alternative regimes. Results demonstrate that resilience thresholds are surpassed, and regime shifts, including early warning signals, occurred in multiple locations of a transboundary basin. The present study highlights the importance of assessing the carrying capacity of a river basin; hence, evaluating regime transitions, including identifying early warning signals and thresholds, is critical in managing for sustainability and ecological resilience of SESs. Looking ahead, the integration of ecological resilience theory into water management has the potential to recognize the sustainable carrying capacity of river basins at the local, regional, and international scale. [ABSTRACT FROM AUTHOR]

Environmental data are the key to defining and addressing water quality and quantity challenges at the catchment scale. Here, we present the first large-sample water quality data set for 1386 German catchments covering a large range of hydroclimatic, topographic, geologic, land use, and anthropogenic settings. QUADICA (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany) combines water quality with water quantity data, meteorological and nutrient forcing data, and catchment attributes. The data set comprises time series of riverine macronutrient concentrations (species of nitrogen, phosphorus, and organic carbon) and diffuse nitrogen forcing data (nitrogen surplus, atmospheric deposition, and fixation) at the catchment scale. Time series are generally aggregated to an annual basis; however, for 140 stations with long-term water quality and quantity data (more than 20 years), we additionally present monthly median discharge and nutrient concentrations, flow-normalized concentrations, and corresponding mean fluxes as outputs from Weighted Regressions on Time, Discharge, and Season (WRTDS). The catchment attributes include catchment nutrient inputs from point and diffuse sources and characteristics from topography, climate, land cover, lithology, and soils. This comprehensive, freely available data collection with a large spatial and temporal coverage can facilitate large-sample data-driven water quality assessments at the catchment scale as well as mechanistic modeling studies. QUADICA is available at 10.4211/hs.0ec5f43e43c349ff818a8d57699c0fe1 (Ebeling et al., 2022b) and 10.4211/hs.88254bd930d1466c85992a7dea6947a4 (Ebeling et al., 2022a). [ABSTRACT FROM AUTHOR]

Accessible urban nature is a key component of creating sustainable urban communities and promoting human health and well-being. To balance the economic, social, and environmental dimensions of sustainable development, the United Nations adopted several sustainable developmental goals (SDGs), such as SDG 11 for sustainable cities and communities, which aims to improve urban planning and management, including equitable access to urban nature. However, more information is still needed regarding how planners and managers can promote urban nature visitation and equitable access during health and environmental crises, such as the COVID-19 pandemic. The purpose of this study was to examine trends in urban nature visitation during the pandemic and then to determine if the effect of the pandemic on the frequency of urban nature site visitation varied by distance to home, using an innovative approach of analyzing both publicly available large-scale mobility data and a web-based survey of urban residents of Tel Aviv-Yafo, Israel. The mobility data results showed that there was a negative mean % difference in park visits compared to baseline during the first and third lockdowns, but an increase compared to baseline between lockdowns and even during the second lockdown. This suggests that urban residents had greater need to reconnect with urban nature during and after periods of intense stress. In addition, the survey results showed an increasing negative effect of distance on urban nature site visitation during the pandemic, specifically for urban nature sites located more than 1 km from home. Altogether, the mobility data and survey results suggest that people who lived within 1 km of their preferred urban nature site had disproportionate access to the benefits of urban nature during and after lockdowns than others. To effectively make social and ecological transitions toward urban sustainability, it is vital that cities promote urban nature accessibility during current and future environmental and health crises. Cities should collaborate with diverse stakeholders to create/maintain accessible urban nature sites nearby all sociodemographic groups, provide sustainability education and training to convey the benefits of urban nature, and pursue participatory solutions for understanding urban nature needs and preferences. In this manner, it will be possible to address the growing influence of proximity/travel distance and additional factors that affect urban nature visitation and ultimately, human health and urban sustainability., (© 2023. Springer Nature Limited.)

To meet the demand of the fast increasing population, enhancing the wheat (Triticum aestivum L.) yield and resource use efficiency by optimizing water and nitrogen (N) management can greatly improve agricultural sustainability and enhance regenerative farming in developing countries such as China. Based on 126 studies conducted in China between 1996 and 2018, using meta-analysis in combination with decision regression tree modeling and life cycle assessment (LCA), this study aimed to (1) quantify the effect of water and N input on wheat yield, water productivity (WP c ), and N use efficiency (NUE f ), and evaluate the subsequent environmental impact in different regions using LCA; and (2) evaluate, model, and rank the roles of environmental (e.g., soil nutrient status and climatic factors) and agronomic factors (e.g., water and N management practices) affecting wheat yield, WP c , and NUE f . The results showed that irrigation and N addition increased the average yield and WP c by 40 and 15%, respectively, relative to control treatments with no irrigation or fertilizer application. The mean water saving potential (WSP) and N saving potential (NSP) in China were estimated at 11 and 10%, respectively. Soil nutrient status [e.g., initial soil phosphorus (P) and potassium (K)] and soil organic carbon content affected the wheat yield, WP c , and NUE f more significantly than climatic factors [mean annual temperature (MAT)] or water and N management practices. The structural equation-based modeling indicated that initial soil nutrient condition impacted productivity and resource use efficiency more at the below optimal water and N levels than above. The risk-factor-based feature ranking indicated that site-specific environmental and soil condition was highly informative toward model construction but split input of N or water had less impact on yield and input use efficiency. LCA demonstrated that to further mitigate greenhouse gas emissions, water- or N-saving management should be promoted in China. Collectively, our research implies that long-term soil health and nutrient enhancement should be more beneficial for increasing yield and resource use efficiency in wheat production. [ABSTRACT FROM AUTHOR]

Wetlands are highly productive ecosystems that host unique biota and provide vital ecosystem services. Despite their critical importance, we still lack a fundamental understanding of factors controlling species suitability and climate change impacts in wetlands essential to conserving these imperiled ecosystems in the face of anthropogenic threats. Here, we applied ecological niche models (ENMs) inferred from a massive nationwide data set including field surveys and herbarium specimens representing 101 dominant plant species of China's inland wetlands, to explore the potential climate‐driven shifts in these ecosystems. Our optimal models applying machine learning and integrating key aspects of climate, soil, topographic hydrology, and anthropogenic disturbance, identified that (a) species of different types (e.g., woody/nonwoody) showed significant differences in their sensitivity to extracted key environmental factors (e.g., isothermality, water table depth) in wetlands; (b) native wetland species likely face habitat shrinkage under climate change, especially in Northeast China, as well as potential encroachment of xeric and nonnative species; (c) only less than 20% of projected conservation hotspots are currently protected. We propose four protection gaps that need to be addressed urgently in China's current wetland protection framework. This study clarifies the conservation priorities of China's inland wetlands and provides practical guidelines for the improvement of wetland conservation efforts. Plain Language Summary: Wetlands are among the most productive ecosystems on Earth and provide multiple key ecosystem services for wildlife and human societies. However, global wetlands have been experiencing extensive degradation and loss under anthropogenic threats, and anthropogenic climate change poses new challenges and uncertainties to wetland conservation efforts. Thus, there is a pressing need to assess climatic risks in wetlands, to identify potential climate‐change hotspots and develop climate‐smart conservation strategies. This study compiled a massive wetland data set and applied machine learning tools to perform the first nationwide climatic risk assessment in China's inland wetlands. The results revealed that native wetland species would face habitat shrinkage, especially woody species, whereas nonwetland and nonnative species would benefit from climate change and expand their distributions. Northeast China is a hotspot with its native wetland species under high climatic risk, while in the Tibetan Plateau, the habitats for native wetland species and nonwetland species are both likely to expand in the future. This study highlights the conservation priorities in China's inland wetlands and offers guidelines for filling the current wetland protection gaps. Key Points: Different species types vary in their sensitivity to the key environmental factors in inland wetlandsNative wetland species will overall face habitat loss, especially in Northeast China, as well as invasion of xeric and alien speciesLess than 20% of projected conservation hotspots are currently protected [ABSTRACT FROM AUTHOR]

How does climate control river chemistry? Existing literature has examined extensively the response of river chemistry to short‐term weather conditions from event to seasonal scales. Patterns and drivers of long‐term, baseline river chemistry have remained poorly understood. Here we compile and analyze chemistry data from 506 minimally impacted rivers (412,801 data points) in the contiguous United States (CAMELS‐Chem) to identify patterns and drivers of river chemistry. Despite distinct sources and diverse reaction characteristics, a universal pattern emerges for 16 major solutes at the continental scale. Their long‐term mean concentrations (Cm) decrease with mean discharge (Qm), with elevated concentrations in arid climates and lower concentrations in humid climates, indicating overwhelming regulation by climate compared to local Critical Zone characteristics such as lithology and topography. To understand the CmQm pattern, a parsimonious watershed reactor model was solved by bringing together hydrology (storage–discharge relationship) and biogeochemical reaction theories from traditionally separate disciplines. The derivation of long‐term, steady state solutions lead to a power law form of CmQm relationships. The model illuminates two competing processes that determine mean solute concentrations: solute production by subsurface biogeochemical and chemical weathering reactions, and solute export (or removal) by mean discharge, the water flushing capacity dictated by climate and vegetation. In other words, watersheds function primarily as reactors that produce and accumulate solutes in arid climates, and as transporters that export solutes in humid climates. With space‐for‐time substitution, these results indicate that in places where river discharge dwindles in a warming climate, solute concentrations will elevate even without human perturbation, threatening water quality and aquatic ecosystems. Water quality deterioration therefore should be considered in the global calculation of future climate risks. Key Points: Continental‐scale river chemistry data show that mean discharge predominantly regulates mean concentrations of 16 solutesA simple watershed hydro‐biogeochemical reactor model illuminates that river chemistry is driven by the relative rates of solute addition (by reactions and input) and solute exportWhere river discharge dwindles in a warmer climate, higher concentrations will deteriorate water quality even without human perturbations [ABSTRACT FROM AUTHOR]

Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high-resolution gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within the United States (US) Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population Map for the conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas dasymetric population map, we found that IDM's population estimates for the US Census Bureau blocks improved across all states in the CONUS. We found that IDM performed better in states with larger urban areas than in states that are sparsely populated. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas. The updated 30 m population density for the CONUS is available via the EPA's Environmental Dataset Gateway (Baynes et al., 2021, 10.23719/1522948) and the EPA's EnviroAtlas (https://www.epa.gov/enviroatlas , last access: 15 June 2022; Pickard et al., 2015). [ABSTRACT FROM AUTHOR]

When crisis strikes, where do evacuees go? This question affects how policymakers and first responders allocate their time, funds, and resources after disaster. While past research compared evacuation rates of cities within the same disaster, evacuation among different types of disasters remain under-examined. This mixed methods study compares evacuation patterns from 7,631 cities among 10 major disasters in the US and Japan between 2019 and 2020, combining social network analysis, modeling, and visualization. This study highlights that evacuation from some hazards is more alike than others; large, sprawling disasters, including some storms, fires, and power outages trigger both clustered and dispersed evacuation networks, while smaller, focused disaster result in mainly dispersed evacuation networks. Further, cities with similar levels of social capital tend to see greater evacuation between them. By uncovering the different shapes and drivers of evacuation networks across different disasters, scholars can clarify where evacuees go and which kinds of cities need additional support after crisis. [ABSTRACT FROM AUTHOR]