Variable Climatic Conditions Research Articles

Variable climatic conditions dominate decreased wetland vulnerability on the Qinghai‒Tibet Plateau: Insights from the ecosystem pattern-process-function framework

Wetlands are of global importance in providing essential ecosystem services but are also sensitive to climate change and human activities. Monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies. However, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied. In this study, the spatiotemporal variations in wetland vulnerability on the Qinghai‒Tibet Plateau (QTP) between 1990 and 2020 were investigated based on the ecosystem pattern-process-function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models. Our results showed that the wetland ecosystem pattern index (EPI), ecosystem process index (EPOI), ecosystem function index (EFI), and wetland vulnerability index (WVI) all demonstrated an increasing pattern from northwest to southeast. Between 1990 and 2020, the mean WVI values gradually decreased from 0.616 to 0.588, indicating a steady improvement in the wetland ecosystem on the QTP. Rapid urbanization increased the EPOI, while rugged topography increased both the EPI and EPOI, and the increase in hydrological abundance enhanced the EFI, which in turn contributed to an increase in the WVI. Conversely, climatic conditions led to a reduction in the EPI, which in turn resulted in a significant decrease in the WVI. Therefore, although urbanization and topographical and hydrological factors have somewhat exacerbated the WVI on the QTP, variable climatic conditions have driven the decline in wetland vulnerability in the last three decades. Furthermore, our results indicated that the proposed framework could provide a comprehensive approach for wetland vulnerability assessment and useful implications for wetland conservation and management.

Archaeobotanical data from the Sabor Valley reveal shifting moments in landscape and agriculture in NW Iberia during the Holocene

NW Iberia is dominated by Atlantic climate areas that favour pollen preservation, useful for palaeoecological studies. However, the region also includes Mediterranean sectors in which preservation of such palaeoenvironmental evidence is more difficult. To overcome these constraints, archaeological plant macroremains can be used to help characterize flora and vegetation dynamics at a local and regional level. To fill the gap in knowledge in an understudied Mediterranean region, a large archaeobotanical study was conducted at the river Sabor valley, NE Portugal. With 13 archaeological sites sampled for charcoal, fruits and seeds, it allowed the study of vegetation throughout the Holocene, starting in the Mesolithic up to Modern times, with some chronological gaps. Tree taxa dominates the older period and an expansion of shrubby taxa since Bronze age was observed. Diversification of plants used during the Iron Age and Roman period suggests an intensification of resources exploitation and deforestation. These trends seem to be related with changes in human settlements and productive strategies. During Prehistory, agricultural fields were established in flat and wide areas, and, during Iron Age, fortified granaries were used to store large amounts of grains, particularly free-threshing wheat. Drastic changes in settlement during Roman times and the establishment of small farms producing wine and/or olive oil were testified by the occurrence of Olea and Vitis in both anthracological and carpological datasets. Data from more recent periods is scarce. Results highlight that the combination of several proxies and integration of archaeological evidence helps to understand ecological dynamics in areas without pollen data and contributes to the characterization of heterogeneous areas under diverse climatic conditions and with a variety of social trends.

Environmental impacts on intraspecific variation in Ambrosia artemisiifolia genome size in Slovakia, Central Europe

The quantity of DNA in angiosperms exhibits variation attributed to many external influences, such as environmental factors, geographical features, or stress factors, which exert constant selection pressure on organisms. Since invasive species possess adaptive capabilities to acclimate to novel environmental conditions, ragweed (Ambrosia artemisiifolia L.) was chosen as a subject for investigating their influence on genome size variation. Slovakia has diverse climatic conditions, suitable for testing the hypothesis that air temperature and precipitation, the main limiting factors of ragweed occurrence, would also have an impact on its genome size. Our results using flow cytometry confirmed this hypothesis and also found a significant association with geographical features such as latitude, altitude, and longitude. We can conclude that plants growing in colder environments farther from oceanic influences exhibit smaller DNA amounts, while optimal growth conditions result in a greater variability in genome size, reflecting the diminished effect of selection pressure.

Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers

Improving streamflow prediction plays a significant role in flood warning, mitigation and development purposes. Therefore, this paper aims to compare the prediction capability of a useful conceptual hydrologic model hydrologiska byråns vattenbalansavdelning (HBV) enhanced and equipped with an automatic calibration procedure (Genetic Algorithm) along with two well-known deep learning (DL) algorithms, including Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This study was concentrated on the daily streamflow of two river basins with diverse climatic and hydrological conditions, namely the Halilrood River with fragmented and intermittent flows (located in Iran) and the Tweed River with a permanent stream flow (Located across the border of Scotland and northern England). These were particularly selected to address both seasonal and perennial riverine cases. For reproducing the streamflow, 10-year records of the daily discharge, precipitation, and temperature (from satellite and ground resources), and evapotranspiration were used as the main input data. The accuracy of the implemented models was assessed using several indices. The findings show that 1) the LSTM and GRU show better accuracy than the employed hydrologic model for estimating daily discharges (especially in the seasonal river basin); 2) the DL approaches need only meteorological data inputs (precipitation and temperature) and are independent of involved uncertainties in hydrological modeling such as physical characteristics of basins; and 3) best streamflow simulations were achieved by the LSTM (best agreement with the observations). In general, LSTM has the highest priority to be connected with online hydrological rainfall-runoff simulations and dynamical forecast models.

Carbon offset potential of rooftop photovoltaic systems in China

As a promising tool for mitigating carbon emission, rooftop photovoltaics have attracted worldwide attention. This study examines the carbon offset potential of rooftop photovoltaics in 31 provinces in China. The future trends of carbon offset potentials over the lifetime of rooftop photovoltaic systems are investigated. The findings reveal that the carbon offset potentials vary significantly across different regions in China due to the combined effects of variations in climate conditions and the energy mix for electricity generation. The northern regions possess significantly higher carbon offset potentials than the southwestern areas. In 2021, the annual total carbon offset potential ranged from the lowest at −2.0 kg CO2/m2 in Tibet to the highest at 169.3 kg CO2/m2 in Beijing. The projected future trends indicate that Beijing and Tibet will respectively maintain their highest and lowest carbon offset potential rankings. Most provinces are projected to witness decreasing carbon offset potentials. In terms of the carbon offset potential over the lifetime of rooftop photovoltaics, the northern regions − Beijing, Tianjin and Inner Mongolia − rank the top three. Not only can these findings serve as reference for developing targeted policies for carbon mitigation in China, the methodology of this study can also be applied in other places to determine their carbon offset potentials of rooftop photovoltaics.

Plant selection for rain gardens in temperate climates: The case of Izmir, Turkey

Rain gardens have gained importance in recent years as a green infrastructure strategy. These gardens, created to capture, filter, and absorb runoff from impervious surfaces, offer a sustainable method for addressing water-related challenges in urban areas. Incorporating rain gardens into urban areas not only addresses the challenges of heavy rainfall and flooding but also brings about ecological advantages by encouraging biodiversity, improving water quality, increasing resilience, and enhancing the aesthetic appeal of urban settings. Plant selection in rain gardens plays a crucial role in their effectiveness and sustainability. The research focused on exploring the importance of carefully choosing plants for rain gardens, aiming to help in selecting the most suitable flora and creating visually appealing, resilient, and ecologically important landscapes. For this purpose, Izmir Katip Celebi University was selected as a study area. The first step was to locate an appropriate space for a rain garden and evaluate its potential for a rain garden implementation. Next, plants suitable for a rain garden in a temperate climate were listed. Among these plants, those that can be found in Izmir were selected after contacting nurseries. Only fourteen of them were available. Utilizing the plants listed that align with the project requirements and the plant design criteria such as diverse color, high density, and proportion outlined in the literature review, a proposal for a rain garden design was recommended. Since the rain garden consists of three different zones (dry, wet, and moderate), the plants were arranged accordingly. This design was tailored to suit existing conditions, such as a temperate climate and proximity to the building. Factors like varying climate conditions or alternative rain garden placements were not accounted for in this design. Given the necessity for diverse plant selections in varying climates, research carried out across different regions holds significant value. This study, conducted in Izmir province, will enrich existing literature and provide municipalities with crucial guidance in plant selection in a rain garden project, offering valuable insights.

Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling

This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km2. However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake’s range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species.

Chorematic modeling to represent dynamics in the quinoa agroecosystems in Peru.

Our research occurred in the Andean region, one of the eight global centers of domestication of plant species grown for agriculture. The shores of Lake Titicaca (located between Peru and Bolivia), at 3800 meters above sea level, are recognized as the center of origin of quinoa (Chenopodium quinoa Willd.). In this region, complex societies have emerged, thanks to the development of water and soil management technologies. They have managed to overcome high mountain territories' extreme and variable climatic conditions. These societies have traditionally protected and preserved quinoa as food for present and future generations through their long-standing knowledge and cultivation practices. The fieldwork occurred in the context of Andean family farming, and our study analyzes nature-society dynamics with a chorematic approach and interviews with local communities. The interest of this work is the transformation of the landscape at the scale of the mountain agroecosystem to understand better the impacts of rural development policies. Chorematic modeling was applied to two periods, before and after 1970, a pivotal year in Peru for agriculture, to show how socio-spatial dynamics in the Andean environment are changing, particularly concerning the evolution of quinoa cultivation. The results show that wild quinoa relatives' distribution is strongly linked to the socio-spatial organization of the agroecosystem. Different species of wild quinoa relatives are maintained by villagers for their multiple foods, medicinal and cultural uses in natural areas, grazed areas, on edge, and also within cultivated fields. However, this management is changing under the pressure of global issues related to the international quinoa market, whose requirements imply reducing the presence of wild relatives in cultivated fields.

The Economic and Environmental Aspects of Miscanthus × giganteus Phytomanagement Applied to Non-Agricultural Land

Miscanthus × giganteus (M × g) is a promising energy crop in phytotechnology with biomass production. Despite considerable vegetation and harvest under varying climate conditions and across different soils, field-scale studies on utilising M × g remain scarce. Analysing the literature and our own findings, this study intends to highlight the potential of M × g phytotechnology for revitalising non-agricultural lands (NAL), including brownfields, and illustrate the expediency of applying biochar to enhance biomass yield, energy efficiency, and economic feasibility. To validate the feasibility of M × g production on brownfields, two scenarios within the value chain “biomass–biogas–electricity” for green harvest were examined. The assumptions were as follows: (1) a methane yield of 5134 m3 ha−1 y−1, and (2) substrate-specific methane yields of 247 and 283 mL (g oDM)−1 for the first and subsequent years, respectively. The findings suggest that Scenario 2 is better suited for cultivating M × g on brownfields/NAL, being more sensitive and eliminating inaccuracies and the generalisations of results. From the third year onward, the revenue of M × g production on biochar-amended brownfields showed greater potential for future profitability. Future research should confirm the positive trend in the energy efficiency ratio of M × g phytotechnology on a larger scale, particularly in real brownfield applications.

Risk assessment of mold growth across the US due to weather variations

Mold growth in residential and commercial buildings poses a serious problem to various communities. There are several facets of the problem, the most prominent of which are chronic public health risks, business losses, and monetary and labor resources needed for mold containment and mitigation. In addition, there may be severe renovation work needed if the problem is left unattended. This paper describes a probabilistic framework for mold growth risk assessment on various construction materials and shows its application across a variety of climate conditions. Mold growth is contingent upon three ingredients: temperature, moisture, and nutrients. A specific construction material of interest represents the latter, while the other two are modeled through Monte-Carlo simulations. Mold growth is characterized by the mold growth index, which is tracked over ten years, with hourly time resolution. Analysis of the temperature and relative humidity empirical data revealed that these quantities are statistically dependent. Accounting for these interdependencies is crucial for realistic simulations of temperature and humidity and were numerically reproduced in the developed method. The developed framework allowed for realistic assessment of mold growth risk. It was applied across 60 US metropolitan areas in different climatological zones to quantify mold growth risk. The proposed framework allows for quantitative risk assessment of mold growth in different climatological conditions. Such a capability is needed for decision makers for timely mold mitigation, preventing health hazards, and avoiding large financial losses for emergency mold remediation and building repair.

Continuous In-woods Production of Biochar Using a Trailer-mounted Air Curtain Burner.

Fuel treatments and other forest restoration thinning practices aim to reduce wildfire risk while building forest resilience to drought, insects, and diseases and increasing aboveground carbon (C) sequestration. However, fuel treatments generate large amounts of unmerchantable woody biomass residues that are often burned in open piles, releasing significant quantities of greenhouse gases and particulates, and potentially damaging the soil beneath the pile. Air curtain burners offer a solution to mitigate these issues, helping to reduce smoke and particulates from burning operations, more fully burn biomass residues compared to pile burning, and eliminate the direct and intense fire contact that can harm soil beneath the slash pile. In an air curtain burner, burning takes place in a controlled environment. Smoke is contained and recirculated by the air curtain, and therefore burning can be conducted under a variety of climatic conditions (e.g.,wind, rain, snow), lengthening the burning season for disposal of slash material. The mobile pyrolysis unit that continuously creates biochar was specifically designed to dispose of residual woody biomass at log landings, green wood at landfills, or salvaged logged materials and create biochar in the process. This high-carbon biochar output can be used to enhance soil resilience by improving its chemical, physical, and biological properties and has potential applications in remediating contaminated soils, including those at abandoned mine sites. Here, we describe the general use of this equipment, appropriate siting, loading methods, quenching requirements, and lessons learned about operating this new technology.

Impact of cultivation conditions on physicochemical characteristics of Miscanthus × giganteus biomass

AbstractMiscanthus × giganteus has emerged as a successful energy crop for remediating marginal land, offering potential as an alternative energy source for climate change mitigation. This study aimed to assess the physicochemical properties for gasification of Miscanthus × giganteus biomass from plantations with diverse soil origins, characteristics, contamination levels, age, and climate conditions, aiming to determine their influence on the energy properties. Fuel parameters, i.e.: ash content, volatile matter, calorific value, ultimate analysis, and ash melting behavior were measured. Analysis of biomass samples revealed significant variations in the ash content and volatile matter with the average values of 4.5 and 78.8%, respectively, depending of the produced biomass. The calorific value remained consistently high, ranging from 18 to 19 MJ kg−1, even when biomass was derived from the contaminated soil. Additionally, biomass ash exhibited complete melting at temperatures exceeding 1300 °C, signifying its suitability for gasification without encountering technical obstacles. Employing PCA analysis, the significance of plantation age, location, and soil characteristics in shaping the physicochemical properties of Miscanthus × giganteus biomass for gasification was highlighted. The results illustrated that soil contamination by trace elements had a minimal impact on the physicochemical properties, whereas plantation age and climate conditions significantly influenced to the fuel properties of biomass.

Analysis of recent decade rainfall data with new exponential-exponential distribution: Inference and applications

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

Real time performance assessment of utility grid interfaced solar photovoltaic plant

Continuous monitoring of large-scale solar photovoltaic (PV) installations is necessary to check the deterioration and monitor the performance of the PV plant. Fault diagnosis is crucial to ensure the PV plant operates safely and reliably. This paper presents a diagnosis methodology based on current-voltage (I-V) and PV characteristics to monitor and assess the behavior of solar PV. In this paper, I-V curve characterization using an I-V curve tracer is used to check the deterioration and diagnosis of the PV panels. The real-time performance of the 50.4 kWp rooftop solar grid interfaced PV plant is investigated and analyzed using I-V and PV curve tracers in real-time conditions. The overall performance of solar PV is assessed on a real-time test system in different scenarios such as variable climatic conditions, partial shading conditions, aging of solar panels, short circuit conditions, and dust decomposition. Furthermore, the performance assessment of solar PV is evaluated using performance indicators such as open circuit voltage index, short circuit current index, fill factor, and performance ratio.

EXPLORING THE MICRO AND MACRO TERROIR OF FETEASCĂ NEAGRĂ WINE FROM MOLDOVA

This study delves into the intricate realm of Moldovan winemaking, focusing on the iconic Fetească Neagră grape variety. The research highlights the importance of indigenous grape varieties in shaping a unique wine identity for the region. Drawing on the concept of terroir, the study investigates the microbial terroir specific to three designated geographical regions in Moldova during the 2022 vintage. The research employs a multifaceted approach, combining climate data analysis, mini vinification experiments, microbial DNA extraction, and sensory evaluation techniques. Results reveal significant variations in climatic conditions and microbial diversity across the three regions, influencing the sensory characteristics of the resulting wines. On vintage 2022 the Codru is temperature， and the Stefan-Voda and Vadul lui Traian are warm climates. All the 3 regions are moderately dry. The Stefan Voda is 14.0%， and manifests the highest ethanol content among the regions, alongside the lowest pH level. The sensory evaluations show Stefan Voda evincing heightened creaminess, Codru showcasing prominent herbal nuances, and Vadul lui Traian displaying elevated scores in berry, cherry, violet, and black pepper descriptors. Through meticulous analysis, the study underscores the dynamic interplay between environmental factors, microbial communities, and wine attributes, shedding light on the complex tapestry of Moldovan winemaking. Ultimately, this research contributes to a deeper understanding of terroir-driven wine production and emphasizes the importance of preserving indigenous grape varieties to uphold the distinctiveness of Moldovan wines on the global stage.

CSM-CROPGRO model to simulate safflower phenological development and yield.

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016-17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3kgha-1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8kgha-1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions.

Sustainable and self-sufficient social home through a combined PV‑hydrogen pilot

Utilizing surplus renewable energy to produce and store green hydrogen can help meet energy demands during periods of low renewable energy production, and thus contributing greatly to decarbonize emission-intensive sectors, such as the residential and building sector. In this context, this manuscript reports the design, construction and operation of a hybrid PV‑hydrogen demonstrative pilot (PVHyP) for the 100% electrical self-sufficiency of a social housing in the town Novales (Spain). The work addresses i) the collection of real consumption data from the dwelling, ii) the design considering real characteristics and market availability of the devices, iii) final balance of plant implementation, and iv) the energy management strategy followed. The implemented control system allows the facility to be tracked and operated remotely. Besides, this flagship implementation in the SUDOE region (Spain, Portugal and southwest of France) has achieved the electrical independence from the grid under diverse climate conditions, eliminating 2260 kg of CO2 emissions associated to electricity consumption, saving almost 15,200 kWh of primary energy during two years of monitoring. Thus, around 1170 € where avoided in electricity bills (100% savings), resulting on multiple benefits for social dwellers in risk of energy poverty.

Ancient prairies as a reference for soil organic carbon content and microbial community structure

The historic remnant prairies of Missouri display diversity above and below the soil surface and represent the few remaining acres of unconverted grassland habitat in the state. The soil microbiome is foundational to nutrient acquisition, resource partitioning, and ecosystem resilience; therefore, the intact plant-soil interactions of remnant prairies can serve as a reference for microbial community structure and ecosystem services lost due to land conversion. This research was conducted to show the importance of reference soil conditions and the variability that lies therein while contrasting degraded soil ecosystems to provide a standard to which comparable soils should be held. In this study, 35 sites across Missouri representing three prairie management conditions on similar soils and variable climatic conditions were compared to assess differences in the microbial community structure and biomass using phospholipid fatty acid (PLFA) analysis. Surface (0–7.5 cm) soil organic carbon (SOC) content provided a measure of substrate available to support microbial community health. Total microbial biomass, the biomass of individual microbial groups, selected biomarker ratios, and SOC, were significantly different across treatments and highest in remnant prairies apart from Saprophytic fungi and eukaryotes. This study demonstrated that the soil microbial community in reconstructed and restored prairie systems remained significantly diminished in biomass relative to remnant prairies, even after several decades. This study highlights the need to preserve remnant prairies, where soil is in its native state under native flora and abiotic conditions. These results also illustrate the importance of selecting remnant systems as the ecological reference standard for prairie lands.

Risk-based hydrologic design under climate change using stochastic weather and watershed modeling

Water resources planning and management requires the estimation of extreme design events. Anticipated climate change is playing an increasingly prominent role in the planning and design of long-lived infrastructure, as changes to climate forcings are expected to alter the distribution of extremes in ways and to extents that are difficult to predict. One approach is to use climate projections to force hydrologic models, but this raises two challenges. First, global climate models generally focus on much larger scales than are relevant to hydrologic design, and regional climate models that better capture small scale dynamics are too computationally expensive for large ensemble analyses. Second, hydrologic models systematically misrepresent the variance and higher moments of streamflow response to climate, resulting in a mischaracterization of the extreme flows of most interest. To address both issues, we propose a new framework for non-stationary risk-based hydrologic design that combines a stochastic weather generator (SWG) that accurately replicates basin-scale weather and a stochastic watershed model (SWM) that accurately represents the distribution of extreme flows. The joint SWG-SWM framework can generate large ensembles of future hydrologic simulations under varying climate conditions, from which design statistics and their uncertainties can be estimated. The SWG-SWM framework is demonstrated for the Squannacook River in the Northeast United States. Standard approaches to design flows, like the T-year flood, are difficult to interpret under non-stationarity, but the SWG-SWM simulations can readily be adapted to risk and reliability metrics which bare the same interpretation under stationary and non-stationary conditions. As an example, we provide an analysis comparing the use of risk and more traditional T-year design events, and conclude that risk-based metrics have the potential to reduce regret of over- and under-design compared to traditional return-period based analyses.

Energetic analysis and economic viability of active atmospheric water generation technologies

Water scarcity is a growing global and systematic problem in regions with low groundwater availability. Atmospheric water generation (AWG) technologies are an innovative solution to the water shortage problem, as atmospheric water vapor is a readily available resource even in arid regions, with the drawback of high energy consumption. In this paper, the viability of AWG technologies on an energy and economic level is investigated by thermodynamic modeling of three main active AWG systems consisting of cooling condensation, adsorption and absorption processes. A location analysis model is developed to evaluate the performance based on representative weather data of temperature, pressure and relative humidity over a period of one year to account for seasonal shifts and daily variations in climatic conditions. The specific energy consumption kWh/kg, water production trend and total specific cost are calculated for each technology. Water production by seawater desalination at the nearest coastline and transportation to the site by tanker truck, as well as bottled water prices, are used as benchmarks to assess economic viability. The results show that active AWG systems can only be an economically viable alternative if the water consumption site is relatively far from the coast or other water-rich regions and low electricity costs are available (distance >600 km, electricity price <0.10 US$/kWh). Compared to bottled water, all AWG technologies are in a competitive price range. Absorption systems have an energy efficiency advantage over conventional cooling condensation and adsorption systems (cooling condensation: average 0.42 kWh/kg; absorption: average 0.38 kWh/kg; adsorption: average 1.16 kWh/kg), but require a higher degree of process and plant design development. However, because of the high fluctuation in water production, atmospheric water generation technologies should be considered as a complementary supply to conventional water sources.