Annual Scale Research Articles

Surface energy and mass balance of Mera Glacier (Nepal, Central Himalaya) and their sensitivity to temperature and precipitation

Abstract The sensitivity of glacier mass balance to temperature and precipitation variations is crucial for informing models that simulate glaciers’ response to climate change. In this study, we simulate the glacier-wide mass balance of Mera Glacier with a surface energy-balance model, driven by in situ meteorological data, from 2016 to 2020. The analysis of the share of the energy fluxes of the glacier shows the radiative fluxes account for almost all the energy available during the melt season (May–October). However, turbulent fluxes are significant outside the monsoon (June–September). On an annual scale, melt is the dominant mass flux at all elevations, but 44% of the melt refreezes across the glacier. By reshuffling the available observations, we create 180 synthetic series of hourly meteorological forcings to force the model over a wide range of plausible climate conditions. A +1 (−1)°C change in temperature results in a −0.75 ± 0.17 (+0.93 ± 0.18) m w.e. change in glacier-wide mass balance and a +20 (−20)% change in precipitation results in a +0.52 ± 0.10 (−0.60 ± 0.11) m w.e. change. Our study highlights the need for physical-based approaches to produce consistent forcing datasets, and calls for more meteorological and glaciological measurements in High Mountain Asia.

Enhancing Daily Crash Count Prediction using Deep Learning with Window Size Selection and Seasonality Predictor Integration

The escalating need for proactive safety measures, coupled with advancements in data collection and analytical techniques, has significantly refined the accuracy of crash count predictions, shifting from annual scales to finer daily or hourly estimates. This research places emphasis on recurrent neural networks (RNNs), specifically the long short-term memory (LSTM) model, acknowledged for effectively managing sequential data in time series predictions. Paramount considerations encompass the treatment of input data, including the decision to incorporate temporal features alongside endogenous historical target values, and the establishment of an optimal window size for data input. Despite the critical nature of these facets, exhaustive studies concurrently investigating both under controlled conditions are scarce. This research addresses this gap, assessing diverse scenarios featuring distinct temporal treatments and window sizes, and employing an LSTM model with uniform fine-tuned parameters to ensure a fair comparison. Findings indicate a significant variation in performance among models employing different window sizes and month predictor integration, under identical RNN structures and LSTM configurations. The best model outperformed the least effective by roughly 30% concerning root mean square error (RMSE) and ranking correlation between predicted and actual target crash counts in the test datasets. Unlike seasonality predictor treatment, diverse window size selections did not lead to statistically significant differences in model performance. Generally, a window size of 3 performed worst under most conditions, followed by a size of 14. Sizes of 7 and 28 performed best in nearly an equal numbers of cases.

Planktonic to sessile: drivers of spatial and temporal variability across barnacle life stages and indirect effects of the Pacific Marine Heatwave

ABSTRACT Barnacles are a foundation species in intertidal habitats. During the Pacific Marine Heatwave (PMH), intertidal barnacle cover increased in the northern Gulf of Alaska (GoA); however, the role of pelagic larval supply in this increase was unknown. Using long-term monitoring data on intertidal benthic (percent cover) and pelagic larval populations (nauplii and cyprid concentrations), we examined potential environmental drivers (temperature, chlorophyll-a, mixed layer depth) of larval concentration and whether including larval concentration at regional and annual scales improved intertidal barnacle percent cover models in two study regions in the GoA. In both regions, larval concentrations were slightly higher following the PMH. Percent cover models were improved by including cyprid concentrations (but not nauplii), and the effect strength varied by site and tidal elevation. This indicates that larval concentration contributes as a bottom–up driver of benthic barnacle abundance. There is little evidence of a direct effect of the PMH on either life stage. Instead, our results may illustrate the positive feedback between life stages, where higher adult benthic abundance increased larval concentrations, which then supplied more new recruits to the benthos. As heatwaves continue to occur, integrating various data types can provide insights into factors influencing both benthic and pelagic communities.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

Four years of landslide observation with anthropogenic loading as an additional trigger - Analysis of seasonal and annual variability of physical parameters

Determining the structure and evolution of landslides is crucial for geophysical hazard assessment. In this study, we employed an approach integrating the methodologies of multi-channel analysis of surface waves (MASW) and electrical conductivity to image temporal and spatial changes within a landslide in southern Poland. The area, located in the Outer Carpathians, experiences significant climate fluctuations, compounded by anthropogenic activities such as recreational skiing requiring artificial snow.Our combined seismic and electrical methods techniques reveal the landslide's susceptibility to environmental factors on both annual and seasonal scales. Additional analysis, including data clustering and remote sensing, identifies three distinct landslide zones with varying vulnerability to natural and anthropogenic influences.While focusing on a specific area, our approach has global applicability to similar mass movements. This research addresses a gap in understanding time-dependent geophysical observations of moisture-driven landslides, providing valuable insights for hazard identification and mitigation strategies.

Seasonal variability in the global relevance of mountains to satisfy lowland water demand

Abstract Mountain areas play a vital role in global water resources as they often generate disproportionally high runoff and seasonally delay runoff due to storage as snow and ice. Water originating from mountains is used to satisfy human water demand further downstream in the lowlands of the corresponding river basins. Although the relevance of mountains for water supply is widely acknowledged, our current quantitative knowledge of their relevance for human water use on a global scale remains limited to decadal averages. As both water demand and mountain water supply have a strong seasonality, it is crucial to assess the global relevance of mountain areas beyond the annual time scale. To this end, we examined the share of lowland surface water abstraction (LSWA) stemming from mountain runoff in all river basins larger than 10,000 km2 globally from 1990 to 2019, focusing on the intra-annual variability. We distinguished between essential runoff contributions from low and high mountains and potential mountain runoff contributions to LSWA. Essential mountain contributions are defined as the share of water abstractions in the lowlands that can solely be satisfied by mountain runoff, whereas potential mountain contributions are the share that can originate from the mountains but does not necessarily have to. Our results confirm a strong spatial heterogeneity in the contribution of mountain runoff to LSWA. Globally, 15% of annual LSWA can solely be satisfied by mountain runoff, with monthly variations between 9 and 23%, highlighting the strong seasonality in the reliance on mountain runoff for lowland water use. The share of potential mountain contributions is much higher (51% annually). Slightly less than half of the essential mountain contributions to LSWA are sourced from high mountains. This shows the disproportional relevance of these regions, constituting only around one-third of the total mountain area. Furthermore, our results show an increasing dependence of lowlands on mountain water contributions.

Retrieval of total suspended matter concentration in the yellow river estuary offshore area based on QAA-RF model

ABSTRACT Total suspended matter is one of the crucial water quality parameters for both inland and marine environments, and a key role in evaluating the water quality of estuaries and offshore areas. Each year, the Yellow River carries a significant amount of sediment into the semi-enclosed Bohai Sea, results in a prolonged high concentration of total suspended matter in the offshore areas of the Yellow River Estuary. This study focuses on the offshore region of the Yellow River Estuary in China. Utilizing Sentinel-2 satellite imagery data from 2020 to 2023 and in-situ measured data from August 2020 to August 2022, to address the lack of physical mechanisms currently studied in machine learning retrieval methods, a model that integrates the physics-driven Quasi-Analytical Algorithm (QAA) and data-driven Random Forest (RF) is employed for the retrieval of total suspended matter concentration in the study area. The fused model (QAA-RF) is compared and analysed against regression models and standalone machine learning models. The results indicate that the accuracy of machine learning retrieval models is consistently higher than that regression models. The QAA-RF model demonstrates the highest accuracy (R 2 = 0.87, MAE = 5.01 mg L−1, RMSE = 6.39 mg L−1). Based on the QAA-RF model and the imagery data, monthly total suspended matter concentration is conducted in the study area. The retrieval results indicates that: (1) the total suspended matter concentrations is primarily concentrated in the near estuary region, with concentrations decreasing as distance from the estuary increases. (2) the total suspended matter concentration exhibits a distribution pattern with higher values in spring and winter, and lower values in summer and autumn. (3) the concentration of total suspended matter shows relatively small fluctuations at the annual scale from 2020 to 2023.

On the long-term stability of the association between foF2 and EUV solar proxies

Solar extreme ultraviolet (EUV) radiation is the main source of heating and ionization of the Earth's upper atmosphere, forcing most of this system's time variability, which in annual scales corresponds to the solar activity ∼11-year cycle. Due to the difficulties in obtaining solar EUV time series covering extended periods of time or during periods without measurements available, the use of solar EUV proxies became a solution. In the case of the ionosphere, and in particular the F2-layer critical frequency (foF2), in addition to the solar activity cycle variation, it may also exhibit the effect of long-term trend forcings, like the monotonous increasing greenhouse gas concentration since the industrial revolution. To accurately detect and measure this weak trend against the solar activity variability, it is crucial to account for the solar forced variation. Traditionally, it is modeled as a linear association between foF2 and a given solar EUV proxy. However, the stability of this association has become a controversial issue. It would be reasonable to assume, in turn, that if the ionospheric environment is undergoing a trend forced by a non-solar diver, like the greenhouse gas concentration increase, the relationship between foF2 and solar proxies may be affected, ceasing to be stable if this additional driver is not introduced in the modeled association. Using rolling regressions over the period 1960–2023 to analyze this stability, our results suggest that the issue may not only lie in the steady trend expected in foF2 from a non-solar source or the need to include terms in the simple linear regression commonly used, but also in the possible deviation of the different proxies from the 'true' EUV solar flux, which is the ultimate main driver of F2 region ionization, a deviation that has been intensifying over the last two decades. We assert that it is a deviation from the actual EUV behavior because the indices diverge from one another, something that should not occur if they all reflect the same solar EUV.

Modeling the influences of rainfall conditions on nitrogen removal effects of a permeable pavement system

Permeable pavement systems are important facilities for alleviating runoff pollution. However, few studies have focused on outflow nitrogen processes under varying rainfall conditions. In this study, we proposed a permeable pavement system model that can simulate the nitrogen transformation processes. The model was verified with data on outflow rates and outflow nitrogen concentrations (ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), and total nitrogen (TN)) processes for six consecutive rainfall events of a permeable pavement system in Shenzhen, China. Based on the validated model, scenarios were designed to simulate the influences of rainfall conditions on nitrogen removal effects at the single rainfall event and annual scale, respectively. The results indicate that: (i) the model can explicitly describe hydrological and nitrogen processes of the system, and the Nash Sutcliffe Efficiency and percent bias of outflow rates and outflow nitrogen concentrations range from 0.5 to 0.9 and from −22.4% to 24.9%, respectively; (ii) the sensitivity analysis reveals that the empirical coefficient (C) related to evaporation in the gravel layer is sensitive at the annual scale but not at rainfall events scale. Parameters related to mineralization and microbial assimilation (i.e., mineralization rate constant in the gravel layer (k3N,mine), microbial assimilation rate constant of NH4-N, NO3-N, and ON (k1N,bio, k2N,bio, k3N,bio)) are sensitive annually but not at rainfall events scale; (iii) in the single rainfall event simulations, nitrogen removal efficiencies decrease with increasing rainfall return periods but is not significantly affected by rainfall peak coefficients. Nitrogen removal efficiencies improve notably with antecedent dry period extension for single rainfall event with antecedent dry period less than 2 days. In addition, we found that the outflow process significantly affects the outflow nitrogen concentrations process. In annual-scale simulations, extending the antecedent dry period only significantly boosts runoff reduction efficiency and nitrogen removal efficiencies for rainfall events with less than 10 mm.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Evaluating the performance of carbon dioxide and methane observations from carbon-monitoring satellite products over China

As the primary greenhouse gases contributing to climate change, carbon dioxide (CO₂) and methane (CH4) play a critical role in the Earth's radiative balance and temperature regulation. Continuous and accurate satellite monitoring of CO₂ and CH₄ is essential for developing effective policies and strategies to mitigate their impacts. This study utilizes ground-based Fourier-transform infrared measurements from the Total Carbon Column Observing Network (TCCON) to evaluate the performance of current mainstream carbon-monitoring satellites over China, the world's largest carbon emitter. Our findings show that TanSat performs excellently for CO₂ observations at both Hefei and Xianghe sites, achieving a standard deviation (SD) of about 2 ppm and a maximum bias of 0.25 ppm, dropping to 0.02 ppm at Xianghe. In contrast, Orbiting Carbon Observatory (OCO)-3 and Greenhouse Gases Observing Satellite (GOSAT)-2 CO2 measurements are slightly less reliable. For CH₄ monitoring, GOSAT outperforms GOSAT-2, with a SD of around 14 ppb and bias within 2 ppb, compared to GOSAT-2's 17.58 ppb SD and 2.15 ppb bias at Hefei. These results indicate that the latest carbon-monitoring satellites are less precise and accurate than their predecessors. Additionally, we provide detailed assessments of the data products based on different spatial matching criteria. We also discuss the variation in satellite accuracy over time, revealing periodic bias variations and the instability in satellite performance. Furthermore, while the spatial distribution trends of satellite acquisitions are generally consistent on an annual scale, we observe non-negligible differences in the annual averages across specific land surfaces. Our study presents a meticulous evaluation of the reliability of satellite-based carbon-monitoring products over the Chinese region and provides scientific evidence for analyzing uncertainty in carbon source-sink studies.

Rainfall And Drought Characteristics For Crop Planning In Cuttack District Of Odishastate, India

The increasing frequency of agricultural droughts and variability in rainfall patterns over the past three decades prompted this study, which analyzed 34 years of block-wise daily rainfall data (1988-2021) from Cuttack district, Odisha. The aim was to determine the long-term average and temporal variability of rainfall on weekly, monthly, seasonal, and annual scales, with a focus on identifying strategies to mitigate production risks in the region. The average annual rainfall in Cuttack was 1597.6 mm, with August receiving the highest amount of rainfall (476 mm), followed by July (407 mm) during the monsoon season. A trend analysis on rainfall over the past 34 years showed an increasing pattern of an average 6.03 mm and 3.6 mm per year in annual and kharif season rainfall, respectively. Agricultural droughts were frequently observed during the early (22-28 Standard Meteorological Week) and late (36-42 SMW) stages of kharif crops. The study also revealed that the station is prone to mild-moderate meteorological droughts of different intensities, including mild,moderate, and severe. To minimize production risks, short duration, low water-requiring, high-value crops such as maize, pulses, oilseeds, and some vegetables are recommended for this region.

How do gridded meteorological datasets perform in a typical data-scarce cryospheric basin?

Gridded meteorological datasets have been widely used in meteorological and hydrological studies, especially in data-scarce regions. However, assessments of these datasets in cryospheric basins, which are highly sensitive to global warming, remain inadequate. Therefore, three representative gridded meteorological datasets, i.e., the China meteorological forcing dataset (CMFD), CN05.1, and the meteorological forcing dataset for the third-pole region (TPMFD), were systematically evaluated in the Nachitai Basin based on comparisons with limited gauge observations, pairwise intercomparisons, and hydrological simulations. All gridded precipitation and temperature datasets were in good agreement with gauge observations on an intra-annual scale, while substantial differences existed among them in terms of precipitation and temperature on an annual scale. Additionally, pairwise correlation analyses indicated that the temporal consistency between the datasets was higher than their spatial consistency. Furthermore, all three datasets achieved satisfactory and reasonable results in simulating glacier area and streamflow variations when used as inputs for the hydrological−hydrodynamic model, exhibiting the robustness of the model in this data-scarce basin. This study provides a more profound understanding of the significance of gridded datasets in data-scarce areas situated on the Tibetan Plateau.

Addressing large scale patterns of no-flow events in rivers: An in-depth analysis with Achelous software

Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.

Water Budget Input Linked to Atmospheric Rivers in British Columbia's Nechako River Basin

ABSTRACTThis study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use.

Local Weather and Global Climate Data-Driven Long-Term Runoff Forecasting Based on Local–Global–Temporal Attention Mechanisms and Graph Attention Networks

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff.

Analysis of Meteorological Drought in Cases of North Wello Zone, Ethiopia

Drought is a commonly used term, but the most complex and least realized of all the natural hazards affecting more people than any other hazards. The main objective of the study was to investigate the spatial-temporal occurrence of Meteorological drought indices analysis in the study areas. The study was undertaken to investigate the magnitude, frequency, and trends of drought incidence in North Wello Zone Ethiopia using monthly rainfall records for the period 1990 to 2020 of North Wello zone stations and CHIRPS satellite data. Drought Index Calculator used to analyse standard precipitation index (SPI). The correlation between station data and CHIRPS data in this study was 0.89 which shows highly correlation between them. the result of meteorological drought indices map analysis on the years 1999, 2000, and 2008 severely dry to extremely dry conditions in the Belg season, similarly in 19990, 199308, 201507, 201508, and 201509 have got meteorological drought indices with extremely dry Kiremt season on the North Wello Zone. The drought magnitude of different time scales varied from slight to extremely severe in the studied stations. The identification of the temporal and spatial characteristics of droughts in the North Wello Zone will be useful for the development of a drought preparedness plan in the Zone. Generally increasing tendencies of drought were observed during the main rainy season and decreasing tendencies of drought during the short rainy season and annual scale were observed in the study area.

Remotely Sensed Comparative Spatiotemporal Analysis of Drought and Wet Periods in Distinct Mediterranean Agroecosystems

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI12) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI12 = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI12 = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI12 = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI12 values, areal extent and duration (SPI12 = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI12 = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI12 = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions.

How Do Schumann Resonance Frequency Changes in the Vertical Electric Field Component Reflect Global Lightning Dynamics at Different Time Scales?

AbstractThe electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth‐ionosphere cavity confined between the Earth's surface and the lower ionosphere. The peak frequencies of SR are known to vary with source‐observer distance (SOD), while the daily frequency range (DFR: fmax − fmin) is also indicative of the average size of thunderstorm regions. This paper provides observational evidence for these relationships based on SR frequency observations of the vertical electric (EZ) field component at Nagycenk (NCK), Hungary in Central Europe from the period 1994–2015. Variations of the peak frequencies are considered on the annual, seasonal and diurnal time scales as well as during a specific event when squall‐line formation of lightning activity in South America moves toward NCK. DFR is studied in relation to the El Niño Southern Oscillation (ENSO). Increasing area of lightning activity in mid‐high Northern hemisphere latitudes has been identified by DFR variations during the transition from warm to cold episodes of the ENSO in 1998 and 2010. The extension of the lightning area is considered as a consequence of energy released in the tropics and exported to higher latitudes with some months of delay from the end of the El Niño episodes. The frequency variations are interpreted via model calculations and supported with satellite‐based optical lightning observations (Optical Transient Detector, Geostationary Lightning Mapper). The described variations of SR peak frequencies and DFR yield information on the global/regional lightning dynamics and on this basis they have important application to climate issues as well.

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.