Long-term Trend Analysis Research Articles

Logistic-growth models measuring density feedback are sensitive to population declines, but not fluctuating carrying capacity.

Analysis of long-term trends in abundance of animal populations provides insights into population dynamics. Population growth rates are the emergent interplay of inter alia fertility, survival, and dispersal. However, the density feedbacks operating on some vital rates ("component feedback") can be decoupled from density feedbacks on population growth rates estimated using abundance time series ("ensemble feedback"). Many of the mechanisms responsible for this decoupling are poorly understood, thereby questioning the validity of using logistic-growth models versus vital rates to infer long-term population trends. To examine which conditions lead to decoupling, we simulated age-structured populations of long-lived vertebrates experiencing component density feedbacks on survival. We then quantified how imposed stochasticity in survival rates, density-independent mortality (catastrophes, harvest-like removal of individuals) and variation in carrying capacity modified the ensemble feedback in abundance time series simulated from age-structured populations. The statistical detection of ensemble density feedback from census data was largely unaffected by density-independent processes. Long-term population decline caused from density-independentmortalitywas the main mechanism decoupling the strength of component versus ensemble density feedbacks. Our study supports the use of simple logistic-growth models to capture long-term population trends, mediated by changes in population abundance, when survival rates are stochastic, carrying capacity fluctuates, and populations experience moderate catastrophic mortality over time.

Distinct characteristics of western Pacific atmospheric rivers affecting Southeast Asia

The dynamic characteristics of atmospheric rivers (ARs) have been researched over the western North Pacific and East Asia due to their close linkage to disastrous precipitation extremes, while very little attention has been paid to the AR features from the western Pacific to Southeast Asia. This study aims to quantify the climatology, long-term trends and variability of different AR properties from the western Pacific to Southeast Asia using an objective identification algorithm, the ERA5 reanalysis dataset and the APHRODITE precipitation dataset for the period 1951-2015. The results indicate a belt of frequent AR activities from the western Pacific to the Andaman Sea during the boreal autumn-winter season. The long-term trend analyses show a significantly increasing trend in AR frequency and an eastward shift of AR plumes. These dynamic changes contribute to the increasing trend of extreme precipitation amounts in the coastal areas surrounding the South China Sea. The intraseasonal variability of the AR associated with the Madden-Julian oscillation (MJO) shows a pronounced enhancement of AR activity in the MJO phase-2 to phase-3 due to the steeper gradient of low-level geopotential height between the Northwestern Pacific and the tropical Indian Ocean. The modulation is partly explained by the enhanced MJO convection and the adiabatic heating in the vicinity of the trough of the 200-500 hPa geopotential thickness of the region. This study shows that ARs are important mechanisms behind the climatology, trends and variability of the regional precipitation in Southeast Asia. This study implies that more attention is required toward the dynamics of these tropical weather systems, particularly for their interactions with other synoptic processes and their response to future climate warming.

The using of data on the moult progress in the study of migration periods of the annual cycle of passerines

Using trapping and ringing of birds for studying the ecology and the demographic parameters of populations is necessary to determine as accurately as possible in which period of the annual cycle each individual was caught. The first-year passerine birds undergo juvenile migration (usually in form of postfledging dispersal) and postmoulting (autumn) migration in summer-autumn period. These stages of annual cycle are difficult to identify in trapping birds by calendar dates because the timing of migration seasons can significantly overlap in the same location. The study of moult at the Ladoga Ornithological Station made it possible to determine the states of plumage that mark juvenile and postmoulting migrations in first-year birds. The article presents two examples of the use of moult progress in the study of bird migrations. The first one is an analysis of the ratio of the terms of departure and passage in three long-distant and three short-distant migrants of passerines. It has been shown that the timing of the departure of local birds is earlier than the timing of the passage of other populations in all studied species, while the last local individuals in long-distance migrants are recorded long before the end of the passage of the species, and in short-distance migrants they can be found until its end. As another example, an analysis of long-term trends in the timing of summer-autumn movements in the Garden Warbler was performed. It is shown that for almost a fifty-year observation period, neither the timing of juvenile migration nor the timing of postmoulting migration of first-year birds has changed. At the same time, the timing of the spring passage of the species shifted to earlier. As a result, the duration of stay of the Garden Warbler in the Ladoga region has increased.

Spatiotemporal distributions of air-sea CO2 flux modulated by windseas in the Southern Indian Ocean

The Southern Indian Ocean is a major reservoir for rapid carbon exchange with the atmosphere, plays a key role in the world’s carbon cycle. To understand the importance of anthropogenic CO2 uptake in the Southern Indian Ocean, a variety of methods have been used to quantify the magnitude of the CO2 flux between air and sea. The basic approach is based on the bulk formula—the air-sea CO2 flux is commonly calculated by the difference in the CO2 partial pressure between the ocean and the atmosphere, the gas transfer velocity, the surface wind speed, and the CO2 solubility in seawater. However, relying solely on wind speed to measure the gas transfer velocity at the sea surface increases the uncertainty of CO2 flux estimation. Recent studies have shown that the generation and breaking of ocean waves also significantly affect the gas transfer process at the air-sea interface. In this study, we highlight the impact of windseas on the process of air-sea CO2 exchange and address its important role in CO2 uptake in the Southern Indian Ocean. We run the WAVEWATCH III model to simulate surface waves in this region over the period from January 1st 2002 to December 31st 2021. Then, we use the spectral partitioning method to isolate windseas and swells from total wave fields. Finally, we calculate the CO2 flux based on the new semiempirical equation for gas transfer velocity considering only windseas. We found that after considering windseas’ impact, the seasonal mean zonal flux (mmol/m2·d) increased approximately 10%-20% compared with that calculated solely on wind speed in all seasons. Evolution of air-sea net carbon flux (PgC) increased around 5.87%-32.12% in the latest 5 years with the most significant seasonal improvement appeared in summer. Long-term trend analysis also indicated that the CO2 absorption capacity of the whole Southern Indian Ocean gradually increased during the past 20 years. These findings extend the understanding of the roles of the Southern Indian Ocean in the global carbon cycle and are useful for making management policies associated with marine environmental protection and global climatic change mitigation.

Aerosol impact on light pollution in cities and their environment

Measurements of artificial light at night represent an incredible challenge as the optical state of the atmosphere is highly unstable thus making both long-term trend analyses and inter-comparison of multiple observations difficult. Variations of atmospheric parameters, caused by either natural or anthropogenic processes, can massively influence the level of resulting night sky brightness caused by light pollution. Focusing on six parameters, either from aerosol optics or emission properties of light sources, this work literarily and numerically examines defined variations in aerosol optical depth, asymmetry parameter, single scattering albedo, ground surface reflectance, direct uplight ratio, and aerosol scale height. For each individual element the effect size and angular reliance is investigated, with results indicating that besides the aerosol scale height all play non-negligible roles in forming skyglow and environmental impact. Especially variations in aerosol optical depth and city emission function displayed severe discrepancies in consequential light pollution level. Hence, future improvement on atmospheric condition, i.e., air quality, focusing particularly on discussed elements indicates to positively influence the level of environmental impact caused by artificial light at night. We underline the need of inclusion of our outcomes to urban development and civil engineering processes in order to create or protect habitable areas for humans, wildlife and nature.

Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change

Extreme climate is one of the important variables which determine the capability of tropical peatland to act as either carbon sink and/or carbon source. The purpose of this study is to reveal the spatio-temporal trend in the long-term time series of extreme rainfall and temperature in Sarawak peatland cause by climate change. Gridded-based Princeton datasets were used for trend analysis spanning 68-year (1948–2016) based on Modified Mann-Kendall (m-MK) test which has the capability of distinguishing unidirectional trend with multi-scale variability. The m-MK test was also used to confirm the increasing or decreasing trend produce by Mann-Kendall (MK), and to discriminate the exaggeration in trend caused by serial auto-correlation due to the high effect of large scale climate events regulating the climate in the region. By using R-based program, RClimDex for extreme climate indices output, extreme climate under Northeast (NE) and Southwest (SW) monsoon showed lower grid point with significant changes under m-MK test compared to MK test at 95% significance level. Here, the exaggeration of trend by MK test has been reduced by using m-MK test which can accommodate the scaling effect in the time series due to inherent natural climate variability. Diurnal temperature range (DTR) was expected to decrease for both monsoons in the central-coastal region as minimum temperature (TN) increased more than maximum temperature (TX). Significant increase in extreme rainfall (R10, R20, Rnn) was spatially observed more during SW monsoon compared to NE monsoon, although with high spatial variability. Significant increase of TN indices of TNn and TN90p might cause increased rainfall intensity in the south and central-coastal region, while high TX indices of TXn might cause increased rainfall intensity in the north. Due to the imminent threat of climate change, this study gives scientists an essential view on the behavior of different extreme climate variables and its potential impact on the peatland area which is susceptible to flood and risk of fire during the NE and SW monsoon, respectively.

Global long term daily 1 km surface soil moisture dataset with physics informed machine learning

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

Analysis of Long-Term Trend of Stream Flow and Interaction Effect of Land Use and Land Cover on Water Yield by SWAT Model and Statistical Learning in Part of Urmia Lake Basin, Northwest of Iran

The water yield produced at the outlet of a sub-basin is the combination of multiple interacting land uses. In the majority of previous research, while accounting for the effect of land use and land cover (LULC) on water yield, the hydrologic components of a watershed have been attributed to the dominant land use class within that sub-basin. We adopted an approach to investigate the interaction effect of LULC on water yield (WYLD) using the Johnson–Neyman (JN) method. The soil and water assessment tool (SWAT) model was employed in the Urmia Lake Basin (ULB) to estimate the WYLD following successful calibration and validation of the model by stream flow. It was found that in each sub-basin, the effect of the soil class on the WYLD was statistically significant only when the area of rangeland was less than 717 ha and when the area of agricultural lands was less than 633 ha. On the other hand, the trend of stream flow was assessed over 70 years at two stations in the Urmia Lake Basin (ULB) using the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). The year 1991 turned out to be the most likely change point in both stations. A significant decrease in Urmia Lake’s water level started in 1995, which indicated that part of this shrinkage was most likely caused by water inflow reduction over a 4-year time delay. Besides identifying the most probable seasonal and trend change points, this method has the additional capability to analyze the uncertainty of estimated points, which was lacking in earlier methods.

Understanding Changes in the Hydrometeorological Conditions towards Climate-Resilient Agricultural Interventions in Ethiopia

Climate resilient agriculture (CRA) is very important to achieve long-term improvement in productivity and farm incomes under climate uncertainty. The present study focuses on investigating the plausible changes in the hydrometeorological conditions using big-data analysis techniques in the study of Ethiopia. The original contribution of this work envisages the importance of the CRA system in water-scarce areas for sustainable agriculture planning and management under changing climatic conditions. In the present research, a TerraClimate model was the basis for weather (precipitation and temperature) and hydrological data (runoff, actual evapotranspiration, potential evapotranspiration, vapor pressure deficit and climate water deficit); these data were used to determine the spatial distribution of the standardized anomaly index (SAI) and the slope of the linear regression for long-term (1958–2020) trend analysis. Future climate trend analysis (2021–2100) has been performed through the CMIP6 (EC-Earth3) shared socio-economic pathway (SSP 2) 4.5 dataset. Gravity Recovery and Climate Experiment (GRACE) with CSR and JPL data were utilized for the generation of water storage heat maps from 2002 to 2021. The results show that the average annual rainfall data for over 62 years was found to be 778.42 mm and the standard deviation is 81.53 mm. The results also show that the western part of the study area has the highest temperature trend, which diminishes as one moves eastward; the minimum temperature trend has been found in the western part of the study area. It was found that the equivalent water thickness (EWT) range of both CSR and JPL products was −15 to 40 cm. These results can help local climate-resilient development planning and enhance coordination with other institutions to access and manage climate finance.

A novel framework for vegetation change characterization from time series landsat images

Understanding terrestrial ecosystem dynamics requires a comprehensive examination of vegetation changes. Remote sensing technology has been established as an effective approach to reconstructing vegetation change history, investigating change properties, and evaluating the ecological effects. However, current remote sensing techniques are primarily focused on break detection but ignore long-term trend analysis. In this study, we proposed a novel framework based on a change detection algorithm and a trend analysis method that could integrate both short-term disturbance detection and long-term trends to comprehensively assess vegetation change. With this framework, we characterized the vegetation changes in Zhejiang Province from 1990 to 2020 using Landsat and landcover data. Benefiting from combining break detection and long-term trend analysis, the framework showcased its capability of capturing a variety of dynamics and trends of vegetation. The results show that the vegetation was browning in the plains while greening in the mountains, and the overall vegetation was gradually greening during the study period. By comparison, detected vegetation disturbances covered 57.71% of the province's land areas (accounting for 66.92% of the vegetated region) which were mainly distributed around the built-up areas, and most disturbances (94%) occurred in forest and cropland. There were two peak timings in the frequency of vegetation disturbances: around 2003 and around 2014, and the proportions of more than twice disturbances in a single location were low. The results illustrate that this framework is promising for the characterization of regional vegetation growth, including long-term trends and short-term features. The proposed framework enlightens a new direction for the continuous monitoring of vegetation dynamics.

Population aging from 1950 to 2010 in seventeen transitional countries in the wider region of South Eastern Europe

Aim: Population aging has profoundly reshaped demographic landscapes in all South Eastern European (SEE) countries. The aim of this study was to provide a thorough comparative inter-country assessment on the speed of population aging in the entire SEE region for the period 1950-2010. Methods: Descriptive observational analysis of long-term trends on core primary and composite indicators of population aging across seventeen countries of the wider SEE region, with panel data sets at a national level. Results: During the past six decades, the entire SEE region has experienced a rapid increase in the median age (from 25.2 years in 1950 to 37.9 years in 2010), with a simultaneous fall of fertility rates for two children per woman (from 3.55 children per each childbearing woman in 1950 to 1.49 in 2010), coupled with significant rise in the population of elderly citizens. The speed of population aging has vastly accelerated (with a 2.5 fold increase) over the past three decades. The percentage of individuals over 65 years has doubled from 7% in 1950 to 14% in 2010. Conclusion: Complex national strategies are needed to cope with the shrinking labour force coupled with the growing proportion of the older population. With all likelihood, population aging will further accelerate in the near future. This profound long-term demographic transition will threaten financial sustainability of current health systems in all SEE countries.

A new merged dataset of global ocean chlorophyll-a concentration for better trend detection

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS (Sea-viewing Wide Field-of-view Sensor) in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve climate change related studies, it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI (Ocean Colour Climate Change Initiative), which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the inter-mission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the SeaWiFS, MODIS (Medium-spectral Resolution Imaging Spectrometer), MERIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), and OLCI (Ocean and Land Colour Instrument) with inter-mission differences corrected in this work. The fitness of the dataset on long-term Chla trend study was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. The results suggest that our dataset can be used for long-term series analysis and trend detection. We also provide the global trend map in Chla over 23 years (1998–2020) and present a significant positive global trend with 0.67% ± 0.37%/yr.

A geospatial approach for understanding the spatio-temporal variability and projection of future trend in groundwater availability in the Tawi basin, Jammu, India

The overexploitation of groundwater in the foothills of the Himalayas at Jammu has led in a steady decline of groundwater levels in the recent years. The causes of the groundwater scarcity, however, is unknown due to a lack of long-term trend analysis of groundwater monitoring and scientific investigations. Therefore, the objective of this research is to investigate the groundwater variability in the alluvial aquifers of Tawi River basin of the Jammu district by applying a non-parametric approach using Mann-Kendall and Sen's slope approach. In addition, a stochastic seasonal Autoregressive Integrated Moving Average (ARIMA) model was also used for forecasting time series groundwater level. The results revealed that during the pre-monsoon (PRM) season, 66.6% of the monitoring wells suffered groundwater decreases, compared to 53% during the post-monsoon (POM) season across the whole study area. The steepest slope was found in the Kandi belt with maximum water table declined to 26 m below ground level (bgl) in 2018 compared to 8.44 m bgl in 2009 with an average rate of decline of 1.109 m/yr in PRM season. Siwalik region reported a decline of groundwater level at the rate of 0.173 m/yr and 0.27 m/yr in PRM and POM respectively. However, water table in Sirowal region improved marginally in PRM and POM season with a mean raise of 0.05 m/yr and 0.08 m/yr respectively. According to the time series forecasting by stochastic simulation, if the current situation continues, the groundwater level in the Kandi region would decline by 9.46 m and 9.38 m respectively, over the observed values in 2018 to reach a level of 27.82 and 24.38 m bgl in PRM and POM respectively by 2025. The spatio-temporal analysis showed that the groundwater extraction in the aquifers of Kandi region was higher than that of the Siwalik and Sirowal region. The spatiotemporal trend and projection of groundwater levels is quite helpful in identifying the most vulnerable places, as well as addressing the current and future condition of groundwater levels for optimal resource planning and management of groundwater resources in the sustainable ways.

Assessment of Runoff Components of River Flow in the Karakoram Mountains, Pakistan, during 1995–2010

Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. Thus, this study is based on a long-term trend analysis of the computed runoff components using satellite data with continuous spatial and temporal coverage incorporated into a simple degree day Snowmelt Runoff Model (SRM). The trends of melt runoff components can help us understanding the future scenarios of the glaciers in the study area. The SRM was calibrated against the recorded river flows in the Hunza River Basin (HRB). Our simulations showed that runoff contribution from rain, snow, and glaciers are 14.4%, 34.2%, and 51.4%, respectively during 1995–2010. The melting during the summer has slightly increased, suggesting overall but modest glacier mass loss which consistent with a few recent studies. The annual stream flows showed a rising trend during the 1995–2010 period, while, rainfall and temperatures showed contrasting increasing/decreasing behavior in the July, August, and September months during the same period. The average decreasing temperatures (0.08 °C per annum) in July, August, and September makes it challenging and unclear to explain the reason for this rising trend of runoff but a rise in precipitation in the same months affirms the rise in basin flows. At times, the warmer rainwater over the snow and glacier surfaces also contributed to excessive melting. Moreover, the uncertainties in the recorded hydrological, meteorological, and remote sensing data due to low temporal and spatial resolution also portrayed contrasting results. Gradual climate change in the HRB can affect river flows in the near future, requiring effective water resource management to mitigate any adverse impacts. This study shows that assessment of long-term runoff components can be a good alternative to detect changes in melting glaciers with minimal field observations.

Quantifying Uncertainties in CERES/MODIS Downwelling Radiation Fluxes in the Global Tropical Oceans

The Clouds and the Earth's Radiant Energy System program, which uses the Moderate Resolution Imaging Spectroradiometer (CM), has been updated with the launch of new satellites and the availability of newly upgraded radiation data. The spatial and temporal variability of daily averaged synoptic 1-degree CM version 3 (CMv3) (old) and version 4 (CMv4) (new) downwelling shortwave (Q S ) and longwave radiation (Q L ) data in the global tropical oceans spanning 30°S–30°N from 2000 to 2017 is investigated. Daily in situ data from the Global Tropical Moored Buoy Array were used to validate the CM data from 2000 to 2015. When compared to CMv3, both Q S and Q L in CMv4 show significant improvements in bias, root-mean-square error, and standard deviations. Furthermore, a long-term trend analysis shows that Q S has been increasing by 1 W m −2 per year in the Southern Hemisphere. In contrast, the Northern Hemisphere has a −0.7 W m −2 annual decreasing trend. Q S and Q L exhibit similar spatial trend patterns. However, in the Indian Ocean, Indo-Pacific warm pool region, and Southern Hemisphere, Q L spatial patterns in CMv3 and CMv4 differ with an opposite trend (0.5 W m −2 ). These annual trends in Q S and Q L could cause the sea surface temperature to change by −0.2 to 0.3 °C per year in the tropical oceans. These results stress the importance of accurate radiative flux data, and CMv4 can be an alternative to reanalysis or other model-simulated data.

Changes in Rainfall Pattern in Bengawan Solo Sub-Watershed

<p>Rainfed farming is vulnerable to climate variability, which changes rainfall patterns. Rainfall variability disrupts rainfed rice cultivation because a change in rainfall will affect the rice crop calendar. An analysis of long-term trends over a specific area is required to understand rainfall variability. The aim of this study was to assess climate variability in terms of rainfall magnitude and frequency by analyzing spatial and temporal rainfall trends in Bengawan Solo Sub-Watershed as well as the rainfed rice production. Daily rainfall data from 10 rain gauge stations over the sub-watershed area from the years 1975 to 2020 were used. The data was managed and collected by the Bengawan Solo Watershed authority. Pearson, Mann-Kendall, and Sen’s Slope tests were applied to assess the recorded data correlation, rainfall trends, and magnitude of trends into annual, monthly, and 10-day. The findings of the study indicated the spatial and temporal inhomogeneous rainfall pattern for all locations for 10-day, monthly and annual patterns. The mountainous regions at Tawang Mangu and Ngrambe stations tend to experience an upward trend (positive magnitude), while the coastal regions at Nglirip and Bojonegoro stations have a downward trend(negative magnitude). Those trends also confirmed that coastal regions would be drier than mountainous regions in the future. Understanding this rainfall trend can assist with rainfed farming strategic planning.</p>

Tunceli iklim parametrelerine ait zaman serilerinin farklı istatistiksel analiz yöntemleriyle değerlendirilmesi

Günümüz dünya toplumunun karşılaştığı en zorlu çevre sorunlarından biri olan iklim değişikliğinininsan ve doğal sistemler üzerindeki olumsuz etkileri giderek artmaktadır. İklim değişikliğinin etkilerinianlamak için iklim verilerindeki trendlerin geniş çapta araştırılması gerekmektedir. Bununlabirlikte, küresel ölçekte yapılan iklim gözlemlerinin yerel ve bölgesel ölçeklerde yapılan planlamalarakatkısı düşüktür. Bu nedenle iklim değişikliklerinin zararlı etkilerini en aza indirmeye yönelik stratejilerideğerlendirebilmek ve geliştirebilmek için lokal iklim analizlerinin yapılması gerekmektedir.Kentleşme oranının düşük olduğu Tunceli ilinde tarım ve hayvancılık özellikle de mera hayvancılığıyaygın ekonomik aktivite olarak öne çıkmaktadır. İklim değişimlerinden büyük ölçüde etkilenentarım ve hayvancılıkta uygulanabilir adaptasyon stratejileri geliştirebilmek için iklim parametrelerininözelliklerinin ve trendlerinin belirlenmesi gerekir. Bu çalışmanın amacı iklim değişikliğine karşıhassas bir sahada yer alan Tunceli meteoroloji istasyonuna ait iklim parametrelerinin uzun dönemtrend analiz sonuçlarını ve parametrelerin birbirleri ile olan ilişkilerini açıklamaktır. Çalışmada 15parametreye ait uzun dönem zaman serilerine 5 farklı istatistiksel analiz yöntemi uygulanmıştır.İlk olarak iklim parametrelerine ait zaman serilerinin homojen yapıda olup olmadığını sorgulamakve homojen olmayan veri setlerinde değişim noktasını tespit etmek için her bir zaman serisineBuishand homojenlik testi uygulanmıştır. İkinci olarak zaman serilerine, herhangi bir trend olupolmadığını saptamak için Mann-Kendall ve Yenilikçi Şen trend analiz yöntemleri uygulanmıştır.Mann-Kendall ve Yenilikçi Şen trend analiz yöntemlerinden elde edilen sonuçlar karşılaştırılarakparametrelerde tespit edilen trendin tutarlılığı test edilmiştir. Dördüncü olarak iklim parametrelerineait zaman serilerine regresyon analiz yöntemi uygulanmıştır. Regresyon analizi ile her bir zamanserisinde yıllık ortalama değişim ve uzun dönem değişim ortalamaları ile oranları tespit edilmiştir.Son olarak parametrelerin birbirleriyle olan ilişkileri korelasyon analizi yöntemiyle sorgulanmış veelde veriler ışığında ilişki şiddeti ve yönü açıklanmaya çalışılmıştır. Analizlerin ortak sonuçlarınagöre 1965-2020 yılları arasındaki dönemde, Tunceli’de maksimum sıcaklık, minimum sıcaklık, ortalamasıcaklık ve sisli gün sayısı değerlerinde artış; toplam yağış, yağışlı gün sayısı, maksimum nisbinem, ortalama rüzgar hızı ve kar yağışlı gün sayısı değerlerinde azalış şeklinde bir değişim tespitedilmiştir. Elde edilen analiz sonuçları, Tunceli’de yakın gelecekte ciddi kuraklık ve kuraklığın nedenolabileceği su yetersizliği sorunlarının yaşanabileceğini göstermektedir.

Temperature Variation in Kerala: Present and Future

Temperature is changing worldwide and a warming trend is expected in most parts. This study investigates the long-term (1983-2020) trend analysis of the maximum temperature in Kerala and the future trend in maximum temperature. The future temperature has been downloaded from GFDL-CM3, which is most suited for Kerala conditions was used to assess the future climate change under different representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The Mann-Kendall test was also used for trends analysis for the downloaded data for each RCPs. The baseline (1980 – 2020) temperature was compared with future (2021-2050 and 2051-2080) simulations for the 14 districts of Kerala under different RCPs. An increasing trend of maximum temperature was observed in all districts of Kerala during the period of 1980 -2020. The increasing temperature trend was confirmed by Mann- Kendall test at a 0.01 significant level. The warming is expected to increase in future simulations under RCP 4.5 and 8.5. In the high-range zone, a decrease in temperature is expected during the beginning of the mid-century, but it continues to show an increasing trend in the projected climate. Under RCP 4.5 in most districts, an increase in temperature by 0.03ºC per year is expected in the future. While under RCP 8.5 the temperature is expected to increase by 0.05ºC per year and 0.06ºC per year by mid and end century respectively.

Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration

Estimating urban evapotranspiration (ET) is of great significance for urban water resource allocation and assessing the urban heat island effect. However, most current urban ET models are based on the energy balance theory to estimate urban ET. These models lead to significant errors in urban ET simulation due to the surface heterogeneity and the existence of anthropogenic heat fluxes in urban areas. To solve this issue, this study proposes a modified machine learning-based urban ET method that can estimate urban ET at the site and regional scales. To better characterize the heterogeneity of urban surfaces, the flux footprint of in-situ ET and physical mechanism of ET process are integrated into the convolutional neural network (CNN) model. The modified CNN model is tested in a fast-developing city: Shenzhen, China based on two Eddy Correlation (EC) observations. The verification results indicated that coupling flux footprint and physical mechanism into the CNN model could effectively improve the accuracy of urban ET simulation at the site scale. The modified CNN model significantly reduced the root-mean-square-error (RMSE) of 25.8 W/m2 and increased the determination coefficient (R2) of 0.17 compared to the CNN-O model (The CNN-O model is defined as the CNN model do not integrate flux footprint and physical mechanism of ET). Further analyses suggested that fusing flux footprint data into a machine learning model helps enhance ET estimation in regions with high heterogeneity and highly variable wind directions. Moreover, the integration of physical mechanisms significantly enhanced the model capability to simulate extreme ET events. The modified CNN model is further applied to map the spatial distribution of urban ET and reconstruct long-term urban ET changes. The spatial pattern of urban ET exhibited large spatial variability, where the urban ET in water bodies (mean λET larger than 480 W/m2) and vegetation-covered areas (mean λET larger than 260 W/m2) are substantially higher than the impervious surfaces (mean λET less than 30 W/m2). Long-term trend analyses demonstrated that urbanization resulted in decline in urban ET. The average decreasing rate of urban ET is 1.61 mm/yr (P < 0.05), with a 18 % decrease relative to the long-term ET average. The leading causes for the decline of urban ET are the increased impervious surfaces and the decreased radiation. This study improved the simulation accuracy of urban ET and revealed the response of urban ET to urbanization.

Marine heat waves: Characterizing a major climate impact in the Mediterranean

Marine heat waves (MHW), considered as persistent and spatially extensive sea surface temperature (SST) anomalies, have emerged as one of the global change-induced high impact events on the oceans. The study of MHWs received significant progress in recent years, although many unknowns remain. One of the most notable weaknesses is related to the absence of a universally established definition that would allow better intercomparison of results. It is our aim to contribute to this debate by considering the spatial extent to define a MHW. By applying this hypothesis to a relatively small, but complex, basin such as the Mediterranean, MHWs have been characterized and long-term trends assessed from SST satellite data analysis. Our results show that the inclusion of a minimum area threshold, 5 % of the area basin, greatly reduces the population of MHW events by not considering local SST anomalies that do not constitute a MHW event. A trend to more frequent, intense, and longer MHWs is found in the 1982–2021 period in the Mediterranean. In the spatial characterization and long-term trend analysis, regional differences were apparent. Results evidenced variations in MHWs characteristics and trends across the different sub-basins evidencing the fact that, even in a relatively small basin such as the Mediterranean, significant regional differences make it necessary to include a spatial perspective in the studies, beyond purely local analysis at each observation point in a large basin or even in the global ocean. Regarding the characterization of MHWs and trend analysis in the Mediterranean basin, a growing trend has been found in terms of frequency, duration, and intensity that accelerated since 2000 and especially in the last decade, pointing not just to a steady intensification and higher frequency of MHWs but to the emergence of a new set of more intense, long-lasting and spatially extensive MHWs in the recent years.