 Subjects -> METEOROLOGY (Total: 106 journals)
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- Intercomparison of CORDEX-CORE and CORDEX-SA model experiments in
assessing Indian summer monsoon-
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Abstract: Abstract The present study compares two different modelling suites such as Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) and Coordinated Regional Climate Downscaling Experiment-Coordinated Output for Regional Evaluation (CORDEX-CORE) to represent ISM rainfall (ISMR) over India and monsoon core zone (MCZ) and examine for the subsequent improvement in high-resolution CORDEX-CORE. The model performances are evaluated on the basis of the presentation of spatial and temporal distribution, interannual variability (IAV), and intraseasonal oscillation (ISO) during the historical (present) period (1979–2005). The spatial distribution of precipitation is better represented in the high-resolution CORDEX-CORE than CORDEX-SA, and Consortium for Small Scale Modelling (COSMO) model experiments resemble more to the corresponding Indian Meteorological Department (IMD) observation. Highest magnitude of grid-specific correlation coefficients over India as well as MCZ are found for MPI-ESM-LR_COSMO and ERAINT_COSMO members under CORDEX-CORE Regional Climate Model (RCM) framework. The shifting of precipitation maxima from July–August to August–September is captured in IMD observation; however, none of the models is able to capture this precipitation shift. CORDEX-CORE suites show better evolution of active and break spells than CORDEX-SA. An improvement is observed in CORDEX-CORE modelling suite in representing spatial distribution, intraseasonal oscillation. COSMO model experiments outstand all other model experiments.
PubDate: 2023-09-30
- The spatiotemporal variations of freezing index and its relationship with
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Abstract: Abstract The freezing index (FI) is one of the most important indicators that shows the variation of permafrost. However, the relationship between climate change and the thermal conditions of permafrost is not understood well. This study analyzed the variation of FI based on 5-cm soil temperature derived from 74 meteorological stations from 1977 to 2016 on the Qinghai-Tibet Plateau (QTP). Furthermore, the factors affecting the FI variation and its relationship with permafrost degradation were also discussed. The results showed that FI was much smaller in the interior than other areas of the QTP, and it increased at a rate of 53.0 °C d/10a during the 40 years. FI in the main body of the QTP was relatively stable than surrounding areas; it was more stable in the northern part than in the southern part. On average, the FI variation coefficient was larger than 10%, indicating the large fluctuation of FI during the 40 years. FI decreased with the increasing altitude; it was more sensitive to the altitude in the south of 33° N than in the north. The variation of FI was closely related to the maximum freezing depth (MFD) and the active layer thickness (ALT). It was observed that MFD decreased and ALT increased by approximately 1.4 cm and 1.6 cm, respectively, with each 10.0 °C d increase in FI. The results exhibited the thermal condition variation of the permafrost in QTP and revealed a degrading trend of the permafrost.
PubDate: 2023-09-30
- Statistical downscaling for precipitation projections in West Africa
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Abstract: Abstract The West Africa region (5 \(^\circ\) to 20 \(^\circ\) N and 10 \(^\circ\) E to 20 \(^\circ\) W) is particularly vulnerable to climate change due to a combination of unique geographic features, meteorological conditions, and socio-economic factors. Drastic changes in precipitation (e.g., droughts or floods) in the region can have dramatic impacts on rain-fed agriculture, water availability, and disease risks for the region’s population. Quantifying these risks requires localized climate projections at a higher resolution than is generally available from general circulation models. Using self-organizing maps, we produce station-based downscaled precipitation projections for medium and high-emission climate scenarios for this region. Compared to historical observations, the downscaled values are able to match the historical range of the distribution, and recreate the seasonal variability for the inland portions of the region, but struggle with the seasonal cycle along the coast. We find a decrease in the interior Sahel region by an average of 10% by 2100 under the high greenhouse gas-emission scenario of Shared Socioeonomic Pathway 5 \(-\) 8.5. Precipitation decreases in the Sahel are primarily driven by reductions in the number of rainy days during the wet season, rather than by consistent decreases in the magnitude of the precipitation amounts or decreases in the average length of the wet season.
PubDate: 2023-09-29
- A new approach for estimating solar radiation using Gaussian distribution
and developing new model-
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Abstract: Abstract The main aim of this study is to develop a new model using the Gaussian distribution in global solar radiation estimation. In this study, a new global solar radiation (SR) model was developed using the Matlab program, and the performance of this developed model was examined in different regions. With this newly developed model, the estimated values were compared with the actual measured values in the determined regions. When the literature is examined, it is seen that although many different methods and approaches have been used in SR estimation, no model has been developed with the Gaussian distribution. Thus, the performance of this new developed model was observed in detail in different regions. The performance of the developed model was examined in six different statistical error tests. The developed new model was compared with twenty different models. Global SR data used in this study were obtained from the Turkish State Meteorological Service. When the results are evaluated in general, it has been observed that the Gaussian model exhibited the best performance with RPE = 0.3574, MPE = 0.7794, R2 = 0.9848, MAPE = 6.7525, SSRE = 0.0997, and t-stat = 0.1913 resulting in the amongst all the models which were compared. In addition to these results, it was observed that the performance of the new model developed gave acceptable results in different regions. The findings of this study are expected to make significant contributions to the growing literature on advancing forecasting model development efforts to predict solar energy production.
PubDate: 2023-09-29
- Rainfall interception loss as a function of leaf area index and rainfall
by soybean-
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Abstract: Abstract Canopy interception affects the effective rainfall for plant growth. Extensive studies of canopy interception have focused on trees, but few on crops, due to the longer canopy duration of trees. However, overlooking the canopy rainfall interception results in an overestimate of effective water for crop growth and development. It is still unclear how crop canopy interception is influenced. In this study we examined the effect of leaf area index (LAI) and rainfall characteristics on soybean canopy interception. The results showed that the LAI, rainfall intensity and rainfall duration were the most relevant factors affecting canopy interception. The relationship between canopy interception and LAI was expressed by a linear function, as well as the relationship between canopy interception and rainfall amount. We proposed a canopy interception model versus LAI and rainfall characteristics to simulated the water loss by canopy interception. The results indicated that canopy interception loss increased with bigger LAI and decreased with rainfall amount increasing, indicating that canopy interception can’t be ignored in the crop production, especially with small LAI and high precipitation.
PubDate: 2023-09-29
- In what conditions an urban heat island can initiate deep convection'
Theoretical estimations-
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Abstract: Abstract There is strong empirical evidence that the urban heat islands can initiate storms due to the triggering of the deep convection, which then leads to severe weather events over the large urban areas. However, the conditions under which a triggering may occur are still not well understood. In this study, we present an idealized theoretical framework based on the assumption that the triggering is produced by the buoyant convective plumes generated by the urban heat islands, and we find a theoretical estimation for the conditions under which deep convection may be triggered. We show that the excess virtual potential temperature of the urban heat island is the principal factor which controls the triggering, but only if the convective inhibition is not too large. We also highlight the importance of the urban dimensions on the convective flow of the urban plume, and thus, on the deep convection triggering. It is also demonstrated, on the basis of dimensional arguments, that the mechanically induced convergence, associated with the urban friction, has a negligible impact on the convergence dynamics, which shows that this mechanism cannot lead to deep convective deep convective initiation by itself.
PubDate: 2023-09-29
- Elevation-dependent precipitation in the Indian Himalayan Region
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Abstract: Abstract The linkage between elevation and precipitation in the mountainous regions across the world including the Indian Himalayan Region (IHR) is very complex. Various meteorological parameters, viz., albedo, shortwave and longwave radiations, humidity, and mass-energy balance, play a major role in the physical processes occurring in these places. The present study examines the changes in precipitation across the IHR. The precipitation patterns act differently from east to west, and north to south due to the varying elevation. The study employed high-resolution observational gridded precipitation analyses (CHELSA) for the period 1980–2018 and the GTOPO 30 DEM for the analyses. The two major precipitating seasons over the IHR such as the monsoon (JJAS) and winter (DJF) precipitation are considered. Characteristics of the precipitation with altitude over subregions of IHR such as Shivalik’s IHR (SH), Lesser IHR (LH) and Higher IHR (HH) are presented. In addition, longitudinal variations over western IHR (WH), central IHR (CH) and eastern IHR (EH) are presented. Further, a non-parametric Mann–Kendall method has been used for trend analysis of precipitation, while the Pettitt test is used for change point detection. A positive precipitation trend is observed over HH and the western part of SH whereas a negative trend is found over the eastern part of SH. In most of the regions in SH, change in mean precipitation is observed during the recent time (1999–2018). However, HH located in the east of CH and west of EH shows a change in mean precipitation quite early (1979–1998).
PubDate: 2023-09-28
- Evolution of long-term trends and variability in air temperatures of
Kazakhstan for the period 1963–2020-
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Abstract: Abstract The annual, seasonal, and monthly trends of air temperatures were analyzed for thirteen urban and five rural meteorological stations in Kazakhstan for the 1963–2020 period. The non-parametric Mann–Kendall (M–K) rank correlation and Sen’s slope estimator methods and the parametric least-squares linear regression (LSLR) were used to determine whether there were positive or negative statistically significant trends in mean, average maximum, and minimum air temperature time series along with diurnal temperature ranges (DTRs), and temperature differences between five large and small cities. In addition, Kazakhstan’s annual and seasonal air temperature series were analyzed in terms of autocorrelation (serial correlation) coefficients. Coefficients of variations indicated that mean annual temperature variability is the highest in northern cities. Results of the M–K trend test indicated that the highest and lowest increases in the mean air temperatures were observed in spring and autumn, respectively. The magnitudes of the significant increasing trends in annual air temperature ranged between 0.23 °C/decade at Karagandy and 0.54 °C/decade at Kyzylorda. Annual and seasonal diurnal temperature ranges (DTRs) reveal insignificantly decreasing and increasing trends at most of the stations characterized by urbanization. According to the results of both M–K and LSLR tests, annual and winter air temperature differences of some station pairs tend to significantly increase, which may mean that the differences in the calculated temperature range between large and small cities might have widened significantly. However, significance test for the calculated autocorrelation coefficients of the annual and seasonal air temperature data showed that most of the series clearly appear as a low-frequency variability on the significantly increased long-term averages.
PubDate: 2023-09-28
- Another scanning test of trend change in regression coefficients applied
to monthly temperature on global land and sea surfaces-
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Abstract: Abstract Two algorithms have been proposed in this paper. One is another scanning t test of trend change points in regression slope coefficients in two phases, along with a coherency analysis of trend changes between two time series. It is different from the previously published scanning Fmax test of trend changes. The second is a fuzzy weighted moving average (FWMA). Then, the algorithms were applied to two series of monthly temperatures over global land and ocean surfaces for 1850–2021. The applied results show that significant changes in segment trends appeared in two gradations on the interdecadal and intradecadal scales. All subsample regression models were found to fit well with those data. Global warming started in April 1976 with a good coherency of warming trends between land and sea. The global warming “hiatus” mainly occurred in SST cooling from November 2001 to April 2008, but was not evinced over land on interdecadal scales. The “land/sea warming contrast” was detected only in their anomaly series, but disappeared in their standardized differences. We might refer to the anomalies in distribution N(0,s) as “perceptual” indicators and refer to the standardized differences in N(0,1) as “net” indexes.
PubDate: 2023-09-28
- A CMIP6 multi-model ensemble-based analysis of potential climate change
impacts on irrigation water demand and supply using SWAT and CROPWAT
models: a case study of Akmese Dam, Turkey-
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Abstract: Abstract This study details an integrated framework for assessing the water supply reliability of a multi-purpose reservoir under different climate change scenarios, with the case of the Akmese Project in northwest Turkey. In this assessment, the precipitation and temperature simulations of 24 Global Circulation Models (GCMs) from the Couple Model Intercomparison Project phase 6 (CMIP6) are analyzed using two statistical bias correction methods, namely, linear scaling and distribution mapping, to produce the best-performing multi-model ensemble predictions under two different Shared Socio-economic Pathway (SSP) scenarios (SSP245 and SSP585). The future inflow rates of the Akmese reservoir are simulated using the Soil and Water Assessment Tool (SWAT) model. The CROPWAT model is utilized to estimate crop water and crop irrigation requirements under the projected climate conditions. The effects of changing climate on the lake evaporation rates are also taken into consideration in analyzing the future reservoir water availability for domestic usages, irrigation demands, and downstream environmental flow requirements. The 25-year monthly reservoir operations are conducted with the changing inputs of the projected inflows, lake evaporation rates, and irrigation requirements for the historical period of 1990–2014 and near-, mid-, and long-future periods of 2025–2049, 2050–2074, and 2075–2099, respectively. The results indicate that the projected changes in the hydro-climatic conditions of the Akmese Basin will adversely impact the reservoir water availability. Under the high-forcing scenario SSP585, 9.26 and 22.11% of the total water demand, and 20.17 and 38.89% of the total irrigation requirement cannot be supplied, in turn, in the mid- and long-future periods.
PubDate: 2023-09-27
- Terrestrial water storage and climate variability study of the Volta River
Basin, West Africa-
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Abstract: Abstract The Volta Basin in West Africa plays a crucial role in supporting the livelihoods of millions of people, and effective management of its water resources is essential for climate change adaptation. This study utilized remote sensing technology, specifically the Gravity Recovery and Climate Experiment (GRACE), to assess terrestrial water storage (TWS) and its response to climate variability within the Volta Basin. The methodology involved integrating GRACE data with ground-based measurements, climate models, and other satellite observations to enhance the accuracy of TWS assessment. Despite numerous studies conducted within the basin, this research employed additional statistical techniques such as Independent Component Analysis (ICA) and El Niño Southern Oscillation (ENSO). It also utilized Climate Hazard Group Infrared Precipitation with Station (CHIRPS) to determine variations in TWS and climate variability observed within the Volta Basin. The results provide valuable insights into TWS dynamics, highlighting the complex interplay between precipitation patterns, groundwater storage, and surface water availability. Also, it was revealed that rainfall signals were strongest in the northernmost part of the basin, reaching a maximum value of 10 mm, while the lowest value of 5.5 mm was recorded in the southern part of the basin. Similarly, TWS signals were highest in the northern and lowest in the southern part of the basin, exhibiting values related to that of rainfall. Additionally, the highest TWS value of 250 mm was identified between 2010 and 2012. The increase in TWS during this period correlates with the occurrence of La Niña that happened between 2010 and 2012. This study offers essential information for water resource management, drought monitoring, flood forecasting, and climate change adaptation strategies not only within the Volta Basin but also in other basins across the globe.
PubDate: 2023-09-26
- Multiscale analysis of drought, heatwaves, and compound events in the
Brazilian Pantanal in 2019–2021-
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Abstract: Abstract In this study, a comprehensive multiscale analysis of compounding drought-heat events in the Pantanal region is presented. The goal is to assess the multiscale nature of drought and determine whether the combined effects of drought and heatwaves, as driving factors, are more relevant than the effects of each event separately. The study describes a persistent interannual extreme event characterized by drought and heatwaves in the Pantanal, lasting from 2019 to 2021. The extreme event involved a prolonged dry season, a shortened and delayed rainy season, and persistent heatwaves, resulting in the emergence of drought-heat compound events. Despite experiencing consecutive months of increasing drought hazards and a delayed rainy season in late 2020 to 2021, the northern Pantanal region was unable to recover from the water deficit accumulated due to water stress in the previous year. This emphasizes the long-lasting impacts of compound events on water availability and ecosystem health. Furthermore, the study suggests that interannual water stress played a crucial role in explaining the context that led to record-breaking daily maximum temperatures during the austral spring of 2020. The regions most at risk for such compound extreme events are the northern and central Pantanal. Looking at longer timescales, the analysis of compound drought-heat events can provide insights essential for understanding and preventing their impacts, particularly those that could trigger fire outbreaks.
PubDate: 2023-09-26
- Performance evaluation of climate models in the simulation of
precipitation and average temperature in the Brazilian Cerrado-
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Abstract: Abstract Expected changes in climate variables, such as precipitation and temperature, can change the hydrological regime, impacting water availability in already stressed watersheds. The predictions indicate annual surface temperature increase trends in the Cerrado region. Climate models are essential tools in predicting future climate. To increase the degree of confidence in the projections of these climate models, it is necessary to understand the performance of the models and identify and correct the biases observed in the climate variables simulated by them. This study aimed to evaluate global climate models nested with regional climate models in the simulation of precipitation and average temperature in localities in the Brazilian Cerrado. A comparison of historical data from climate models (Eta-HadGEM2-ES, Eta-MIROC5, Eta-BESM, and Eta-CANESM2) was carried out with data observed at the climatological stations present in the area through statistical metrics. In general, the model with the best statistical fits for precipitation and average temperature in the Cerrado region was Eta-HadGEM2-ES. The Run, Mann-Kendall, Pettitt, and Sen’s slope tests demonstrated that a reduction in precipitation and an increase in temperature are expected in the studied locations in the Cerrado region by the end of the 21st century.
PubDate: 2023-09-26
- Highlighting climate change by applying statistical tests and climate
indices to the temperature of Kébir Rhumel watershed, Algeria-
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Abstract: Abstract A number of statistical methods (Mann-Kendall, Pettitt test, etc.) and climatic indices (Emberger, Euverte) were used to highlight climate change in the Kébir Rhumel watershed, at seven geographically selected stations, by studying temperature on a monthly scale. The time series used was from 1901 to 2021 (121 years). The results show a breakpoint in the series, identified at 1980, for all the stations. Analysis of the monthly temperature values, before and after the breakpoint, shows an increase of between 1.08 and 1.18 °C. The graphical representation of the climatic indices indicates that most of the stations moved from the sub-humid stage (before the break) to the semi-arid stage (after the break). Euverte’s method displays that the temperatures for 80% of the months are shifting towards a warmer, less humid climate.
PubDate: 2023-09-22
- Analysis of the spatial variability of temperature with the aim of
improving the location of wind machines-
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Abstract: Abstract Spring frosts after budburst are responsible for crop losses and threaten local economies. As global warming brings forward the phenological stages of plants, they are increasingly confronted with long periods of frost. Nowadays, many solutions exist to fight frost, including the wind machines that dot the Quincy vineyard in France. However, their placements often do not consider the spatial variability of spring frost, leading to some zones being insufficiently protected, while others may have overprotection. The temperature within a specific area can vary spatially due to factors such as the meteorological conditions, the topography of the site, and the soil characteristics. Radiative frost can cause the temperature in an area to differ by a few degrees Kelvin, creating colder areas that must be known to protect plants from frost effects. This study, transferable to any other orchard equipped with wind machines, first analyzes the spatial variability of the minimum temperature according to the type of frost. Meteorological variables from a national synoptic weather station, topographic parameters, and local daily minimum temperatures from a network of connected sensors scattered throughout the vineyard are retrieved for the last three spring seasons of 2020, 2021, and 2022. Then, thanks to hierarchical ascendant clusterings, the spatial variability of temperatures is linked to the synoptic situation and the topography of the domain. In a second step, the current implantation of the wind machines is compared with the frost areas previously identified to propose an optimal positioning for the wind machines in the Quincy vineyard.
PubDate: 2023-09-21
- COVID-19 and the impact of climatic parameters: a case study of Bangladesh
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Abstract: Abstract This study examines the relationship between climatic factors and the prevalence of COVID-19 in Bangladesh. The Pearson correlation coefficient, the Spearman correlation coefficient, and Kendall's correlation coefficient have all been used to assess the intensity and direction of the relationship between climatic factors and COVID-19. The lagged effects of climatic parameters on COVID-19 daily confirmed cases from Bangladesh are being investigated using the Auto Regressive Distributed Lag (ARDL) model. As a result, one non-climatic variable, such as a daily lab test, is considered a control variable. As climatic variables, average temperature (°C), average humidity (percent), average rainfall (mm), and average wind speed (km/h) were well chosen and the same time one environmental variable (a proxy of air quality) like average particulate matter ( \({PM}_{2.5})\) is considered into account. The time series data used in this analysis was from May 1, 2020, to April 14, 2021. The findings of correlation analysis indicate that there is an important /strong, significant, and positive relationship between COVID-19 widespread and temperature (°C), humidity (percent), rainfall (mm), and wind speed (km/h), whereas there is a negative, weak, and significant relationship between \({PM}_{2.5}\) and COVID-19 widespread. In addition, the ARDL findings suggest that temperature (°C), \({PM}_{2.5}\) , and wind speed (km/h) have major lag effects on COVID-19 in Bangladesh, while humidity (percent) and rainfall (mm) have negligible lag effects. This study will be helpful to environmental activists and policymakers in creating future sustainable improvement plans for climate and weather conditions in Bangladesh.
PubDate: 2023-09-20
- The hottest center: characteristics of high temperatures in midsummer of
2022 in Chongqing and its comparison with 2006-
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Abstract: Abstract In the midsummer of 2022, a rare regional high-temperature event occurred in the Yangtze River Basin. Chongqing, as the center of this event, also experienced its strongest high temperature since 1961. In this paper, the characteristics of this event in Chongqing are reviewed in detail and compared with those of 2006. We have confirmed the following conclusions with detailed data: (1) In the midsummer of 2022, the number of days that the daily maximum temperature is exceeding 35 ℃ in Chongqing was the most since 1961, with the number of days that the daily maximum temperature above 40 ℃ increasing significantly compared with 2006. (2) The impact range of this event in Chongqing was quite extensive, especially the range with the daily maximum temperature exceeding 40 ℃ was the widest since 1961. (3) The high-temperature event was more extreme in 2022 than that in 2006. The maximum of daily maximum temperature was 45 ℃, and this is the first time that China has recorded a high temperature of 45 ℃ outside Turpan. There were 5 stations with daily maximum temperature exceeding 44 ℃, accounting for 1/3 of the number of stations with such temperature (15 stations) in China. (4) In the midsummer of 2022, Chongqing was dominated by the compound high-temperature day. Compared with 2006, the daytime high-temperature day decreased significantly, while the compound one increased obviously.
PubDate: 2023-09-20
- Daily precipitation concentration and Shannon’s entropy characteristics:
spatial and temporal variability in Iran, 1966–2018-
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Abstract: Abstract Iran’s diverse climate and topography lead to variable precipitation distribution over different time scales. This study examines daily rainfall distribution across 37 Iranian synoptic stations for 53 years (1966–2018) using the daily precipitation concentration index (CI) and Shannon’s entropy (H). The analysis reveals significant insights into precipitation patterns and their implications. The concentration index (CI) highlights irregular daily precipitation, particularly along the Persian Gulf and Caspian Sea coastlines, and in southeastern Iran, where CI values range from 0.58 to 0.72. In contrast, Shannon’s entropy indicates higher entropy in northern and northwestern Iran, reflecting more uniform northwest rainfall (entropy values 6.48 to 11.63), while entropy decreases from northwest to south and southeast, indicating varying levels of precipitation evenness. The Lorenz asymmetric coefficient (LAC) indicates uneven rainfall distribution, characterized by light rain in the west/northwest and heavy rain elsewhere. The interplay between diminished precipitation, heightened entropy, and increased rainfall variability contributes to an elevated likelihood of droughts and flooding in the southern regions. For instance, regions like the Persian Gulf coast have CI values of 0.71, indicating moderate-high rainfall concentration. In the north, where CI values range from 0.57 to 0.71, amplified rainfall, entropy, and variability enhance the risk of flooding. Notably, the Shannon entropy index responds more significantly to changes in rainfall uniformity compared to increased daily precipitation classes, thereby significantly impacting the concentration index (CI). These findings provide valuable insights into the spatial and temporal dynamics of precipitation, with implications for risk assessment, and water resource management.
PubDate: 2023-09-19
- High-performance prediction model combining minimum redundancy maximum
relevance, circulant spectrum analysis, and machine learning methods for
daily and peak streamflow-
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Abstract: Abstract Streamflow predictions play a crucial role in the planning and management of water structures. However, accurately predicting streamflow data, which exhibits nonlinear and nonstationary characteristics, is a challenging problem. In this study, a novel approach was proposed for the prediction of both overall and peak streamflows, aiming to achieve high performance. The data used included precipitation and streamflow time series, as well as lagged data from the empirical mode decomposition (EMD), variational mode decomposition (VMD), and circulant spectrum analysis (ciSSA) subbands. The minimum redundancy maximum relevance (MRMR) method was employed for feature selection from these datasets. The selected features were used to develop daily streamflow prediction models using Gaussian process regression (GPR), ensemble (boosting and bagging), support vector regression (SVR), and artificial neural network (ANN) methods. The performance of the developed MRMR-, EMD-MRMR-, VMD-MRMR-, and ciSSA-MRMR-machine learning models was evaluated using mean squared error (MSE), mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R2) metrics. Additionally, the Bland-Altman plots and the Kruskal–Wallis test were used to determine the statistical significance of the results. According to the results, the ciSSA-MRMR-machine learning models achieved higher performance compared to the other models (R2 value of 0.956, an MSE of 0.0001, and an MAE of 0.0049 for overall streamflow prediction). For peak streamflow prediction, the ciSSA-MRMR-ANN model yielded an R2 value of 0.956, an MSE of 0.0002, and an MAE of 0.0217. It was observed that the proposed method significantly improved the prediction of not only overall streamflow but also peak streamflow values.
PubDate: 2023-09-19
- Trend and variability analysis in rainfall and temperature records over
Van Province, Türkiye-
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Abstract: Abstract The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman’s Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen’s Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies.
PubDate: 2023-09-18
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