 Subjects -> METEOROLOGY (Total: 106 journals)
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- Decadal trend of synoptic temperature variability over the Northern
Hemisphere in winter-
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Abstract: Abstract Synoptic temperature variability gives rise to cold waves and extreme cold events in winter. Based on four reanalysis datasets, this study investigates the decadal trend of synoptic temperature variability in boreal winter during the period from 1980 to 2019, with particular focus on the sharp drops in synoptic-scale temperature, which are associated with cold waves. The result shows that the synoptic-scale standard deviation of temperature decreases significantly with a trend of − 0.15 K/decade (− 0.09 to − 0.21 K/decade among reanalysis datasets) over continental regions in mid-high latitudes. Correspondingly, the rapid cooling events (RCEs), defined based on the day-to-day temperature decrease exceeding 6 K, also show a general decreasing trend in terms of their frequency and intensity. The strongest decrease occurs over eastern North America (ENA) and western Eurasia (WE). The weakening of the RCEs is closely connected to the decreased trend of eddy kinetic energy (EKE), suggesting that the weakened transient eddy activities may have mitigated the synoptic-scale temperature variability and the associated RCEs over mid-high latitudes. This study highlights that the decreased synoptic temperature variability leads to fewer and weaker RCEs on the synoptic scale over mid-high latitudes in winter though the mean state of winter temperature continues to warm.
PubDate: 2023-03-18
- Impact of climate change on future productivity and water use efficiency
of wheat in eastern India-
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Abstract: Abstract High temperature and elevated CO2 under future climate change will influence the agricultural productivity worldwide. Burgeoning population along with climate change situation is going to threaten the food security of India. According to IPCC 5th Assessment Report, global mean surface temperature and concentration of carbon dioxide (CO2) at the end of twenty-first century will increase by 4.8 °C and 539 ppm respectively under representative concentration pathway (RCP) 8.5 scenario. Considering the burning issue, the present study aims to find out the probable change in different climatic parameters under high greenhouse gas emission (RCP 8.5) scenario during 2021–2095 and their impact on wheat yield and water productivity over six locations (Jalpaiguri, Nadia, Murshidabad, Malda, Birbhum, and South 24 Parganas) covering five major agro-climatic zones of West Bengal, a state of eastern India. Results showed that maximum temperature (Tmax) and minimum temperature (Tmin) will increase by 5.3 °C and 5.9 °C during the end of this century. The increase in annual rainfall will be maximum (22%) at Murshidabad. Wheat yield will increase by 3 to 28% across the study sites. The seasonal crop evapotranspiration value will decline by 1 to 21%. Both water use efficiency (WUE) and transpiration use efficiency (TUE) will increase at all the study sites.
PubDate: 2023-03-14
- Analyzing future rainfall variations over southern malay peninsula based
on CORDEX-SEA dataset-
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Abstract: Abstract Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme conditions implied from individual RCM models could be more alarming. The study result could be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.
PubDate: 2023-03-14
- Spatiotemporal variability and controlling factors of ecosystem water use
efficiency in India-
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Abstract: Abstract Ecosystem water use efficiency (WUEe), defined as the amount of carbon biomass produced to water loss, is an important ecohydrological index characterizing the relationship between the carbon and water cycles. Understanding the WUEe dynamics and its controlling factors is essential for ecosystem management and restoration. This study analyzed spatiotemporal variations and controlling factors of WUEe over major basins, climate zones, and land covers in India during 2002–2015 using remote sensing-based datasets. A substantial spatial variation in WUEe was observed in India across different spatial scales. WUEe was high in shrubland ecosystems, followed by forest, cropland, and grassland ecosystems. The country-average WUEe showed a significant increasing trend over the study duration. Eleven biotic and abiotic controlling factors were analyzed in this study, namely, CO2 concentration, evapotranspiration (ET), humidity, leaf area index (LAI), normalized difference vegetation index (NDVI), precipitation, soil moisture, solar radiation, temperature, vapor pressure deficit (VPD), and wind speed. Among these factors, solar radiation, CO2 concentration, and temperature were found most sensitive factors to WUEe at the country scale. Other factors, such as NDVI, soil moisture, and humidity play a significant role at local scales in some regions. The inland drains in Rajasthan and west-flowing rivers from Kutch to Saurashtra were found most sensitive to controlling factors than other basins. These findings provide important insights into ecosystem functioning and water use patterns across different scales in India and will be helpful for water resources and ecosystem management.
PubDate: 2023-03-13
- Correction to: Assessment of climate change impacts on the hydrological
response of a watershed in the savanna region of sub-Saharan Africa-
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PubDate: 2023-03-10
- The effect of air quality parameters on new COVID-19 cases between two
different climatic and geographical regions in Turkey-
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Abstract: Abstract Different health management strategies may need to be implemented in different regions to cope with diseases. The current work aims to evaluate the relationship between air quality parameters and the number of new COVID-19 cases in two different geographical locations, namely Western Anatolia and Western Black Sea in Turkey. Principal component analysis (PCA) and regression model were utilized to describe the effect of environmental parameters (air quality and meteorological parameters) on the number of new COVID-19 cases. A big difference in the mean values for all air quality parameters has appeared between the two areas. Two regression models were developed and showed a significant relationship between the number of new cases and the selected environmental parameters. The results showed that wind speed, SO2, CO, NOX, and O3 are not influential variable and does not affect the number of new cases of COVID-19 in the Western Black Sea area, while only wind speed, SO2, CO, NOX, and O3 are influential parameters on the number of new cases in Western Anatolia. Although the environmental parameters behave differently in each region, these results revealed that the relationship between the air quality parameters and the number of new cases is significant.
PubDate: 2023-03-10
- A study on the spatial and temporal evolution of multi-year extreme
precipitation in the Huaihe River Basin-
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Abstract: Abstract The scientific analysis of the precipitation evolution characteristics of watersheds is of great value and significance in revealing the spatial and temporal patterns of regional precipitation and in predicting flooding and climate. Based on precipitation data from 45 meteorological stations in the Huaihe River basin from 1960 to 2019, 12 extreme precipitation indices were selected, and ESMD-R-MK trend analysis and cross-wavelet transform were used to study-in-depth the spatial and temporal distribution characteristics of extreme precipitation in the Huaihe River basin and its relationship with sunspots and atmospheric circulation anomalies factors. The results of the study show that between 1960 and 2019, the extreme rainfall indices in the middle and lower reaches of the Yangtze River basin, except for CDD and CWD, all followed a decreasing and then increasing trend, with a significant increase in rainfall at a rate of 33.3 mm/10 years since 1990. With a design return period of 20 years, all the extreme precipitation indices, except CDD and CWD, show a semi-annular trend of decreasing from the southeastern part of the basin to the third side, and most of the extreme precipitation indices show a significant upward trend in the Gaoyou Lake basin, the lower reaches of the Huai River and the Hongze Lake basin; some of the anomalous distributions may be related to topographic factors; sunspots and atmospheric circulation anomalies have a strong influence on the changes of extreme precipitation.
PubDate: 2023-03-10
- A method for selecting a climate model: an application for maximum daily
temperature in Southern Spain-
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Abstract: Abstract General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented.
PubDate: 2023-03-09
- Regional and seasonal variability in human thermal stress in Poland
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Abstract: Abstract The study objective was to determine the spatial and temporal variability of Poland’s bioclimatic conditions and to designate heat-stress regions with the Universal Thermal Climate Index (UTCI) application. The study was based on daily data from the multiannual period 1966–2021 obtained for 37 stations in Poland, provided from the resources of the Institute of Meteorology and Water Management–National Research Institute (IMGW-PIB). The aforementioned data provided the basis for the calculation of the Universal Thermal Climate Index (UTCI). The study revealed high variability of bioclimatic conditions in Poland, both in temporal and spatial terms. Bioclimatic regions characterised by the different occurrence of heat stress were distinguished and characterised. Regions in the south-west and west of Poland proved the most favourable in bioclimatic terms, with the highest number of days with no thermal stress. In these regions, the highest UTCI values were observed, while the lowest were recorded in the northeast of Poland and at the east coast of the Baltic Sea. Among unfavourable biometeorological conditions, the ones causing hypothermia have so far occurred more frequently than the ones causing overheating of the human organism. However, UTCI has increased during the study period; therefore, under proceeding global warming, an increase in the frequency of occurrence of heat stress can be expected. So far, the most intensive increase in UTCI values in Poland was recorded in spring and the weakest in winter.
PubDate: 2023-03-09
- Spatial coherence patterns of extreme winter precipitation in the U.S.
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Abstract: Abstract Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.
PubDate: 2023-03-08
- Numerical simulation and evaluation of soil temperature on the
Qinghai–Xizang Plateau by the CLM4.5 model-
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Abstract: Abstract The CRUNCEP V7 dataset was used to drive the Community Land Model version 4.5 (CLM4.5) to simulate the spatiotemporal changes in soil temperature in the Qinghai–Xizang Plateau from 1981 to 2016. The simulation results were compared with observations, ERA-Interim, and Global Land Data Assimilation System-Community Land Model (GLDAS-CLM) soil temperature reanalysis data. The results show that (1) the CLM4.5 can accurately reproduce the dynamic changes in the observed soil temperature over time in two soil layers (0–10 cm and 10–50 cm) at most sites, with a significant positive correlation with the observations. (2) The CLM4.5 can depict the spatial distribution of soil temperature in the plateau area, and the distribution characteristics are consistent with those of the reanalysis data. The soil temperature increases from north to south, and the values in the Qaidam Basin are significantly higher than those in the surrounding area. The CLM4.5 results are similar in value to the ERA-Interim product, while the GLDAS-CLM soil temperature values are generally higher. (3) The simulated value shows the trend of ‘increasing–decreasing-increasing–decreasing’ (+ − + −) from the west to the east in summer and autumn, while in winter and spring, the trend is generally increasing, but a decreasing trend is observed in some isolated locations. The temperature variation trends in the ERA-Interim data in winter and the GLDAS-CLM data in the middle of Sanjiangyuan in spring and the Qinghai Plateau in winter are consistent with those of the simulated data. The above results are all tested with 95% confidence. (4) From 1981 to 2016, the soil temperature on the plateau showed a significant upward trend, especially in spring and autumn. The two layers of the plateau have obvious seasonal changes, with the whole year characterised by a ‘single peak shape’. From March to September, the shallow soil temperature is higher than the deep soil temperature, and from October to February, the deep soil temperature is higher than the shallow soil temperature.
PubDate: 2023-03-07
- An appraisal of the NEX-GDDP precipitation dataset across homogeneous
precipitation sub-regions of Iran-
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Abstract: Abstract Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs’ average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models’ estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs’ efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively.
PubDate: 2023-03-06
- Competing effects of vegetation on summer temperature in North Korea
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Abstract: Abstract Vegetation reduction could affect regional climate by perturbing the surface energy and moisture balances via changes in albedo and evapotranspiration. However, it is unknown whether vegetation effects on climate occur in North Korea, where a severe reduction in forest cover has been observed. This study aimed to identify the biogeophysical processes in vegetation and climate interactions in North Korea, using the Normalized Difference Vegetation Index (NDVI) from the AVHRR GIMMS NDVI3g and the climate reanalysis data from the ERA5-Land over the period of 1982‒2015. As per the NDVI regression trend results, the highest rates of decreasing NDVI (10–20%/decade) were detected in the western region of North Korea during the summer. Based on the detrended correlation analysis of NDVI with surface energy variables at each grid point, including solar radiation, sensible and latent heat fluxes, Bowen ratio, and temperature, we identified the distinct biogeophysical effects of vegetation between the western and northern regions of North Korea. In the western (northern) region, a cooling (warming) effect of vegetation on the local temperature was approximately by 0.2–0.3 °C/0.1 NDVI during the summer. The competitive biogeophysical effects were induced by the geographical factors of relatively lower (higher) values of NDVI, altitude, and latitude in the western (northern) region. Particularly, if the current rate of deforestation continues, the increasing summer temperature would be up to 0.5 °C by the end of this century in the western region of North Korea, where large-scale human-induced forest loss has been observed. Thus, we urgently suggest that sustainable management and restoration of forests are needed in North Korea, which is among the countries most vulnerable to climate change now and in the future.
PubDate: 2023-03-06
- WetSpass to model the components of hydrologic cycle in the big watershed
of Khafr affected by land use-
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Abstract: Abstract In the present research, the hydrologic cycle in the big watershed of Khafr (1108.05 km2), Fars province, Iran, affected by land use in a 9-year period (2009–2017) was investigated. The flow of water and the trends of temperature (6.1–27.2 °C) and rainfall (300–600 mm) (1967–2018) were investigated using Mann–Kendall and Sen’s tests. The hydrologic cycle was investigated using 22 observing wells and was subsequently simulated using the WetSpass (Water and Energy Transfer between Soil, Plants and Atmosphere under quasi Steady State) model in which the input variables including climate, potential evapotranspiration, soil layer, ground water level, topography, slope and land use were integrated in ArcView GIS maps. The land use in the region was illustrated by Landsat, and was processed by ENVI for the production of the final maps for use in the model. The output variables of the model were plant cover, land use, flooding pathways, rain-harvesting, rainfall-runoff, and charging and discharging of groundwater level. The maximum total yearly runoff of 724 mm, cold season of 632 mm and warm season of 250 mm were estimated by the model. The maximum of yearly groundwater charging of 91 mm, discharge (warm season) of 25 mm and yearly rain-harvesting of 145 mm were also estimated by the model. The WetSpass model simulated the hydrologic cycle in the region with a high accuracy. If land use is managed properly in the region, it would be possible to increase the efficiency of water sources.
PubDate: 2023-03-06
- Unraveling diurnal asymmetry of surface temperature under warming
scenarios in diverse agroclimate zones of India-
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Abstract: Abstract Diurnal temperature range (DTR) which reflects the difference between the daily maximum (Tmax) and minimum temperature (Tmin) is an important indication of changing climate and a critical thermal metric to assess the impact on agriculture, biodiversity, water resources, and human health. The major aim of this study is to assess the probable future spatio-temporal changes in the Tmax, Tmin, and DTR and their long-term warming trend from 2006 to 2099 under two representative concentration pathways (hereafter RCP4.5 and RCP8.5) over diverse agroclimatic regions of India. The observed data from India Meteorological Department (IMD) was used to evaluate the performance of climate models (1970–2005). The result shows a very slight underestimation in DTR by models compared to the observed. In future projections, we found a reduction in DTR (0.001 to 0.020 °C/year) partly linked to the substantial increase in Tmin (0.020 to 0.071 °C/year) than Tmax (0.031 to 0.060 °C/year) that was stronger in far twenty-first-century future under RCP8.5. The decline in DTR was profound and consistent over northern India (up to 3 °C) surrounding the Indo-Gangetic Plain, western dry region, and part of central India with the highest decline observed in winter and pre-monsoon season. However, a decline in DTR was also anticipated over the plateau, coastal, and eastern Himalayas region. Change in land use land cover (LULC) also complimented the decline in DTR. The main findings of the study advocate implementation of a robust framework for climate change adaptation strategies to mitigate adverse consequences to the natural ecosystem and human health over specific regions arising due to declining DTR.
PubDate: 2023-03-03
- Maximum Northern Hemisphere warming rates before and after 1880 during the
Common Era-
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Abstract: Abstract We find that maximal decadal Northern Hemisphere warming increases as rapidly before as after the industrial revolution (0.86 °C decade−1 before 1880 and 0.60–0.68 °C decade−1 after 1880). However, whereas the number of decadal periods with large increase and decrease rates were about equal before 1880 (267 vs. 273), after 1880 there are more periods with high increase rates (35) than with high decrease rates (9). The same patterns hold for bi-decadal rates. However, for time windows greater than about 20 years, the slope in global warming with time becomes greater after 1880. After 1971, there is only one short 11 year period with negative slopes. This reflects the higher frequency of positive slopes during the industrial period caused by the contribution of greenhouse gases (GHG). Maximum temperature changes for detrended series were associated with the beginning and end of extreme warm or cold sub periods. They occurred throughout all of the Common Era. Because the detrended temperature series showed sign of a pacemaker mechanism (regular cycle periods) we suggest that ocean variabilities were a dominating mechanism for multidecadal temperature variability during the Common Era.
PubDate: 2023-03-03
- Dynamical downscaling using CGCM ensemble average: an application to
seasonal prediction for summer precipitation over South Korea-
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Abstract: Abstract This study investigates how to properly downscale the coupled general circulation model (CGCM) ensemble prediction dynamically more efficiently than conventional method. Specifically, the ensemble seasonal prediction skill of dynamically downscaled precipitation over South Korea is evaluated by comparing two experiments. The first experiment (EXP1) involves conventional ensemble forecasts. Five ensemble members (EMs) are downscaled dynamically with initial and lateral boundary conditions obtained from the outputs of five CGCM EMs. The results of each EM are averaged for ensemble prediction utilizing a simple composite method. The second experiment (EXP2) is the same as EXP1, but the initial and lateral boundary conditions are obtained by arithmetically averaging the outputs of the five CGCM EMs. Therefore, five integrations are carried out for the EXP1, but only one integration is performed for the EXP2. The results show that EXP2 simulates closer to the observed precipitation than EXP1. This improvement is attributed to the strongly simulated upper zonal wind that can influence the vertically integrated moisture flux convergence. EXP2 shows comparable or better performance in simulating the interannual variability of summer precipitation than EXP1. Unlike conventional methods, such as EXP1, EXP2 provides a prediction in a single integration, and the prediction is similar to or even better than the one obtained conventionally. Hence, EXP2 can be a powerful means to drastically reduce the prediction time by reducing the number of ensemble integration to just one.
PubDate: 2023-03-02
- Drought mitigation through a hedging-based model of reservoir-farm systems
considering climate and streamflow variations-
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Abstract: Abstract For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM).
PubDate: 2023-03-01
- Regional precipitation frequency analysis for 24-h duration using GPM and
L-moments approach in South China-
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Abstract: Abstract There are little researchers that combined satellite-based products and L-moments methods in frequency analysis. The objective of this study was to explore and evaluate the satellite precipitation data applied in univariate L-moments method. The Shaoguan City of South China was selected as the study area. Annual maximum precipitation (AMP) respectively from observational rainfall and Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) gridded rainfall was used for the calculation of estimates using regional L-moments analysis (RLMA) method. Then, the quantiles at grids were corrected by mean bias correction (MBC) method with the estimates at sites. The results showed that the number of homogeneous regions as well as the spatial distribution of parameters ( \({C}_{v}\) , \({C}_{s}\) , and \({C}_{k}\) ) for two data sets are different. When the return periods are larger than 40 years, the estimates at sites are also larger than those at grids and the difference will increase as return period increases. After being modified, the quantiles from IMERG are more reasonable and combine the spatial distribution characteristic of estimates from two cases, which indicates that the estimation calculated by satellite products with L-moments approach is applicable and more reasonable. This study can also provide reference for solving the issues regarding the density and coverage of rain gauges that affect the accuracy of frequency analysis, especially in remote areas of China or most parts of the word.
PubDate: 2023-03-01
- A feasibility study of using a best group fitting method to determine wind
data probability distribution-
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Abstract: Abstract Probability distributions of wind data in a short time scale, such as hourly, are critically important for the risk assessment in relation to wind energy, optimal route scheduling for commercial airlines and unmanned aerial vehicles. It is known that the probability distributions of wind direction, wind speed, and wind gust have a large variety—ranging from Weibull to Lognormal distributions. A systematic method is needed to determine which distribution best fits a given wind dataset in a very efficient manner. This paper attempts to provide such a method. It is the best group fitting (BGF) method that efficiently fits the hourly wind data to a large number of distributions simultaneously, in contrast to the traditional one-by-one data fitting. The BGF method utilizes a Python package named Fitter which uses Scipy package, tries 80 distributions simultaneously, and checks what is the most probable distribution. We then apply the BGF method for the hourly data of u-wind, v-wind, wind speed, and wind gust in four geologically different locations, including the Alta Wind Energy Center, the Rocky Mountain region, the Colorado Plateau, and the San Diego urban district. The performance of the commonly used distributions and a wider selection of distributions are ranked for these locations at different times of a day. Our datasets and results show that, in addition to the traditional distributions such as Weilbull distribution and Lognormal distribution, the Burr distribution class stands out among the wind distributions and even outperforms the most used distributions in the literature. Our fitting results to the operational wind data have demonstrated that it is feasible to use BGF to make an efficient group fitting, which may imply that BGF can be used to search for the best probability distribution from different hourly weather datasets.
PubDate: 2023-03-01
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