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Theoretical and Applied Climatology
Journal Prestige (SJR): 0.867
Citation Impact (citeScore): 2
Number of Followers: 14  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1434-4483 - ISSN (Online) 0177-798X
Published by Springer-Verlag Homepage  [2469 journals]
  • Correction to: Evaluation of a multi-model approach to estimate leaf
           wetness duration: an essential input for disease alert systems

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      PubDate: 2022-07-01
       
  • Spatiotemporal analysis of drought and rainfall in Pakistan via
           Standardized Precipitation Index: homogeneous regions, trend, wavelet, and
           influence of El NiƱo-southern oscillation

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      Abstract: Abstract The phenomenon of drought is common in the world, especially in Pakistan. El Niño-Southern Oscillation (ENSO) influences the spatial and temporal variability of drought and rainfall in Pakistan. Therefore, the objectives of this study are to identify homogeneous rainfall regions and their trend regions, as well as the impact of ENSO phases. In this study, monthly rainfall data from 44 weather stations are used during 1980–2019. Moreover, descriptive and exploratory statistics tests (e.g., Pettitt and Mann-Kendall—MK), Sen method, and cluster analysis (CA) are evaluated along with the annual Standardized Precipitation Index (SPI) on spatiotemporal scales. ENSO occurrences were classified based on the Oceanic Nino Index (ONI) for region 3.4. Using the cophenetic correlation coefficient (CCC) and a significance level of 5%, seven methods were applied to the rainfall series, with the complete method (CCC > 0.9082) being the best. According to the CA method, Pakistan has four groups of homogeneous rainfall (G1, G2, G3, and G4). Descriptive and exploratory statistics showed that G1 differs from the other groups in size and spatial distribution. Pettitt’s technique identified the most extreme El Niño years in terms of spatial and temporal drought variability, along with the wettest months (March, August, September, June, and December) in Pakistan. Non-significant increases in Pakistan’s annual precipitation were identified via the MK test, with exceptions in the southern and northern regions, respectively. No significant increase in rainfall in Pakistan was found using the Sen method, especially in regions G2, G3, and G4. The severity of the drought in Pakistan is intensified by El Niño events, which demand attention from public managers in the management of water resources, agriculture, and the country’s economy.
      PubDate: 2022-07-01
       
  • Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual
           evapotranspiration of wheat crop

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      Abstract: Abstract Evapotranspiration is one of the main components of water balance and its accurate estimation is of great importance in planning and optimizing water consumption. In this study, therefore, it was tried to calculate the actual evapotranspiration rate of wheat crop in the Pars Abad section of Moghan plain, northwestern Iran, which is one of the main agricultural hubs in Iran. The research tools were Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), and Analytical Land Atmosphere Radiometer Model (ALARM) methods. For this purpose, 12 images of Landsat 7 and 8 satellites were used, all of which were in the product development period between 2016 and 2019, and the results were compared with lysimeter data. The results indicated that the highest actual evapotranspiration rate of wheat crop during the development period was related to 2018.07.01 (7.86 mm/day) in the ALARM method and the lowest rate in the mid-growth period belonged to 2017.01.30 (0.32 mm/day) in the METRIC method. Among the investigated methods, the SEBAL method with an RSME of 0.633 had the lowest error rate and the highest \({R}^{2}\) (0.9307) compared with the lysimeter data, followed by the METRIC and ALARM methods with the lowest error (RMSE = 0.761 and 0.855 mm/day) and the highest correlation \(({R}^{2}\) = 0.9057 and 0.8709), respectively.
      PubDate: 2022-07-01
       
  • Performance of gridded precipitation products in the Black Sea region for
           hydrological studies

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      Abstract: Abstract Gridded precipitation products are becoming good alternative data sources for regions with limited weather gauging stations. In this study, four climate gridded precipitation products were utilized, namely Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim/land, the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). The key novelty of this study is to fill the gap in one of important areas of the transcontinental region of Eurasia, namely Rize province in the Black Sea region being selected as a study area since it has complex topography and climatology in addition to a limited number of gauging stations. A set of precipitation products were assessed for performance with the observed precipitation data before using a hydrological model (SWAT) to evaluate the basin response for the climate products. Three methods were considered in this study: (i) spatial comparison and (ii) hydrological and (iii) statistical evaluations. Along with precipitation forcing, the SWAT model simulations were analyzed in conjunction with streamflow observations. In an overall evaluation, the percentage bias of ERA-Interim/land, CFSR, APHRODITE, and MSWEP mean monthly precipitation is 19.9%, 33.4%, 41.4%, and 85.0% respectively. For the flow simulations, the CFSR and MSWEP have resulted in exaggerated peak flows in the high flow season due to overestimated precipitation forcing (Nash Sutcliffe efficiency [NS] equal to 0.22 and −0.73, respectively). On the contrary, the APHRODITE underestimated the peak flows due to lower precipitation estimates (NS = 0.38). The ERA-Interim land showed good agreement with the observed flows (NS = 0.53). From these readings, we stated that the ERA-Interim land exhibited improved performance with the observed precipitation whereas the CFSR showed the worst performance. The study suggests that gridded precipitation products could supplement observed precipitation data for observational data scarcity in mountainous regions.
      PubDate: 2022-07-01
       
  • Seasonal extreme rainfall variability over India and its association with
           surface air temperature

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      Abstract: Abstract In recent decades, a significant rise in extreme rainfall events has been reported across India, accompanied by large-scale flood/drought-like conditions and catastrophic loss of life. Large-scale climate variability modes like the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) tend to influence the surface air temperature and rainfall variability over India. In what follows, the complete and independent influences of ENSO using Niño3.4, Niño3, and Niño4 indices, and IOD using Dipole Mode Index (DMI) on seasonal mean and extreme surface air temperature and rainfall over India are examined. A non-stationary generalized extreme value (GEV) distribution is implemented to analyze the seasonal extremes over the period 1979‒2019. Niño3.4 induces deficit rainfall over regions such as the Gangetic plains, the entire Deccan plateau, and western India in boreal summer. In autumn, strong positive seasonal mean rainfall responses are evident in peninsular India and eastern parts of Madhya Pradesh (MP). A dipole pattern evident in north India during summer reverses its polarity by autumn. Yet, Niño3 significantly reduces the intensity of rainfall over large parts of MP in summer. Contrarily, Niño4 strengthens the rainfall in similar regions, thereby significantly impacting the rainfall variability over India. Likewise, positive (negative) phases of the IOD lead to wet (dry) conditions over northwestern India in summers and central India in autumn. Overall, a coherent inverse relationship between rainfall and daily maximum temperature is observed. For Niño3.4 independent of IOD (Niño3.4 IOD), a weaker intensity in rainfall is found in northern India compared to the original Niño3.4 response. However, the IOD independent of Niño3.4 (IOD Niño3.4) rainfall response is weaker in northern India and stronger in central India compared to original IOD responses. Importantly, a composite analysis of rainfall and temperature anomalies during different phase combinations of ENSO and IOD also shows that the IOD mitigates the influence of ENSO in boreal summer and fall whenever such events occur in-phase.
      PubDate: 2022-07-01
       
  • Optimal selection of representative climate models and statistical
           downscaling for climate change impact studies: a case study of Rhode
           Island, USA

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      Abstract: Abstract The future climate impact studies rely on future projections obtained from downscaling of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. The main challenge is large, yet growing pool of CMIP5 models, posing a high computational cost for analyzing the climate model ensemble. The optimal model selection from the large pool is hence necessary. It is critical that the selected climate models represent the uncertainty range of possible future and have high skill in past performance. In this study, we use a multi-criteria decision process based on the coupling of the envelope and past performance approach to identify potential future climate stresses for RI, USA. The selection is based on projected changes in annual climatic means (precipitation and temperature) followed by a range of projected changes in climatic extremes and past performance among these models. From a pool of 109 models from RCP4.5 and 79 models from RCP8.5, a final subset of 4 models was selected for RCP4.5 and RCP8.5 respectively. The change in annual climatic means for temperature varied + 1.7 to + 3.0 °C in RCP4.5 and + 2.4 to + 5.8 °C in RCP8.5, and the range in climate mean of annual precipitation varied from 0.2 to 12.7% in RCP4.5 and − 4.1 to 15.9% in RCP8.5. The selected climate models are statistically downscaled to produce reliable local-scale climate estimates. Various variants of quantile mapping are studied, and quantile delta mapping is applied to systematically reduce biases and preserve raw GCM signals.
      PubDate: 2022-07-01
       
  • Realization of rainfed wheat and barley production environment based on
           drought patterns in the northeast Iran

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      Abstract: Abstract High spatiotemporal climate variability in the northeast Iran has led to increased production risk for rainfed crops. Hence, to explore potential opportunities for reducing the probable risk, it is necessary to characterize the main rainfed growing regions based on drought patterns. The CSM-CERES-Wheat and -Barley models were applied to simulate yield and to achieve input for estimating water supply demand ratio (WSDR) in 10 reference weather stations (RWSs) for the period 1980–2009. Simulated WSDR was averaged for every 100°Cd to and from flowering for each location (each RWS and each season). The environmental classes and their optimum number for each RWSs and the region were derived by k-means clustering. The soil diversity combined with spatiotemporal variations in rainfall caused rainfed wheat and barley to be exposed to drought stresses, which were very different across locations and among seasons. Nevertheless, four main environmental classes could represent the variability at local and regional scales. The first environmental class corresponded to environments with low drought stress with a frequency of 1.10 and 12.01% for wheat and barley, respectively. For wheat, environmental classes 2, 3, and 4 with a frequency of 22.62, 51.46, and 24.82% begin from 1100, 900, and 700°Cd (degree days) before flowering, respectively, and lasted until maturity. For barley, these three environmental classes with the frequency of 26.50, 32.86, and 28.62%, respectively, corresponded to sever drought stress levels with different degrees that begin from 500°Cd before flowering and was maximized at about 300°Cd after flowering which coincided with the grain filling. The occurrence frequency of environmental classes differed temporally and spatially. Over time from 1980 to 2009, environmental class 4 increased and environmental class 1 decreased for wheat and barley, respectively. In most locations, wheat and barley yield tended to decline from environmental classes 1 to 4. Such characterization of production environment can be used to help breeders focus on genes and traits adapted to target production environments.
      PubDate: 2022-07-01
       
  • Frequency-intensity-distribution bias correction and trend analysis of
           high-resolution CMIP6 precipitation data over a tropical river basin

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      Abstract: Abstract Advancements in computational power have enabled general circulation models (GCMs) to simulate climate variables at a higher resolution. However, GCM outputs often deviate from the observed climatological data and therefore need bias correction (BC) before they are used for impact studies. While there are several BC methods, BCs considering frequency, intensity and distribution of rainfall are few. This study proposes a BC method which focuses on separately correcting the frequency, intensity and distribution of precipitation. This BC was performed on high-resolution daily precipitation simulations of Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 with a 20-km grid size (MRI-AGCM3-2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6) on Netravati basin, a tropical river basin in India. The historical rain gauge station data was considered for testing the effectiveness of the BC method applied. The quantile–quantile (Q–Q) plot, Taylor diagram, Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS) and correlation coefficient (R) are employed for the evaluation of the BC method. Higher R and R2 and lower RMSE, MAE and PBIAS values were observed for the bias-corrected GCM data than raw simulation. The PBIAS reduced from 15.6 to 6% when BC was applied. The analysis suggested that the proposed method effectively corrects the bias in rainfall over the basin. Furthermore, an attempt has been made to analyse the trend of historical and future rainfall in the basin. The analysis revealed a declining trend of precipitation in monsoon months with the magnitude of 12.44 mm and 56.7 mm in the historical and future periods respectively. This study demonstrates that BC should be applied before the use of GCM simulated precipitation for any analysis or impact studies to improve the predictions.
      PubDate: 2022-07-01
       
  • Determining the impact of climate change on land suitability for rice
           paddy cultivation using GIS and RS on FAO maximum limitation approach

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      Abstract: Abstract In the present study, we aimed to understand the current condition of land suitability and how climate change will affect its suitability for rice paddy cultivation in the Edirne Province of Turkey in the future. We used RS and the GIS-supported FAO Maximum Limitation Approach to perform land suitability analysis for the current conditions and 20-year periodic times from 2020 to 2100. The results of the current land suitability assessment indicated that 81.39% of the study area is suitable for rice paddy cultivation. Two climate change models (HadGEM2-ES and MPI-ESM-MR) and related scenarios RCP4.5 and RCP8.5 showed that the climate conditions in the region will change significantly, therefore, the suitable lands for rice paddy cultivation in the study area will increase. However, the amount of change varies across models and scenarios. Further land suitability for rice paddy cultivation in the study area will be positively affected by temperature and solar radiation changes and negatively affected by changes in humidity and precipitation. Lastly, the agricultural irrigation infrastructure is expected to be unfavorably affected by an increase in extreme climatic events. These findings can guide policymakers and stakeholders to select suitable land for future rice paddy cultivation. To adapt to climate change and reduce its effects, we recommend choosing an agricultural production model that is suitable for climate change scenarios.
      PubDate: 2022-07-01
       
  • Differential signal of change among multiple components of West African
           rainfall

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      Abstract: Abstract Rainfall components likely differ in the magnitude and direction of their long-term changes for any given location, and some rainfall components may carry a greater regional signal of change than rainfall totals. This study evaluates the magnitude of change of multiple rainfall components relative to other components, and the greatest regions of change across all rainfall components in West Africa. Hourly rainfall data from the ERA5 reanalysis dataset was used to derive twelve rainfall components, which were evaluated for long-term means, interannual variability, and long-term changes. For rainfall totals and rainfall intensity, the central Sahel is witnessing increasing trends while the western Sahel is experiencing significant decreasing trends. In general, decreasing trends predominate in the study domain, especially in the northwestern Congo Basin, where annual rainfall is decreasing by 120 mm per decade. Importantly, rainfall frequency accounts for 49% of all significant grid-point trends for the whole domain. In contrast, rainfall totals account for 26% of all combined significant trends across the domain, while rainfall intensity (12.6%), rainy season length (9.5%), and seasonality (3.3%) account for the remaining signals of change. Most of the changes among the rainfall components are in the tropical wet and dry regions (59% of all significant trends); the Saharan and equatorial regions account for the least changes. This study finds evidence that rainfall frequency is changing more across the regions compared to rainfall totals and should be explored as rainfall inputs in climate models to potentially improve regional predictions of future rainfall.
      PubDate: 2022-07-01
       
  • Coupling relationship between meteorological factors and precipitation: an
           empirical study from Xinjiang Province, China

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      Abstract: Abstract The present study examines the spatio–temporal trend analysis of rainfall and temperature over time (annual, warm season, and cold season) from 1981 to 2010 in Xinjiang Province, China. The Mann–Kendall test, least square linear regression, and Spearman rank correlation were used to detect the direction and magnitude of change in rainfall and temperature. A panel data set of 8 meteorological factors during 1981–2010 was used to detect the statistical relationship between rainfall and other meteorological factors in Sect. 4.4. The results showed that annual rainfall and seasonal rainfall increased steadily, and the increasing rainfall trend during the cold season was more significant compared to the warm season. For temperature, average, high, and low temperatures increased on the annual and seasonal scales. Spatially, the northern parts of Xinjiang received more rainfall and showed higher variability. Based on the rainfall trend, the province was clustered into three parts using the K–means method. Based on this clustering, the statistical relationships between rainfall and other meteorological factors were investigated. The results showed that temperature and relative humidity were significantly correlated with rainfall in the three clusters, whereas sunshine, wind speed, and cloud cover had small influences on rainfall.
      PubDate: 2022-07-01
       
  • Observed and projected changes in temperature and precipitation extremes
           based on CORDEX data over Iran

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      Abstract: Abstract Due to increasing greenhouse gases, Iran is experiencing changes in patterns and trends of extreme climate events. Future climate extremes are one of the hottest topics affecting agricultural, economical, and environmental balances in many regions. Here, changes in temperature and precipitation extremes are investigated for the observational (1976–2005) and future (2025–2054) periods over Iran. For this, temperature and precipitation data of the CORDEX project acquired from three regional climate models with RCP4.5 and RCP8.5 scenarios are simulated using a developed multiscale bias correction downscaling method. For identifying trends and changes in 14 extreme climate indices used in this study from 54 climatic stations, the Mann–Kendall test and Sen’s slope estimator are used. Results show that notable changes can be seen in the climate extremes across Iran for both observed and projected data. Overall, more warming and increasing trends are found for both maximum and minimum values of daily maximum and minimum temperatures, which rise from RCP4.5 to RCP8.5. The number of cold days and nights indicate decreasing trends; alternatively, the number of warm days and nights show opposite behaviors. Changes in the monthly maximum 1-day and consecutive 5-day precipitation illustrate an increasing trend in most model-scenarios for the projected data in the future compared to the observed one. The spatial analysis results reveal that changes in the climate extremes are more noticeable in the northwestern and western regions of Iran. These areas will probably experience heavy disastrous rainfall because the number of very wet days and heavy precipitation days will increase in the future. Changes in climate extremes will likely increase the risk of severe extreme events in the future in these areas and, consequently, make society more vulnerable to natural disasters. The findings of this study can help decision-makers consider appropriate management in the face of climate change consequences.
      PubDate: 2022-07-01
       
  • Cooling island effect of urban lakes in hot waves under foehn and climate
           change

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      Abstract: Abstract The central region of Vietnam has a tropical monsoon climate but often undergoes heat waves due to uncontrolled urbanization, foehn winds, and climate change. Water bodies are considered effective candidates for heat mitigation in cities through the water cooling island (WCI) effect. Quantifying the WCI capacity of water areas and related factors is necessary for sites with advantages of surface water. The current attempt used the WCI effect range (Lmax), temperature drop amplitude (ΔTmax), and temperature gradient (Gtemp) to investigate the cooling effect of 20 lakes in the Thanh Noi region, Hue City. Data derived from high-resolution Google Earth, Landsat-8 Satellite Imagery Data, and ground truth. The results show that the average water temperature of the 20 studied lakes was about 36.61 °C, lower than the average temperature in the area with an urban heat island (UHI) of about 2.82 °C. The mean Lmax was 150 m, ΔTmax was 1.52 °C, and Gtemp was 10.16 °C /km or 0.01 °C/m. Climate characteristics and human impacts had reduced the ability of the lakes to create WCI during the period when the lake water level was low. The factors that influenced the WCI significantly were the landscape shape index (LSI), the proportion of green (PG), and the percentage of impervious surfaces (PI). Most lakes with relatively simple LSI, high PG, and low PI obtained high WCI, suggesting that structural and landscape characteristics played a critical role in urban cooling.
      PubDate: 2022-07-01
       
  • Effect of land cover change and elevation on decadal trend of land surface
           temperature: a linear model with sum contrast analysis

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      Abstract: Abstract Land surface temperature (LST) is a significant factor in surface energy balance and global climatology studies. Land cover (LC) and elevation are two factors that affect the change of LST, and their effects depend on different geography. This study aims to demonstrate an alternative approach to examine the change of LST during 20 years (2001 to 2020) on Taiwan Island and to investigate the effect of LC change and elevation on a decadal trend of LST using a linear model that adjusting for each determinate factor. MODIS LST and LC data, as well as GMTED2010 elevation product, were downloaded available website. The natural cubic spline function was used to model annual seasonal patterns in LST. Linear regression model was used to estimate decadal change of long-term LST time series. Weighted sum contrasts linear regression was used to assess the effect of LC transformation and elevation on the decadal LST change by comparing adjusting mean of all factors. The adopted analysis method was an appropriate approach to assess categorical factors than those based on treatment contrasts, requiring specifying a control group to compare means and confidence intervals. Results showed that there was an increase in LST for most of the island. The average daytime and nighttime LST trends were 0.12 and 0.31 °C/decade, respectively. However, areas in the southern part of the north–south direction mountain range show a statistically significant increase in LST in both daytime and nighttime. The major landslides caused this noticeable change of surface temperature due to the catastrophic damage of typhoon Morakot in 2009. The results also revealed that the different pattern of LC change has a significant effect on daytime LST, but not on nighttime LST trends. The elevation above 600 m had affected both daytime and nighttime LSTs.
      PubDate: 2022-07-01
       
  • Spatial and temporal drought projections of northwestern Turkey

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      Abstract: Abstract In recent years, natural calamities like droughts have caused disruptive impacts on the environment and society. Drought monitoring and its relations with different climatic parameters have been analyzed with various indices. This study assessed the drought condition with the Standardized Precipitation and Evapotranspiration Index (SPEI) and self-calibrated Palmer Drought Severity Index (sc-PDSI), which were calculated with monthly meteorological measurements between 1971 and 2018 in the northwestern part of Turkey. In addition, the projected drought events of the region were analyzed with the Coordinated Regional Downscaling Experiment (CORDEX) data obtained from five climate models with multi-model ensemble (MME) and under optimistic (RCP 4.5) and pessimistic (RCP 8.5) greenhouse gas and aerosol concentrations for the periods 2019–2050 and 2051–2099. The bias correction of projection data was carried out using meteorological data from the reference period (1971–2000) measurements. Projected drought conditions were analyzed according to the innovative trend analysis (ITA) methods. As a result of the trend analysis of SPEI, it was determined that the drought in the region would increase. Trend analysis of sc-PDSI indicated that drought intensity was different between the stations in RCP 4.5 and 8.5. In addition, the rate of moderate and higher dryness (drought occurrence) in the region was 17.2–30.3% in the measurement period with SPEI and sc-PDSI, respectively. Drought occurrence would increase by 38.3–35.3% in RCP 4.5 and 47–41% in RCP 8.5 for the years 2051–2099, respectively.
      PubDate: 2022-07-01
       
  • Twentieth century precipitation trends in the upper Mzingwane
           sub-catchment of the northern Limpopo basin, Zimbabwe

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      Abstract: Abstract This study evaluates precipitation trends in the upper Mzingwane sub-catchment (UMS) of Zimbabwe using ten World Meteorological Organization (WMO)’s Expert Team for Climate Change Detection Monitoring and Indices climate indices. Trend analysis for variables such as annual total precipitation, extremely wet days, consecutive wet and dry days is analysed. The UMS is of strategic socio-economic significance in terms of water security and sustenance to livelihoods in this region and the city of Bulawayo. The analysis is undertaken at four stations: Bulawayo Goetz, Filabusi, Mbalabala and Matopos National Park (MNP), for the period 1921–2000. In general, no statistically significant trends were detected for all indices in all stations (with the exception of MNP (in the westernmost extent of UMS)) which showed significant increasing (decreasing) trends for most dryness (total precipitation) extreme indices. This is indicative of persistent drying coupled with a shift towards shorter very wet periods with more intense precipitation in the past twentieth century. There is also an indication of a general north to south-western declining precipitation gradient during the past ~ 69 years over this region. These findings are not only useful in explaining the historical evolution of observed extremes and their associated socio-economic impacts but also present a baseline for comparative follow-up studies assessing the twenty-first century and future trends in precipitation extreme events in the UMS.
      PubDate: 2022-07-01
       
  • Evaluation of a multi-model approach to estimate leaf wetness duration: an
           essential input for disease alert systems

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      Abstract: Abstract Disease alert systems (DAS) of the AgroClimate platform are intended to facilitate grower decision-making when planning fungicide applications. These DAS provide disease risk estimates that are linked to management recommendations. If disease risk exceeds a threshold, a fungicide application is recommended. Temperature and leaf wetness duration are often required inputs to calculate daily disease risk. While temperature estimation is straightforward, calculating leaf wetness duration lacks standardization, being estimated by predictive models and/or by sensors. For the DAS on the AgroClimate platform, a four-model leaf wetness system and dielectric Campbell 237-L sensors are used. However, the four-model system was never formally assessed. Our objectives were to compare the performance of four leaf wetness models and their two-, three-, and four-model combinations—number of hours with relative humidity equal or greater than 90% (NHRH90), dew point depression (DPD), classification and regression tree (CART), and Penman–Monteith (PM)—to well-calibrated 237-L sensors. The performance of each model was satisfactory and overall comparable among the models used individually or in combinations of three and four models. Three of the two-model combinations did not perform well. PM tended to overestimate wetness, whereas NHRH90 tended to underestimate wetness. CART- and DPD-estimated leaf wetness resulted in satisfactory disease management recommendations. We confirmed the potential for operational use of models or their combinations in our DAS, although PM and NHRH90 should be used carefully. The other models and three- or four-model combinations are viable options for operational use in automated disease alert systems.
      PubDate: 2022-07-01
       
  • New method for estimating reference evapotranspiration and comparison with
           alternative methods in a fruit-producing hub in the semi-arid region of
           Brazil

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      Abstract: Abstract Analysing alternative methods for estimating reference evapotranspiration (ET0) is essential for improving the management of water resources, especially where meteorological data are not fully available for application of the standard Penman–Monteith (PM) method. In this study, the aim was (a) to propose a model for estimating ET0 and (b) to investigate its application together with alternative methods in an area of fruit production in the semi-arid region of Brazil. The Souza-Silva model was generated to estimate ET0 and together with other original methods based on thermal-water variables and global incident solar radiation was tested relative to the PM method. The Souza-Silva method (ET0SS =  − 0.00728 + 1.356325DPV + 0.174658Qg, where VPD is the water vapour-pressure deficit and Qg the global incident solar radiation) showed excellent performance in estimating ET0 with a low level of error (RMSE = 0.467 mm day−1 and MBE =  − 0.034 mm day−1). Among the original alternative methods, those for Hargreaves (ET0HS) and Camargo (ET0C) models did not perform well in the region under study. The Benevides and Lopes (ET0BL) and Valiantzas1 (ET0V1) methods gave excellent results; these were however even lower than methods based on the solar radiation. Compared to each of the original methods, the result of the Souza-Silva method showed greater precision and accuracy, with smaller estimation errors. It is concluded that this method, based on the water vapour-pressure deficit and solar radiation, can be used to estimate ET0 in places where data collection is limited.
      PubDate: 2022-07-01
       
  • A new method of identifying an appropriate distance between independent
           extreme annual rain events for a 5-min time resolution precipitation data
           network

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      Abstract: Abstract This paper deals with precipitation events, particularly the problem of recognising events individually, of distinguishing one local meteorological/precipitation event from another. The final aim was to arrive at a more appropriate conceptualization of “distance” between individual annual events. A criterion for identifying annual precipitation events, inside a 5 min-timestep series of pluviometric data, was proposed. Using this numerical criterion, a series of annual distances between events were obtained and compared with those obtained by other two known and applied statistical methods. This comparison would be important for linking the so-called statistical methods to the real data of the series, analysed through a numerical process. This would make it possible to provide a kind of “measure” for the understanding of real data via a statistical model. Results showed that distances between events, obtained through numerical analyses, are almost always different from those obtained by statistical methods. The supposed Poisson-process, generally matched to, and widely applied in the field of rainfall data, showed some contradiction with the analysis conducted in this paper. The so-called Lag method shows a numerical method type distribution, but produces some important internal deviations from numerical results. It seems that the method proposed in this paper can reproduce the real distances between events more realistically compared to the statistical methods considered here. Exponential distribution does not seem highly performing, at least in the case of the annual analysis, with series of a 5-min time-step resolution, obtained from rain gauges installed in the Veneto Region of Italy. It is believed that the numerical criterion presented in this paper can be considered adequate for determining the distances between events, within an annual time-window.
      PubDate: 2022-07-01
       
  • The role of short-wave troughs on the formation and development of
           sea-effect snowbands in the western Black Sea

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      Abstract: Abstract The short waves (less than 6000 km) embedded within the long waves and the resulting short-wave troughs have an effect on the sea-effect snow (SES). The short-wave troughs, which accompany most of the SES events, directly affect the formation and intensity of the bands, especially along their trajectories. In this study, the structure and characteristics of short-wave troughs and long waves during the occurrence of SES bands over the western Black Sea during nine winter periods (2010–2018) were investigated. A total of 48 short-wave troughs and long waves that concurred with snow events were detected in the period. In the classification made according to the movement direction followed by the short-wave troughs, it was determined that the western movement was dominant. This was mostly observed due to the latitudinal movements of the long waves. The average duration of the short-wave troughs over the region was found to be 27.8 h, while the longest duration trough lasted 60 h (LWT-Type). The most obvious effects of long waves were in the form of handling short waves. Apart from these, it also played a critical role in lowering arctic and polar air longitudinally to the south. The short-wave troughs allowed the convection to increase and contributed to the formation of severe SES bands by playing a role in the deepening of the convective boundary layer. The SES bands mostly had more than one parallel band formation in longitudinal direction. Movement directions of short-wave troughs and long waves mostly concurred with the SES bands (77–79%). Therefore, it is possible to talk about the effects of short and long waves not only in the change of boundary layer properties, but also in the direction of the upper atmospheric level (sub-inversion wind directions) movements.
      PubDate: 2022-07-01
       
 
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