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Theoretical and Applied Climatology
Journal Prestige (SJR): 0.867
Citation Impact (citeScore): 2
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1434-4483 - ISSN (Online) 0177-798X
Published by Springer-Verlag Homepage  [2467 journals]
  • The effect of the seasonality of moisture sources on moisture flux and
           precipitation stable isotopes in the Shiyang River Basin

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      Abstract: Abstract As one of the inland rivers in arid areas of Northwest China, precipitation is an important supply for the Shiyang River. Daily-scale stable isotopes of precipitation can provide record evidence for the moisture sources, so we collected daily δ2H and δ18O data of precipitation obtained from 11 sampling points and reanalysis meteorological data in the Shiyang River Basin from 2016 to 2020, to explore the moisture sources and the impact on moisture fluxes and precipitation isotope. The results showed the δ2H and δ18O values increase from upstream area to midstream and downstream areas, but the spatial variation of d-excess values is reversed. Dominated by westerly moisture, the moisture flux is higher when the westerly wind strengthens in winter. And the strength of the monsoon determines the water vapor content in summer. The δ18O is depleted in precipitation related to maritime sources than continental sources. Significant depletion of δ18O in precipitation is associated with the Atlantic Ocean (− 13.9‰) and Indian Ocean (− 13.5‰) sources, attributed to enhanced Rayleigh distillation caused by longer transport distances. The enrichment of δ18O in precipitation is associated with continental sources affected by evapotranspiration and secondary evaporation, such as inland China (− 8.0‰), Europe (− 8.6‰), and Mongolia-Siberia (− 9.0‰). This study indicates the moisture sources and transport processes in inland river basins of arid area, contributing to the understanding of the water cycle.
      PubDate: 2022-11-25
       
  • Assessment of H SAF satellite snow products in hydrological applications
           over the Upper Euphrates Basin

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      Abstract: Abstract Satellite technology offers alternative products for hydrological applications; however, products should be validated with benchmark models and/or data sets for operational purposes. This study assesses the performance of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) snow products of snow detection, SE-E-SEVIRI(H10), and snow water equivalent, SWE-E(H13), data sets over a mountainous catchment in the Upper Euphrates, Turkey. Moderate Resolution Imaging Spectroradiometer (MODIS) snow extent is used as a benchmark. Two different conceptual hydrological models are employed to obtain reliable results over the period 2008–2020. First, the spatio-temporal assessment of satellite-derived snow cover area (SCA) data is evaluated, followed by the calibration/validation of hydrological models, SRM and HBV, for impact analysis and hydro-validation of satellite snow products, respectively. SRM, demanding SCA as one of the primary forcings, reveals high Kling Gupta Efficiency, KGE, (0.75–0.89) in the impact analysis of satellite data. In hydro-validation analysis, noteworthy Nash–Sutcliffe Efficiency, NSE (0.89–0.92), values are obtained for SCA derived by SE-E-SEVIRI(H10) and MODIS as compared to simulated HBV model results. SWE-E(H13) product is also valuable since snow water equivalent (SWE) values are rarely available for mountainous areas. However, this product seems to need further attention. Overall results show the degree of applicability and usefulness of H SAF snow data in hydrological applications; thus, the strong need to disseminate the products is highlighted.
      PubDate: 2022-11-25
       
  • Assessment and characterization of the monthly probabilities of rainfall
           in Midwest Brazil using different goodness-of-fit tests as probability
           density functions selection criteria

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      Abstract: Abstract The probable rainfall is an excellent climatic parameter of information since it consists of the highest/lowest expected rainfall for a particular period of the year considering a certain level of probability. This study aims to evaluate traditional PDF performance using different goodness-of-fit tests to establish a criterion for choosing them on a monthly rainfall scale, in Mato Grosso do Sul, Brazil. The Cramer-von Misses (CVM) and Anderson–Darling (AD) tests were the most rigorous in rejecting the hypothesis of PFD suitable for monthly rainfall data, while the Kolmogorov–Smirnov (KS) was the least rigorous. The application of stricter goodness-of-fit tests as the CVM implies the use of up to 60% fewer series compared to the KS test. However, suitable series by the KS test presented erroneous estimates of probable monthly rainfall. The CVM and AD tests indicate the PDF with the best statistical performance (higher precision and accuracy between the observed and estimated frequency by the PDF) in more than 60% of situations. The most suitable PDFs for total monthly rainfall by the goodness-of-fit tests was gamma (12 months of the year). The exponential and Generalized Extreme Values (GEV) can be used for both the dry and rainy periods, respectively. The parameters of PDF are correlated with geographical variables, describing the total monthly rainfall distribution such as, for example, the influence of the South Atlantic Subtropical Anticyclone, the South Atlantic Convergence Zone in the rainy period, and the orographic effect in the dry period.
      PubDate: 2022-11-23
       
  • Regional selection of satellite estimates over the Northwest Himalayan
           region using the merged ranking methods

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      Abstract: Abstract Mountainous regions are often faced with various challenges of monitoring and predicting accurate rainfall due to complex topography. Satellite precipitation estimates serve as rich repositories of data, which are highly valuable for varied applications. However, it is essential to prioritize satellite estimates based on their performance in capturing the weather patterns over mountainous terrains like Northwest Himalayas (NWH). The present study has spatially ranked five satellite estimates, namely, APHRODITE-V1901, CHIRPS-V2.0, CMORPH-V1.0, PERSIANN-CDR-V1, and TMPA-3B42-V7 against the Indian Meteorological Department observed data at 0.25° × 0.25° resolution over the period 1998–2018. An ensemble methodology that incorporates the ranking methods like GRA, MAUT, TOPSIS, and WASPAS and the statistical metrics is proposed. The analysis is based on precipitation indices such as consecutive wet days, R20mm, Rx1day, Rx5day, total precipitation, R95p, R99p, and SDII. The results show that, in order to monitor rainfall across the NWH region, APHRODITE-V1901 secured the first ranking, followed by PERSIANN-CDR-V1, while CHIRPS-V2.0 received the last rank. Further, NWH is divided into five regions, i.e., region I, region II, region III, region IV, and region V concerning elevation. The ensemble merged ranking method is applied in each of the classified regions. The results reveal that APHRODITE-V1901 secured the first rank in region II, CHIRPS-V2.0 in region IV, CMORPH-V1.0 in region I, PERSIANN-CDR-V1 in region V, and TMPA-3B42-V7 in region III. The analysis of the extreme rainfall events is performed towards the end using the best satellite estimates for the designated locations.
      PubDate: 2022-11-23
       
  • Analysis of climate change in the middle reaches of the Yangtze River
           Basin using principal component analysis

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      Abstract: Abstract Global warming and associated frequent extreme hydrological events with increasingly severe climate change threaten human life and economic development. Consequently, climate change has become a significant focus of the scientific community. The middle reaches of the Yangtze River Basin (MYRB) contain abundant water resources and is an area with intensive human activities; it is not only the center of social and economic development in China but also the region with the highest frequency of drought and flood disasters. This study analyzed the spatiotemporal characteristics of several climate change indicators, including temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI), in the MYRB during 1961–2017, using principal component analysis (PCA). The main results are as follows: (1) mean temperature, monthly air temperature sum, precipitation sum, maximum daily precipitation, and number of days with precipitation exceeding 30 mm were the primary variables that experienced variations in the MYRB; (2) the average temperature during spring and autumn and precipitation intensity in summer increased significantly in the MYRB, primarily in the east and the north. Increased aridity during spring, autumn, and winter was apparent in the MYRB; and (3) changes in the distribution of temperature and the intensification of drought were the principal variables that changed across the study period in the Hanjiang River Basin, while the dominant variables in Dongting Lake Basin and Yichang to Hukou Basin were precipitation regime and temperature-related indicators. The results of this study can improve our understanding of how climate change affects the MYRB, allowing the development of mitigation measures for meteorological disasters.
      PubDate: 2022-11-22
       
  • On the quality of satellite-based precipitation estimates for time series
           analysis at the central region of the state of São Paulo, Brazil

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      Abstract: Abstract With the advance of remote sensing technologies, meteorological satellites have become an alternative in the process of monitoring and measuring meteorological variables, both spatially and temporally. The present study brings some additional elements to the existent validations of satellite-based precipitation estimates from CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station) all around the world, by analyzing its monthly product in the period 1981–2019 over the central region of the state of São Paulo, Brazil. There are significant variations over time in the number of rain gauges used by CHIRPS at the region, and the product quality has been evaluated under these conditions. Initially, the general relationship between satellite estimates and surface rainfall data is assessed using the linear adjustment and error analysis in both temporal and spatial perspectives, followed by a trend analysis using Laplace test. Results show an average decrease of 20% in R2 values when gauges were not used as anchor/reference stations by CHIRPS; the same behavior is observed for the other metrics. The monthly map analysis, besides the evident impact of the use or not of the gauges as reference stations, showed a better performance of CHIRPS (in terms of R2) during the dry period (April to August) than for the wet period (October to March), especially when anchor stations were not available. On the other hand, CHIRPS tends to underestimate (overestimate) low (high) rain rate events. Finally, despite the changes in product over time, monthly trends showed, in general, the same pattern of variability in rainfall over 38 years and a prevalence toward the reduction of rainfall. In summary, CHIRPS product seems a reasonable alternative for regions that lack historical rainfall information, but a careful analysis on the product diagnosis should be made when temporal analysis is conducted.
      PubDate: 2022-11-22
       
  • Forecasting seasonal to sub-seasonal rainfall in Great Britain using
           convolutional-neural networks

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      Abstract: Abstract Traditional weather forecasting approaches use various numerical simulations and empirical models to produce a gridded estimate of rainfall, often spanning multiple regions but struggling to capture extreme events. The approach presented here combines modern meteorological forecasts from the ECMWF SEAS5 seasonal forecasts with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall across Great Britain. The CNN is trained using mean sea-level pressure and 2-m air temperature forecasts from the ECMWF C3S service using three lead-times: 1 month, 3 months and 6 months. The training is supervised using the equivalent benchmark rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). Comparing the CNN to the ECMWF predictions shows the CNN out-performs the ECMWF across all three lead times. This is done using an unseen validation dataset and based on the root mean square error (RMSE) between the predicted rainfall values for each region and benchmark values from the CEH-GEAR dataset. The largest improvement is at a 1-month lead time where the CNN model scores a RMSE 6.89 mm lower than the ECMWF. However, these differences are exacerbated at the extremes with the CNN producing, at a 1-month lead time, RMSEs which are 28.19 mm lower than the corresponding predictions from the ECMWF. Following this, a sensitivity analysis shows the CNN model predicts increased rainfall values in the presence of a low sea-level pressure anomaly around Iceland, followed by a high sea-level pressure anomaly south of Greenland.
      PubDate: 2022-11-19
       
  • Groundwater level prediction based on GMS and SVR models under climate
           change conditions: Case Study—Talesh Plain

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      Abstract: Abstract This study compared the capability of GMS and SVR models for groundwater modeling and evaluated the impact of climate change on future aquifer quantity in Talesh Plain. Groundwater level modeling was performed using GMS and SVR models for the period 2005–2018 (base period). Also, the effects of climate change on temperature and precipitation in the study area were estimated based on the HadGEM2-ES GCM model considering RCP 2.6, RCP 4.5, and RCP 8.5 emission scenarios in 2020–2034 (future period). A correlation of greater than 0.70 was found between the observed and estimated groundwater levels in both models. Moreover, in the base period, the average decline in groundwater level was 0.86 m. SVR model exhibited that the average groundwater level will drop by 0.94, 0.98, and 1.04 m in RCP2.6, RCP4.5, and RCP8.5 emission scenarios, respectively. While in the GMS modeling, under the same emission scenarios, these values were 0.91, 0.95, and 1.06 m, respectively. Moreover, the current trend of groundwater withdrawal may significantly increase the groundwater deficit and aquifer imbalance. It is therefore essential to apply artificial intelligence and mathematical models to accurately predict groundwater level fluctuations in this region to optimize groundwater management. Overall, our results revealed that SVR and GMS models perform almost similarly in simulating groundwater levels in the study area, suggesting that artificial intelligence can serve as a fast decision-making tool in groundwater management in similar aquifers.
      PubDate: 2022-11-19
       
  • Analysis of precipitation extremes related to agriculture and water
           resources sectors based on gridded daily data in Romania

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      Abstract: Abstract Climate change is one of the most frequent topics in climatic literature over the last three decades. One of the significant concerns with a potential change in climate is that extreme events will occur with a higher frequency. This paper focuses on spatial distribution and changes that occurred in extreme precipitation indices in Romania over a 53-yr period: 1961–2013. Gridded daily precipitation data have been used at a spatial resolution of 0.1° × 0.1° (about 11 km × 11 km). A set of 14 indices established by the Expert Team for Sector-Specific Indices for agriculture and water resources sectors has been calculated. They are both frequency and intensity indices: four are fixed threshold indices (R10, R20, CDD, and CWD), four are station-related thresholds (R95p, R99p, R95pTOT, R99pTOT), and six indices were detected without using a threshold (Rx1day, Rx3days, PRECPTOT, SDII, SPI, SPEI). The study’s main finding is that most of the indices registered increasing trends but not statistically significant at the country level. The only exceptions are the drought-related indices (CDD, SPI, and SPEI), for which we found a dominant decreasing trend. In the northern half of the country, increasing trends were prevailing, and in the southern one, those decreasing registered a broader spatial coverage. SPI and SPEI recorded mainly significant changes: SPI trends are almost equally divided between increasing and decreasing. For SPEI, more than 70% of the country was characterized by a significant decrease.
      PubDate: 2022-11-17
       
  • Non-stationary modeling of wet-season precipitation over the Inner
           Mongolia section of the Yellow River basin

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      Abstract: Abstract Climate system anomalies and intensified human disturbances have different impacts on the hydrological cycle at different scales, chiefly leading to prominent spatio-temporal heterogeneities in precipitation distributions. It is of great significance to accurately detect the non-stationary changes of precipitation. Focusing on the Inner Mongolia section of the Yellow River basin which is considered as the key ecological barrier in northern China, the best probabilistic model with time-varying moments was established to fit the wet-season precipitation series from 1988 to 2017 for 38 meteorological stations. Considering four three-parameter distributions, time was used as covariate to describe the linear or nonlinear change of each parameter, and model optimization was performed by Akaike Information Criterion. Combined with conventional methods including the Trend-Free Pre-Whitening Mann–Kendall trend test and Sen’s slope estimator, the non-stationary behaviors of wet-season precipitation variability were quantitatively captured. Results showed that the generalized gamma distribution performed best in fitting the wet-season precipitation series in the study area, characterized with high skewness and heavy tails. The non-stationary characteristics of the wet-season precipitation were obvious in most areas during the past 30 years, especially in the central region. The non-stationarities of wet-season precipitation manifested in a downward trend in the mean value, an increase in dispersion degree and significant changes in distribution shape with time, consequently adding the uncertainty to wet-season precipitation process and raising the risk of extreme conditions. The findings of this study provide scientific references to water resources management, drought resistance, and disaster reduction.
      PubDate: 2022-11-17
       
  • Synoptic climatology of weather parameters associated with tropical
           cyclone events in the coastal areas of Bay of Bengal

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      Abstract: Abstract Tropical cyclones (TCs) are the most devastating weather phenomena that trigger massive loss of property and life in the coastal areas of the Bay of Bengal (BoB). Scientific understanding of TC occurrence can aid policy-makers and residents in coastal areas to take the necessary actions and do appropriate planning in advance. In this study, we aimed to examine the possible linkage of weather parameters with the deadly 22 TC events in the BoB from 1975 to 2014 using principal component analysis, K-mean clustering, and general circulation model (GCMs). Results showed that among 22 TCs, cluster 1 belongs to 12 TCs that occurred under the same atmospheric situation when the sea level pressure (SLP) was below 990 hPa, and the temperature ranged from 30 to 39 °C. A deep negative anomaly in SLP and temperature was observed up to 500 hPa levels. In contrast, a negative depression was found at 300 hPa geopotential height (GPH) over the study area. Cluster 2 consisted of 9 TCs when SLP was below 1000 hPa, and the average temperature was 33.5 °C. A strong negative anomaly was noticed at surface level up to 500 hPa GPH, but dramatically, this depression was completely absent at 300 hPa geopotential height over the BoB and entire coastal region. Cluster 3 contained only 1 TC when the atmospheric circumstances were completely diverse, and the SLP was above 1000 hPa. The results of the GCM model revealed that the SLP was lower, and the temperature was higher over BoB compared to the North Indian Ocean. We identified the larger depression of SLP and unpredictable temperature anomalies in the upper atmosphere that can trigger enormous unpredictability throughout the atmospheric level, leading to severe TCs. The outcomes of this study can improve our understanding of weather variables in the upper atmospheric column for forecasting the TC system more accurately in the future.
      PubDate: 2022-11-17
       
  • Winter wheat evapotranspiration and irrigation requirements across
           tropical and sub-tropical producing regions in Brazil

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      Abstract: Abstract Agricultural intensification is often seen as an appropriate approach to meet the growing demand for agricultural products. In regions affected by seasonal or chronic water scarcity, closing the yield gap depends on irrigation. Brazil is the second-largest wheat importer of the world and irrigated wheat has high yield potential in Brazil, especially in tropical and sub-tropical regions of the country. The crop coefficient (Kc) is still a practical and simple way of quantifying crop irrigation requirements. In this paper, we provided Kc values based on a robust experimental dataset across different wheat-producing regions in Brazil. Four experiments were conducted in three sites [season 2017 in Piracicaba, State of São Paulo (Southeast region); season 2012 Maringá, State of Paraná (Southern region); and seasons 2016 and 2017 Rondonópolis, State of Mato Grosso) (Midwest region)] using lysimeters and the Bowen ratio energy balance method for crop evapotranspiration measurements. Our data showed that Kc values varied among the regions analyzed. For the Midwest region, Kc values ranged from 0.88 to 1.36; the Southeastern region from 0.81 to 1.15, and the Southern region from 0.67 to 1.01. Considering values suggested by FAO, those for arid climates should be used in the Midwest region, sub-humid values for the Southeast, and humid values in the Southern. Differently, that was observed for other crops, our database showed wheat Kc values being stable disregarding the ETo range, which might be related to relatively lower ETo during the winter.
      PubDate: 2022-11-17
       
  • Sensitivity analysis of convective and PBL parameterization schemes for
           Luban and Titli tropical cyclones

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      Abstract: Abstract Tropical cyclones (TCs) are the most distractive natural weather phenomena and cause extensive damage and socioeconomic loss over the North Indian Ocean (NIO) region. Convection and planetary boundary layer (PBL) system play a vital role in the origin and strengthening of the TCs. The various convective and PBL parameterization schemes are available in the statistical model, which integrates these processes. The efficient incorporation of these schemes is vital to enhance the performance of the numerical weather prediction (NWP) model. In the present study, twelve experiments have been designed to carry out the numerical simulations using Advance Research Weather Research and Forecasting (ARW) model. The behavior and performance of the schemes have been evaluated to verify the instantaneous forecast of the TCs. The simulated cyclone track, which is assessed with the Indian Meteorological Department (IMD) best track data, indicates that the vector displacement error and RMSE for the experiment MWBM and YWBM are < 100 km and < 10 km, respectively. The maximum sustained 10-m wind prediction shows MWKF for Luban and YWKF for Titli have the least RMSE value, accounting for 7.13 ms−1 and 9.75 ms−1. The equitable threat score (ETS) at 24-h accumulated rainfall is > 0.4 for MLBM and up to 60 mm in Luban. However, it is > 0.6 for YLBM and up to 40 mm for Titli. Based on the results and keeping the cyclone track, intensity, and rainfall, the BMJ convective scheme with the YSU and MYJ PBL has better predicting skills over the NIO region. The KF scheme has better skills in the prediction of TC intensity.
      PubDate: 2022-11-16
       
  • A critical analysis of the effect of projected temperature and rainfall
           for differential sowing of maize cultivars under RCP 4.5 and RCP 6.0
           scenarios for Punjab

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      Abstract: Abstract A simulation study was conducted for two maize cultivars (PMH1 and PMH2) under two Representative Concentration Pathways (RCPs) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and variability in rainfall. The temperature and rainfall varied from location to location so agroclimatic zone II (Ballowal Saunkhri), zone III (Ludhiana, Amritsar, and Patiala), zone IV (Bathinda), and zone V (Abohar and Faridkot) were selected for the study. The bias-corrected ensemble model data from seventeen global circulation models (GCMs) for RCP 4.5 and RCP 6.0 was used to simulate maize yield for a period of 70 years (2025–2095) using calibrated and validated CERES-Maize model. The simulated yield trend in Punjab under current dates of sowing indicated a strong negative correlation between the yield and the weather parameters under the two scenarios. Agroclimatic zones II, III, and V (Faridkot) observed an increase in temperature by 1 °C over the 70 years’ time period which led to lowering of the maize yield from high yield category (> 5000 kg/ha) to low (< 3000 kg/ha). The cv PMH1 was able to compensate these effects and performed better in agroclimatic zones II, III, and V (Faridkot). In agroclimatic zones IV (Bathinda) and V (Abohar), an increase in temperature by 2 °C is observed, which led to decline of yield categories from medium yield years to low yield years. Though the rainfall in the region was higher for low yield years, but the rainfall amount was insufficient to mitigate the impact of temperature. Under the future stabilization scenarios, amongst the current sowing dates, mid-June was found suitable in agroclimatic zones II and III and in agroclimatic zone V (Faridkot) both early and mid-June sowing dates performed well. Considering the suitable sowing dates under current farming practices, it would be easy to determine the future sowing window for maize cultivars in Punjab.
      PubDate: 2022-11-16
       
  • Correction to: Daily precipitation grids for Austria since
           1961—development and evaluation of a spatial dataset for hydroclimatic
           monitoring and modelling

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      PubDate: 2022-11-15
       
  • Anomalous rainfall trends in the North-Western Indian Himalayan Region
           (NW-IHR)

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      Abstract: Abstract The North-Western Indian Himalayan Region (NW-IHR) is the source of some of India’s major rivers and hosts a few of the highest mountain peaks in the world. The region is also a major source of water for the population living in the plains. We study the rainfall trends, magnitudes, and seasonal and annual variations in the NW-IHR during the last 120 years (1901–2020). The temporal trends in rainfall are estimated using the Mann-Kendall Test (MKT) and  Modified Mann-Kendall Test (MMKT) at 10%, 5%, and 1% significance levels. The ACC at lag-1 was calculated and tested at 5% significance level for the MMKT. The magnitude of the seasonal and annual rainfall was calculated using Sen’s Slope (SS), Simple Linear Regression (SLR), Innovative Trend Analysis (ITA), and Spearman’s Rho (SR). The variability of seasonal and annual rainfalls was studied using the Coefficient of Variation (CV). The results of our analysis revealed both increasing and decreasing trends at various levels of significance during seasonal and annual rainfalls. The majority of the districts in the northern region showed increasing trends, while the districts in the southern region showed decreasing trends. CV was higher for seasonal rainfalls as compared to annual rainfalls. Our analysis of trends and variability will help the local stakeholders for efficient planning and understand the risk and vulnerability of the region at large.
      PubDate: 2022-11-15
       
  • Evaluation of gridded precipitation products in the selected sub-basins of
           Lower Mekong River Basin

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      Abstract: Abstract Hydrological and meteorological studies demand accurate, continuous, long-term, reliable, and uniformly distributed precipitation data. Considering low density rain gauges with incomplete data in developing nations, a plethora of gridded precipitation products (GPPs) have made their place as an alternative to rain gauge records. However, GPPs house inherent errors depending on the type of data, gauge density, gridding algorithm, etc. Hence, it is crucial to evaluate them prior to their application. This study evaluated monthly products of eight GPPs over 17 years (1998-2014) – Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation data (APHRODITE), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Southeast Asian Observed dataset (SA-OBS), Climate Prediction Center Morphing Technique (CMORPH), The Tropical Rainfall Measuring Mission (TRMM)-daily products, Climate Research Unit (CRU), and Global Precipitation Climatology Center (GPCC). An entropy-based weight calculation for each statistical index and compromise programming was employed to rank the GPPs in the selected sub-basins (Nam Ngum River Basin, NRB, and Vietnam Mekong Delta, VMD) of the Lower Mekong Region (LMR) for mean and six extreme precipitation indices. The correlation coefficient (r), root mean square error (RMSE), skilled score (SS), and bias were the continuous statistical indices and probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) were the categorical indices used in this study. In terms of capturing mean monthly precipitation, GPCC outweighed all other products for both the studied basins. However, APHRODITE ranked first for daily precipitation products based on compromise programming algorithm for NRB. APHRODITE consistently recorded r between 0.85 and 0.95, RMSE between 50 and 100 mm/month, and SS between 0.72 and 0.90 for the 5 observed stations. Similarly, in case of VMD, TRMM ranked first for the daily precipitation products with r between 0.8 and 0.95, RMSE between 50 and 70 mm/month, and SS between 0.56 and 0.9 when evaluated with 11 observed stations. The APHRODITE for NRB and TRMM for VMD can be used as alternate to gauge data for hydrological and meteorological studies.
      PubDate: 2022-11-15
       
  • Evaluation of the CMIP5 GCM rainfall simulation over the Shire River Basin
           in Malawi

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      Abstract: Abstract Data scarcity globally has impeded our understanding of hydrological processes. This study was aimed at evaluating skills of models in reproducing past climate in the Shire River Basin (SRB) in Malawi for future climate impact assessments. The study used observed and simulated data by Global Climate Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A total of 52 models were considered comprising a mixture of models in the representative concentration pathways of RCP4.5 and RCP6.0. The mean annual bias, correlation, extreme precipitation indices obtained from the RClimdex package of R software program, and frequency distributions were used to quantify the accuracy of the GCM simulations. On the precipitation indices, emphasis was placed on the frequency indices (number of heavy precipitation days (RR ≥ 10 mm), R10mm; number of very heavy precipitation days (RR ≥ 20 mm), R20mm; number of extremely heavy precipitation days (RR ≥ 25 mm), R25mm; consecutive dry days (RR < 1 mm), CDD; and consecutive wet days (RR ≥ 1 mm), CWD) and on the intensity indices (daily maximum precipitation, RX1day; 5-day maximum precipitation, RX5days; annual total wet-day precipitation, PRCPTOT; and very wet days, (R95P)). Study results have revealed that there is variation in the performances of individual models and that the overall performance of the models over the SRB is generally low. Some individual models perform better than the multi-model ensemble. Results have also shown the better performance of the following models: ACCESS1-3_rcp45_r1i1p1, BNU-ESM_rcp45_r1i1p1, CSIRO-Mk3-6-0_rcp45_r3i1p1, CSIRO-Mk3-6-0_rcp45_r8i1p1, and GFDL-ESM2G_rcp45_r1i1p1 of medium–low emission pathway, RCP4.5, in replicating the historical extreme precipitation for Shire River Basin.
      PubDate: 2022-11-15
       
  • Croatian high-resolution monthly gridded dataset of homogenised surface
           air temperature

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      Abstract: Abstract Homogenised climatological series and gridded data are the basis for climate monitoring and climate change detection. Considering this, monthly mean temperatures from 122 Croatian stations were homogenised, and high-resolution monthly gridded data were developed for the 1981–2018 period. Homogenisation needs to be performed on stations from the same climate region; therefore, hierarchical clustering is introduced to define those climate regions in Croatia. The breaks of homogeneity were detected by the standard normal homogeneity test on 54 stations. Regression kriging was applied to produce monthly grids for each month in the analysed period. The quality of the interpolation assessed by leave-one-out cross-validation resulted in a root mean square error of 0.7 °C. The quality of spatial interpolation is supplemented with normalised error maps. The derived homogenised station data and monthly grids are necessary for national climate monitoring, the production of climate normals and the estimation of trends. After 1999, average annual anomalies from the 30-year climate standard normal 1981–2010 were positive and up to 1.4 °C warmer than the average and only occasionally negative. The measured amount, sign and significance of the trend were accurately captured on the trend maps calculated from the monthly maps. Significant strong warming was observed and mapped over the entire Croatian territory in April, June, July, August and November. It was stronger inland than on the coast. Annual trends were significant and ranged from 0.3 °C/decade to 0.7 °C/decade. There was no observational evidence of enhanced elevation-dependent warming over elevations from 750 to 1594 m.
      PubDate: 2022-11-14
       
  • Probability distribution characteristics of summer extreme precipitation
           in Xinjiang, China during 1970–2021

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      Abstract: Abstract Based on the daily precipitation data of 96 stations in Xinjiang, China, from 1970 to 2021, the trend of summer extreme precipitation indices and their regional characteristics are analyzed. The generalized extreme value (GEV) model is used to investigate the probability distribution characteristics of summer extreme precipitation indices in northern, southern, and eastern Xinjiang. The results show that (1) The summer maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day) of most stations in Xinjiang showed an increasing trend, while the summer consecutive dry days (CDD) showed a decreasing trend. (2) The climatology (mean intensity) of RX1day, RX5day, and CDD at most stations in northern Xinjiang were more than 10 mm, more than 15 mm, and less than 25 days, respectively, while those at most stations in southern and eastern Xinjiang were less than 10 mm, less than 15 mm, and more than 25 days. The regional averaged climatology and inter-annual variability of RX1day/RX5day (CDD) in southern and eastern Xinjiang were smaller (larger) than that in northern Xinjiang. (3) The 20-year return level (RL20) of RX1day, RX5day, and CDD at stations in northern Xinjiang were 19.38–56.57 mm, 28.05–70.91 mm, and 22.51–51.05 days, respectively. The RL20 of RX1day, RX5day, and CDD at stations in southern Xinjiang were 21.31–46.07 mm, 23.99–72.89 mm, and 14.94–89.80 days, respectively. The RL20 of RX1day, RX5day, and CDD at stations in eastern Xinjiang were 8.89–36.36 mm, 10.13–50.66 mm, and 26.75–92.00 days, respectively. Compared with northern Xinjiang, there were lesser RX1day and RX5day events, with weaker intensity and smaller variability in southern and eastern Xinjiang. And the CDD events were opposite.
      PubDate: 2022-11-12
       
 
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