Bivariate drought frequency analysis using the copula method

Droughts are major natural hazards with significant environmental and economic impacts. In this study, two-dimensional copulas were applied to the analysis of the meteorological drought characteristics of the Sharafkhaneh gauge station, located in the northwest of Iran. Two major drought characteristics, duration and severity, as defined by the standardized precipitation index, were abstracted from observed drought events. Since drought duration and severity exhibited a significant correlation and since they were modeled using different distributions, copulas were used to construct the joint distribution function of the drought characteristics. The parameter of copulas was estimated using the method of the Inference Function for Margins. Several copulas were tested in order to determine the best data fit. According to the error analysis and the tail dependence coefficient, the Galambos copula provided the best fit for the observed drought data. Some bivariate probabilistic properties of droughts, based on the derived copula-based joint distribution, were also investigated. These probabilistic properties can provide useful information for water resource planning and management.     

Changes in future climate indices using Statistical Downscaling Model in the upper Baro basin of Ethiopia

Climate indices are the diagnostic tools used to define the state of climate system. The main objective of this study was to investigate the climate index change in future time periods in the upper Baro basin of Ethiopia. The daily precipitation and maximum and minimum temperature data were downscaled using Statistical Downscaling Model (SDSM). The precipitation and temperature data were estimated according to UK Hadley Centre Coupled Model version 3 (HadCM3) global circulation model with medium-high (A2) and medium-low emission (B2) scenarios in three future time interval periods. The De Martonne Aridity Index and Pinna Combinative Index change of the future time periods centered at 2020s, 2050s, and 2080s was computed. The analysis was based on percentage change between the baseline and three future time periods. The monthly De Martonne Aridity Index result showed that there are months in the dry season classified as semi-dry with value of less than 20 and the land needs irrigation in these months. The Pinna Combinative Index value also showed the same trend like that of the De Martonne Aridity Index and a high correlation coefficient was noticed, verifying similar trend of the two indices for the three future time period changes. Overall, humidity is expected to decrease in most of the months in the three future time periods for both A2 and B2 emission scenarios because of the increment of temperature in the future.     

Evaluation of Global Gridded Precipitation and Temperature Datasets against Gauged Observations over the Upper Tekeze River Basin,Ethiopia

The availability of satellite and reanalysis climate datasets and their applicability have been greatly promoted in hydro-climatic studies. However, such climatic products are still subject to considerable uncertainties and an evaluation of the products is necessary for applications in specific regions. This study aims to evaluate the reliability of nine gridded precipitation and temperature datasets against ground-based observations in the upper Tekeze River basin(UTB) of Ethiopia from 1982 to 2016. Precipitation, maximum temperature(T_(max)), minimum temperature(T_(min)), and mean temperature(T_(mean)) were evaluated at daily and monthly timescales. The results show that the best estimates of precipitation are from the Eart H2 Observe, WFDEI, and ERA-Interim reanalysis data Merged and Bias-corrected for the Inter-Sectoral Impact Model Intercomparison Project(EWEMBI), and the Climate Hazards Group Infra Red Precipitation with Station data(CHIRPS) datasets. The percentage biases and correlation coefficients(CCs) are within ±15% and 0.5, respectively, for both EWEMBI and CHIRPS at the two timescales. All products underestimate the drought conditions indicated by the standardized precipitation index(SPI), while the EWEMBI and CHIRPS datasets show higher agreement with the observations than other datasets. The Tmean estimates produced by the ECMWF ReAnalysis version 5(ERA5) and the Climate Hazards Group Infra Red Temperature with Station data(CHIRTS) are the closest to the observations, with CCs of 0.65 and 0.55, respectively, at the daily timescale. The CHIRTS and EWEMBI datasets show better representations of Tmax(T_(min)), with CCs of 0.69(0.72) and 0.62(0.68), respectively,at the monthly timescale. The temperature extremes are better captured by the ERA5(T_(mean)), CHIRTS(T_(max)), and EWEMBI(T_(min)) datasets. The findings of this study provide useful information to select the most appropriate dataset for hydrometeorological studies in the UTB and could help to improve the regional representation of global datasets.     

Modeling fields of river runoff (a case study for the Lena River basin)

The potential of the model approach to the construction of mean annual fields (maps) of specific runoff for large territories from meteorological data is demonstrated for the Lena River basin. The ECOMAG (ECOlogical Model for Applied Geophysics), the physically based distributed model of river runoff formation is used to simulate hydrological parameters. Methodological aspects of calibration of the spatial fields of model parameters are discussed. The results of runoff hydrograph calculations are compared with the data of hydrometric observations at 12 gaging stations for the period of 1966-2009. The field of mean annual specific runoff in the Lena River batin computed with the ECOMAG model is compared with the map of specific runoff constructed from the data on water discharge in the river network. The comparative analysis of consistency between the fields is provided, and the possible sources of errors are considered.     

Trend analysis of rainfall time series for Sindh river basin in India

The main goal of this paper is to estimate a set of optimal seasonal, daily, and hourly values of atmospheric turbidity and surface radiative parameters Ångström’s turbidity coefficient (β), Ångström’s wavelength exponent (α), aerosol single scattering albedo (ω_o), forward scatterance (F_c) and average surface albedo (ρ_g), using the Brute Force multidimensional minimization method to minimize the difference between measured and simulated solar irradiance components, expressed as cost functions. In order to simulate the components of short-wave solar irradiance (direct, diffuse and global) for clear sky conditions, incidents on a horizontal surface in the Metropolitan Area of Rio de Janeiro (MARJ), Brazil (22° 51′ 27″ S, 43° 13′ 58″ W), we use two parameterized broadband solar irradiance models, called CPCR2 and Iqbal C, based on synoptic information. The meteorological variables such as precipitable water (u_w) and ozone concentration (u_o) required by the broadband solar models were obtained from moderate-resolution imaging spectroradiometer (MODIS) sensor on Terra and Aqua NASA platforms. For the implementation and validation processes, we use global and diffuse solar irradiance data measured by the radiometric platform of LabMiM, located in the north area of the MARJ. The data were measured between the years 2010 and 2012 at 1-min intervals. The performance of solar irradiance models using optimal parameters was evaluated with several quantitative statistical indicators and a subset of measured solar irradiance data. Some daily results for Ångström’s wavelength exponent α were compared with Ångström’s parameter (440–870 nm) values obtained by aerosol robotic network (AERONET) for 11 days, showing an acceptable level of agreement. Results for Ångström’s turbidity coefficient β, associated with the amount of aerosols in the atmosphere, show a seasonal pattern according with increased precipitation during summer months (December–February) in the MARJ.     

On the application of copula theory for determination of probabilistic characteristics of springflood

Considered is the possibility of using copula theory for creating joint probability distributions of springflood peak discharges and flow volumes taking account of the relations between discharges and flow volumes. For approximation of marginal distributions, Gumbel distribution was used for peak discharges, and two-parameter gamma distribution, for flow volumes. Joint two-dimensional distribution was built as a marginal distribution function which was set as one of the three one-parameter Archimedean copulas using different ways of determining their parameters. The best results were obtained for Gumbel-Hougaard copula using the method of maximum likelihood to determine its parameters. Major flood risk estimates determined from one- and two-dimensional probability distributions of their characteristics were compared with each other. Demonstrated are the benefits of using two-dimensional probability distributions of flood characteristics as compared with one-dimensional distributions for probabilistic estimation of floods. The data on springflood peak discharges and flow volumes in the Belaya and Vyatka rivers were used for this study.     

Estimation of possible climatic changes of river runoff in the Northern Dvina River basin in the 21st century

Presented is a method of studying possible climate-driven changes of river runoff characteristics, according to which the atmosphere-ocean general circulation models are used for computing the climate system impact, whereas the response of a watershed to given disturbance is estimated using the model of runoff formation in a river basin. The study is carried out for the Northern Dvina River basin. Estimates of possible changes (relative to the reference period) by the middle and late 21st century of such water regime characteristics as mean annual and maximum water discharge, as well as mean discharge values for the spring, summer-fall and winter seasons, are presented.     

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Theoretical and Applied Climatology - For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water...     

Probabilistic forecasting of seasonal drought behaviors in the Huai River basin,China

Results from high resolution 7-km WRF regional climate model (RCM) simulations are used to analyse changes in the occurrence frequencies of heat waves, of precipitation extremes and of the duration of the winter time freezing period for highly populated urban areas in Central Europe. The projected climate change impact is assessed for 11 urban areas based on climate indices for a future period (2021–2050) compared to a reference period (1971–2000) using the IPCC AR4 A1B Scenario as boundary conditions. These climate indices are calculated from daily maximum, minimum and mean temperatures as well as precipitation amounts. By this, the vulnerability of these areas to future climate conditions is to be investigated. The number of heat waves, as well as the number of single hot days, tropical nights and heavy precipitation events is projected to increase in the near future. In addition, the number of frost days is significantly decreased. Probability density functions of monthly mean summer time temperatures show an increase of the 95th percentile of about 1–3 °C for the future compared with the reference period. The projected increase of cooling and decrease of heating degree days indicate the possible impact on urban energy consumption under future climate conditions.     

2022年夏季长江流域干旱的水循环模拟和分析

基于WRF-WVT水汽追踪模式,对2022年6—8月长江流域极端干旱情况下的水循环进行模拟研究,分析了长江流域蒸散发对长江流域局地和非局地降水的影响。结果表明,2022年夏季干旱导致长江中下游陆地水储量在5—8月期间减少100～150 mm。6—8月长江流域约45％的蒸散发在当地和华北形成降水,其中6月长江流域蒸散发主要贡献当地降水,而7、8月对当地和华北降水的贡献大致相等。6—8月长江流域蒸散发贡献的当地降水逐月减少,总量为8.2×10⁷ m³(长江流域平均91.2 mm),并且降水强度越高当地蒸散发贡献率越小,对当地降水贡献最大的区域为四川盆地附近(最大超过40％)。长江流域蒸散发为华北提供的降水在6—8月先增多后小幅度减少,总量为5.3×10⁷ m³(华北平均58.4 mm),并且降水强度越高长江流域蒸散发贡献率越大。2022年夏季长江流域蒸散发对当地和华北地区暴雨的贡献率都为12％左右。     

Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River,Cambodia

Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site).     

Observed changes of drought/wetness episodes in the Pearl River basin, China, using the standardized precipitation index and aridity index

Monthly precipitation data of 42 rain stations over the Pearl River basin for 1960–2005 were analyzed to classify anomalously wet and dry conditions by using the standardized precipitation index (SPI) and aridity index (I) for the rainy season (April–September) and winter (December–February). Trends of the number of wet and dry months decided by SPI were detected with Mann-Kendall technique. Furthermore, we also investigated possible causes behind wet and dry variations by analyzing NCAR/NCEP reanalysis dataset. The results indicate that: (1) the Pearl River basin tends to be dryer in the rainy season and comes to be wetter in winter. However, different wetting and drying properties can be identified across the basin: west parts of the basin tend to be dryer; and southeast parts tend to be wetter; (2) the Pearl River basin is dominated by dry tendency in the rainy season and is further substantiated by aridity index (I) variations; and (3) water vapor flux, moisture content changes in the rainy season and winter indicate different influences of moisture changes on wet and dry conditions across the Pearl River basin. Increasing moisture content gives rise to an increasing number of wet months in winter. However, no fixed relationships can be observed between moisture content changes and number of wet months in the rainy season, indicating that more than one factor can influence the dry or wet conditions of the study region. The results of this paper will be helpful for basin-scale water resource management under the changing climate.     

Modeling water and heat balance components for the river basin using remote sensing data on underlying surface characteristics

The method of the AVHRR-3 (NOAA) radiometer measurement data subject processing is produced for the retrieval of underlying surface temperature and several vegetation characteristics under cloud-free conditions. A technology for deriving the values of these parameters from the MODIS (EOS/Terra and Aqua) radiometer data is developed. The estimation of the temperature and vegetation characteristics is carried out for the Seim River basin (Kursk region) with the catchment area of 7460 km² for 2003–2005 vegetation seasons. Practical coincidence of estimations of AVHRR- and MODIS-derived temperatures, as well as the coincidence with ground observation results, is revealed. Statistics of these estimation errors is analyzed. Satellite-derived estimations of land surface temperature (LST) and vegetation characteristics are used for the calibration and verification of the developed model of the vertical heat and water transfer in the soil-vegetation-atmosphere system (SVAT). The model is intended for calculations of evapotranspiration, soil water and heat content, latent and sensible heat fluxes, and other water and heat balance components. The abilities to compute these parameters using the satellite estimations of the leaf area index and projective vegetation cover fraction as the model parameters and LST satellite estimations as the model input variable are investigated.     

Assessment of projected agro-climatic indices over Awun river basin,Nigeria for the late twenty-first century

This paper examines changes in rainfall effectiveness indices of the Awun basin in Nigeria during the late twenty-first century for agricultural applications with outputs from high-resolution regional climate model (RCM) simulations. The RCM simulations are driven by two global climate models for a reference period (1985–2004) and a future period (2080–2099) and for RCP4.5 (a scenario with some mitigation) and RCP8.5 (a business as usual scenario) forcings. Simulations are provided for the control (1985–2004) and scenario (2080–2099) periods. Observations from synoptic station are used for bias-correction. Three indices being local onset date, seasonality index (SI), and hydrologic ratio (HR) are analyzed. Onset and HR are tested with two evapotranspiration (ET_p) models. Farmers’ perceptions are also collected to validate trends of rainfall indices for the present-day climate. We found that onset dates do not depend much on the ET_p models used, and farmers’ perceptions are consistent with predicted rainfall patterns. Present-day climate trend shows an early onset. However, onset is projected to be late in future and the delay will be magnified under the business as usual scenario. Indeed, average onset date is found on the 5th May for present-day while in the future, a delay about 4 and 8 weeks is projected under RCP4.5 and RCP8.5 scenarios respectively. SI is between 0.80 and 0.99, and HR is less than 0.75 for all scenarios, meaning respectively that (i) the rainy season will get shorter and (ii) the area will get drier in the future compared to the present-day. Local stakeholders are forewarned to prepare for potential response strategies. A continuous provision of forecast-based rainfall indices to support farmer’s decision making is also recommended.     

Climatic change of inland river basin in an arid area: a case study in northern Xinjiang, China

We examined climate variability at two timescales for northern Xinjiang, China: one is of the past 500?years using dendrochronology data and the other is of the past 50?years using meteorological station data. The regression models built from the 50-year period were used to reconstruct the climate of the 500-year period. The results indicate that climate underwent many alternating warm–cold and wet–dry periods in the past 500?years. For the 50-year period, we applied the Mann–Kendall jump test to data from 48 meteorological stations to identify possible transition points of temperature and precipitation. For this period, we also analyzed the impacts of latitude, altitude, slope aspect, and human activities on climate variability, aiming to recognize major factors that influence regional climate variability. The results show a warming and wetting trend in the recent 50?years in northern Xinjiang. We determined that natural pattern variability is dominant in the long-term climate variability in the region, but human impacts are non-negligible in the past 50?years. Regional climate variability may be associated with or driven by latitude, altitude, ecosystems, topography, and human activities. The study provides an empirical evidence of the unique regional characteristics of inland river basin in an arid area over the global climate change background.     

北方地区小麦蚜虫气象适宜度预报模型构建

根据1958—2015年我国北方地区8个主产省（市）小麦蚜虫分省发生面积和发生程度资料、1958—2015年601个气象站点相应逐日气象资料和农业气象站小麦发育期资料,采用相关分析、主成分分析和逐步回归等方法,并利用相关系数法进行因子普查,结合小麦蚜虫适宜生理气象指标和华北、黄淮小麦生育期规律,筛选影响小麦蚜虫年发生程度的关键气象因子,构建分区域的小麦蚜虫气象适宜度预报模型,并将气象适宜度指数划分为非常适宜、适宜、较适宜、不适宜4个等级,以反映气象条件对小麦蚜虫发生发展的适宜程度。结果表明：筛选出影响华北小麦蚜虫年发生程度的8个关键气象因子,影响黄淮小麦蚜虫年发生程度的6个关键气象因子。建立的华北、黄淮模型回代检验等级准确率分别为91.2%,93.1%,2016—2018年3年外推预报平均准确率均在75%以上;利用黄淮模型反演苏皖两省2016—2018年小麦蚜虫发生等级、异地检验3年预报效果均较理想。模型适用于从气象角度对华北、黄淮及江淮地区小麦蚜虫发生等级进行监测和预报。     

用逐步判别分析方法作铜仁地区夏旱的长期预报

利用1959-1998年北半球500hPa、100hPa高度、北太平洋海温等网格点资料以及本区地面气象要素等资料,应用具有引进和剔出因子双重功能的逐步判别方法,建立了铜仁地区关于夏旱和夏季降水3个预报对象的判别方程.结果表明,该方法的历史拟合率和独立试报都较理想.