Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin,Iran

ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).     

Spatio-temporal trends of precipitation,its aggressiveness and concentration,along the Pacific coast of South America (36–49°S)

ABSTRACT

Precipitation is the most critical climatic element that directly affects the availability of water resources. The objective of this study was to describe and discuss spatio-temporal patterns of annual precipitation, its aggressiveness, and its concentration along the southwest coast of South America (36°–49°S) from 1930 to 2006. An annual and multi-decadal analysis was applied to 107 sampling stations distributed throughout this region, using the Mann-Kendall test (MK), and the Sampling Uncertainty Analysis (SUA) coupled with Gumbel probability density function (SUA-Gumbel). The analysis revealed positive but not significant trends in annual precipitation and aggressiveness for the region between 36° and 44°S, at least during the last 50 years of the analysed period. However, a significant decrease in annual precipitation and aggressiveness was observed between 44° and 49°S during the same period. The annual concentration of precipitation became slightly more seasonal in the last 50 years within the entire study area.     

Mapping of climatic parameters under climate change impacts in Iran

ABSTRACT

This research aims to provide a comprehensive evaluation of climate change effects on temperature, precipitation and potential evapotranspiration over the country of Iran for the time periods 2010–2039, 2040–2069 and 2070–2099, and under scenarios A2 and B2. After preparation of measured temperature and precipitation data and calculation of potential evapotranspiration for the base time period of 1960–1990 for 46 meteorological stations (with a nationwide distribution), initial zoning of these three parameters over the country was attempted. Maximum and minimum temperatures and values of precipitation were obtained from the HadCM3 model under scenarios A2 and B2 for the three time periods, and these data were downscaled. Corresponding maps were prepared for the three parameters in the three time periods, and spatial and temporal variations of these climatic parameters under scenarios A2 and B2 were extracted and interpreted. Results showed that the highest increase in temperature would occur in western parts of the country, but the highest increase of potential evapotranspiration would occur in the central region of Iran. However, precipitation would vary temporally and spatially in different parts of the country depending on the scenario used and the time period selected.

Editor Z. W. Kundzewicz; Associate editor not assigned     

Analysis of trends of annual and seasonal precipitation from 1956 to 2000 in Guangdong Province,China

Abstract

The trends of annual, seasonal and monthly precipitation in southern China (Guangdong Province) for the period 1956–2000 are investigated, based on the data from 186 high-quality gauging stations. Statistical tests, including Mann-Kendall rank test and wavelet analysis, are employed to determine whether the precipitation series exhibit any regular trend and periodicity. The results indicate that the annual precipitation has a slightly decreasing trend in central Guangdong and slight increasing trends in the eastern and western areas of the province. However, all the annual trends are not statistically significant at the 95% confidence level. The average precipitation increases in the dry season in central Guangdong, but decreases in the wet season, meaning that the precipitation becomes more evenly distributed within the year. Furthermore, the analysis of monthly precipitation suggests that the distribution of intra-annual precipitation changes over time. The results of wavelet analysis show prominent precipitation with periods ranging from 10 to 12 years in every sub-region in Guangdong Province. Comparing with the sunspot cycle (11-year), the annual precipitation in every sub-region in Guangdong province correlates with Sunspot Number with a 3-year lag. The findings in this paper will be useful for water resources management.

Editor Z.W. Kundzewicz; Associate editor Sheng Yue

Citation Dedi Liu, Shenglian Guo, Xiaohong Chen and Quanxi Shao, 2012. Analysis of trends of annual and seasonal precipitation from 1956 to 2000 in Guangdong Province, China. Hydrological Sciences Journal, 57 (2), 358–369.     

Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach

Assessing the probability of extreme precipitation events is consequential in civil planning. This requires an understanding of how return values change with return periods, which is essentially described by the generalized extreme value (GEV) shape parameter. Some works in the field suggest a constant shape parameter, while our analysis indicates a non-universal value. We re-analysed an older precipitation dataset (169 stations) extended by Norwegian data (71 stations). We showed that while each set seems to have a constant shape parameter, it differs between the two datasets, indicating regional differences. For a more comprehensive analysis of spatial effects, we examined a global dataset (1495 stations). We provided shape parameter maps for two models and found clear evidence that the shape parameter depends on elevation, while the effect of latitude remains uncertain. Our results confirm an explanation in terms of dominating precipitation systems based on a proxy derived from the Köppen-Geiger climate classification.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR not assigned     

Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales

Abstract

The aim of this paper is to quantify meteorological droughts and assign return periods to these droughts. Moreover, the relation between meteorological and hydrological droughts is explored. This has been done for the River Meuse basin in Western Europe at different spatial and temporal scales to enable comparison between different data sources (e.g. stations and climate models). Meteorological drought is assessed in two ways: using annual minimum precipitation amounts as a function of return period, and using troughs under threshold as a function of return period. The Weibull extreme value type 3 distribution has been fitted to both sources of information. Results show that the trough-under-threshold precipitation is larger than the annual minimum precipitation for a specific return period. Annual minimum precipitation values increase with spatial scale, being most pronounced for small temporal scales. The uncertainty in annual minimum point precipitation varies between 68% for the 30-day precipitation with a return period of 100 years, and 8% for the 120-day precipitation with a return period of 10 years. For spatially-averaged values, these numbers are slightly lower. The annual discharge deficit is significantly related to the annual minimum precipitation.

Citation Booij, M. J. & de Wit, M. J. M. (2010) Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales. Hydrol. Sci. J. 55(8), 1289–1301.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.

Editor Z. W. Kundzewicz; Associate editor Q. Zhang     

Comparative analysis of the seasonality of hydrological characteristics in Slovakia and Austria / Analyse comparative de la saisonnalité de caractéristiques hydrologiques en Slovaquie et en Autriche

Abstract

The main objective of this study is to compare the seasonality of selected precipitation and runoff characteristics in Austria and Slovakia. Monthly seasonality indices are analysed to interpret the long-term climatic behaviour, while the seasonality of extremes is analysed to understand flood occurrence. The analysis is based on mean monthly precipitation data at 555 (Austria) and 202 (Slovakia) stations, annual maximum daily precipitation at 520 (Austria) and 56 (Slovakia) stations, and mean monthly runoff and annual maximum floods at 258 (Austria) and 85 (Slovakia) gauging stations. The results suggest that the seasonality of the selected hydrological characteristics is an important indicator of flood processes, but varies considerably in space. The seasonality of extreme flood events and, hence flood processes, tends to change with the flood magnitude. This change is more pronounced in the lowland and hilly regions than it is in the mountains. Both in Austria and Slovakia, decades of flood seasonality exist.     

Climate changes and their impacts on water resources in semiarid regions: a case study of the Wei River basin,China

Understanding the impacts of climate change and human activity on the hydrological processes in river basins is important for maintaining ecosystem integrity and sustaining local economic development. The objective of this study was to evaluate the impact of climate variability and human activity on mean annual flow in the Wei River, the largest tributary of the Yellow River. The nonparametric Mann–Kendall test and wavelet transform were applied to detect the variations of hydrometeorological variables in the semiarid Wei River basin in the northwestern China. The identifications were based on streamflow records from 1958 to 2008 at four hydrological stations as well as precipitation and potential evapotranspiration (PET) data from 21 climate stations. A simple method based on Budyko curve was used to evaluate potential impacts of climate change and human activities on mean annual flow. The results show that annual streamflow decreased because of the reduced precipitation and increased PET at most stations. Both annual and seasonal precipitation and PET demonstrated mixed trends of decreasing and increasing, although significant trends (P < 0.05) were consistently detected in spring and autumn at most stations. Significant periodicities of 0.5 and 1 year (P < 0.05) were examined in all the time series. The spectrum of streamflow at the Huaxian station shows insignificant annual cycle during 1971–1975, 1986–1993 and 1996–2008, which is probably resulted from human activities. Climate variability greatly affected water resources in the Beiluo River, whereas human activities (including soil and water conservation, irrigation, reservoirs construction, etc.) accounted more for the changes of streamflow in the area near the Huaxian station during different periods. The results from this article can be used as a reference for water resources planning and management in the semiarid Wei River basin. Copyright © 2012 John Wiley & Sons, Ltd.     

Investigation of spatial and temporal variability of precipitation in Iran over the last half century

Investigation of the precipitation phenomenon as one of the most important meteorological factors directly affecting access to water resources is of paramount importance. In this study, the precipitation concentration index (PCI) was calculated using annual precipitation data from 34 synoptic stations of Iran over a 50-year period (1961–2010). The trend of precipitation and the PCI index were analyzed using the Mann–Kendall test after removing the effect of autocorrelation coefficients in annual and seasonal time scales. The results of zoning the studied index at annual time scale revealed that precipitation concentration follows a similar trend within two 25-year subscales. Furthermore, the PCI index in central and southern regions of the country, including the stations of Kerman, Bandarabbas, Yazd, Zahedan, Shahrekord, Birjand, Bushehr, Ahwaz, and Esfahan indicates a strong irregularity and high concentration in atmospheric precipitations. In annual time scale, none of the studied stations, had shown regular concentration (PCI < 10). Analyzing the trend of PCI index during the period of 1961–2010 witnessed an insignificant increasing (decreasing) trend in 16 (15) stations for winter season, respectively, while it faced a significant negative trend in Dezful, Saghez, and Hamedan stations. Similarly, in spring, Kerman and Ramsar stations exhibited a significant increasing trend in the PCI index, implying significant development of precipitation concentration irregularities in these two stations. In summer, Gorgan station showed a strong and significant irregularity for the PCI index and in autumn, Tabriz and Zahedan (Babolsar) stations experienced a significant increasing (decreasing) trend in the PCI index. At the annual time scale, 50 % of stations experienced an increasing trend in the PCI index. Investigating the changes in the precipitation trend also revealed that in annual time scale, about 58 % of the stations had a decreasing trend. In winter, which is the rainiest season in Iran, about 64 % of stations experienced a decreasing trend in precipitation that caused an increasing trend in PCI index. Comparing the spatial distribution of PCI index within two 25 years sub-periods indicated that the PCI index of the second sub-period increased in the spring time scale that means irregularity of precipitation distribution has been increased. But in the other seasons any significant variations were not observed. Also in the annual time scale the PCI index increased in the second sub-period because of the increasing trend of precipitation.     

Suitability of CLIGEN precipitation estimates based on an updated database and their impacts on urban runoff: a case study of the Great Lakes Region,USA

ABSTRACT

Weather generators rely on historical meteorological records to simulate time series of synthetic weather sequences, the quality of which has direct influence on model applications. The climate generator CLIGEN’s database has recently been updated to comprise consistent historical records from 1974 to 2013 (updated CLIGEN database, UCD) compared to the current database in which records are of different lengths. In this study, CLIGEN’s performance in estimating precipitation using UCD (eight stations) and the subsequent impact on urban runoff simulations (371 stations) were evaluated in the Great Lakes Region, USA. Generally, UCD-based precipitation could replicate observed daily precipitation up to the 99.5th percentile, but maximum precipitation was underestimated. Results from the Long-Term Hydrologic Impact Assessment model using UCD-based precipitation showed about 0.57 billion cubic meters more (14.9%) average annual runoff being simulated compared with simulations based on the current CLIGEN database. Overall, CLIGEN with the updated database was found suitable for providing precipitation estimates and for use with modeling urban runoff or urbanization effects.     

Using entropy theory to improve the definition of homogeneous regions in the semi-arid region of Brazil

ABSTRACT

Although the semi-arid region of Brazil appears to be homogeneous due to drought conditions, there is a great deal of variability in climatic elements in the region, so that the definition of homogeneous regions will provide the deployment of measures appropriate for each locality. However, the limited information on climatic parameters in the region makes it difficult to define these regions. This problem can, however, be alleviated by the use of entropy theory. Therefore, this study aimed to investigate the potential of the theory to identify hydrologically homogeneous regions for conditions of the semi-arid region of Brazil. Entropy-based Disorder Index (DI) data were computed, based on monthly precipitation and monthly water balance (precipitation – reference evapotranspiration) for 290 gauge stations. For defining homogeneous regions, cluster analysis was utilized, using the data on geographical information about rain gauges (latitude and longitude), annual precipitation, annual water balance, coefficient of skewness, coefficient of kurtosis and DI. The identification of homogeneous regions in the Brazilian semi-arid region was only possible when the grouping of stations was performed, based on DI for precipitation and latitude. Results showed the definition of seven homogeneous regions in the semi-arid region of Brazil.     

Wavelet combined innovative trend analysis for precipitation data in the Euphrates-Tigris basin,Turkey

ABSTRACT

In this study, annual and seasonal precipitation trend analysis was performed in the Euphrates-Tigris basin, Turkey, using innovative trend analysis (ITA) and discrete wavelet transformation. In this context, it was seen that there is a downward trend in winter, spring and annual precipitation, whereas precipitation has an increasing tendency in summer and autumn seasons, in the greater part of the basin. When annual and seasonal data were decomposed into wavelet components, the most significant trends were observed for high-periodic wavelet components, such as D3 (8-year), D4 (16-year) and D5 (32-year), where these components represent the periods of the precipitation data. Then, the relationship between North Atlantic Oscillation (NAO) and trend in precipitation was investigated. In this regard, it was found that there could be a significant relationship between the NAO and precipitation trends of the Euphrates-Tigris basin, especially in winter, based on the wavelet ITA.     

洪涝异常与云南楚雄地区强震的相关分析

利用云南省123个气象台站1960年以来的降雨量资料,使用年降雨量距平百分率方法,分析楚雄强震与降雨量的关系,结果发现：年降雨量距平百分率≥0.25的台站数大于8,次年云南地区皆有Ms≥6.0地震,也是楚雄地区发生6级强震背景条件;楚雄地区强震前表现涝异常,即该区域强震具有涝—震特征.     

Long‐term change of daily and multi‐daily precipitation in southern Sweden

Daily rain series from southern Sweden with records dating back to the 1870s have been analysed to investigate the trends of daily and multi‐day precipitation of different return periods with emphasis on the extremes. Probabilities of extreme storms were determined as continuously changing values based on 25 years of data. An extra set of data was used to investigate changes in Skåne, the southernmost peninsula of Sweden. Another 30‐year data set of more than 200 stations of a dense gauge network in Skåne was used to investigate the relation between very large daily rainfall and annual precipitation. The annual precipitation has increased significantly all over southern Sweden due to increased winter precipitation. There is a trend of increasing maximum annual daily precipitation at only one station, where the annual maximum often occurs in winter. The number of events with a short return period is increasing, but the number of more extreme events has not increased. Daily and multi‐daily design storms of long return periods determined from extreme value analysis with updating year by year are not higher today than during the last 100 years. The largest daily storms are not related to stations with annual rainfall but seem to occur randomly. Copyright © 2013 John Wiley & Sons, Ltd.     

A new temperature based method to separate rain and snow

This paper presents the development and testing of a new method to estimate daily snowfall from precipitation and associated temperature records. The new method requires two variables; the threshold mean daily air temperature at which 50% of precipitation is considered snow, and the temperature range within which mixed precipitation can occur. Sensitivity analyses using 15 climate stations across south‐western Alberta, Canada, and ranging from prairie to alpine regions investigates the sensitivity of those two variables on mean annual snowfall (MAS), the coefficient of determination, and the MAS‐weighted coefficient of determination. Existing methods, including the static threshold method, one linear transition method used by Quick and Pipes, and the Leavesley method employed in the PRMS hydrological modelling system are compared with the new method, using a total of 963 years of daily data from the 15 climate stations used for the sensitivity analyses. Four different approaches to using the two input variables (threshold temperature and range) were tested and statistically compared: mean annual variables based on the 15 stations, mean annual variables for each station, mean monthly variables for each station, and a sine curve representing seasonal variation of the variables. In almost all cases the proposed new method resulted in higher MAS‐weighted coefficients of determination, and, on average, they were significantly different from those of other methods. The paper concludes with a decision tree to help decide which method and approach to apply under a variety of data availabilities. Copyright © 2008 John Wiley & Sons, Ltd.     

Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions

ABSTRACT

In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions.     

Evaluating the suitability of three gridded-datasets and their impacts on hydrological simulation at Scotty Creek in the southern Northwest Territories,Canada

In the southern Northwest Territories (NWT), long time series of historical observations of climate and hydrology are scarce. Gridded datasets have been used as an alternative to instrumental observations for climate analysis in this area, but not for driving models to understand hydrological processes in the southern NWT. The suitability of temperature and precipitation from three-gridded datasets (Australian National University Spline [ANUSPLIN], ERA-Interim, and Modern-Era Retrospective Analysis for Research and Application, Version 2 [MERRA-2]) as forcings for hydrological modelling in a small subcatchment in the southern NWT are assessed. Multiple statistical techniques are used to ensure that structural and temporal attributes of the observational datasets are adequately compared. Daily minimum and maximum air temperatures in gridded datasets are more similar to observations than precipitation. The ANUSPLIN temperature time series are more statistically similar to observations, based on population statistics and temporal structure, than either of ERA-Interim or MERRA-2. The gridded datasets capture the seasonal and annual seasonal variability of precipitation but with large biases. ANUSPLIN precipitation compares better with observations than either ERA-Interim or MERRA-2 precipitation. The biases in these gridded datasets affect run-off simulations. The biases in hydrological simulations are predictable from the statistical differences between gridded datasets and observations and can be used to make informed choices about their use.     

Comparative performance analysis of climate re-analysis approaches in Angola

This paper examines the performance of three climate re-analysis datasets in Angola, namely the ERA-40, NCEP-r1 and JRA-55, by comparing annual and seasonal estimates of precipitation, surface air temperature and evaporation with ground observation measurements. The observational dataset describes a region poorly covered by international climate databases and it is believed that most of its data have not been used in the data assimilation procedures of the climate models. This paper therefore provides a fresh look at the performance of these climate re-analysis datasets in a vast area where distance and civil war have hindered ground monitoring efforts. The re-analysis exercises offer better temperature estimates than precipitation estimates. When offered, the evaporation estimates from all three products are very poor. The three products are able to describe the main features of the spatial distribution of average annual precipitation and temperature, but struggle to reproduce the temporal changes of these variables. The results from a set of performance criteria show that the correlation between the observed ground measurements and re-analysis estimates is poor overall and the NSE values indicate that the average measured value at each location is usually a better estimate than the re-analysis estimate.

EDITOR A. Castellarin

ASSOCIATE EDITOR S. Kanae     

Evaluation of efficiency of different estimation methods for missing climatological data

Reliable estimation of missing data is an important task for meteorologists, hydrologists and environment protection workers all over the world. In recent years, artificial intelligence techniques have gained enormous interest of many researchers in estimating of missing values. In the current study, we evaluated 11 artificial intelligence and classical techniques to determine the most suitable model for estimating of climatological data in three different climate conditions of Iran. In this case, 5 years (2001–2005) of observed data at target and neighborhood stations were used to estimate missing data of monthly minimum temperature, maximum temperature, mean air temperature, relative humidity, wind speed and precipitation variables. The comparison includes both visual and parametric approaches using such statistic as mean absolute errors, coefficient of efficiency and skill score. In general, it was found that although the artificial intelligence techniques are more complex and time-consuming models in identifying their best structures for optimum estimation, but they outperform the classical methods in estimating missing data in three distinct climate conditions. Moreover, the in-filling done by artificial neural network rivals that by genetic programming and sometimes becomes more satisfactory, especially for precipitation data. The results also indicated that multiple regression analysis method is the suitable method among the classical methods. The results of this research proved the high importance of choosing the best and most precise method in estimating different climatological data in Iran and other arid and semi-arid regions.