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Monthly precipitation samples from Singapore were collected between 2013 and 2019 for stable isotope analysis to further our understanding of the drivers of tropical precipitation isotopes, in particular, 17O‐excess. δ18O ranges from –11.34‰ to –2.34‰, with a low correlation to rainfall (r = –0.31, p = 0.014), suggesting a weak amount effect. d‐excess is relatively consistent and has an average value of 10.89‰ ± 3.45‰. Compared to high‐latitude regions, 17O‐excess in our samples generally falls in a narrower range from 2 to 47 per meg with an average of 21 ± 11 per meg. Moreover, 17O‐excess shows strong periodic variability; spectral analysis reveals 3‐month, 6‐month, and 2.7‐year periodicities, likely corresponding to intraseasonal oscillations, monsoons, and the El Niño–Southern Oscillation (ENSO), respectively. In contrast, d‐excess shows no clear periodicities. Although spectral analysis only identifies 6‐month periodicity in the δ18O time series, δ18O tracks the Nino3.4 sea surface temperature variability; the average δ18O value (–5.2‰) is higher during El Niño years than ENSO neutral years (–7.6‰). Therefore, regional convection associated with monsoons and ENSO has different impacts on δ18O, d‐excess, and 17O‐excess. 17O‐excess and d‐excess are anticorrelated and do not relate to the relative humidity in moisture source regions. Extremely low humidity and drought conditions in moisture source regions would be required to account for high 17O‐excess. Processes during transport and precipitation likely modify these two parameters, especially 17O‐excess, which no longer record humidity conditions of moisture source regions. Our findings will be useful for further modeling studies to identify physical processes during convection that alter d‐excess and 17O‐excess. Key Points: Monthly tropical precipitation in Singapore exhibits a weak amount effectRegional convection has different impacts on precipitation δ18O, d‐excess, and 17O‐excessProcesses during transport and precipitation have modified 17O‐excess, which no longer records the relative humidity in moisture source regions [ABSTRACT FROM AUTHOR]

The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean Oscillation-Algiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models. [ABSTRACT FROM AUTHOR]

This paper proposes an interpolation model for monthly rainfall in large areas of complex orography. It has been implemented in the Iberian Peninsula (continental territories of Spain and Portugal), Balearic and Canary Islands covering a territory of almost 600.000 km2. To do this a data set that comprises a total number of 11,822 monthly precipitation series has been created (11,042 provided by the Spanish Meteorological Agency and 780 provided by the National Water Resources Information System of the Portuguese Water Institute). The data set covers the period from October 1940 until September 2005. The interpolation model has been based on the assumption of two different components on monthly precipitation. The first component reflects local and seasonal characteristics and 24 different mean monthly precipitation maps (12) and SDs maps (12) compose it. It considers the varying influence of physiographic variables such as altitude and orientation. The second precipitation component reflects the synoptic pattern that dominated each month of the series and it is composed by series of anomalies of monthly precipitation (780). Anomalies have been interpolated by means of ordinary kriging once local spatial continuity was assumed. Gridded maps of each variable have been developed at 200 m resolution following a hybrid methodology that implements two different interpolation techniques. The first technique applies a regression analysis to derive maps depending on altitude and orientation; the second one is a weighting technique to consider the non‐linearity of the precipitation/altitude dependence. Cross validation has been applied to estimate the goodness of both techniques. Results show an average annual precipitation of 655 mm/year. Although this figure is only 4% less than the estimate of MAGRAMA (2004), regional and local differences are highlighted when the spatial distribution is considered. The model constitutes a comprehensive implementation considering the availability of historical records and the need of avoiding slow calculations in large territories. [ABSTRACT FROM AUTHOR]

Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961-2014 are collected, among which the data during 1961-2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions. [ABSTRACT FROM AUTHOR]

The present article investigates dry and wet periods in a large area of the Mediterranean basin. First, a stochastic model was applied to a homogeneous database of monthly precipitation values of 46 rain gauges in five regions of southern Italy. In particular, after estimating the model parameters, a set of 104 years of monthly precipitation for each rain gauge was generated by means of a Monte Carlo technique. Then, dry and wet periods were analyzed through the application of the standardized precipitation index (SPI) over 3-month and 6-month timespan (short-term) and 12-month and 24-month period (long-term). As a result of the SPI application on the generated monthly precipitation series, higher occurrence probabilities of dry conditions than wet conditions have been detected, especially when long-term precipitation scales are considered. [ABSTRACT FROM AUTHOR]

Determining the hydroelectric power potential of ungauged or poorly gauged basins gains importance parallel with the increasing electricity consumption. This study presents some simple methods to predict flow to determine the hydroelectric power potential of poorly gauged basins, such as the precipitation-elevation, average precipitation, and average basin elevation methods. Results of these methods are compared with the available flow measurements. The poorly gauged Solaklı Basin, which is located in Trabzon, in the Eastern Black Sea Region of Turkey, is selected as the pilot area. The hydroelectric power potential of the planned small hydroelectric power plants in this area is estimated using different flow prediction methods. [ABSTRACT FROM AUTHOR]

Atmospheric variables (being predictors) play a crucial role in statistical downscaling (SD) studies, which subsequently are used to assess the hydrological impacts of climate change on water resources development and management. The spatial extent of these variables has an utmost importance in development of the SD model and for projecting the precipitation series. In this paper, an attempt is made to identify the spatial extent of atmospheric variables in terms of National Center for Environmental Prediction (NCEP) grid points that result in development of an efficient SD model for precipitation. A case study of the Yamuna-Hindon interbasin is considered to illustrate the applicability of the methodology proposed for capturing the effect of spatial domain of atmospheric variables on development of the SD model. The precipitation in the area is likely to be influenced by surrounding climate, topography, atmospheric circulation, thermodynamic processes, etc. Various cases of spatial extent, which covers a combination of NCEP grid points (containing atmospheric data), are constructed and SD models are developed for each case. SD Models are evaluated for performance using statistical measures. The statistical analysis revealed that though the complete coverage of atmospheric variables gives a better result, the atmospheric activities on NCEP grid points located in Himalayan region have a higher influence on precipitation in the Yamuna-Hindon interbasin. The best performing SD model is then practiced with various emission scenarios. [ABSTRACT FROM AUTHOR]

A new statistical downscaling method was developed and applied to downscale monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical downscaling: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better downscaling results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical downscaling. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical downscaling. In conclusion, the downscaling method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically downscale large-scale variables. [ABSTRACT FROM AUTHOR]

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Rainfall represents an important factor in agriculture and food security, particularly, in the low latitudes. Climatological and hydrological studies which attempt to diagnose the hydrological cycle, require high-quality precipitation data. In West Africa, like in many parts of the world, the density of observational data is low and climate models are needed in order to perform homogeneous and complete data sets. However, climate models tend to produce systematic errors, especially, in terms of rainfall and cloud processes, which are usually approximated by physical parameterizations. In this study, a 25-year climatology of monthly precipitation in West Africa is presented, derived from a regional climate model simulation, and evaluated with respect to observational data. It is found that the model systematically underestimates the rainfall amount and variability and does not capture some details of the seasonal cycle in sub-Saharan West Africa. Thus, in its present form the precipitation climatology is not appropriate to draw a realistic picture of the hydrological cycle in West Africa nor to serve as input data for impact research. Therefore, a statistical model is developed in order to adjust the simulated rainfall data to the characteristics of observed precipitation. Assuming that the regional climate model is much more reliable in terms of atmospheric circulation and thermodynamics, model output statistics is used to correct simulated rainfall by means of other simulated parameters of the near-surface climate like temperature, sea level pressure and wind components. Monthly data is adjusted by a cross-validated multiple regression model. The resulting adjusted rainfall climatology reveals a substantial improvement in terms of the model deficiencies mentioned above. In part II of this publication, the characteristics of simulated daily precipitation is adapted to station data by applying a weather generator. Once the postprocessing approach is trained, it can be extrapolated to simulation periods, for which observational data do not exist like for instance future climate. [ABSTRACT FROM AUTHOR]

The stable isotopic composition (δ2H and δ18O) of short-term (daily) precipitation collected from October 2002 to September 2003 at two stations in a coastal, karstic area in south-western Slovenia was investigated. In addition, monthly composite samples were collected and analysed for comparison with amount-weighted monthly means. The δ2H and δ18O values obtained show a wide range and reflect seasonal climatic variations. Deuterium excess and local meteoric water lines (LMWLs) were determined and cumulative frequency analysis and coincidence tests were performed. The statistical coincidence test showed that the LMWLs calculated from monthly data for Portoroz and Kozina are coincident, but the LMWLs calculated from daily precipitation events are not. This difference could be explained by the greater variance of the isotopic composition of daily precipitation in comparison to monthly composite samples and also to the influence of evaporation during events below<1 mm at Portoroz during the extremely dry and warm spring-summer season of 2003. Finally, synoptic maps and backward trajectories of a selected precipitation event showed that changes of isotopic composition are related to mixing of air masses originating from the continent and Mediterranean cyclogenesis. [ABSTRACT FROM AUTHOR]

The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen's slope, Spearman's rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Zc = −0.784) and July (Zc = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Zc > Z0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for "high" monthly precipitation. According to the "low" monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Zc. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as "high" and "low" values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods. [ABSTRACT FROM AUTHOR]