Distribution Of Mean Annual Precipitation Research Articles

Elevation Dependency of Precipitation over Southern Slope of Central Himalaya

Precipitation plays vital roles in the global water cycle, knowledge of the spatial and temporal variation of the precipitation is essential to understanding extreme environmental phenomena such as floods, landslides, and drought. In this paper, the integrated characteristics of precipitation during 1980–2016 over Nepal along with the seasonal elevation dependency of precipitation were examined for three different regions over the country using Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. The spatial distribution of mean annual precipitation varies significantly with the highest (lowest) precipitation of ~5500 (~100) mm/year in the Arun valley (Manang and Mustang). The precipitation regime of the country is determined by the contribution of the monthly precipitation amount with distinct spatial gradients between the eastern and the western sides during pre-monsoon, post-monsoon, and winter seasons. On the contrary, the spatial distribution of monsoon precipitation tends to more heterogeneous with visible differences between the lowland, midland, and highlands as similar to the annual one. Further, elevation dependency of seasonal precipitation revealed that the winter and post-monsoon precipitation distribution in western and central are very similar, whereas post-monsoon precipitation was found slightly higher than winter season in the eastern region. The highest precipitation areas in eastern and central region are located between 2000-2500 m, which is between 500 and 1000 m in the western region of the country. Overall, the pre-monsoon, summer monsoon and annual precipitation increases gradually with elevation upto 2500 m and then decreases with increasing elevation, whereas winter and post-monsoon precipitation are almost identical to each elevation interval of 500 m.

Applying support vector machines optimized by genetic algorithm for estimating the spatial distribution of mean annual precipitation

The main goal of the present study was to present an alternative spatial interpolation technique in order to estimate the mean annual precipitation distribution within a research area. The novel spatial interpolation technique combined evolutionary algorithms and data mining methods. Specifically, a support vector machine (SVM) was used to produce the mean annual precipitation map, the structural parameters of which (cost, epsilon, and gamma) were tuned by a genetic algorithm (GA). To evaluate the performance of the developed methodology, 80 rainfall gauge stations (covering the time period 1970–2010) located within the Macta basin, Northwestern Algeria, were analyzed. The conceptual model which was formulated in order to create the mean annual precipitation map had seven topographic variables, namely latitude, longitude, altitude, the minimum, maximum, and mean altitude within a radius of 5 km from each station and the distance from the coastline. To estimate the efficiency and accuracy of the developed model, three statistical criteria, the root mean square error, the mean square error, and the r square, had been used. The results were compared with the findings obtained by multi-linear regression analysis (MLR) with the optimized SVM model showing higher learning and predictive r square values (0.835 and 0.848, respectively) than the values obtained by the MLR model (0.248 and 0.388, respectively). Another goal was the examination of the spatio-temporal pattern concerning the mean annual precipitation. The spatial-temporal trend was estimated using the non-parametric Mann-Kendall approach and Sen’s slope estimator. The analysis revealed significant spatial variability with 14 stations showing statistical significant downward trend, reaching −6.62 mm/year, and only 1 station showing statistical significant upward trend, +3.62 mm/year. The knowledge of the spatio-temporal trend of mean annual precipitation is of high significance in studies concerning climate change, groundwater assessments, and flood-related applications.

Exploring Mean Annual Precipitation Values (2003–2012) in a Specific Area (36°N–43°N, 113°E–120°E) Using Meteorological, Elevational, and the Nearest Distance to Coastline Variables

Gathering very accurate spatially explicit data related to the distribution of mean annual precipitation is required when laying the groundwork for the prevention and mitigation of water-related disasters. In this study, four Bayesian maximum entropy (BME) models were compared to estimate the spatial distribution of mean annual precipitation of the selected areas. Meteorological data from 48 meteorological stations were used, and spatial correlations between three meteorological factors and two topological factors were analyzed to improve the mapping results including annual precipitation, average temperature, average water vapor pressure, elevation, and distance to coastline. Some missing annual precipitation data were estimated based on their historical probability distribution and were assimilated as soft data in the BME method. Based on this, the univariate BME, multivariate BME, univariate BME with soft data, and multivariate BME with soft data analysis methods were compared. The estimation accuracy was assessed by cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results showed that multivariate BME with soft data outperformed the other methods, indicating that adding the spatial correlations between multivariate factors and soft data can help improve the estimation performance.

Seasonal and Spatial Variations of Bulk Nitrogen Deposition and the Impacts on the Carbon Cycle in the Arid/Semiarid Grassland of Inner Mongolia, China.

Atmospheric nitrogen (N) deposition is an important component that affects the structure and function of different terrestrial ecosystem worldwide. However, much uncertainty still remains concerning the magnitude of N deposition on grassland ecosystem in China. To study the spatial and temporal patterns of bulk N deposition, the levels of N (NH4 +-N and NO3 --N) concentration in rainfall were measured at 12 sites across a 1200 km grassland transect in Inner Mongolia, China, and the respective N deposition rates were estimated. The inorganic N deposition rates ranged from 4.53 kg N ha-1 to 12.21 kg N ha-1 with a mean value of 8.07 kg N ha-1 during the entire growing season, decreasing steadily from the eastern to the western regions. Inorganic N deposition occurred mainly in July and August across meadow steppe, typical steppe, and desert steppe, which corresponded to the seasonal distribution of mean annual precipitation. A positive relationship was found between inorganic N deposition and mean annual precipitation (R2 = 0.54 ~ 0.72, P < 0.0001) across the grassland transect. Annual estimation of inorganic N deposition was 0.67 Pg yr-1 in Inner Mongolia, China based on the correlation between N deposition rates and precipitation. N deposition was an important factor controlling aboveground biomass and ecosystem respiration, but has no effect on root biomass and soil respiration. We must clarify that we used the bulk deposition samplers during the entire sampling process and estimated the dissolved NH4 +-N and NO3 --N deposition rates during the entire growing season. Long-term N deposition monitoring networks should be constructed to study the patterns of N deposition and its potential effect on grassland ecosystem, considering various N species, i.e., gaseous N, particle N, and wet N deposition.

English

Based on the interpolation method ordinary kriging, it was compared the spherical, exponential, Gaussian and circular models that best fits in the spatial distribution of mean annual precipitation for the state of Mato Grosso do Sul (MS). The rainfall data from 32 sites were obtained from the database of the National Water Agency - ANA, in the period 1954-2013. The variographic parameters evaluated were nugget effect, level and reach. From these, were calculated the Index of Spatial Dependence. The criteria used to choose the best transitive theoretical mathematical model were the root of the root mean square error (RMSE), Pearson‟s correlation coefficient (d), mean absolute error (EMA) and mean error percentage (EMP), index of agreement (d) and coefficient of determination (R²). The transitive theoretical mathematical models circular, spherical and Gaussian and can be used with satisfactory performance for data interpolation of annual rainfall in State of Mato Grosso do Sul.

Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru

Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

Mapping the mean annual precipitation of China using local interpolation techniques

Spatially explicit precipitation data are required in the research of hydrology, agriculture, ecology, and environmental sciences. In this study, two established techniques of local ordinary linear regression (OLR) and geographically weighted regression (GWR) and two new local hybrid interpolation techniques of local regression-kriging (LRK) and geographically weighted regression kriging (GWRK) were compared to predict the spatial distribution of mean annual precipitation of China. Precipitation data from 684 meteorological stations were used in the analysis, and a stepwise regression analysis was used to select six covariates, including longitude, latitude, elevation, slope, surface roughness, and river density. The four spatial prediction methods (OLR, GWR, LRK, and GWRK) were implemented with local regression techniques with different number of neighbors (50, 100, 150, and 200). The prediction accuracy was assessed at validation sites with the root mean squared deviation, mean estimation error, and R-square values. The results showed that LRK outperforms OLR and GWRK outperforms GWR, indicating that adding the kriging of regression residuals can help improve the prediction performance. GWRK gives the best prediction but the accuracy of estimation varies with the number of neighborhood points used for modeling. Although LRK is outperformed by GWRK, LRK is still recommended as a powerful and practical interpolation method given its computation efficiency. However, if LRK and GWRK are used to extrapolate prediction values, post-processing in the areal interpolation will be needed.

Analysis on Variation Characteristics of Precipitation Time Series in Weihe River Basin during the Past 55 Years

By using linear regression, moving average, Mann-Kendall test and Morlet wavelet analysis method, the annual, flood seasonal and dry seasonal precipitation series during 1954~2008 in Weihe River basin are analyzed. The results indicate that: ①Annual precipitation and precipitation in flood season and dry season present decreasing trends during the past 55 years, and the variation trends are not significant. ②There is an irregular spatial distribution of mean annual precipitation, which shows a decreasing trend from southeast to northwest. ③Annual precipitation exists principal periods of about 2, 10, 17 and 50 years, and the phase changes of 2 and 50 years period are most obvious. Time scales of phase change of precipitation series in flood season and dry season are roughly consistent with those in annual. The research results will provide reference for climate analysis and future weather forecasting in Weihe River basin.

Isoscapes of tree‐ring carbon‐13 perform like meteorological networks in predicting regional precipitation patterns

AbstractStable isotopes in tree rings provide climatic information with annual resolution dating back for centuries or even millennia. However, deriving spatially explicit climate models from isotope networks remains challenging. Here we propose a methodology to model regional precipitation from carbon isotope discrimination (Δ13C) in tree rings by (1) building regional spatial models of Δ13C (isoscapes) and (2) deriving precipitation maps from Δ13C‐isoscapes, taking advantage of the response of Δ13C to precipitation in seasonally dry climates. As a case study, we modeled the spatial distribution of mean annual precipitation (MAP) in the northeastern Iberian Peninsula, a region with complex topography and climate (MAP = 303–1086 mm). We compiled wood Δ13C data for two Mediterranean species that exhibit complementary responses to seasonal precipitation (Pinus halepensis Mill., N = 38; Quercus ilex L.; N = 44; pooling period: 1975–2008). By combining multiple regression and geostatistical interpolation, we generated one Δ13 C‐isoscape for each species. A spatial model of MAP was then built as the sum of two complementary maps of seasonal precipitation, each one derived from the corresponding Δ13C‐isoscape (September–November from Q. ilex; December–August from P. halepensis). Our approach showed a predictive power for MAP (RMSE = 84 mm) nearly identical to that obtained by interpolating data directly from a similarly dense network of meteorological stations (RMSE = 80–83 mm, N = 65), being only outperformed when using a much denser meteorological network (RMSE = 56–57 mm, N = 340). This method offers new avenues for modeling spatial variability of past precipitation, exploiting the large amount of information currently available from tree‐ring networks.

Analysis on Characteristic of Precipitation in Poyang Lake Basin from 1959 to 2008

Abstract Based on the daily precipitation observational data in 78 counties and municipalities in Jiangxi province in Poyang Lake Basin from 1959 to 2008, day-to-day observation data of the past 50 year are firstly completed with inverse distance weighting (IDW) and complete day-to-day precipitation observation data on PoYang Lake Basin has been established; Secondly, Mexican Hat wavelet analysis is used on periodic law of annual rainfall and precipitation in flood season in this basin. Spatial distribution of annual rainfall and precipitation in flood season in this basin is technically analyzed with GIS geo-statistics; variation tendency of total annual rainfall, sprinkle, downfall, rainstorm days and successive no-rain days are studied respectively with linear regression and credit analysis is also made on these variation tendencies. Results show that: (1) No significant changes have taken place on annual rainfall and precipitation in flood season in this basin in the past 50 years and periodic fluctuation is presented. Wavelet analysis reveals that annual rainfall has a quasi-period of 20 years and precipitation in flood season has a quasi-period of 6 years. (2) Annual rainfall and precipitation in flood season are featured by basically similar spatial distribution. Areas with heavy precipitation are concentrated in Raohe basin and Xinjiang basin in the northeast of Jiangxi province. (3) Total rainfall days and days of sprinkle in this basin are in a descending tendency; on the contrary, accumulated longest time of no rainfall in all observation station are in an ascending tendency; which indicates that risks of draught and flood occurrence in this basin are in an ascending tendency. (4) Spatial distribution of total rainfall days and mean annual precipitation differs a lot; Total rainfall days in west Jinggangshan area and central and east part of the basin are the most; spatial distribution of rainstorm days and mean annual precipitation are similar. Areas with the most rainstorm days are also concentrated in Raohe basin and Xinjiang basin in the northeast of Jiangxi province. It can be seen from this that spatial distribution of mean annual precipitation, storm rainfall and rainstorm days are closely linked with each other.

Mapping snow characteristics based on snow observation probability

AbstractMeasurement/estimation of snow water equivalent (SWE) is a difficult task in water resources studies of snowy regions. SWE point data is measured at snow courses that are normally operated with low density owing to high costs and great difficulty in reaching the stations in cold seasons. Moreover, snow is known to exhibit high spatial variability, which makes SWE studies based solely on sparse station data more uncertain. Ever‐increasing availability of satellite images is a promising tool to overcome some of the difficulties associated with analyzing spatial variability of snow. Although National Oceanic and Atmospheric Administration (NOAA) satellite images have low spatial resolution with approximately 1.1‐km pixel size, they are adequate for mapping snow cover at regional scales and enjoy a moderate length of record period. In this paper, rain and snow records of synoptic stations and the time series of NOAA‐based snow cover maps were used to map average SWE of a vast area in southwestern Iran. First, monthly and annual snow coefficient (SC) at synoptic stations were determined on the basis of analysis of hourly observation of type and amount of precipitation. Then, two new spatially distributed snow characteristics were introduced, namely, average frequency of snow observation (FSO) and monthly frequency of maximum snow observation (FMSO), on the basis of existing satellite snow observations. FSO and monthly FMSO maps were prepared by a geographic information system on the basis of snow map time series. Correlation of these two parameters with SC was studied and spatial distribution of SC was estimated on the basis of the best correlation. Moreover, the distribution of mean annual precipitation was derived by comparing a number of interpolation methods. SWE map was generated by multiplying SC and precipitation maps and its spatial variability in the region was analyzed. Copyright © 2007 Royal Meteorological Society

Identification and explanation of continental differences in the variability of annual runoff

Continental differences in the variability of annual runoff were investigated using an expanded and improved database to that used in previous work. A statistical analysis of the data, divided by continent and Köppen climate type, revealed that continental differences exist in the variability of annual runoff. The variability of annual runoff for temperate Australia, arid southern Africa and possibly temperate southern Africa were noted to be generally higher than that of other continents with data in the same climate type. A statistical analysis of annual precipitation by continent and Köppen climate type revealed that differences in the variability of annual precipitation could account for some but not all the observed differences in the variability of annual runoff. A literature review of potential causes of continental differences in evapotranspiration resulted in the hypothesis that the significantly higher variability of annual runoff in temperate Australia and possibly temperate southern Africa may be due to the distribution of evergreen and deciduous vegetation. The process model Macaque was used to test this hypothesis. The model results indicate that the variability of annual runoff may be between 1 and 99% higher for catchments covered in evergreen vegetation as opposed to deciduous vegetation, depending on mean annual precipitation and the seasonality of precipitation. It is suggested that the observed continental differences in the variability of annual runoff are largely caused by continental differences in the variability of annual precipitation and in temperate regions the distribution of evergreen and deciduous vegetation in conjunction with the distribution of mean annual precipitation and precipitation seasonality.

Statistical Relationships between Topography and Precipitation Patterns

Abstract Statistical relationships between topography and the spatial distribution of mean annual precipitation are developed for ten distinct mountainous regions. These relationships are derived through linear bivariate and multivariate analyses, using six topographic variables as predictors of precipitation. These predictors are elevation, slope, orientation, exposure, the product (or interaction) of slope and orientation, and the product of elevation and exposure. The two interactive terms are the best overall bivariate predictors of mean annual precipitation, whereas orientation and exposure are the strongest noninteractive bivariate predictors. The regression equations in many of the climatically similar regions tend to have similar slope coefficients and similar y-intercept values, indicating that local climatic conditions strongly influence the relationship between topography and the spatial distribution of precipitation. In contrast, the regression equations for the tropical and extratropical regi...