Accuracy of precipitation network in different geophysical regions in India

For any type of hydro meteorological studies it Is imperative that an optimum design of network of raingauge stations is determined taking into consideration various factors influencing specific purpose for which such designs are envisaged. In the present paper an attempt has been made to determine the relative accuracy of the precipitation network designed for estimation of normal areal precipitation in comparison to the standard prescribed by World Meteorological Orgamsatlon. It is observed that III the present case the proposed network is fairly accurate for the purpose for which it has been designed.

Disentangle Kinetic From Equilibrium Fractionation Using Primary (δ17O, δ18O, δD) and Secondary (Δ17O, dex) Stable Isotope Parameters on Samples From the Swiss Precipitation Network

Since 1971 water isotope measurements are being conducted by the Climate and Environmental Physics Division at the University of Bern on precipitation, river- and groundwater collected at several places within Switzerland. The water samples were stored in glass flasks for later analyses with improved instrumentation. Conventional isotope ratio measurements on precipitated water from all stations of the network are well correlated as expected. However, Δ17O as well as dex is anticorrelated to these isotope ratio. The combination of these parameters allow to investigate dependencies on temperature, turbulence factor, and humidity of these values as well as to look into the importance and relative contributions of kinetic to equilibrium fractionations. We used published temperature dependent fractionation factors in combination with a simple Rayleigh model approach to investigate the importance of the meteorological parameters on the isotope ratios. A direct comparison of measured and modeled isotope ratios for primary (δ17O, δ18O and (δD) as well as secondary isotope parameters (Δ17O and dex) is shown.

Dependence Types in a Binarized Precipitation Network

<p>In a network of binarized precipitation (i.e., wet or dry value), the connection or dependence between each pair of nodes can occur following one or more of the following conditions: wet‐wet, dry‐dry, wet‐dry, or dry‐wet. Here, we firstly investigate the different types of dependence, year by year, within a precipitation network of binarized variables. We compare the sample estimate of the probability of co‐occurrence (or occurrence with a lag time within ±3 days) of each of the four possible combinations with respect to the correspondent confidence interval in hypothesis of independence. We develop a procedure to efficiently assess the dependence behavior of all couples of nodes within the network and apply the methodology to a network of rain gauges covering Europe and north Africa.</p>

Network-based approach to explore basin network comparing observed and satellite dataset

<p>Complex network is a relatively young, multidisciplinary field with an objective to unravel the spatiotemporal interaction in natural processes. Though network theory has become a very important paradigm in many fields, the applications in the hydrology field are still at an emerging stage.  In this study, we employed the Pearson correlation coefficient and Spearman correlation coefficient as a similarity measure with varying threshold ranges to construct the precipitation network of the Ganga River Basin (GRB). Ground-based observed dataset (IMD) and satellite precipitation product (TRMM) are used. Different network properties such as node degree, degree distribution, clustering coefficient, and architecture were computed on each resultant precipitation network of GRB. We also ranked influential grid points in the precipitation network by using weighted degree betweenness to identify the importance of each grid station in the network Our results reveal that the choice of correlation method does not significantly affect the network measures and reconfirm that the thresholds significantly influence network construction and network properties in the case of both datasets. The spatial distribution of the clustering coefficient value is high to low from center to boundary and inverse in the case of degree.  In addition, there is a positive correlation between the average neighbor degree and node degree. Again, we analyzed the architecture of precipitation networks and found that the network has a small world with random network behavior.   Our results also indicated that both products have similar network measures and showed similar kinds of spatial patterns.</p>

Exploring the Clustering Property and Network Structure of a Large-Scale Basin’s Precipitation Network: A Complex Network Approach

Understanding of the spatial connections in rainfall is a challenging and essential groundwork for reliable modeling of catchment processes. Recent developments in network theory offer new avenues to understand of the spatial variability of rainfall. The Yellow River Basin (YRB) in China is spatially extensive, with pronounced environmental gradients driven primarily by precipitation and air temperature on broad scales. Therefore, it is an ideal region to examine the availability of network theory. The concepts of clustering coefficient, degree distribution and small-world network are employed to investigate the spatial connections and architecture of precipitation networks in the YRB. The results show that (1) the choice of methods has little effect on the precipitation networks, but correlation thresholds significantly affected vertex degree and clustering coefficient values of precipitation networks; (2) the spatial distribution of the clustering coefficient appears to be high–low–high from southeast to northwest and the vertex degree is the opposite; (3) the precipitation network has small-world properties in the appropriate threshold range. The findings of this paper could help researchers to understand the spatial rainfall connections of the YRB and, therefore, become a foundation for developing a hydrological model in further studies.

Reflecting on the status of precipitation data collection in Alaska: a case study

Measuring precipitation, especially solid, at high latitudes is a challenge. In Alaska (USA), the extreme topography, large regional extent, and varying climate result in annual precipitation values ranging from 120 in. (3,050 mm) to 10 in. (254 mm). The state's precipitation network recently has expanded significantly, but there is still room for improvement. A recent intensity-duration-frequency (idf) exercise for the state showed that: (1) although density and spatial coverage of stations have increased, large areas in northern and western Alaska are still without gauge coverage; (2) the number of gauges at higher elevations is insufficient, although growing (e.g., the number of stations above 1,000 ft (305 m) increased from 26 gauges in 1963 to 134 gauges in 2012); (3) solid precipitation is difficult to quantify, and at unmanned sites, the phase of precipitation (liquid or solid) is hard to determine, as air temperature is often the only other measured variable; (4) corrections for gauge undercatch need to be made but too often information on the shielded status of gauges and wind speed is lacking; and (5) in the recent idf analysis only about one-third of the existing and historical stations were used because of data-quality issues. Obviously, overall improvements in precipitation data collection can and should be made.

A monthly precipitation database for Spain (1851–2008): reconstruction, homogeneity and trends

The compilation and reconstruction of a dataset integrated by 66 long monthly precipitation series, covering mainland Spain and the Balearic Islands, is presented. The reconstruction is based on the hypothesis that the cessation of data recording at one observatory is followed by the establishment of a new observatory very close to the closed one. In order to detect and adjust for possible multiple change points or shifts that could exist in the precipitation series, the R-package CLIMATOL V2.0 is used. This method enables to take advantage of the whole historical Spanish precipitation network in the detection and correction of inhomogeneities. The analysis of annual precipitation trends indicate a high temporal variability. Negative trends dominate for the period 1951–2008 but not for all observatories. On the other hand, positive trends can be detected in the northern Spain for 1902–2008.