Evaluation of Some Path Reduction Factor Models Performance i n Tropical Location

Performance evaluation of the ITU-R. P.530-17, Ghiani and Budalal model are considered for this work. It is found that the predicted values from the ITU-R and Ghiani distance factor models are seen to gradually decrease with an increase in path length for distances below 1km. Results further suggest that for a link length of 300 m, the Ghiani model predicts a 0.2499 dB (1.059 w) to 0.3273 dB (1.078 w) precipitation loss across all four (4) stations. For the ITU-R. P.530-17 model, a 3.4741 dB (2.225 w) to 5.329 dB (3.411 w) precipitation loss is estimated across all stations while the Budalal model estimated a 2.8608 dB (1.932 w) to 4.6250 dB (2.901 w) precipitation loss across all stations. The ITU-R. P.530-17, Ghiani and Budalal model further suggest a precipitation loss in the Received Signal Strength (RSS) of a typical 5G base station operating in the four (4) stations considered to be at least -9.4733 dBm, -8.8601 dBm, and -6.2489 dBm respectively. Generally, all models are found to predict rain attenuation and distance factor values with disparities especially for link lengths above 300 m. Further research is recommended on the models for accurate prediction and improve agreement with measured values.

Geographic Location System for Identifying Urban Road Sections Sensitive to Runoff Accumulation

This paper concerns the design of a geographic location system to identify urban road sections susceptible to runoff accumulation through the analysis of the efficiency of surface drainage networks. To this end, a combination of Geographic Information Systems (GISs) and stormwater models was proposed. First, GIS hydrology tools were employed to generate all the information required to characterise urban catchments geometrically. Then, a synthetic storm was created from precipitation data obtained through spatial interpolation for a given return period. Finally, the three main hydrological processes occurring in catchments (precipitation loss, transformation and routing) were simulated using the Hydrologic Modeling System (HEC-HMS). The system was tested through a case study of an urban catchment located in the city of Santander (Spain). The results demonstrate its usefulness in detecting critical points in terms of runoff accumulation, according to the efficiency of the existing surface drainage network.

Urbanization—Its Hidden Impact on Water Losses: Prądnik River Basin, Lesser Poland

Urban development causes multiple water losses. Some of them may be ignored but some could have a huge influence on the whole catchment, including soil drought. As urban sprawl rises, space for unaffected infiltration and retention is increasingly limited. The objective of this study was to backcast and to estimate water-retention loss due to urbanization during the period of 1990–2018. We used landcover data, meteorological and hydrological data and data on soil water-holding capacity. Water-retention loss was expressed as soil water retention capacity loss, net precipitation loss and total sum of precipitation loss. Historical change in urban extension has led to large impacts on the hydrological cycle of the study area. Progressive urban development caused water-retention losses which range from 3.380 to 14.182 millions of cubic meters—depending on the methodology used. Hydrological analysis showed the lack of a significant trend (decrease trend) of low flow which is caused by the high percentage of natural land use in the upper part of catchment. Our results show that backcasting of water retention change using CLC data (a) brings new and plausible data on retention loss, (b) is possible to replicate and (c) data used are common and easy-to-get.

Global warming precipitation accumulation increases above the current-climate cutoff scale

Precipitation accumulations, integrated over rainfall events, can be affected by both intensity and duration of the storm event. Thus, although precipitation intensity is widely projected to increase under global warming, a clear framework for predicting accumulation changes has been lacking, despite the importance of accumulations for societal impacts. Theory for changes in the probability density function (pdf) of precipitation accumulations is presented with an evaluation of these changes in global climate model simulations. We show that a simple set of conditions implies roughly exponential increases in the frequency of the very largest accumulations above a physical cutoff scale, increasing with event size. The pdf exhibits an approximately power-law range where probability density drops slowly with each order of magnitude size increase, up to a cutoff at large accumulations that limits the largest events experienced in current climate. The theory predicts that the cutoff scale, controlled by the interplay of moisture convergence variance and precipitation loss, tends to increase under global warming. Thus, precisely the large accumulations above the cutoff that are currently rare will exhibit increases in the warmer climate as this cutoff is extended. This indeed occurs in the full climate model, with a 3 °C end-of-century global-average warming yielding regional increases of hundreds of percent to >1,000% in the probability density of the largest accumulations that have historical precedents. The probabilities of unprecedented accumulations are also consistent with the extension of the cutoff.

Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Precipitation measurements exhibit large cold-season biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibration of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions. In 2010, a comprehensive test site for precipitation measurements was established on a mountain plateau in southern Norway. Automatic precipitation gauge data are compared with data from a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which serves as the reference. A large number of other sensors are provided supporting data for relevant meteorological parameters. In this paper, data from three winters are used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics are used to evaluate and objectively choose the model that best describes the data. A continuous adjustment function and its uncertainty are derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis does not reveal any significant misspecifications for the adjustment function, but shows that the chosen model does not describe the regression noise optimally. The adjustment function is operationally usable because it is based only on data available at standard automatic weather stations. The results show a non-linear relationship between under-catch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allow, for the first time, derivation of an adjustment function based on measurements above 7 m s−1. This extended validity of the adjustment function shows a stabilization of the wind-induced precipitation loss for higher wind speeds.

Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Precipitation measurements exhibit large cold-season biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibrations of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions. In 2010, an extensive test-site for precipitation measurements was established at a mountain plateau in Southern Norway. Precipitation data of automatic gauges were compared with a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which served as the reference. Additionally, a large number of sensors were monitoring supportive meteorological parameters. In this paper, data from three winters were used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics were used to evaluate and objectively choose the model that is describing the data best. A continuous adjustment function and its uncertainty were derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis did not reveal any significant misspecifications for the adjustment function, but showed that the chosen model uncertainty is slightly insufficient and can be further optimized. The adjustment function is operationally usable based only on data available at standard automatic weather stations. Our results show a non-linear relationship between under-catch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allowed for the first time to derive an adjustment function with a data-tested validity beyond 7 m s−1 and proved a stabilisation of the wind-induced precipitation loss for higher wind speeds.