Actual Precipitation Data Research Articles

Моделювання впливу кліматичних змін на продуктивність агросектора

The article proposes a conceptual approach to modeling the impact of climate change on the productivity of the agrarian sector, substantiates the factors of climate change, and forms the information base of the study. Specifically, rainfall data was taken from the Climate Hazards Group InfraRed Precipitation with Station database (CHIRPS), which is a quasi-global dataset of precipitation amounts over 30+ years. CHIRPS incorporates 0.05° resolution satellite imagery together with data from local stations to create gridded rainfall time series to analyze trends and monitor seasonal drought. In order to obtain rainfall data for each of the selected countries, the Large-Scale International Borders (LSIB) database was used. This data is taken from two other datasets: the LSIB Lines Vector File and the World Vector Shoreline (WVS) from the National Geospatial-Intelligence Agency (NGA). Additional precipitation information was obtained using the Google Earth Engine after performing procedures with a sample of geolocation data for each country, obtaining actual precipitation data for the observation period, exporting the data and processing them to bring the results to the annual figure. The formed information base on the factors of climate change has allowed to develop models of productivity of grain production in the countries that are the largest wheat producers in the world. The results of the development of models led to the conclusion that climate change affects crop yields in countries that are leading producers of cereals. The obtained results can be used to predict changes in yield and production depending on climatic parameters such as temperature and precipitation, as well as to determine the optimal and extreme values of climatic factors.

Ranch Economics of Using Targeted Grazing to Create Wildfire Fuel Breaks on Public Land

Targeted grazing in the Great Basin has been used to reduce cheatgrass fuel loads and enhance wildfire control. In this project, we evaluate the economic impact of targeted grazing on cow-calf ranches across southeast Oregon, northeast Nevada, and southwest Idaho when practices such as fencing, water hauling, and herding are necessary for producers to accomplish desired grazing outcomes. Large and small representative ranch models were developed for major land resource areas 23, 24, and 25 where applicable. Typical targeted grazing costs and practices were obtained from producer and agency focus groups in each state and introduced into ranch economic models. Targeted grazing periods begin 1 mo before typical Bureau of Land Management turnout in the spring and again in the fall after typical public land grazing ends. In each year, targeted grazing would occur when the previous growing season (September to March and April to August) had more than 25% of median precipitation based on PRISM historical data. Hence, targeted grazing could occur in the spring, fall, or both depending on precipitation. In both seasons, targeted grazing continues until the desired animal unit months of forage are removed. One hundred precipitation data sets were randomly generated using Excel to mimic the actual number of drought years in the spring and fall. The model is a 40-yr recursive linear programming model using 100 cattle price sets and the 100 precipitation sets. Results are averaged over 10 000 model runs and compared with scenarios with no targeted grazing and targeted grazing based on the actual precipitation data set. Results show changes in cattle herd size, hay sales, and the economic impacts to the public land ranch operation for two ranch sizes in each of the three major land resource areas by state.

Comparison of Satellite Precipitation Products: IMERG and GSMaP with Rain Gauge Observations in Northern China

Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probability of Detection (POD), Relative Bias (RB), Root-Mean-Squared Error (RMSE), and Fractional Standard Error (FSE) are among the assessment metrics, as are the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). We examined the spatial distribution of cumulative precipitation and the temporal distribution of hourly average precipitation for three severe precipitation occurrences using these assessment metrics. The IMERG products capture strong precipitation centers that are compatible with the gauge observations, especially in extreme precipitation events in areas with relatively flat terrain and low-altitude (≤1000 m). Both IMERG (National Aeronautics and Space Administration, NASA) and GSMaP (Japan Aerospace Exploration Agency, JAXA) satellite-based QPE products have precipitation peaks in advance (2–4 h) and generally underestimate (overestimate) precipitation when the actual precipitation is heavy (light). The satellite-based QPE products generally overestimate the heavy rainfall caused by non-typhoons and underestimate the heavy rainfall caused by typhoons. The GSMaP products may have the capacity to detect short-term rainstorm events. The accuracy of satellite-based QPE products may be influenced by precipitation intensity, sensors, terrain, and other variables. Therefore, in accordance with our recommendations, more ground rainfall stations should be used to collect actual precipitation data in regions with high levels of spatial heterogeneity and complex topography. The data programmers should strengthen the weights computation retrieval technique and fully utilize infrared (IR)-based data. Furthermore, this study is expected to give helpful feedback to the algorithm developers of IMERG and GSMaP products, as well as those researchers into the use of IMERG and GSMaP satellite-based QPE products in applications.

Financial and Social Factors Influencing the Use of Unconventional Water Systems in Single-Family Houses in Eight European Countries

A modern model of water management should take into account, first of all, its responsible consumption of both tap water and water from unconventional sources. With this in mind, a study was conducted to determine the financial efficiency of rainwater harvesting systems (RWHSs) and greywater recycling systems (GWRSs) in residential buildings located in eight European countries. At the first stage, volumetric reliability was determined for different tank capacities for actual precipitation data. An economic analysis was carried out for six variants in which rainwater and greywater were used in various combinations for toilet flushing, washing, and garden watering. The implementation of alternative water systems was found to be financially unprofitable in four cities: Warsaw, Bratislava, Budapest, and Stockholm. For these cities, the variant with the lowest life cycle cost (LCC) level was always Variant 0, with conventional installations. The opposite situation was observed in the other four locations (Lisbon, Madrid, Rome, and Prague), where Variant 0 was not found to be financially profitable for any of the calculation cases analyzed. Additionally, a survey was conducted to determine the effect of social aspects, which is often the greatest barrier to the implementation of new or unknown technologies. In most of the countries surveyed, rainwater is more acceptable to society as an alternative water source than greywater. For hygiene reasons, the use of these two systems for washing clothes was of greatest concern.

Numerical Modeling of the Behavior of a Destructive Rain Flood on a Mountain River

The objective of this study was to provide the most accurate presentation of the behavior of a disastrous rain flood, which had resulted in destruction of a dam, casualties, and significant material damage. The problems set were solved by methods of numerical modeling in the two-dimensional setting applying the STREAM 2D CUDA software package. To calculate the rain flood, a catchment model of the Durso River was developed on a triangular non-uniform mesh, adapted to the river channel and the main inflows. To ensure direct numerical modeling of the dam’s destruction, a model was developed, which included the water area of the reservoir, the dam, composed of non-homogeneous earth material, and a downstream section of the river from the dam to the river mouth. The main results of the study were: the hydrograph of the water discharge of the rain flood, calculated by the actual precipitation data; a description of the washout of the earth dam composed of non-homogeneous materials, resulting from water spill over the dam crest, and a desctiption of the downstream spread of the breach wave.

The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China

Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.

Error correction on summer model precipitation of China based on the singular value decomposition

In this paper, singular value decomposition (SVD) is used to correct the National Climate Center business model CGCM, thus improving the forecast results. By performing the SVD decomposition using field coupling between the summer rainfall patterns forecasted in 1983-2011 and actual rainfall values, by selecting the mode forecast and 3-7 modals that are best correlated with the live precipitation, and comparing these revised results of the different numbers of modal number, and by selecting the number of modals with the best correction effect in the China region, the revised results are obtained from the model predictions. The 2004-2009 cross hindcast experiments and the return results tested by using the anomaly correlation coefficient and the root mean square error as evaluation criterion show that the best corrections are obtained by taking the first five modals in 2004-2009 as the number of modal revised in the year. The examinations of 2004-2009 mode and actual precipitation data demonstrate the correction effect obtained by the five modals is indeed best. A comparison between the result and the systematic errors shows that the Dhamma revised results are better than the systematic errors. Taking the year of 2010 as the forecast year, using the results of the cross-examination of 27 years 1983-2009 to determine the number of modals, and analyzing the forecast results by the determined number of modals constitute a method that has a potential business value.

Statistical analysis and modeling of precipitation data

The objective of the present paper is to perform statistical analysis and modeling of actual precipitation data collected in 20 stations in Greece. Although our analysis and statistical modeling procedure utilizes precipitation data from Greece, the same methodology can be applied to similar data or other environmental variables. We identify the probability density function (PDF) that best characterizes the behavior of the data, that being the Frechet PDF, a member of the family of the Generalized Extreme Value (GEV) family of distributions. In addition to obtaining useful information, such as the expected rainfall along with its variance and confidence limits, we are able to cluster the stations into 7 groups that have similar response. Furthermore, we introduce time dependence into the Frechet probability distribution by characterizing the location and scale parameters to be functions of time and other well known, but more complex covariates such as the North Atlantic Oscillation index. This approach enables a more flexible and realistic model to quantify the uncertainty inherent into the system, and allows for more complex dependence patterns.

Development and Application of a Multipollutant Model for Atmospheric Mercury Deposition

Abstract A multipollutant model, the Community Multiscale Air Quality model paired with the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (CMAQ-MADRID), is extended to include a comprehensive treatment of mercury processes and is applied to the simulation of the atmospheric deposition of sulfate and mercury over the United States during 1996. Model performance is evaluated first by comparison with annual sulfate wet deposition data from the National Atmospheric Deposition Program’s National Trends Network; the coefficient of determination r 2 is 0.77, and the model normalized error and bias are 53% and −8%, respectively. When actual precipitation data are used to scale the deposition fluxes, r 2 improves to 0.91 and the error and bias change to 42% and −41%, respectively. The scaled results underscore a tendency of the model to underestimate sulfate wet deposition. Model performance for mercury wet deposition is then evaluated by comparison with data from the Mercury Deposition Network. For annual mercury wet deposition, r 2 is 0.28 and the normalized error and bias are 81% and 73%, respectively, when the modeled precipitation data are used. Model performance improves when actual precipitation data are used to scale deposition fluxes: r 2 increases to 0.41 and the error and bias decrease to 40% and 29%, respectively. The model reproduces the spatial pattern of sulfate wet deposition adequately with an increasing gradient from the upper Midwest to the Northeast, that is, from upwind to downwind of large sulfur dioxide sources in the Ohio River Valley. However, the model tends to overestimate mercury wet deposition in the Northeast downwind of these sources that also emit significant amounts of mercury. This “Pennsylvania anomaly” may be due to a partial misrepresentation of the mercury reduction–oxidation cycle, uncertainties in the dry deposition of divalent gaseous mercury HgII, incorrect speciation of mercury emissions, and/or uncharacterized emissions in the upper Midwest.

A Semi-Quantitative, Regional Precipitation Data Set for Studying African Climates of the Nineteenth Century, Part I. Overview of the Data Set

This article describes a historical archive of proxy and actual precipitation data that extends the African climate record back to the early nineteenth century. The `proxy' archive includes verbal, documentary references which contain information related to rainfall conditions, such as references to famine, drought, agriculture or the nature of the rainy season. The precipitation archive includes all observations made in Africa during the nineteenth century. It consists of records for 60 stations in Algeria, 87 stations in South Africa and 304 stations scattered over the rest of Africa. Information is particularly plentiful for the 1880s and 1890s. The two parts have been be combined into a semi-quantitative regional data set indicating annual rainfall conditions in terms of anomaly classes (e.g., normal, dry, wet). This data set extends from the early nineteenth century to 1900 and distinguishes seven anomaly classes, using numbers ranging from –3 to +3 to represent very wet, wet, good rains, normal, dry, drought, and severe drought. The regionalization is based on 90 geographical regions shown via studies of the modern precipitation record to be climatically homogeneous with respect to the interannual variability of rainfall. The regional aggregation allows the voluminous fragmentary information available in historical sources to be used systematically to produce multi-year time series that can be directly integrated into the modern record for each region. The resultant time series can also be subjected to statistical analysis, in order to investigate nineteenth century climate over Africa. Spatial detail is added to the data set by utilizing a unique methodology based on climatic teleconnections established from studies of rainfall variability over Africa. The historical information and station records have been combined into a file containing a regional anomaly value for up to 90 geographic regions and the years 1801–1900. Gaps necessarily remain in the matrix, but as early as the 1820s over 40 regions are represented. By the 1880s generally around 70 regions or more are represented.