scholarly journals Monthly Precipitation Patterns in a Region Vulnerable to Climate-Related Hazards—A Case Study from Poland

Water ◽  
2016 ◽  
Vol 8 (9) ◽  
pp. 362 ◽  
Author(s):  
Wiesława Kasperska-Wołowicz ◽  
Karolina Smarzyńska ◽  
Zygmunt Miatkowski ◽  
Tymoteusz Bolewski ◽  
Ryszard Farat
Keyword(s):  
Monthly Precipitation ◽  
Precipitation Patterns

scholarly journals Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China

2021 ◽  
Author(s):  
Yujie Li ◽  
Bin Xu ◽  
Dong Wang ◽  
QJ Wang ◽  
Xiongwei Zheng ◽  
...  
Keyword(s):  
General Circulation ◽  
Critical Role ◽  
Joint Probability ◽  
General Circulation Models ◽  
Mean Value ◽  
Monthly Precipitation ◽  
Post Processing ◽  
Probabilistic Evaluation ◽  
Forecasting Performance

Abstract Monthly Precipitation Forecasts (MPF) play a critical role in drought monitoring, hydrological forecasting and water resources management. In this study, we applied two advanced Machine Learning Models (MLM) and latest General Circulation Models (GCM) to generate deterministic MPFs with a resolution of 0.5° across China. Then the Bayesian Joint Probability (BJP) modeling approach is employed to calibrate and generate corresponding ensemble MPFs. Raw and post-processing MPFs were put against gridded observations over the period of 1981–2015. The results indicated that: (1) for deterministic evaluation, the forecasting performance of MLMs was more inclined to generate random forecasts around the mean value, while the GCMs could reflect the increasing or decreasing trend of precipitation to some degree; (2) for probabilistic evaluation, the four BJP calibrated ensemble MPFs were unbiased and reliable. Compared to climatology, reliability and sharpness were all significantly improved. However, in terms of overall accuracy metric, the ensemble MPFs generated from MLMs were similar to climatology. In contrast, the ensemble MPFs generated from GCMs achieved better forecasting skill and was not dependent on forecasting regions and months. Moreover, the post-processing method is necessary that achieve not only bias-free but also reliable as well as skillful ensemble MPFs.

ANN-RBF Hybrid Model for Spatiotemporal Estimation of Monthly Precipitation Case Study

2013 ◽  
Vol 4 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Vahid Nourani ◽  
Ehsan Entezari ◽  
Peyman Yousefi
Keyword(s):  
Hybrid Model ◽  
Black Box ◽  
Monthly Precipitation ◽  
Study Region ◽  
Spatial Estimation ◽  
Box Models ◽  
Proposed Model ◽  
Temporal Estimation ◽  
Monthly Precipitation Data

For estimation of monthly precipitation, considering the intricacy and lack of accurate knowledge about the physical relationships, black box models usually are used because they produce more accurate values. In this article, a hybrid black box model, namely ANN-RBF, is proposed to estimate spatiotemporal value of monthly precipitation. In the first step a Multi Layer Perceptron (MLP) network is used for temporal estimation of monthly precipitation using the value of precipitation in previous months in the same gauging station. In the second step, Radial Basis Function (RBF) is used to estimate the value of precipitation in specific month and a spatial point within the study region, considering the value of monthly precipitation in other stations. In this regard, three commonly used RBFs’ Multi Quadric (MQ), Inverse Multi Quadric (IMQ) and Gaussian (Ga), are used for spatial estimation. Finally, the combination of these two steps leads to ANN-RBF hybrid model. The model is examined using monthly precipitation data of Ardabil plain located north western of Iran. All results show the reliable accuracy of ANN-RBF model for spatiotemporal estimation of precipitation. Furthermore, IMQ RBF yields more accurate results for spatial estimation in comparison with two other RBFs. The cross-validation scheme was also employed to validate the spatial estimation performance of the proposed model.

scholarly journals Extreme Variabilities of Rainfall and Runoff Regime within Vulnerable Hydrographic River Basins. Case Study: Elan River Basin, 2016 (Romania)

2019 ◽  
Vol 13 (1) ◽  
pp. 129-136
Author(s):  
Andreea-Violeta Tudorache
Keyword(s):  
River Basin ◽  
Surface Runoff ◽  
River Basins ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Runoff Regime ◽  
Torrential Rainfall ◽  
Maximum Flows ◽  
The Impact

Abstract The present paper analyzes the extreme variabilities of rainfall and runoff regime within vulnerable hydrographic river basins, focused on a case study: Elan river basin, year 2016. This year, due to excess rainfall, the Elan River basin was affected by torrential rainfall, warned against by orange and red code hydrological forecasts. For this reason, this study makes an analysis of the spatial and temporal variability of the surface runoff also considering the main flood events occurring in this river basin. The impact of liquid precipitation on the surface runoff will be highlighted by a statistical analysis of the relationship between monthly average flows and the sum of monthly precipitation in the river basin. The monthly flows series from the Murgeni and Poșta Elan hydrometric stations were capitalized through appropriate statistical analyses. Maximum flows were reported to the thresholds values corresponding to the Defense Levels.

scholarly journals ANALYSIS OF TREND OF THE PRECIPITATION DATA: A CASE STUDY OF KANGRA DISTRICT, HIMACHAL PRADESH

2015 ◽  
Vol 3 (9) ◽  
pp. 87-95
Author(s):  
Arijit Ganguly ◽  
Ranjana Ray Chaudhuri ◽  
Prateek Sharma
Keyword(s):  
Flood Control ◽  
Rain Gauge ◽  
Himachal Pradesh ◽  
Flood Frequency ◽  
Negative Trend ◽  
Annual Rainfall ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Level Of Significance

The current study is carried out to determine the potential trend of rainfall and assess its significance in Kangra district of Himachal Pradesh. Rainfall is a key characteristic of any watershed which plays a significant role in flood frequency, flood control studies and water planning and management. In this case study,mean monthly rainfall has been analysed to determine the variability in magnitude over the period 1950-2005.  Trend in mean monthly precipitation data and mean seasonal trends are analysed using Mann-Kendall test and Sen’s slope estimation for the data period 1950-2005. Analysis of monthly trend in precipitation shows negative trend for the months of July, August, September and October in all the rain gauge stations. However, the falling trend is significant for the month of August for Dharamshala(0.05 level of significance). Interestingly the month of June shows rising trend of rainfall in all the stations, however, at Dharamshala the trend is significant (0.01 level of significance). The winter rainfall in the month of January and February record decreasing trend, with DeraGobipur and Kangra recording significant decreasing trend for the month of January at 0.01 level of significance and 0.05 level of significance respectively. Trend analysis for annual rainfall data shows significant negative trend for Dharamshala.

scholarly journals Examining patterns and drivers of variability in playa water status on the High Plains of western Kansas, 2016–2019

2021 ◽  
Vol 2 ◽  
pp. 15-32
Author(s):  
Mark W. Bowen ◽  
Luis Lepe
Keyword(s):  
Ecosystem Services ◽  
Land Cover ◽  
Water Status ◽  
Regional Climate ◽  
Water Storage ◽  
High Plains ◽  
Monthly Precipitation ◽  
Standing Water ◽  
Precipitation Patterns ◽  
Data Layers

Playa wetlands are widely distributed across the High Plains of the central United States, providing a range of ecosystem services, such as groundwater recharge, surface water storage, and wetland habitat. Although playas are essential resources, few studies have examined the variability and controls on playa water storage. The purpose of this project is to determine how playa and watershed morphology, watershed land cover, and precipitation patterns affect timing and duration of water storage in playas. This project focuses on 92 playas distributed throughout a 10-county region in western Kansas. Playa and watershed morphology were calculated in a GIS environment and classified into quartiles based on playa and watershed surface area. Watershed tilled index (i.e., percent cropland versus grassland) was determined using 2016, 2017, 2018, and 2019 Cropland Data Layers available from the National Agricultural Statistics Service and classified as either cropland (more than 75% cropland), grassland (more than 75% grassland), or mixed. Monthly precipitation data for 2016–2019 were compiled from the Oakley 22S High Plains Regional Climate Center weather station. Playa water status for 2016–2019 was classified monthly as either standing water or dry (i.e., no visible standing water) by visually examining four-band satellite imagery with 3.7 m resolution available from Planet Explorer (www.planet.com). Playa water status is influenced by a combination of factors, including playa and watershed morphology, watershed land cover, and precipitation patterns. Larger playas have larger watersheds and standing water more frequently and for longer periods than smaller playas. Playas in cropland watersheds store water more frequently and for longer periods than playas in grassland watersheds, though differences are not statistically significant. Standing water within playas is positively correlated with monthly precipitation and reflects a short-term response to precipitation patterns, regardless of playa size or watershed land cover. The strongest controls on playa water status are playa area, monthly precipitation, and watershed area. Playas are critical resources for the High Plains, providing a range of ecosystem services that are dependent upon the playa’s ability to store water. Playa functions are under continued threat from cropland expansion, climate change, and playa and watershed modifications. To sustain playa functions in Kansas, efforts should focus on conserving larger grassland playas and reducing sediment inputs to playas in cropland watersheds.

scholarly journals Estimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Nor Zaimah Che Ghani ◽  
Zorkeflee Abu Hasan ◽  
Lau Tze Liang
Keyword(s):  
Hydrological Modeling ◽  
Gene Expression Programming ◽  
Flood Management ◽  
Hydraulic Modeling ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Urban Flood ◽  
Rainfall Station ◽  
Study Gene Expression

Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R2 is 0.886.

Effect of Climate Change on Precipitation Patterns in an Arid Region Using GCM Models: Case Study of Isfahan-Borkhar Plain

2020 ◽  
Vol 21 (2) ◽  
pp. 04020006
Author(s):  
Kaveh Ostad-Ali-Askari ◽  
Hossein Ghorbanizadeh Kharazi ◽  
Mohammad Shayannejad ◽  
Mohammad Javad Zareian
Keyword(s):  
Climate Change ◽  
Arid Region ◽  
Precipitation Patterns

scholarly journals Downscaling heavy rainfall in the subtropics – a simple approach for dynamical nesting

2007 ◽  
Vol 10 ◽  
pp. 9-16 ◽  
Author(s):  
H. Huebener ◽  
K. Born ◽  
M. Kerschgens
Keyword(s):  
Heavy Rainfall ◽  
Scale Model ◽  
Small Scale ◽  
Rainfall Events ◽  
Nested Model ◽  
Nested Models ◽  
Precipitation Patterns ◽  
Model Domain ◽  
Small Scale Model

Abstract. The simulation of local scale precipitation with nested models often suffers from large errors in the boundary rows. Advection of precipitation into the model domain of the small scale model can lead to an overestimation of precipitation in the boundary grid cells of the nested model and a drying of the interior grid area. Consequently, the finer scale structure of rainfall events of the small scale model can not evolve. These errors result from three main sources: "dynamical", "scale", and "parameterization" problems. As a first step to reduce the "parameterization" boundary errors, we propose a nesting procedure where rainwater from the driving larger scale model is converted to cloud water in the smaller scale model. The nesting method is applied to a case study of heavy rainfall in semi-arid southern Morocco. The results show the elimination of erroneous excessive rainfall in the boundary rows and slightly enhanced rainfall in the interior of the nested model domain. Additionally, fine scale structures in the precipitation patterns develop. The excessive surface runoff is clearly diminished in comparison to the standard nesting procedure. The proposed approach enables scale consistent precipitation patterns resulting from model physics and grid-resolution of the smaller scale model for the complete model domain.

scholarly journals Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil

2020 ◽  
Vol 35 (2) ◽  
pp. 187-196
Author(s):  
Sílvio Fernando Alves Xavier Júnior ◽  
Jader da Silva Jale ◽  
Tatijana Stosic ◽  
Carlos Antonio Costa dos Santos ◽  
Vijay P. Singh
Keyword(s):  
Spatial Dependence ◽  
Kendall Test ◽  
Dependence Structure ◽  
Monthly Precipitation ◽  
Data Set ◽  
Geostatistical Interpolation ◽  
Precipitation Records ◽  
Spatial Dependence Structure ◽  
Best Fit

Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.

scholarly journals Investigation of impact of climate change on small catchments using different climate models and statistical approaches

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala ◽  
M. Sheikhvaisi
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Gene Expression Programming ◽  
Monthly Precipitation ◽  
Global Circulation Models ◽  
Systems Model

Abstract The use of a statistical downscaling technique is needed to investigate the hydrological consequences of climate change on the local hydropower capacity. Global Circulation Models (GCMs) are crucial tools used in various simulations for potential climate change effects, including precipitation and temperature. Statistical downscaling methods comprise the improvement of relations between the large-scale climatic parameters and the local variables. This study presents the trend analysis of the observed variables compared to the statistically downscaled emission scenarios that are adopted from the Canadian Second Generation Earth Systems Model (CanESM2) in the basin of Göksu River which is located in Turkey. The key purpose of the research is to evaluate both the predicted monthly precipitation and the projections of GCMs within the three simulated scenarios of RCP2.6, RCP4.5, and RCP8.5 by Gene Expression Programming (GEP). In addition, the findings of statistical downscaling of monthly mean precipitation will be compared to the Linear Regression model (LR). The R-value is 0.827 and 0.755 for precipitation of the GEP model for the periods of calibrating and validation. In comparison with the LR model for the validation and calibration periods (1971–2005), the results of the GEP model prove its applicability in projecting the data of the monthly mean rainfall. Generally, in the simulated periods of 2021–2100, the mentioned scenarios forecast a decline in the monthly mean precipitation in the basin. Moreover, the scenario of RCP8.5 projects more suitably for the case study than expected under the scenarios of the RCP4.5 and RCP2.6. The mean statistically downscaled CanESM2 model was compared with the trend analysis of the areal mean precipitation (PM) over the case study area, and the trend was shown decreasing. However, the RCP 8.5 scenario has the more quasi-asymptotic for trend.

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