scholarly journals Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

2021 ◽  
Vol 10 (10) ◽  
pp. 689
Author(s):  
Muhamad Afdal Ahmad Basri ◽  
Shazlyn Milleana Shaharudin ◽  
Kismiantini ◽  
Mou Leong Tan ◽  
Sumayyah Aimi Mohd Najib ◽  
...  
Keyword(s):  
Missing Values ◽  
Monsoon Season ◽  
Bootstrap Method ◽  
Early Warning Systems ◽  
Rainfall Data ◽  
Economic Damage ◽  
Rainfall Regime ◽  
Monthly Precipitation ◽  
Precipitation Regimes ◽  
Rainfall Regimes

Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.

Experimentally altered rainfall regimes and host root traits affect grassland arbuscular mycorrhizal fungal communities

2019 ◽  
Author(s):  
Coline Deveautour ◽  
Suzanne Donn ◽  
Sally Power ◽  
Kirk Barnett ◽  
Jeff Powell
Keyword(s):  
Fungal Community ◽  
Community Assembly ◽  
Root Length ◽  
Specific Root Length ◽  
Root Traits ◽  
Arbuscular Mycorrhizal ◽  
Fungal Communities ◽  
Rainfall Regime ◽  
Precipitation Regimes ◽  
Rainfall Regimes

Future climate scenarios predict changes in rainfall regimes. These changes are expected to affect plants via effects on the expression of root traits associated with water and nutrient uptake. Associated microorganisms may also respond to these new precipitation regimes, either directly in response to changes in the soil environment or indirectly in response to altered root trait expression. We characterised arbuscular mycorrhizal (AM) fungal communities in an Australian grassland exposed to experimentally altered rainfall regimes. We used Illumina sequencing to assess the responses of AM fungal communities associated with four plant species sampled in different watering treatments and evaluated the extent to which shifts were associated with changes in root traits. We observed that altered rainfall regimes affected the composition but not the richness of the AM fungal communities, and we found distinctive communities in the increased rainfall treatment. We found no evidence of altered rainfall regime effects via changes in host physiology because none of the studied traits were affected by changes in rainfall. However, specific root length was observed to correlate with AM fungal richness, while concentrations of phosphorus and calcium in root tissue and the proportion of root length allocated to fine roots were correlated to community composition. Our study provides evidence that climate change and its effects on rainfall may influence AM fungal community assembly, as do plant traits related to plant nutrition and water uptake. We did not find evidence that host responses to altered rainfall drive AM fungal community assembly in this grassland ecosystem.

scholarly journals Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

2018 ◽  
Vol 10 (12) ◽  
pp. 1879 ◽  
Author(s):  
Véronique Michot ◽  
Daniel Vila ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Josyane Ronchail ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Spatial Coverage ◽  
Rainfall Regimes ◽  
Trmm 3B42 ◽  
Good Agreement

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

scholarly journals A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Siti Mariana Che Mat Nor ◽  
Shazlyn Milleana Shaharudin ◽  
Shuhaida Ismail ◽  
Nurul Hila Zainuddin ◽  
Mou Leong Tan
Keyword(s):  
Missing Values ◽  
Nearest Neighbor ◽  
East Coast ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Peninsular Malaysia ◽  
Partial Least Square ◽  
Least Square ◽  
Rainfall Data ◽  
Efficiency Coefficient

Rainfall data are the most significant values in hydrology and climatology modelling. However, the datasets are prone to missing values due to various issues. This study aspires to impute the rainfall missing values by using various imputation method such as Replace by Mean, Nearest Neighbor, Random Forest, Non-linear Interactive Partial Least-Square (NIPALS) and Markov Chain Monte Carlo (MCMC). Daily rainfall datasets from 48 rainfall stations across east-coast Peninsular Malaysia were used in this study. The dataset were then fed into Multiple Linear Regression (MLR) model. The performance of abovementioned methods were evaluated using Root Mean Square Method (RMSE), Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency Coefficient (CE). The experimental results showed that RF coupled with MLR (RF-MLR) approach was attained as more fitting for satisfying the missing data in east-coast Peninsular Malaysia.

scholarly journals Interdecadal Variability of the South American Precipitation in the Monsoon Season

2015 ◽  
Vol 28 (2) ◽  
pp. 755-775 ◽  
Author(s):  
Alice M. Grimm ◽  
João P. J. Saboia
Keyword(s):  
Function Analysis ◽  
Monsoon Season ◽  
Regional Scale ◽  
The Other ◽  
Interdecadal Variability ◽  
Monthly Precipitation ◽  
Monsoon Precipitation ◽  
Climatic Indices ◽  
Continental Scale ◽  
Sst Anomalies

Abstract Interdecadal variability modes of monsoon precipitation over South America (SA) are provided by a continental-scale rotated empirical orthogonal function analysis, and their connections to well-known climatic indices and SST anomalies are examined. The analysis, carried out for austral spring and summer, uses a comprehensive set of station data assembled and verified for the period 1950–2000. The presented modes are robust, consistent with previous regional-scale studies and with modes obtained from longer time series over smaller domains. Opposite phases of the main modes show differences around 50% in monthly precipitation. There are significant relationships between the interdecadal variability in spring and summer, indicating local and remote influences. The first modes for both seasons are dipole-like, displaying opposite anomalies in central-east and southeast SA. They tend to reverse polarity from spring to summer. Yet the summer second mode and its related spring fourth mode, which affect the core monsoon region in central Brazil and central-northwestern Argentina, show similar factor loadings, indicating persistence of anomalies from one season to the other, contrary to the first modes. The other presented modes describe the variability in different regions with great monsoon precipitation. Significant connections with different combinations of climatic indices and SST anomalies provide physical basis for the presented modes: three show the strongest connections with SST-based indices, and two have the strongest connections with atmospheric indices. However, the main modes show connections with more than one climatic index and more than one oceanic region, stressing the importance of combined influence.

scholarly journals Influence of the sowing depth and amount of sugarcane straw on the emergence of Chloris polydactyla and Eleusine indica and their control by herbicides applied pre-emergence

2017 ◽  
Vol 38 (3) ◽  
pp. 1187
Author(s):  
Marcelo Rafael Malardo ◽  
Patrícia Andrea Monquero ◽  
Paulo Henrique Vieira dos Santos ◽  
Nagilla Moraes Ribeiro ◽  
Paulo Vinicius da Silva ◽  
...  
Keyword(s):  
Rainfall Regime ◽  
Herbicide Application ◽  
Dry Period ◽  
Sowing Depth ◽  
Eleusine Indica ◽  
Sugarcane Straw ◽  
Randomized Design ◽  
Emergence Percentage ◽  
Completely Randomized Design ◽  
Rainfall Regimes

The objective of this study was to evaluate the effect of the sowing depth and amount of sugarcane straw on the soil on the emergence of Chloris polydactyla (‘capim-branco’) and Eleusine indica (Indian goosegrass) and to determine the efficacy of herbicides applied pre-emergence in the control of these species under different straw amount and rainfall regime conditions. The experiments were conducted in a completely randomized design with four replications. In the first experiment, the effects of six sowing depths (0.5, 1, 2, 4, 8, and 10 cm) and six sugarcane straw amounts (0, 1, 2, 4, 8 and 10 t ha -1) were assessed on the emergence of Indian goosegrass and ‘capim-branco’ in a 6 x 6 factorial arrangement. In the second experiment, the efficacy in the control of these species was evaluated for one control without herbicide and five treatments (indaziflam, metribuzin, tebuthiuron, indaziflam + metribuzin, and indaziflam + tebuthiuron) applied pre-emergence over four straw amounts (0, 1, 2, and 4 t ha-1) in a 6 x 4 factorial arrangement. This experiment was evaluated under two rainfall regimes in separate experiments (simulation of 20 mm of rainfall 1 or 10 days after herbicide application). The ‘capim-branco’ showed a marked reduction in emergence beginning at 2 t ha-1 of straw and a 2 cm sowing depth. For the Indian goosegrass, the decline in emergence mainly occurred beginning at 4 t ha-1 of straw and a 4 cm sowing depth. Only some of the Indian goosegrass plants emerged at the greater sowing depths (8 and 10 cm) and straw amounts (8 and 10 t ha-1), whereas no emergence of the ‘capim-branco’ was observed under these conditions. The treatments with sowing at a 1 cm depth and with 0, 1, 2, and 4 t ha-1 of straw provided the highest emergence percentage for the species. Application of the herbicide indaziflam alone was the only ineffective treatment for the control of the weeds regardless of the amount of straw and the water regime used. We concluded that the increase in the sowing depth and the amount of straw significantly reduced the emergence of the species and that the presence of straw and the dry period interfered with the herbicide efficacy.

scholarly journals Spatial and temporal variability of precipitation and drought in Portugal

2012 ◽  
Vol 12 (5) ◽  
pp. 1493-1501 ◽  
Author(s):  
D. S. Martins ◽  
T. Raziei ◽  
A. A. Paulo ◽  
L. S. Pereira
Keyword(s):  
Principal Component Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Temperature ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Precipitation Regimes

Abstract. The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI), was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI) and the modified PDSI for Mediterranean conditions (MedPDSI) were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

scholarly journals Evaluation and application of alternative rainfall data sources for forcing hydrologic models in the Mara Basin

2017 ◽  
Vol 49 (4) ◽  
pp. 1271-1282 ◽  
Author(s):  
Tadesse Alemayehu ◽  
Fidelis Kilonzo ◽  
Ann van Griensven ◽  
Willy Bauwens
Keyword(s):  
Assessment Tool ◽  
Meteorological Data ◽  
Poor Performance ◽  
Rainfall Data ◽  
Rainfall Regime ◽  
Hydrologic Models ◽  
Climate Forecast System Reanalysis ◽  
Daily Streamflow ◽  
Basin Scale ◽  
Spatially Distributed

Abstract Accurate and spatially distributed rainfall data are crucial for a realistic simulation of the hydrological processes in a watershed. However, limited availability of observed hydro-meteorological data often challenges the rainfall–runoff modelling efforts. The main goal of this study is to evaluate the Climate Forecast System Reanalysis (CFSR) and Water and Global Change (WATCH) rainfall by comparing them with gauge observations for different rainfall regimes in the Mara Basin (Kenya/Tanzania). Additionally, the skill of these rainfall datasets to simulate the observed streamflow is assessed using the Soil and Water Assessment Tool (SWAT). The daily CFSR and WATCH rainfall show a poor performance (up to 52% bias and less than 0.3 correlation) when compared with gauge rainfall at grid and basin scale, regardless of the rainfall regime. However, the correlations for both CFSR and WATCH substantially improve at monthly scale. The 95% prediction uncertainty (95PPU) of the simulated daily streamflow, as forced by CFSR and WATCH rainfall, bracketed more than 60% of the observed streamflows. We however note high uncertainty for the high flow regime. Yet, the monthly and annual aggregated CFSR and WATCH rainfall can be a useful surrogate for gauge rainfall data for hydrologic application in the study area.

Untold Tales of Goats in Deadly Indian Monsoons: Adapt or Rain-Retreat under Global Warming?

2016 ◽  
Vol 03 (01) ◽  
pp. 1650001 ◽  
Author(s):  
S. Niggol Seo
Keyword(s):  
Monsoon Rainfall ◽  
Monsoon Season ◽  
Climate System ◽  
Policy Implications ◽  
Monthly Precipitation ◽  
Monsoon Climate ◽  
Monsoon Variability ◽  
Monsoon System ◽  
Variability Index ◽  
First Time

This paper examines the unique characteristics of the monsoon climate system and whether Indian farmers can adapt to an even deadlier monsoon climate caused by global climatic shifts. This paper shows that the monsoon climate system can be best captured by the Monsoon Variability Index (MVI) constructed by the present author which is defined as the coefficient of variation in the ratio of monsoon rainfall over non-monsoon rainfall for the 40-year period from 1971 to 2010. Monthly precipitation data are based on the observations at 304 weather stations located across India. This paper shows that the traditional measures of the monsoon climate such as monsoon precipitation do not explain the Indian farmers’ behaviors in response to the climate system. Second, this paper shows that the number of goats owned by farm households increases as the MVI increases, that is, as the monsoon climate intensifies. This paper finds that 50% increase in the MVI leads to 23% increase in the number of goats owned by farms. This means that farmers adapt to even the deadliest climate system, i.e., the monsoon system which often leaves millions of people homeless or dead in a single year. Third, this paper finds that the number of sheep owned declines as monsoon rainfall intensifies but increases if the non-monsoon season temperature warms up. The sheep number is not sensitive to changes in the MVI. This paper shows for the first time the ways farmers adapt to the deadly monsoon climate system. Past studies of climate change and agriculture in India are re-evaluated based on the results and policy implications are described.

scholarly journals Dynamic and Thermodynamic Factors Associated with Different Precipitation Regimes over South China during Pre-Monsoon Season

Atmosphere ◽  
2018 ◽  
Vol 9 (6) ◽  
pp. 219 ◽  
Author(s):  
Wenqian Ma ◽  
Wenyu Huang ◽  
Zifan Yang ◽  
Bin Wang ◽  
Daiyu Lin ◽  
...  
Keyword(s):  
South China ◽  
Monsoon Season ◽  
Factors Associated ◽  
Precipitation Regimes ◽  
Thermodynamic Factors

scholarly journals Assessment of different methods for estimation of missing data in precipitation studies

2016 ◽  
Vol 48 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Mohammad-Taghi Sattari ◽  
Ali Rezazadeh-Joudi ◽  
Andrew Kusiak
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Regression Method ◽  
Linear Regression Method ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Multiple Regression Method ◽  
Multiple Imputation Method ◽  
Estimation Of Missing Data ◽  
Monthly Precipitation Data

The outcome of data analysis depends on the quality and completeness of data. This paper considers various techniques for filling in missing precipitation data. To assess suitability of the different methods for filling in missing data, monthly precipitation data collected at six different stations was considered. The complete sets (with no missing values) are used to predict monthly precipitation. The arithmetic averaging method, the multiple linear regression method, and the non-linear iterative partial least squares algorithm perform best. The multiple regression method provided a successful estimation of the missing precipitation data, which is supported by the results published in the literature. The multiple imputation method produced the most accurate results for precipitation data from five dependent stations. The decision-tree algorithm is explicit, and therefore it is used when insights into the decision making are needed. Comprehensive error analysis is presented.

Sign in / Sign up

Export Citation Format

Share Document