scholarly journals Quantifying the joint distribution of drought indicators in Borneo fire-prone area

2021 ◽  
Vol 880 (1) ◽  
pp. 012002
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Return Period ◽  
Joint Distribution ◽  
Risk Model ◽  
Peat Soil ◽  
Monthly Precipitation ◽  
Total Precipitation ◽  
Dry Spells ◽  
Dry Conditions ◽  
Precipitation Anomalies ◽  
Drought Indicators

Abstract Borneo island is prone to fire due to its large peat soil area. Fire activity in Borneo is associated with regional climate conditions, such as total precipitation, precipitation anomaly, and dry spells. Thus, knowing the relationship between drought indicators can provide preliminary knowledge in developing a fire risk model. Therefore, this study aims to quantify the copula-based joint distribution and to analyze the coincidence probability between drought indicators in Borneo fire-prone areas. From dependence analysis, we found that the average of 2 months of total precipitation (TP), monthly precipitation anomalies (PA), and the total of 3 months of dry spells (DS) provides a moderate correlation to hotspots in Borneo. The results show the probability of the dry-dry period is 26.63, 17.66, and 18.54 % for TP-DS, PA-DS, and TP-PA, respectively. All of these are higher than the probability of the wet-wet period, which is 25.01, 16.12, and 17.98 % for TP-DS, PA-DS, and TP-PA, respectively. Through the probability, the return period of TP-DS in the dry-dry situation 3.2 months/year, meaning the dry situation in total precipitation and dry spells that occur simultaneously could appear about 3 months in a year on average. Furthermore, the return period of PA-DS and TP-PA in the dry-dry situation is 2.12 and 2.22 months/year, respectively. Moreover, the probability of dry spells in dry conditions when given total precipitation in dry conditions is higher than given precipitation anomalies in dry conditions.

scholarly journals Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

2021 ◽  
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Joint Distribution ◽  
El Nino ◽  
Fire Risk ◽  
Size Reduction ◽  
Total Precipitation ◽  
Climate Conditions ◽  
Dry Spells ◽  
Sample Size Reduction

Abstract The copula-based joint distribution can construct a fire risk model to improve forest fires' early warning system, especially in Kalimantan. In this study, we model and analyze the copula-based joint distribution between climate conditions and hotspots. We used several climate conditions, such as total precipitation, dry spells, and El Nino-Southern Oscillation (ENSO). We used copula functions with sample size reduction to construct the joint distributions and the copula regression model to estimate the fire size. The results show that the probability of extreme hotspots number during normal ENSO conditions is very rare and almost near zero during La Nina. Other than that, extreme hotspot event (more severe than in 2019) during El Nino is more sensitive to total precipitation than dry spells based on the conditional survival function. However, the copula regression model found that the model used dry spells as a climate condition better than total precipitation. In this model, the 95% confidence interval of the expected hotspots can cover all actual hotspots data.

scholarly journals STATISTICAL MODELS FOR FORECASTING AVERAGE MONTHLY TEMPERATURE AND MONTHLY PRECIPITATION AMOUNT IN PERM

2021 ◽  
pp. 84-95
Author(s):  
Valery N. Aptukov ◽  
◽  
Victor Yu. Mitin ◽  
Keyword(s):  
Precipitation Amount ◽  
Forecast Accuracy ◽  
Statistical Characteristics ◽  
Monthly Precipitation ◽  
Total Precipitation ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Precipitation Anomalies ◽  
Fractality Index ◽  
Climate Series

The article proposes an approach to forecasting mean temperature and total precipitation for the upcoming month, based on the study of the regularities of the influence of statistical characteristics of temperature and precipitation of previous periods on them. Among the predictors, along with the basic statistical characteristics, we use the fractality index which is an indicator of the randomness/ determinism of the climate series. Within the framework of this approach, we have developed models of different levels to predict the temperature and total precipitation amount in the upcoming month. The main parameters of these models are described and the possibilities of their variation are indicated. Examples are given to illustrate the forecasting methodology using various types of models and include the results of quality control of the models, calculation of forecast accuracy and dependence of forecast accuracy of average temperature and precipitation on the month (climate season). When tested in 2020, models for forecasting temperature and precipitation for the upcoming month give good results: 9 correct forecasts of temperature anomalies out of 10 (90%) and 7 correct forecasts of precipitation anomalies out of 9 (77,7%).

scholarly journals Multivariate Fire Risk Models Using Sample-Size Reduction and Copula Regression in Kalimantan, Indonesia

2021 ◽  
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati ◽  
Ardhasena Sopaheluwakan
Keyword(s):  
Forest Fires ◽  
El Niño ◽  
Joint Distribution ◽  
El Nino ◽  
Tail Dependence ◽  
Fire Risk ◽  
Total Precipitation ◽  
Climate Conditions ◽  
Dry Spells ◽  
The Relationship

Abstract Forest fires have become a national issue every year and get serious attention from the government and researchers, especially in Kalimantan. The copula-based joint distribution can construct a fire risk model to improve the early warning system of forest fires. This study aims to model and analyze the copula-based joint distribution between climate conditions and hotspots in Kalimantan. We constructed the bivariate joint distributions between climate conditions, either total precipitation or dry spells, and hotspots with sample size reduced by ENSO conditions, i.e., La Nina, normal, and El Nino. From the joint distribution, fire risk models are calculated using conditional probability and copula regression. The results show that the relationship between climate conditions and hotspots in La Nina and normal ENSO conditions have an upper tail dependence but no lower tail dependence. Meanwhile, the relationship has both upper and lower tail dependences during El Nino. There is an outlier in normal ENSO conditions with more hotspots than normally, i.e., in September 2019. The probability is very low during normal ENSO conditions, i.e., less than 2%. The only relatively high probability is during El Nino, i.e., more than 10%. Moreover, the copula regression models show that the model given specific dry spells is better than that given specific total precipitation as climate condition. The copula regression for hotspots given specific total precipitation and ENSO conditions has the RMSE value of 1339 hotspots and the R2 value of 60.70%. Meanwhile, the copula regression for hotspots given specific dry spells and ENSO conditions has the RMSE value of 1185 hotspots and the R2 value of 69.21%.

scholarly journals Dry and Wet Climate Periods over Eastern South America: Identification and Characterization through the SPEI Index

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 155
Author(s):  
Anita Drumond ◽  
Milica Stojanovic ◽  
Raquel Nieto ◽  
Luis Gimeno ◽  
Margarida L. R. Liberato ◽  
...  
Keyword(s):  
South America ◽  
Potential Evapotranspiration ◽  
Monthly Precipitation ◽  
Economic Activities ◽  
Central Brazil ◽  
Moisture Analysis ◽  
Dry Conditions ◽  
Wet Climate ◽  
Climate Events

A large part of the population and the economic activities of South America are located in eastern regions of the continent, where extreme climate events are a recurrent phenomenon. This study identifies and characterizes the dry and wet climate periods at domain-scale occurring over the eastern South America (ESA) during 1980–2018 through the multi-scalar Standardized Precipitation–Evapotranspiration Index (SPEI). For this study, the spatial extent of ESA was defined according to a Lagrangian approach for moisture analysis. It consists of the major continental sink of the moisture transported from the South Atlantic Ocean throughout the year, comprising the Amazonia, central Brazil, and the southeastern continental areas. The SPEI for 1, 3, 6, and 12 months of accumulation was calculated using monthly precipitation and potential evapotranspiration time series averaged on ESA. The analysis of the climate periods followed two different approaches: classification of the monthly SPEI values as mild, moderate, severe, and extreme; the computation of the events and their respective parameters (duration, severity, intensity, and peak). The results indicate that wet periods prevailed in the 1990s and 2000s, while dry conditions predominated in the 2010s, when the longest and more severe dry events have been identified at the four scales.

scholarly journals Temporal Patterns of Precipitation and Vegetation Variability over Zimbabwe during Extreme Dry and Wet Rainfall Seasons

2014 ◽  
Vol 53 (12) ◽  
pp. 2790-2804 ◽  
Author(s):  
Seth Mberego ◽  
Juliet Gwenzi
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Variability ◽  
Temporal Distribution ◽  
Climate Extremes ◽  
Temporal Patterns ◽  
Distribution Patterns ◽  
Dry Spells ◽  
Dry Conditions ◽  
Dry Seasons

AbstractClimatic variability over southern Africa is a well-recognized phenomenon, yet knowledge about the temporal variability of extreme seasons is lacking. This study investigates the intraseasonal progression of extreme seasons over Zimbabwe using precipitation and normalized difference vegetation index (NDVI) data covering the 1981–2005 period. Results show that the greatest deficits/surpluses of precipitation occur during the middle of the rainfall season (January and February), and the temporal distribution of precipitation during extreme dry seasons seems to shift earlier than that of extreme wet seasons. Furthermore, anomalous wet (dry) conditions were observed prior to the development of extreme dry (wet) seasons. Impacts of precipitation variations on vegetation lag by approximately 1–2 months. The semiarid southern region experiences more variability of vegetation cover than do the northern and eastern regions. Three distinct temporal patterns of dry years were noted by considering the maximum NDVI level, the mid-postseason NDVI condition, and nested dry spells. The findings of this study emphasize that climate extremes ought not to be simply understood in terms of total seasonal precipitation, because they may have within them some nested distribution patterns that may have a strong influence on primary production.

On the Development and Demise of the Fall 2019 Southeast U. S. Flash Drought: Links to an Extreme Positive IOD

2020 ◽  
pp. 1-60
Author(s):  
Siegfried D. Schubert ◽  
Yehui Chang ◽  
Anthony M. DeAngelis ◽  
Hailan Wang ◽  
Randal D. Koster
Keyword(s):  
Indian Ocean ◽  
Rossby Wave ◽  
Wave Train ◽  
Temperature And Precipitation ◽  
Ocean Region ◽  
Dry Conditions ◽  
Underlying Causes ◽  
The Indian Ocean ◽  
Precipitation Anomalies ◽  
Rossby Wave Train

AbstractMuch of the southeast United States experienced record dry conditions during September of 2019, with the area in abnormally dry to exceptional drought conditions growing from 25% at the beginning of the month to 80% by the end of the month. The drought ended just as abruptly due to above normal rain that fell during the second half of October. In this study we employed MERRA-2 and the GEOS-5 AGCM to diagnose the underlying causes of the drought’s onset, maintenance, and demise. The basic approach involves performing a series of AGCM simulations in which the model is constrained to remain close to MERRA-2 over pre-specified areas that are external to the drought region. The start of the drought appears to have been forced by anomalous heating in the central/western tropical Pacific that resulted in low level anti-cyclonic flow and a tendency for descending motion over much of the southeast. An anomalous ridge associated with a Rossby wave train (emanating from the Indian Ocean region) is found to be the main source of the most intense temperature and precipitation anomalies that develop over the southeast during the last week of September. A second Rossby wave train (emanating from the same region) is responsible for the substantial rain that fell during the second half of October to end the drought. The links to the Indian Ocean Dipole (with record positive values) as well as a waning El Nino allow some speculation as to the likelihood of similar events occurring in the future.

scholarly journals Synoptic climatology and changes in precipitation associated with the South Atlantic Convergence Zone utilising a cloud band identification technique

2020 ◽  
Author(s):  
Marcia Zilli ◽  
Neil Hart
Keyword(s):  
Deep Convection ◽  
Synoptic Climatology ◽  
Convergence Zone ◽  
Monthly Precipitation ◽  
South American ◽  
Total Precipitation ◽  
The South ◽  
Wet Season ◽  
Cloud Band ◽  
Identification Technique

<p>Austral wet season precipitation (October through March) in the subtropical parts of Brazil is related to the strength and position of the South American Convergence Zone (SACZ), one of the main features of the South American Monsoon System. The SACZ can be defined as the aggregation of individual tropical-extratropical (TE) cloud bands. Such TE cloud bands have deep convection and heavy rainfall linking the tropical convection over the Amazon rain forest to the mid-latitude weather systems in the Southern Ocean. Utilising a cloud band identification technique, which consists of an object-based algorithm that identifies TE interactions, we detected individual weather systems and explored their associated precipitation characteristics and changes since 1980. Each event is characterised by the total precipitation within the contour of the low-value OLR. For this, we considered three different datasets: observed precipitation from various weather stations over Brazil, gridded to a 0.25° lat/lon resolution; satellite-based rainfall from TRMM (version 3B42); and reanalysis-based precipitation from ERA5. Here we explore the spatial characteristics and associated precipitation statistics of the SACZ events identified through the proposed technique. The monthly spatial signature of the selected events is similar among the three data sources and corresponds to the SACZ location. The selected events account for 25% to 50% of the total monthly precipitation during the wet season, with the largest percentages occurring in December and January. Over South-eastern Brazil, we identified a reduction in the number of events and in total precipitation during these events, resulting in a reduction of their contribution to the total precipitation climatology during the last decade. The drying trends occur mostly in December; in January, the areas with reduced precipitation migrate northward and precipitation increases over Southern Brazil, suggesting that the poleward migration of the SACZ is more pronounced during these months. These results demonstrate the relationship between synoptic systems and the changes in the location of the SACZ described in recent studies. In the next steps, we will diagnose the reanalysed and climate-simulated circulations associated with these events, identifying possible mechanisms responsible for the poleward shift of the SACZ.</p>

Sign in / Sign up

Export Citation Format

Share Document