Developing fire danger models using logistic regression analysis for mid-hills of Himachal Pradesh

The present study intended to develop a climatic fire danger model for mid-hills zone of Himachal Pradesh using ten years weather data in relation with forest fire occurrence (2007-2016). Logistic regression technique was used to determine the relationship between fire occurrence and weather parameters viz., maximum temperature (°C), relative humidity (%), and wind speed (ms-1). The model was validated by calculating area under curve (AUC), coefficient of determination (R2) and root mean square Error (RMSE), with estimated values of 88.90%, 0.705 and 0.247, respectively. The fire danger model was verified with actual fire incidences in the study area during the year 2017. Wald's test was carried out to quantify impact climatic parameters on forest fire. Wald's test value was highest for maximum temperature (40.07) followed by relative humidity (1.15) and wind speed (0.75), respectively. In future such model can be utilized for prevention of forest fire hazards in the study area.

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Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽

10.3390/agronomy11061207 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1207

Author(s):

Gonçalo C. Rodrigues ◽

Ricardo P. Braga

Keyword(s):

Wind Speed ◽

Bias Correction ◽

Goodness Of Fit ◽

Mediterranean Climate ◽

Weather Data ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Weather Variables ◽

Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

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SEASONAL AND INTERANNUAL PATTERN OF METEOROLOGICAL VARIABLES IN CUIABÁ, MATO GROSSO STATE, BRAZIL

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v33i3.949 ◽

2015 ◽

Vol 33 (3) ◽

pp. 477 ◽

Cited By ~ 2

Author(s):

Nadja Gomes Machado ◽

Marcelo Sacardi Biudes ◽

Carlos Alexandre Santos Querino ◽

Victor Hugo De Morais Danelichen ◽

Maísa Caldas Souza Velasque

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Meteorological Data ◽

Seasonal Pattern ◽

Global Radiation ◽

Meteorological Variables ◽

Maximum Temperature ◽

Wet Season ◽

Agricultural Producers ◽

Mato Grosso

ABSTRACT. Cuiab´a is located on the border of the Pantanal and Cerrado, in Mato Grosso State, which is recognized as one of the biggest agricultural producers of Brazil. The use of natural resources in a sustainable manner requires knowledge of the regional meteorological variables. Thus, the objective of this study was to characterize the seasonal and interannual pattern of meteorological variables in Cuiab´a. The meteorological data from 1961 to 2011 were provided by the Instituto Nacional de Meteorologia (INMET – National Institute of Meteorology). The results have shown interannual and seasonal variations of precipitation, solar radiation, air temperature and relative humidity, and wind speed and direction, establishing two main distinct seasons (rainy and dry). On average, 89% of the rainfall occurred in the wet season. The annual average values of daily global radiation, mean, minimum and maximum temperature and relative humidity were 15.6 MJ m–2 y–1, 27.9◦C, 23.0◦C, 30.0◦C and 71.6%, respectively. Themaximum temperature and the wind speed had no seasonal pattern. The wind speed average decreased in the NWdirectionand increased in the S direction.Keywords: meteorological variables, climatology, ENSO. RESUMO. Cuiabá está localizado na fronteira do Pantanal com o Cerrado, no Mato Grosso, que é reconhecido como um dos maiores produtores agrícolas do Brasil. A utilização dos recursos naturais de forma sustentável requer o conhecimento das variáveis meteorológicas em escala regional. Assim, o objetivo deste estudo foi caracterizar o padrão sazonal e interanual das variáveis meteorológicas em Cuiabá. Os dados meteorológicos de 1961 a 2011 foram fornecidos pelo Instituto Nacional de Meteorologia (INMET). Os resultados mostraram variações interanuais e sazonais de precipitação, radiação solar, temperatura e umidade relativa do ar e velocidade e direção do vento, estabelecendo duas principais estações distintas (chuvosa e seca). Em média, 89% da precipitação ocorreu na estação chuvosa. Os valores médios anuais de radiação diária global, temperatura do ar média, mínima e máxima e umidade relativa do ar foram 15,6 MJ m–2 y–1, 27,9◦C, 23,0◦C, 30,0◦C e 71,6%, respectivamente. A temperatura máxima e a velocidade do vento não tiveram padrão sazonal. A velocidade média do vento diminuiu na direção NW e aumentou na direção S.Palavras-chave: variáveis meteorológicas, climatologia, ENOS.

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Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation

10.5194/egusphere-egu2020-12520 ◽

2020 ◽

Author(s):

Congying Han

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Spatial Pattern ◽

River Basin ◽

Meteorological Factors ◽

Heihe River Basin ◽

Spatiotemporal Variability ◽

Maximum Temperature ◽

Heihe River ◽

Potential Evaporation

Spatiotemporal Variability of Potential Evaporation in Heihe River Basin Influenced by Irrigation Congying Han1,2, Baozhong Zhang1,2, Songjun Han1,21 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China.2 National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing 100048, China.Corresponding author: Baozhong Zhang ([email protected])Abstract: Potential evaporation is a key factor in crop water requirement estimation and agricultural water resource planning. The spatial pattern and temporal changes of potential evaporation calculated by Penman equation (EPen) (1970-2017) in Heihe River Basin (HRB), Northwest China were evaluated by using data from 10 meteorological stations, with a serious consideration of the influences of irrigation development. Results indicated that the spatial pattern of annual EPen in HRB was significantly different, among which the EPen of agricultural sites (average between 1154 mm and 1333 mm) was significantly higher than that of natural sites (average between 794 mm and 899 mm). Besides, the coefficient of spatial variation of the aerodynamic term (Eaero) was 0.4, while that of the radiation term (Erad) was 0.09. The agricultural irrigation water withdrawal increased annually before 2000, but decreased significantly after 2000 which was influenced by the agricultural development and the water policy. Coincidentally, the annual variation of Epen in agricultural sites decreased at -40 mm/decade in 1970-2000 but increased at 60 mm/decade in 2001-2017, while that in natural sites with little influence of irrigation, only decreased at -0.5mm/decade in 1970-2000 but increased at 11 mm/decade in 2001-2017. So it was obvious that irrigation influenced Epen significantly and the change of Epen was mainly caused by the aerodynamic term. The analysis of the main meteorological factors that affect Epen showed that wind speed had the greatest impact on Epen of agricultural sites, followed by relative humidity and average temperature, while the meteorological factors that had the greatest impact on Epen of natural sites were maximum temperature, followed by wind speed and relative humidity.

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Assessment of the future climate potential for tourism over Europe using a combination of downscaling approaches and quantitative impact models

10.5194/egusphere-egu2020-5037 ◽

2020 ◽

Author(s):

Maria Francisca Cardell ◽

Arnau Amengual ◽

Romualdo Romero

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Future Climate ◽

Cumulative Distribution ◽

Maximum Temperature ◽

Model Errors ◽

Present Climate ◽

Ensemble Strategy ◽

The Future

Europe and particularly, the Mediterranean countries, are among the most visited tourist destinations worldwide, while it is also recognized as one of the most sensitive regions to climate change. Climate is a key resource and even a limiting factor for many types of tourism. Owing to climate change, modified patterns of atmospheric variables such as temperature, rainfall, relative humidity, hours of sunshine and wind speed will likely affect the suitability of the European destinations for certain outdoor leisure activities.Perspectives on the future of second-generation climate indices for tourism (CIT) that depend on thermal, aesthetic and physical facets are derived using model projected daily atmospheric data and present climate &#8220;observations&#8221;. Specifically, daily series of 2-m maximum temperature, accumulated precipitation, 2-m relative humidity, mean cloud cover and 10-m wind speed from ERA-5 reanalysis are used to derive the present climate potential. For projections, the same daily variables have been obtained from a set of regional climate models (RCMs) included in the European CORDEX project, considering the rcp8.5 future emissions scenario. The adoption of a multi-model ensemble strategy allows quantifying the uncertainties arising from the model errors and the GCM-derived boundary conditions. To properly derive CITs at local scale, a quantile&#8211;quantile adjustment has been applied to the simulated regional scenarios. The method detects changes in the continuous CIT cumulative distribution functions (CDFs) between the recent past and successive time slices of the simulated climate and applies these changes, once calibrated, to the observed CDFs.&#160;Assessments on the future climate potential for several types of tourist activities in Europe (i.e., sun, sea and sand (3S) tourism, cycling, cultural, football, golf, nautical and hiking) will be presented by applying suitable quantitative indicators of CIT evolutions adapted to regional contexts. It is expected that such kind of information will ultimately benefit the design of mitigation and adaptation strategies of the tourist sector.

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Future climate uncertainty and spatial variability over Tamilnadu State, India

Global NEST Journal ◽

10.30955/gnj.001516 ◽

2015 ◽

Vol 17 (1) ◽

pp. 175-185

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Minimum Temperature ◽

Climate Models ◽

Regional Climate ◽

Future Climate ◽

Maximum Temperature ◽

Projection Data ◽

Climate Uncertainty

<div> The present study analyses future climate uncertainty for the 21st century over Tamilnadu state for six weather parameters: solar radiation, maximum temperature, minimum temperature, relative humidity, wind speed and rainfall. The climate projection data was dynamically downscaled using high resolution regional climate models, PRECIS and RegCM4 at 0.22°x0.22° resolution. PRECIS RCM was driven by HadCM3Q ensembles (HQ0, HQ1, HQ3, HQ16) lateral boundary conditions (LBCs) and RegCM4 driven by ECHAM5 LBCs for 130 years (1971-2100). The deviations in weather variables between 2091-2100 decade and the base years (1971-2000) were calculated for all grids of Tamilnadu for ascertaining the uncertainty. These deviations indicated that all model members projected no appreciable difference in relative humidity, wind speed and solar radiation. The temperature (maximum and minimum) however showed a definite increasing trend with 1.8 to 4.0°C and 2.0 to 4.8°C, respectively. The model members for rainfall exhibited a high uncertainty as they projected high negative and positive deviations (-379 to 854 mm). The spatial representation of maximum and minimum temperature indicated a definite rhythm of increment from coastal area to inland. However, variability in projected rainfall was noticed. </div>

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Development of 1961-1990 monthly surface climatology of India and patterns of differences of some meteorological parameters with respect to the 1951-1980 climatology

MAUSAM ◽

10.54302/mausam.v63i3.1222 ◽

2022 ◽

Vol 63 (3) ◽

pp. 377-390

Author(s):

A.K. JASWAL ◽

S.R. BHAMBAK ◽

M.K. GUJAR ◽

S.H. MOHITE ◽

S. ANANTHARAMAN ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Spatial Patterns ◽

Large Scale ◽

India Meteorological Department ◽

Cloud Amount ◽

Climatic Conditions ◽

Maximum Temperature ◽

Annual Means ◽

Rainy Days

Climate normals are used to describe the average climatic conditions of a particular place and are computed by National Meteorological Services of all countries. The World Meteorological Organization (WMO) recommends that all countries prepare climate normals for the 30-year periods ending in 1930, 1960, 1990 and so on, for which the WMO World Climate Normals are published. Recently, Climatological Normals for the period 1961-1990 have been prepared by India Meteorological Department (IMD) which will change the baseline of comparison from 1951-1980. In this paper, preparation of the 30-year Climatological Normals of India for the period 1961 to 1990 and spatial patterns of differences of annual means of temperatures, relative humidity, clouds, rainfall and wind speed from the previous normals (1951-1980) are documented.The changes from earlier climatological normals indicate increase in annual means of maximum temperature, relative humidity and decrease in annual means of minimum temperature, cloud amount, rainfall, rainy days and wind speed over large parts of the country during 1961-1990. The spatial patterns of changes in dry bulb temperatures and relative humidity are complementary over most parts of the country. Compared with 1951-1980 climatology, there are large scale decreases in annual mean rainfall, rainy days and wind speed over most parts of the country during 1961-1990. The decrease in wind speed may be partly due to changes in exposure conditions of observatories due to urbanization.

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Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

MAUSAM ◽

10.54302/mausam.v73i1.5090 ◽

2022 ◽

Vol 73 (1) ◽

pp. 173-180

Author(s):

NAVNEET KAUR ◽

M.J. SINGH ◽

SUKHJEET KAUR

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Minimum Temperature ◽

Maximum Temperature ◽

Linear Regression Method ◽

Post Monsoon ◽

Sunshine Hours ◽

Annual Minimum Temperature ◽

Weather Parameters

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

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A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

Journal of Southern Hemisphere Earth System Science ◽

10.1071/es16020 ◽

2016 ◽

Vol 66 (3) ◽

pp. 281

Author(s):

Timothy Brown ◽

Graham Mills ◽

Sarah Harris ◽

Domagoj Podnar ◽

Hauss Reinbold ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Fire Danger ◽

Management Decision ◽

Fire Weather ◽

Near Surface ◽

Australian Water ◽

Hourly Temperature ◽

Extensive Evaluation ◽

Correction Technique

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

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Development of a Fire Weather Index Using Meteorological Observations within the Northeast United States

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0046.1 ◽

2016 ◽

Vol 55 (2) ◽

pp. 389-402 ◽

Cited By ~ 5

Author(s):

Michael J. Erickson ◽

Joseph J. Charney ◽

Brian A. Colle

Keyword(s):

Logistic Regression ◽

Wind Speed ◽

Relative Humidity ◽

Logistic Regression Model ◽

Daily Maximum ◽

Fire Weather ◽

Weather Index ◽

Fire Weather Index ◽

Wildfire Occurrence ◽

Binomial Logistic Regression

AbstractA fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

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Climate change and its impact on Forest Fire in the state of Himachal Pradesh and Uttarakhand states of India: Remote Sensing and GIS Analysis

Contemporary Trends in Geoscience ◽

10.2478/ctg-2018-0016 ◽

2018 ◽

Vol 7 (2) ◽

pp. 229-246 ◽

Cited By ~ 3

Author(s):

Firoz Ahmad ◽

Laxmi Goparaju

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Pearson Correlation ◽

Fire Frequency ◽

Remote Sensing And Gis ◽

Himachal Pradesh ◽

Significant Rise ◽

The State ◽

Annual Precipitation ◽

Maximum Temperature

Abstract We have examined the climate and forest fire data using Remote Sensing and GIS in the state of Himachal Pradesh and Uttarakhand states of India. The significant high forest fire events were observed in district of Pauri Garhwal (22.4%) followed by Naini Tal (16.4%), Tehri Garhwal (8.5%), Almora (7.7%), Chamoli (5.8%), Dehra Dun (4.6%), Uttarkashi (4.3%), Champawat (4.2%), Haridwar (3.6%), Una (3.4%), Bageshwar (3.1%), Udham Singh Nagar (2.9%), Sirmaur (2.7%), Solan (2.3%), Kangra (2.1%), Pithoragarh (1.7%) and Shimla (1.2%). The LULC forest category “Deciduous Broadleaf Forest” occupied 17.2% of total forest area and retain significantly high forest fire percent equivalent to 44.7% of total forest fire events. The study revealed that 79% of forest fire incidence was found in the month of April and May. The fire frequency was found highest in the month of April (among all months) whereas it was spread over the five grids (in the count) where the fire frequencies were greater than 100. The average monthly analysis (from January to June) for maximum temperature (°C), precipitation (mm), solar radiation (MJ/m^2), wind velocity (meter/sec.), wet-days frequency (number of days) and evapotranspiration (mm/day) were found to be in the range of (9.90 to 26.44), (26.06 to 134.71), (11738 to 24119), (1.397 to 2.237), (1.46 to 5.12) and (3.96 to 8.46) respectively. Rapid climate/weather severities which significantly enhance the forest fire events were observed in the month of April and May. The analysis of the Pearson Correlation Coefficient (PCC) values of climate parameters showed a significant correlation with forest fire events. The analysis of predicted (2050) climate anomalies data (RCP-6) for the month of April and annual precipitation manifest the significant rise in April temperature and reduction in annual precipitation observed over large part of high forest fire grids will certainly impact adversely to the future forest fire scenario.

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