A new empirical model of sub-daily rainfall intensity and its application in a rangeland biophysical model

2011 ◽  
Vol 33 (1) ◽  
pp. 37 ◽  
Author(s):  
G. W. Fraser ◽  
J. O. Carter ◽  
G. M. McKeon ◽  
K. A. Day
Keyword(s):  
Empirical Model ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Biophysical Model ◽  
Maximum Temperature ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Data ◽  
Erosion Rates ◽  
Biophysical Models

Sub-daily rainfall intensity has a significant impact on runoff and erosion rates in northern Australian rangelands. However, it has been difficult to include sub-daily rainfall intensity in rangeland biophysical models using historical climate data due to the limited number of pluviograph stations with long-term records. In this paper a new empirical model (‘Temperature I15’ model) was developed to predict the daily maximum 15-min rainfall intensity (I15) using daily minimum and maximum temperature and daily rainfall totals from 12 selected pluviograph stations across Australia. The ‘Temperature I15’ model accounted for 46% (P < 0.01) of the variation in observed daily I15 for an independent validation dataset derived from 67 Australia-wide pluviograph stations and represented both geographical and seasonal variability in I15. The model also accounted for 70% (P < 0.01) of the variation in the observed historical trend in I15 for the full record period (average record period was 37 years) of 73 Australia-wide pluviograph stations. The ‘Temperature I15’ model was found to be an improvement on a past empirical model of I15 and can be easily implemented in biophysical models by using readily available daily climate data. However, as the ‘Temperature I15’ model only represented 46% of the variation in daily observed I15, the model is best used in simulation studies on ‘timeframes’ in excess of 5 years. The new ‘Temperature I15’ model was implemented in the runoff equation of the Australia-wide spatial pasture growth model AussieGRASS, which predicts daily water balance and pasture growth for 185 different pasture communities. This resulted in an improved simulation of green cover for 71% of pasture communities but was worse for 25% of communities, with no change for 4% of communities.

Comparison of Collocated Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina

2006 ◽  
Vol 23 (5) ◽  
pp. 671-682 ◽  
Author(s):  
Christopher Holder ◽  
Ryan Boyles ◽  
Ameenulla Syed ◽  
Dev Niyogi ◽  
Sethu Raman
Keyword(s):  
North Carolina ◽  
Climate Models ◽  
Daily Rainfall ◽  
Maximum Temperature ◽  
Spatial Density ◽  
Climate Data ◽  
Data Resource ◽  
Weather And Climate ◽  
Observation Times ◽  
Mean Square Errors

Abstract The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.

Taking China's Temperature: Daily Range, Warming Trends, and Regional Variations, 1955–2000

2004 ◽  
Vol 17 (22) ◽  
pp. 4453-4462 ◽  
Author(s):  
Binhui Liu ◽  
Ming Xu ◽  
Mark Henderson ◽  
Ye Qi ◽  
Yiqing Li
Keyword(s):  
Atmospheric Aerosols ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Maximum Temperature ◽  
Daily Maximum ◽  
Climate Data ◽  
Temperature Range ◽  
Temperature Trends ◽  
Air Temperatures ◽  
Maximum Temperatures

Abstract In analyzing daily climate data from 305 weather stations in China for the period from 1955 to 2000, the authors found that surface air temperatures are increasing with an accelerating trend after 1990. They also found that the daily maximum (Tmax) and minimum (Tmin) air temperature increased at a rate of 1.27° and 3.23°C (100 yr)−1 between 1955 and 2000. Both temperature trends were faster than those reported for the Northern Hemisphere, where Tmax and Tmin increased by 0.87° and 1.84°C (100 yr)−1 between 1950 and 1993. The daily temperature range (DTR) decreased rapidly by −2.5°C (100 yr)−1 from 1960 to 1990; during that time, minimum temperature increased while maximum temperature decreased slightly. Since 1990, the decline in DTR has halted because Tmax and Tmin increased at a similar pace during the 1990s. Increased minimum and maximum temperatures were most pronounced in northeast China and were lowest in the southwest. Cloud cover and precipitation correlated poorly with the decreasing temperature range. It is argued that a decline in solar irradiance better explains the decreasing range of daily temperatures through its influence on maximum temperature. With declining solar irradiance even on clear days, and with decreases in cloud cover, it is posited that atmospheric aerosols may be contributing to the changing solar irradiance and trends of daily temperatures observed in China.

scholarly journals Different climate response of three tree ring proxies of Pinus sylvestris from the Eastern Carpathians, Romania

2020 ◽  
Author(s):  
Viorica Nagavciuc ◽  
Cătălin-Constantin Roibu ◽  
Monica Ionita ◽  
Andrei Mursa ◽  
Mihai-Gabriel Cotos ◽  
...  
Keyword(s):  
Pinus Sylvestris ◽  
Tree Ring ◽  
Ring Width ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Data ◽  
Maximum Latewood Density ◽  
Latewood Density ◽  
Eastern Carpathians

&lt;p&gt;The aim of this study was to compare the climatic responses of three tree rings proxies: tree ring width (TRW),&lt;br&gt;maximum latewood density (MXD), and blue intensity (BI). For this study, 20 cores of Pinus sylvestris covering&lt;br&gt;the period 1886&amp;#8211;2015 were extracted from living non-damaged trees from the Eastern Carpathian Mountains&lt;br&gt;(Romania). Each chronology was compared to monthly and daily climate data. All tree ring proxies had a&lt;br&gt;stronger correlation with the daily climate data compared to monthly data. The highest correlation coefficient&lt;br&gt;was obtained between the MXD chronology and daily maximum temperature over the period beginning with the&lt;br&gt;end of July and ending in the middle of September (r=0.64). The optimal intervals for the temperature signature&lt;br&gt;were 01 Aug &amp;#8211; 24 Sept for the MXD chronology, 05 Aug &amp;#8211; 25 Aug for the BI chronology, and both 16 Nov&lt;br&gt;of the previous year &amp;#8211; 16 March of the current year and 15 Apr &amp;#8211; 05 May for the TRW chronology. The results&lt;br&gt;from our study indicate that MXD can be used as a proxy indicator for summer maximum temperature, while&lt;br&gt;TRW can be used as a proxy indicator for just March maximum temperature. The weak and unstable relationship&lt;br&gt;between BI and maximum temperature indicates that BI is not a good proxy indicator for climate reconstructions&lt;br&gt;over the analysed region.&lt;/p&gt;

scholarly journals Determination of optimum daily maximum temperature using climate data

2007 ◽  
Vol 12 (5) ◽  
pp. 209-216 ◽  
Author(s):  
Yasushi Honda ◽  
Michinori Kabuto ◽  
Masaji Ono ◽  
Iwao Uchiyama
Keyword(s):  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Data

scholarly journals Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily J. Wilkins ◽  
Peter D. Howe ◽  
Jordan W. Smith
Keyword(s):  
National Park ◽  
National Park Service ◽  
Spatial Behavior ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Weather ◽  
Visitor Behavior ◽  
Park Service ◽  
The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 489
Author(s):  
Jinxiu Liu ◽  
Weihao Shen ◽  
Yaqian He
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Land Cover ◽  
Minimum Temperature ◽  
Negative Impact ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Western India

India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.

scholarly journals The Wet and Dry Spells across India during 1951–2007

2010 ◽  
Vol 11 (1) ◽  
pp. 26-45 ◽  
Author(s):  
Nityanand Singh ◽  
Ashwini Ranade
Keyword(s):  
Climate Change ◽  
Rainfall Intensity ◽  
Total Duration ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Change Scenario ◽  
Monsoon Period ◽  
Northwestern India ◽  
Area Of Interest ◽  
Dry Spells

Abstract Characteristics of wet spells (WSs) and intervening dry spells (DSs) are extremely useful for water-related sectors. The information takes on greater significance in the wake of global climate change and climate-change scenario projections. The features of 40 parameters of the rainfall time distribution as well as their extremes have been studied for two wet and dry spells for 19 subregions across India using gridded daily rainfall available on 1° latitude × 1° longitude spatial resolution for the period 1951–2007. In a low-frequency-mode, intra-annual rainfall variation, WS (DS) is identified as a “continuous period with daily rainfall equal to or greater than (less than) daily mean rainfall (DMR) of climatological monsoon period over the area of interest.” The DMR shows significant spatial variation from 2.6 mm day−1 over the extreme southeast peninsula (ESEP) to 20.2 mm day−1 over the southern-central west coast (SCWC). Climatologically, the number of WSs (DSs) decreases from 11 (10) over the extreme south peninsula to 4 (3) over northwestern India as a result of a decrease in tropical and oceanic influences. The total duration of WSs (DSs) decreases from 101 (173) to 45 (29) days, and the duration of individual WS (DS) from 12 (18) to 7 (11) days following similar spatial patterns. Broadly, the total rainfall of wet and dry spells, and rainfall amount and rainfall intensity of actual and extreme wet and dry spells, are high over orographic regions and low over the peninsula, Indo-Gangetic plains, and northwest dry province. The rainfall due to WSs (DSs) contributes ∼68% (∼17%) to the respective annual total. The start of the first wet spell is earlier (19 March) over ESEP and later (22 June) over northwestern India, and the end of the last wet spell occurs in reverse, that is, earlier (12 September) from northwestern India and later (16 December) from ESEP. In recent years/decades, actual and extreme WSs are slightly shorter and their rainfall intensity higher over a majority of the subregions, whereas actual and extreme DSs are slightly (not significantly) longer and their rainfall intensity weaker. There is a tendency for the first WS to start approximately six days earlier across the country and the last WS to end approximately two days earlier, giving rise to longer duration of rainfall activities by approximately four days. However, a spatially coherent, robust, long-term trend (1951–2007) is not seen in any of the 40 WS/DS parameters examined in the present study.

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

Sign in / Sign up

Export Citation Format

Share Document