scholarly journals Identification of Shift in Sowing and Harvesting Dates of Rice Crop (L. Oryza sativa) through Remote Sensing Techniques: A Case Study of Larkana District

2020 ◽  
Vol 12 (9) ◽  
pp. 3586
Author(s):  
Vipin Kumar Oad ◽  
Xiaohua Dong ◽  
Muhammad Arfan ◽  
Vicky Kumar ◽  
Muhammad Salman Mohsin ◽  
...  
Keyword(s):  
Climate Variability ◽  
Land Surface ◽  
Rice Crop ◽  
Climate Data ◽  
Time Period ◽  
Remote Sensing Techniques ◽  
Landsat Satellite ◽  
Sowing Period ◽  
The Impact

The present study aimed to determine the impact of climate variability on rice crops in terms of sowing and harvesting dates and crop period. The identification of sowing and harvesting dates were spotted by mask identification, variations in land surface temperature (LST) on a temporal scale in the respective months, and a field-level social inquiry. The study was conducted during a time period (1994–2017), in which geo-referenced crop samples, farmer’s perception survey data, Landsat satellite images, and climate data of district Larkana were used. The analysis of satellite imageries revealed that on 20 June 1994, the rice was transplanted on 14.7% of the area of the region while it was only 7.1% of the area in 2017. Similarly, the area under rice crop in the first week of July 1994 was 18.3% compared to 8.15% during the same period in 2017. However, in the first week of October 2017, the rice crop was standing on 46.8% of the area while it was on 34.6% of the area during the year 1994 on the same date. This LST variation depicts a delay in the sowing and harvesting of the rice crop. This changing pattern is further confirmed through mean LST. Mean LST (°C) has been increasing in the sowing period of rice crop from 31.9 °C in June 1994 to 35.8 °C in June 2017, and from 32.8 °C in July 1994 to 36.8 °C in July 2017. Furthermore, the LST decreased during the harvesting period of rice crop from 31 °C in October 1994 to 28.6 °C in October 2017. The present study quantifies a delay of 15–30 days in sowing and harvesting dates of the rice crop in the district due to climate variability.

scholarly journals Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

2015 ◽  
Vol 8 (6) ◽  
pp. 1857-1876 ◽  
Author(s):  
J. J. Guerrette ◽  
D. K. Henze
Keyword(s):  
Land Surface ◽  
Vertical Mixing ◽  
Model Performance ◽  
Land Surface Model ◽  
Sensitivity Study ◽  
Climate Impacts ◽  
Surface Model ◽  
Time Period ◽  
Develop Land ◽  
The Impact

Abstract. Here we present the online meteorology and chemistry adjoint and tangent linear model, WRFPLUS-Chem (Weather Research and Forecasting plus chemistry), which incorporates modules to treat boundary layer mixing, emission, aging, dry deposition, and advection of black carbon aerosol. We also develop land surface and surface layer adjoints to account for coupling between radiation and vertical mixing. Model performance is verified against finite difference derivative approximations. A second-order checkpointing scheme is created to reduce computational costs and enable simulations longer than 6 h. The adjoint is coupled to WRFDA-Chem, in order to conduct a sensitivity study of anthropogenic and biomass burning sources throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. A cost-function weighting scheme was devised to reduce the impact of statistically insignificant residual errors in future inverse modeling studies. Results of the sensitivity study show that, for this domain and time period, anthropogenic emissions are overpredicted, while wildfire emission error signs vary spatially. We consider the diurnal variation in emission sensitivities to determine at what time sources should be scaled up or down. Also, adjoint sensitivities for two choices of land surface model (LSM) indicate that emission inversion results would be sensitive to forward model configuration. The tools described here are the first step in conducting four-dimensional variational data assimilation in a coupled meteorology–chemistry model, which will potentially provide new constraints on aerosol precursor emissions and their distributions. Such analyses will be invaluable to assessments of particulate matter health and climate impacts.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

The Impact of a BI-Supported Performance Measurement System on a Public Police Force

2019 ◽  
pp. 509-527
Author(s):  
Elad Moskovitz ◽  
Adir Even
Keyword(s):  
Performance Measurement ◽  
Measurement System ◽  
Business Intelligence ◽  
Police Force ◽  
Performance Measurement System ◽  
Moderation Effect ◽  
Relative Metrics ◽  
Time Period ◽  
The Impact

Performance measurement, as an effective tool for implementing organizational strategy and assisting ongoing control and surveillance, is broadly adopted today. The performance measurement system (PMS) explored in this case study was implemented, using business intelligence (BI) technologies, for a public police force. The system lets police commanders view and analyze the performance scores of their own units and get feedback on the success of their activities. The study examines the system's impact, through analysis of the metric results over a time period of five years. The results show that the vast majority of the metrics examined indeed improved. Further, the results underscore the moderation effect of relative metrics weights, as well as the different behavior of metrics that reflect activity versus those that reflect outcomes. The study underscores both the positive and the negative aspects of those results, and discusses their implications for future PMS implementation with BI technologies.

scholarly journals Climate Aridity under Changing Conditions and Implications for the Agricultural Sector: Italy as a Case Study

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Luca Salvati ◽  
Marco Zitti ◽  
Rosanna Di Bartolomei ◽  
Luigi Perini
Keyword(s):  
Agricultural Sector ◽  
Potential Evapotranspiration ◽  
Regional Scale ◽  
Aridity Index ◽  
Agricultural System ◽  
Temperature Regimes ◽  
Time Period ◽  
Diachronic Analysis ◽  
The Impact

A comprehensive diachronic analysis (1951–2010) of precipitation and temperature regimes has been carried out at the national and regional scale in Italy to investigate the impact of climate aridity on the agricultural system. Trends in climate aridity have been also analysed using UNEP aridity index which is the ratio between rainfall and potential evapotranspiration on a yearly basis. During the examined time period, and particularly in the most recent years, a gradual reduction in rainfall and growing temperatures have been observed which have further widened the gap between precipitation amounts and water demand in agriculture.

scholarly journals Trends and land surface phenological responses to climate variability in the Argentina Pampas

2020 ◽  
Vol 46 (2) ◽  
pp. 581-602
Author(s):  
B. Lara ◽  
M. Gandini ◽  
P. Gantes ◽  
S.D. Matteucci
Keyword(s):  
Climate Variability ◽  
Land Surface ◽  
Vegetation Index ◽  
Future Climate Change ◽  
Linear Estimator ◽  
Positive Trend ◽  
Land Surface Phenology ◽  
First Case ◽  
Annual Productivity ◽  
The Impact

Understanding the interaction between land surface and atmosphere processes is fundamental for predicting the effects of future climate change on ecosystem functioning and carbon dynamics. The objectives of this work were to analyze the trends in land surface phenology (LSP) metrics from remote sensing data, and to reveal their relationship with precipitation and ENSO phenomenon in the Argentina Pampas. Using a time series of MODIS Normalized Difference Vegetation Index (NDVI) data from 2000 to 2014, the start of the growing season (SOS), the annual integral of NDVI (i-NDVI, linear estimator of annual productivity), the timing of the annual maximum NDVI (t-MAX) and the annual relative range of NDVI (RREL, estimator of seasonality) were obtained for the Argentina Pampas. Then, spatial and temporal relationships with the Multivariate ENSO Index (MEI) and precipitation were analyzed. Results showed a negative trend in annual productivity over a 53.6% of the study area associated to natural and semi-natural grassland under cattle grazing, whereas a 40.3% of Argentina Pampas showed a significant positive trend in seasonality of carbon gains. The study also reveals that climate variability has a significant impact on land surface phenology in Argentina Pampas, although the impact is heterogeneous. SOS and t-MAX showed a significant negative correlation with the precipitation indicating an earlier occurrence. 23.6% and 28.4% of the study area showed a positive correlation of the annual productivity with MEI and precipitation, respectively, associated to rangelands (in the first case) and to both rangeland and croplands, in the second case. Climate variability did not explain the seasonal variability of phenology. The relationships found between LSP metrics and climate variability could be important for implementation of strategies for natural resource management.

scholarly journals Preliminary Study of Total Suspended Solid Distribution in Coastal Ujung Pangkah Gresik Based Reflectance Value of Landsat Satellite Imagery

2019 ◽  
Vol 51 (1) ◽  
pp. 42
Author(s):  
Hendrata Wibisana ◽  
Bangun Muljo Soekotjo ◽  
Umboro Lasminto
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Landsat 8 ◽  
Time Period ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Solid Distribution

Total suspended solid (TSS) is one of the parameters that uses for detecting health in aquatic environments. The distribution of the TSS value in the water body will affect the aquatic ecosystem. In this research will be analyzed the distribution value of TSS during 5 year period by utilizing Landsat 8 satellite image data, where the developed method is extraction of reflectance value from Landsat 8 satellite image for 5 years using SEADASS and then compiled the TSS algorithm with reflectance value that already obtained on the existing conditions, the algorithm obtained is estimated over 5 years back to get a picture of change and distribution of TSS value. As a case study , the coast of Ujung Pangkah Gresik was taken which has the mouth of the river Bengawan Solo. The results obtained from this study illustrate the decrease of TSS value during that time period, so that with this decrease can be concluded that at the point of field coordinate, TSS value was decreasing and causing the erosion in the environment.

scholarly journals Future climate variability impacts on potential erosion and soil organic carbon in European croplands

2014 ◽  
Vol 11 (1) ◽  
pp. 1561-1585 ◽  
Author(s):  
M. van der Velde ◽  
J. Balkovič ◽  
C. Beer ◽  
N. Khabarov ◽  
M. Kuhnert ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Climate Variability ◽  
Future Climate ◽  
Climate Data ◽  
Climate Trend ◽  
Erosion Rates ◽  
Soc Stock ◽  
The Impact ◽  
Earth System Models

Abstract. We investigate the impact of future climate variability on the potential vulnerability of soils to erosion and the consequences for soil organic carbon (SOC) in European croplands. Soil erosion is an important carbon flux not characterized in Earth System Models. We use a~European implementation of EPIC, driven by reference climate data (CNTRL), and climate data with reduced variability (REDVAR). Whether erosion regimes will change across European cropland depends on the spatial conjunction of expected changes in climate variability and physiographic conditions conducive to erosion. We isolated the effect of erosion by performing simulations with and without erosion. Median CNTRL and REDVAR erosion rates equalled 14.4 and 9.1 ton ha−1, and 19.1 and 9.7, for 1981–2010 and 2071–2100, respectively. The total amount of carbon lost from European cropland due to erosion was estimated at 769 Tg C for 1981–2010 (from a total storage of 6197 Tg C without erosion) under CNTRL climate. Climate trend impacts reduce the European cropland SOC stock by 578 Tg C without – and by 683 Tg C with erosion, from 1981 to 2100. Climate variability compounds these impacts and decreases the stock by an estimated 170 Tg without erosion and by 314 Tg C with erosion, by the end of the century. Future climate variability and erosion will thus compound impacts on SOC stocks arising from gradual climate change alone.

scholarly journals The impact of clouds, land use and snow cover on climate in the Canadian Prairies

2016 ◽  
Vol 13 ◽  
pp. 37-42 ◽  
Author(s):  
Alan K. Betts ◽  
Raymond L. Desjardins ◽  
Devon E. Worth
Keyword(s):  
Land Use ◽  
Snow Cover ◽  
Air Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Summer Precipitation ◽  
Climate Data ◽  
Canadian Prairies ◽  
Cloud Forcing ◽  
The Impact

Abstract. This study uses 55 years of hourly observations of air temperature, relative humidity, daily precipitation, snow cover and cloud cover from 15 climate stations across the Canadian Prairies to analyze biosphere-atmosphere interactions. We will provide examples of the coupling between climate, snow cover, clouds, and land use. Snow cover acts as a fast climate switch. With the first snow fall, air temperature falls by 10 °C, and a similar increase in temperature occurs with snow melt. Climatologically, days with snow cover are 10 °C cooler than days with no snow cover in Alberta. However the interannual variability has a larger range, so that for every 10 % decrease in days with snow cover, the mean October to April climate is warmer by 1.4 to 1.5 °C. Snow cover also transforms the coupling between clouds and the diurnal cycle of air temperature from a boundary layer regime dominated by shortwave cloud forcing in the warm season to one dominated by longwave cloud forcing with snow cover. Changing agricultural land use in the past thirty years, specifically the reduction of summer fallowing, has cooled and moistened the growing season climate and increased summer precipitation. These hourly climate data provide a solid observational basis for understanding land surface coupling, which can be used to improve the representation of clouds and land-surface processes in atmospheric models.

scholarly journals Analysis of the Impact of Land Use on Spatiotemporal Patterns of Surface Urban Heat Island in Rapid Urbanization, a Case Study of Shanghai, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1171 ◽  
Author(s):  
Hongyu Du ◽  
Fengqi Zhou ◽  
Chunlan Li ◽  
Wenbo Cai ◽  
Hong Jiang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Land Surface ◽  
Agricultural Land ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The Impact

In the trend of global warming and urbanization, frequent extreme weather influences the life of citizens seriously. Shanghai, as a typical mega-city in China that has been successful in urbanization, suffers seriously from the urban heat island (UHI) effect. The research concentrates on the spatial and temporal pattern of surface UHI and land use. Then, the relation between them are further discussed. The results show that for the last 15 years, the UHI effect of Shanghai has been increasing continuously in both intensity and area. The UHI extends from the city center toward the suburban area. Along with the year, the ratio in area of Agricultural Land (AL), Wetland (WL), and Bare Land (BL) has decreased. On the contrary, Construction Land (CL) and Green Land (GL) have increased. The average land surface temperature (LST) rankings for each research year from high to low were all CL, BL, GL, AL, and WL. CL contributed the most to the UHI effect, while WL and GL contributed the most to mitigate the UHI. The conclusion provides practical advice aimed to mitigate the UHI effect for urban planning authorities.

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