Flood forecasting system for Brahmaputra river basin 

2021 ◽  
Author(s):  
Akash Kale ◽  
Vimal Mishra
Keyword(s):  
Land Surface ◽  
Agricultural Land ◽  
Flow Direction ◽  
Human Life ◽  
Meteorological Data ◽  
Brahmaputra River ◽  
Discharge Data ◽  
Near Surface ◽  
Vic Model ◽  
Major Floods

<p>Assam has always been India’s most flood prone state due to the presence of Brahmaputra river, which is very unstable in terms of its flow direction witnessing 12 major floods from 1950 to 2012. Flooding in the basin has affected around 2.75 million of people and 0.27 million hectares of agricultural land on an average causing catastrophic damage to human life and infrastructure. In this study, we analysed all the major floods across the Brahmaputra river in the past 70 years and established the dependency within discharge and atmospheric parameters. Variable Infiltration Capacity (VIC) model was set up to simulate the flow at two stations namely Yangcun, China and Bahadurabad, Bangladesh. We  used near surface meteorological data for driving land surface modelling systems from 1901 to 2016 as input parameters to the VIC model. To avoid the discontinuity of data after 2016, we used ECMWF reanalysis (ERA5) data for the period of 2016 to 2020. After obtaining the continuous simulated discharge for 120 years, we established the relationship between the observed and simulated discharge data for which the R-squared and Nash Sutcliffe coefficient values were 0.83 and 0.78 respectively. Comparing the simulated discharge with the observed extreme discharge at various locations on the river, we apply the model to address future flood situations.</p>

scholarly journals SENSITIVITY OF ACTUAL EVAPOTRANSPIRATION ESTIMATION USING THE SEBS MODEL TO VARIATION OF INPUT PARAMETERS (LST, DSSF, AERODYNAMICS PARAMETERS, LAI, FVC)

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1193-1200 ◽  
Author(s):  
N. Abid ◽  
C. Mannaerts ◽  
Z. Bargaoui
Keyword(s):  
Energy Balance ◽  
Solar Radiation ◽  
Land Surface ◽  
Agricultural Land ◽  
Meteorological Data ◽  
Actual Evapotranspiration ◽  
Aerodynamic Parameter ◽  
Area Index ◽  
Near Surface ◽  
Sebs Model

<p><strong>Abstract.</strong> Actual Evapotranspiration (AET) is a key component of the water and energy balance and hydrological regime of catchments. A land surface energy balance system model (SEBS) was used to estimate the AET of the 160100-km² Medjerda river basin in Northern Tunisia. This model uses satellite data in combination with meteorological data. In this study, we investigated the sensitivity of the AET model output to five major input variables: the 30-minute Downward Surface Shortwave solar radiation fluxes (DSSF), and Land Surface Temperatures (LST), the roughness height for momentum transfer z<sub>0m</sub>, and the influence of the spatial resolution of satellite-based Leaf Area Index (LAI) and fraction of Vegetation Cover (FVC) estimates. The DSSF product was validated using a comparison to solar radiation estimates by the Angstrom formula based on in-situ station data. Gaps in the 15-min satellite-based land surface temperature time series were filled using a sinusoidal model on pixels containing meteorological stations. One-half to two standard deviations of the errors of the regression curves were applied to analyse the sensitivity of the SEBS output. Two methods to estimate the near surface aerodynamic parameter z<sub>0m</sub> were applied and compared. Maps of LAI and FVC derived from two sensors alternatively applied as an input to the SEBS model. A sensitivity analysis, performed in the first decade of May 2010, showed that SEBS model parameterization is quite sensitive in the forestland cover type. The difference can be up to 0.3&amp;thinsp;mm&amp;thinsp;day<sup>&amp;minus;1</sup>. For agricultural land areas, representing an important percentage of the Medjerda basin, AET estimations based on the SEBS model proved to be used to satisfy the actual evapotranspiration estimates.</p>

scholarly journals Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yi Liu ◽  
Samuel Ortega-Farías ◽  
Fei Tian ◽  
Sufen Wang ◽  
Sien Li
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Northwest China ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Temporal And Spatial

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

scholarly journals Land Surface Precipitation in MERRA-2

2017 ◽  
Vol 30 (5) ◽  
pp. 1643-1664 ◽  
Author(s):  
Rolf H. Reichle ◽  
Q. Liu ◽  
Randal D. Koster ◽  
Clara S. Draper ◽  
Sarith P. P. Mahanama ◽  
...  
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
High Latitudes ◽  
Atmospheric Models ◽  
Near Surface ◽  
Surface Precipitation ◽  
Modern Era ◽  
Better Than

Abstract The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), features several major advances from the original MERRA reanalysis, including the use, outside of high latitudes, of observations-based precipitation data products to correct the precipitation falling on the land surface in the MERRA-2 system. The method for merging the observed precipitation into MERRA-2 has been refined from that of the (land-only) MERRA-Land reanalysis. This paper describes the method and evaluates the MERRA-2 land surface precipitation. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cycling MERRA-2 and MERRA systems. M2CORR is also better than MERRA-Land precipitation over Africa because in MERRA-2 a merged satellite–gauge precipitation product is used instead of the gauge-only data used for MERRA-Land. Compared to 3-hourly TRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere–land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from MERRA-Land, which is important for applications such as land-only modeling studies. Where precipitation observations of sufficient quality are available for use in the reanalysis, the corrections facilitate the seamless spinup of the land surface initial conditions across the MERRA-2 production streams. At high latitudes, however, the lack of reliable precipitation observations results in undesirable land spinup effects that impact mostly the first published year of each MERRA-2 stream (1980, 1992, 2001, and 2011).

scholarly journals A Physically Based Atmospheric Variables Downscaling Technique

2020 ◽  
Vol 21 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Tasnuva Rouf ◽  
Yiwen Mei ◽  
Viviana Maggioni ◽  
Paul Houser ◽  
Margaret Noonan
Keyword(s):  
Land Surface ◽  
Hydrological Modeling ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Shortwave Radiation ◽  
Near Surface ◽  
Physically Based ◽  
Land Data Assimilation System ◽  
Atmospheric Variables

AbstractThis study proposes a physically based downscaling approach for a set of atmospheric variables that relies on correlations with landscape information, such as topography, surface roughness, and vegetation. A proof-of-concept has been implemented over Oklahoma, where high-resolution, high-quality observations are available for validation purposes. Hourly North America Land Data Assimilation System version 2 (NLDAS-2) meteorological data (i.e., near-surface air temperature, pressure, humidity, wind speed, and incident longwave and shortwave radiation) have been spatially downscaled from their original 1/8° resolution to a 500-m grid over the study area during 2015. Results show that correlation coefficients between the downscaled products and ground observations are consistently higher than the ones between the native resolution NLDAS-2 data and ground observations. Furthermore, the downscaled variables present smaller biases than the original ones with respect to ground observations. Results are therefore encouraging toward the use of the 500-m dataset for land surface and hydrological modeling. This would be especially useful in regions where ground-based observations are sparse or not available altogether, and where downscaled global reanalysis products may be the only option for model inputs at scales that are useful for decision-making.

scholarly journals ESTIMATION OF MONTHLY NEAR SURFACE AIR TEMPERATURE USING GEOGRAPHICALLY WEIGHTED REGRESSION IN CHINA

2018 ◽  
Vol XLII-3 ◽  
pp. 1747-1750
Author(s):  
M. M. Wang ◽  
G. J. He ◽  
Z. M. Zhang ◽  
Z. J. Zhang ◽  
X. G. Liu
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Surface Air Temperature ◽  
Weighted Regression ◽  
Good Precision ◽  
Near Surface

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58&amp;thinsp;&amp;deg;C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

FEATURES OF MONITORING OF DEGRADED LANDS TO ASSESS POSSIBILITIES FOR THEIR REHABILITATION

2020 ◽  
pp. 37-43
Author(s):  
B.I. KORZHENEVSKIY ◽  
◽  
N.V. KOLOMIYTSEV ◽  
G.YU. TOLKACHEV
Keyword(s):  
Agricultural Land ◽  
Meteorological Data ◽  
Morphological Characteristics ◽  
Energy Reserves ◽  
Land Restoration ◽  
Degraded Land ◽  
Degraded Lands ◽  
The Past ◽  
Unused Land ◽  
Restoration Work

Putting out of using large areas of agricultural lands in the central region over the past years has led to worsening the prospects of their purposed use, although the problem of the relevance of their restoration still remains. For many years the unused land was exposed to both natural exogenous processes such as erosion, suffusion, etc. and biological and chemical changes, usually for the worse for agriculture. There are considered elements of monitoring aimed at assessing the prospects or lack of perspectives of rehabilitation of degraded lands. An energy approach to assessing the state of slopes and soils located within these slopes is presented. The main factors of natural and anthropogenic character in assessing the prospects for land restoration are their steepness, excess relative to local bases of erosion other morphological characteristics of slopes which in general is reduced to an assessment of the energy provision of slopes and soils. So the higher the energy capacity of slopes – they are less promising for development, for soils – there is a reverse picture – the higher their energy reserves, the more promising is their use. Approaches to zoning the territory for monitoring from larger taxons of natural and anthropogenic genesis to the sites of special surveillance within which the prospects for rehabilitation of the agricultural land are evaluated. The most important factor is the material expediency of such actions, i.e. before starting the restoration work it is necessary to assess the profitability or loss of the proposed event. In cases of the material expediency it is feasible as further actions to include energy assessments of slopes and soils; zoning of the object according to the steepness and oriented characteristics of soil washout; and the possibility of obtaining agronomic and meteorological data on a timely basis. The result of the work is a forecast assessment of the prospects for restoring degraded land for the intended purpose using modern databases and WEB-systems.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

scholarly journals Topoclimate Mapping Using Landsat ETM+ Thermal Data: Wolin Island, Poland

2021 ◽  
Vol 13 (14) ◽  
pp. 2712
Author(s):  
Marta Ciazela ◽  
Jakub Ciazela
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Animal Husbandry ◽  
Boundary Layer Processes ◽  
Lowland Area ◽  
Agricultural Areas ◽  
Glacial Landforms ◽  
Climatic Pattern

Variations in climatic pattern due to boundary layer processes at the topoclimatic scale are critical for ecosystems and human activity, including agriculture, fruit harvesting, and animal husbandry. Here, a new method for topoclimate mapping based on land surface temperature (LST) computed from the brightness temperature of Landsat ETM+ thermal bands (band6) is presented. The study was conducted in a coastal lowland area with glacial landforms (Wolin Island). The method presented is universal for various areas, and is based on freely available remote sensing data. The topoclimatic typology obtained was compared to the classical one based on meteorological data. It was proven to show a good sensitivity to changes in topoclimatic conditions (demonstrated by changes in LST distribution) even in flat, agricultural areas with only small variations in topography. The technique will hopefully prove to be a convenient and relatively fast tool that can improve the topoclimatic classification of other regions. It could be applied by local authorities and farmer associations for optimizing agricultural production.

scholarly journals A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jacob R. Schaperow ◽  
Dongyue Li ◽  
Steven A. Margulis ◽  
Dennis P. Lettenmaier
Keyword(s):  
Land Surface ◽  
Global Scale ◽  
Radiation Budget ◽  
Soil Parameters ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Vic Model ◽  
Spatial Coverage ◽  
Land Surface Parameter ◽  
Vegetation Parameters

AbstractHydrologic models predict the spatial and temporal distribution of water and energy at the land surface. Currently, parameter availability limits global-scale hydrologic modelling to very coarse resolution, hindering researchers from resolving fine-scale variability. With the aim of addressing this problem, we present a set of globally consistent soil and vegetation parameters for the Variable Infiltration Capacity (VIC) model at 1/16° resolution (approximately 6 km at the equator), with spatial coverage from 60°S to 85°N. Soil parameters derived from interpolated soil profiles and vegetation parameters estimated from space-based MODIS measurements have been compiled into input files for both the Classic and Image drivers of the VIC model, version 5. Geographical subsetting codes are provided, as well. Our dataset provides all necessary land surface parameters to run the VIC model at regional to global scale. We evaluate VICGlobal’s ability to simulate the water balance in the Upper Colorado River basin and 12 smaller basins in the CONUS, and their ability to simulate the radiation budget at six SURFRAD stations in the CONUS.

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