scholarly journals Modeling land surface processes over a mountainous rainforest in Costa Rica using CLM4.5 and CLM5

2020 ◽  
Author(s):  
Jaeyoung Song ◽  
Gretchen R. Miller ◽  
Anthony T. Cahill ◽  
Luiza Aparecido ◽  
Georgianne W. Moore
Keyword(s):  
Costa Rica ◽  
Land Surface ◽  
Carbon Flux ◽  
Meteorological Data ◽  
Aerodynamic Resistance ◽  
Mean Annual Precipitation ◽  
Leaf Wetness ◽  
Rainfall Events ◽  
Vapor Flux ◽  
Mountainous Regions

Abstract. This study compares the performance of the Community Land Models (CLM4.5 and CLM5) against tower and ground measurements from a tropical montane rainforest in Costa Rica. The study site receives over 4,000 mm of mean annual precipitation and has high daily levels of relative humidity. The measurement tower is equipped with eddy-covariance and vertical profile systems able to measure various micrometeorological variables, particularly in wet and complex terrain. In this work, results from point-scale simulation for both CLM4.5 and its updated version (CLM5) are compared to observed canopy flux and micro-meteorological data. Both models failed to capture the effects of frequent rainfall events and mountainous topography on the variables of interest (temperatures, leaf wetness, and fluxes). Overall, CLM5 alleviates some errors in CLM4.5 but CLM5 still cannot precisely simulate a number of canopy processes for this forest. Soil, air, and canopy temperatures, as well as leaf wetness, remain too sensitive to incoming solar radiation rates despite updates to the model. As a result, daytime vapor flux and carbon flux are overestimated, and modeled temperature differences between day and night are higher than those observed. Slope effects appear in the measured average diurnal variations of surface albedo and carbon flux, but CLM5 cannot simulate these features. This study suggests that both CLM models still require further improvements concerning energy partitioning processes, such as leaf wetness process, photosynthesis model, and aerodynamic resistance model for wet and mountainous regions.

scholarly journals Modeling land surface processes over a mountainous rainforest in Costa Rica using CLM4.5 and CLM5

2020 ◽  
Vol 13 (11) ◽  
pp. 5147-5173
Author(s):  
Jaeyoung Song ◽  
Gretchen R. Miller ◽  
Anthony T. Cahill ◽  
Luiza Maria T. Aparecido ◽  
Georgianne W. Moore
Keyword(s):  
Costa Rica ◽  
Land Surface ◽  
Carbon Flux ◽  
Complex Terrain ◽  
Aerodynamic Resistance ◽  
Mean Annual Precipitation ◽  
Leaf Wetness ◽  
Rainfall Events ◽  
Vapor Flux ◽  
Mountainous Regions

Abstract. This study compares the performance of the Community Land Models (CLM4.5 and CLM5) against tower and ground measurements from a tropical montane rainforest in Costa Rica. The study site receives over 4000 mm of mean annual precipitation and has high daily levels of relative humidity. The measurement tower is equipped with eddy-covariance and vertical profile systems able to measure various micrometeorological variables, particularly in wet and complex terrain. In this work, results from point-scale simulations for both CLM4.5 and its updated version (CLM5) are compared to observed canopy flux and micrometeorological data. Both models failed to capture the effects of frequent rainfall events and mountainous topography on the variables of interest (temperatures, leaf wetness, and fluxes). Overall, CLM5 alleviates some errors in CLM4.5, but CLM5 still cannot precisely simulate a number of canopy processes for this forest. Soil, air, and canopy temperatures, as well as leaf wetness, remain too sensitive to incoming solar radiation rates despite updates to the model. As a result, daytime vapor flux and carbon flux are overestimated, and modeled temperature differences between day and night are higher than those observed. Slope effects appear in the measured average diurnal variations of surface albedo and carbon flux, but CLM5 cannot simulate these features. This study suggests that both CLMs still require further improvements concerning energy partitioning processes, such as leaf wetness process, photosynthesis model, and aerodynamic resistance model for wet and mountainous regions.

scholarly journals On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

2008 ◽  
Vol 12 (6) ◽  
pp. 1339-1351 ◽  
Author(s):  
G. Laguardia ◽  
S. Niemeyer
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Depth ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Mean Square ◽  
Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999b). Once evaluated the effect of scale mismatch, we calculate the root mean square error and the correlation between the two soil moisture time series on a pixel basis and we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5 pF units. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

scholarly journals On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

2008 ◽  
Vol 5 (3) ◽  
pp. 1227-1265 ◽  
Author(s):  
G. Laguardia ◽  
S. Niemeyer
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Depth ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Mean Square ◽  
Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999). Once calculated the root mean square error and the correlation between the two soil moisture time series on a pixel basis, we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

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 An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4181
Author(s):  
Kunlun Xiang ◽  
Wenping Yuan ◽  
Liwen Wang ◽  
Yujiao Deng
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Statistical Data ◽  
Spatial Information ◽  
Hydrological Cycle ◽  
Mainland China ◽  
Mean Annual Precipitation ◽  
Irrigated Area ◽  
Moderate Resolution

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

scholarly journals A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode

2010 ◽  
Vol 14 (4) ◽  
pp. 613-626 ◽  
Author(s):  
S. Sinclair ◽  
G. G. S. Pegram
Keyword(s):  
South Africa ◽  
Soil Moisture ◽  
Land Surface ◽  
Meteorological Data ◽  
Saturation Index ◽  
Significant Proportion ◽  
Surface Mode ◽  
Good Correspondence ◽  
Percentage Saturation ◽  
Soil Saturation

Abstract. In this paper we compare two independent soil moisture estimates over South Africa. The first estimate is a Soil Saturation Index (SSI) provided by automated real-time computations of the TOPKAPI hydrological model, adapted to run as a collection of independent 1 km cells with centres on a grid with a spatial resolution of 0.125°, at 3 h intervals. The second set of estimates is the remotely sensed ASCAT Surface Soil Moisture product, temporally filtered to yield a Soil Wetness Index (SWI). For the TOPKAPI cells, the rainfall forcing used is the TRMM 3B42RT product, while the evapotranspiration forcing is based on a modification of the FAO56 reference crop evapotranspiration (ET0). ET0 is computed using forecast fields of meteorological variables from the Unified Model (UM) runs done by the South African Weather Service (SAWS); the UM forecast fields were used, because reanalysis is not done by SAWS. To validate these ET0 estimates we compare them with those computed using observed meteorological data at a network of weather stations; they were found to be unbiased with acceptable scatter. Using the rainfall and evapotranspiration forcing data, the percentage saturation of the TOPKAPI soil store is computed as a Soil Saturation Index (SSI), for each of 6984 unconnected uncalibrated TOPKAPI cells at 3 h time-steps. These SSI estimates are then compared with the SWI estimates obtained from ASCAT. The comparisons indicate a good correspondence in the dynamic behaviour of SWI and SSI for a significant proportion of South Africa.

scholarly journals Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

2021 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan I. López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals Spatial Analysis of Urban Thermal Island in Northern Iran (Case Study: Rasht City)

2022 ◽  
Author(s):  
Ali Mohammadpourzeid ◽  
Bohloul Alijani ◽  
Mehry Akbary ◽  
Parviz Zeaieanfirouzabadi
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
Land Use Changes ◽  
Negative Effects ◽  
Northern Iran ◽  
Spatial Trend ◽  
Two Factors ◽  
Temporal And Spatial ◽  
Landsat Satellite ◽  
The City

Abstract Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance.The one-window algorithm of Kim et al. Uses Landsat satellite imagery to model the earth's surface temperature.These trends are validated using meteorological data. Two main and basic factors play a major role in the temporal and spatial trend of the thermal islands of Rasht. These two factors of climate change that have occurred in the last two decades in the region of Gilan province and the city of Rasht. The second factor that has greatly enhanced the effect of the first factor is the human factor that has greatly included other urban factors in Rasht, including urban management and proper urban planning in the province and the city of Rasht. These two factors in the temporal and spatial trend of urban thermal islands have caused thermal islands to rapidly increase the growth of the city and urban population from the urban center to the western and southwestern regions and have very negative effects on land use changes and human areas. It has caused the construction of Rasht city.

scholarly journals Observations and  Modelling of Precipitation  in the Southern Alps of  New Zealand

2021 ◽  
Author(s):  
◽  
Stephen John Stuart
Keyword(s):  
New Zealand ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Southern Alps ◽  
Annual Rainfall ◽  
Mean Annual Precipitation ◽  
Mountain Range ◽  
Mountainous Regions ◽  
Orographic Rainfall

<p>Precipitation in the central Southern Alps affects glaciation, river flows and key economic activities, yet there is still uncertainty about its spatial distribution and primary influences. Long-term and future patterns of New Zealand precipitation can be estimated by the HadRM3P regional climate model (RCM) - developed by the United Kingdom Met Office - but orographic rainfall in the steep and rugged topography of the Southern Alps is difficult to simulate accurately at the 30-km resolution of the RCM. To quantify empirical relationships, observations of surface rainfall were gathered from rain gauges covering a broad region of the South Island. In four transects of the Hokitika, Franz Josef and Haast regions, the mean annual precipitation maxima of objectively interpolated profiles are consistently located 7-11 km southeast of the New Zealand Alpine Fault. The magnitude and shape of the rainfall profile across the Southern Alps are strongly influenced by the 850-hPa wind direction to the north of the mountain range, as determined by comparing rain-gauge observations to wind vectors from NCEP/NCAR Reanalysis 1. The observed profile of orographically enhanced rainfall was incorporated into a trivariate spline in order to interpolate precipitation simulated by the RCM. This downscaling method significantly improved the RCM's estimates of mean annual rainfall at stations in the Southern Alps region from 1971 to 2000, and RCM projections of future rainfall in mountainous regions may be similarly refined via this technique. The improved understanding of the observed rainfall distribution in the Southern Alps, as gained from this analysis, has a range of other hydrological applications and is already being used in 'downstream' modelling of glaciers.</p>

scholarly journals Evaluation of the Performance of CFSR Reanalysis Data Set for Estimating Potential Evapotranspiration (PET) in Turkey

2021 ◽  
Author(s):  
AHMET IRVEM ◽  
Mustafa OZBULDU
Keyword(s):  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Reanalysis Data ◽  
Actual Evapotranspiration ◽  
Observation Data ◽  
Reanalysis Dataset ◽  
Data Set ◽  
Climate Forecast System Reanalysis ◽  
Mountainous Regions ◽  
Weather Stations

Abstract Evapotranspiration is an important parameter for hydrological, meteorological and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, Potential Evapotranspiration (PET) is typically calculated using meteorological data to calculate actual evapotranspiration. However, it is very difficult to get complete and accurate data from meteorology stations in, rural and mountainous regions. This study examined the availability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations in the computation of potential annual and seasonal evapotranspiration. The PET calculations using the CFSR reanalysis dataset for the period 1987-2017 were compared to data observed at 259 weather stations observed in Turkey. As a result of the assessments, it was determined that the seasons in which the CFSR reanalysis data set had the best prediction performance were the winter (C'= 0.76 and PBias = -3.77) and the autumn (C' = 0.75 and PBias = -12.10). The worst performance was observed for the summer season. The performance of the annual prediction was determined as C'= 0.60 and PBias = -15.27. These findings indicate that the results of the PET calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used especially in the summer models.

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