Rigorous evaluation of a soil heat transfer model for mesoscale climate change impact studies

Muerth, Markus; Mauser, Wolfram

doi:10.1016/j.envsoft.2012.02.017

Cited by 10 publications

(3 citation statements)

References 78 publications

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“…The DANUBIA simulation system couples models of different complexity and temporal resolution in a component- and raster-based framework. From the 17 (natural and socio-economic) components of the overall system [ 50 , 51 ], only the components representing plant growth [ 52 ], soil nitrogen transformation [ 53 ], hydrology, and energy balance (based on [ 54 ] and [ 55 ]) were used for the current study. These models simulate fluxes of water, nitrogen and carbon in the soil-vegetation-atmosphere system using physically based process descriptions.…”

Section: Methodsmentioning

confidence: 99%

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

Reichenau

Korres

Montzka³

et al. 2016

PLoS ONE

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The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

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Section: Methodsmentioning

confidence: 99%

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

Reichenau

Korres

Montzka³

et al. 2016

PLoS ONE

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“…The soil water dynamics in PROMET are simulated with a four-layer soil model applying an explicit solution of the Richards equation for flow in unsaturated media [51]. The soil water retention model of Brooks and Corey [52] is used to relate soil suction head to SM content; a detailed description is given in Mauser and Bach [39] and Muerth and Mauser [53]. Muerth [54] evaluated the soil temperature modelling abilities of PROMET in the Upper Danube Catchment with measurements and remote-sensing data.…”

Section: Land-surface Model Prometmentioning

confidence: 99%

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

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“…As a physically based LSHM, PROMET uses a more hydrological view on the land surface with a more detailed spatial resolution of 1 km and different physical formulations from the Noah-LSM (Zabel et al, 2012). Detailed model descriptions of PROMET can be found in Mauser and Bach (2009) and Muerth and Mauser (2012).…”

Section: Methodsmentioning

confidence: 99%

2-way coupling the hydrological land surface model PROMET with the regional climate model MM5

Zabel

Mauser

2013

Hydrol. Earth Syst. Sci.

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Abstract. Most land surface hydrological models (LSHMs) consider land surface processes (e.g. soil-plant-atmosphere interactions, lateral water flows, snow and ice) in a spatially detailed manner. The atmosphere is considered as exogenous driver, neglecting feedbacks between the land surface and the atmosphere. On the other hand, regional climate models (RCMs) generally simulate land surface processes through coarse descriptions and spatial scales but include land-atmosphere interactions. What is the impact of the differently applied model physics and spatial resolution of LSHMs on the performance of RCMs? What feedback effects are induced by different land surface models? This study analyses the impact of replacing the land surface module (LSM) within an RCM with a high resolution LSHM.A 2-way coupling approach was applied using the LSHM PROMET (1 × 1 km 2 ) and the atmospheric part of the RCM MM5 (45 × 45 km 2 ). The scaling interface SCALMET is used for down-and upscaling the linear and non-linear fluxes between the model scales.The change in the atmospheric response by MM5 using the LSHM is analysed, and its quality is compared to observations of temperature and precipitation for a 4 yr period from 1996 to 1999 for the Upper Danube catchment. By substituting the Noah-LSM with PROMET, simulated non-biascorrected near-surface air temperature improves for annual, monthly and daily courses when compared to measurements from 277 meteorological weather stations within the Upper Danube catchment. The mean annual bias was improved from −0.85 to −0.13 K. In particular, the improved afternoon heating from May to September is caused by increased sensible heat flux and decreased latent heat flux as well as more incoming solar radiation in the fully coupled PROMET/MM5 in comparison to the NOAH/MM5 simulation. Triggered by the LSM replacement, precipitation overall is reduced; however simulated precipitation amounts are still of high uncertainty, both spatially and temporally. The distribution of precipitation follows the coarse topography representation in MM5, resulting in a spatial shift of maximum precipitation northwards of the Alps. Consequently, simulation of river runoff inherits precipitation biases from MM5. However, by comparing the water balance, the bias of annual average runoff was improved from 21.2 % (NOAH/MM5) to 4.4 % (PROMET/MM5) when compared to measurements at the outlet gauge of the Upper Danube watershed in Achleiten.

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Rigorous evaluation of a soil heat transfer model for mesoscale climate change impact studies

Cited by 10 publications

References 78 publications

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

2-way coupling the hydrological land surface model PROMET with the regional climate model MM5

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