Search

Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable land test site within the Rur catchment in Western Germany. Surface soil moisture (0-6 cm) has been measured in an approx. 50×50m grid at 14 and 17 dates (May 2007 to November 2008) in both test sites. To analyse spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to connect the pattern to related factors and processes. For the grassland test site, the analysis results in one significant spatial structure (first EOF), which explains about 57.5% of the spatial variability connected to soil properties and topography. The weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable land test site, the analysis yields two significant spatial structures, the first EOF, explaining 38.4% of the spatial variability, shows a highly significant correlation to soil properties, namely soil texture. The second EOF, explaining 28.3% of the spatial variability, is connected to differences in land management. The soil moisture in the arable land test site varies more during dry and wet periods on locations with low porosity. [ABSTRACT FROM AUTHOR]

Due to climate change and global warming, speed and intensity of the hydrological cycle will accelerate. In order to carry out regional risk assessment, integrated water resources management and flood protection, far reaching predictions and future scenarios of climate change effects on extreme precipitation and flooding are of particular relevance. In this study, trends in frequencies of extreme precipitation and floods until 2099 are analysed for the German Rur catchment, which is half located in highlands and half in lowlands and therefore has a high topographical and climatological contrast. To predict future trends, coupled modeling is performed based on NCEP reanalysis data and a General Circulation Model (GCM). Assuming HadCM3 future emission scenarios A2a and B2a, an empirical Statistical Downscaling Model (SDSM) is developed and daily precipitation amounts are projected until 2099 by a stochastic weather generator. The generated precipitation data are used as an input for the ecohydrological Soil & Water Assessment Tool (SWAT model) to simulate daily water discharge until 2099. Statistical trend analyses are implemented based on three annual extreme precipitation indices (EPIs) and the magnitudes of ten flood return periods derived with GEV and Gumbel extreme value distributions for 109 30-year moving periods using regression analyses and Mann-Kendall tendency tests to check for significant trends in the frequencies until 2099. As a result, it could be demonstrated for all EPIs that the frequency of extreme precipitation in the upper Rur catchment will significantly increase by +33% to +51% until 2099 compared to the base period 1961-1990, whereas mostly non-significant negative trends of extreme precipitation can be projected in the lowlands. For runoff, it was found that the magnitudes of the ten flood return periods will significantly increase by +31% for B2a to +36% for A2a until 2099 compared to the base period., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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.