Abstract

Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

Highlights

  • Hydrology and hydrological processes are characterized by high spatiotemporal variability

  • We calculated six catchment attributes describing vegetation indices, which are based on leaf area index (LAI), normalized difference vegetation index (NDVI) and green vegetation fraction (GVF) (Table A7)

  • LamaH includes 10 attributes to characterize soil properties (Table A8), where 8 of them are derived from the 1 km grid sized European Soil Database Derived data (ESDD; Hiederer, 2013a, b)

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Summary

Introduction

Hydrology and hydrological processes are characterized by high spatiotemporal variability. LamaH includes a basin delineation that represents the inter-catchment area (difference area or intermediate catchments) of neighboring gauges, in addition to the usual basin delineation used in CAMELS datasets, which is equivalent to the topographic (delineated only considering terrain features and ignoring potential subsurface cross-basin flows) catchment area of the individual gauges. Supplementary attributes such as the gauge topology, as well as the flow length and gradient between two adjacent gauges, are added to specify the interconnected hydrological network.

Domain of coverage
Basin delineations and aggregation approaches
Runoff data
Meteorological data
Catchment attributes
Topographic indices
Climatic indices
Hydrological signatures
Land cover characteristics
Vegetation indices
Soil characteristics
Geological characteristics
Model setup
Model results
Code availability
Required additional references when using LamaH
10 Summary and conclusions
4558 Appendix C
Findings
A MODIS-Based Global 1-km Maximum Green Vegetation
Full Text
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