<p>Spatially distributed soil information as input for hydrological models has the potential to improve the representation and physical realism of spatio-temporal hydrological processes. Since spatially distributed soil information is often not available, lumped parameters are frequently used in hydrological models to describe soil functions. However, especially the modeling of hydrological processes in the vadose zone &#8211; and consequently groundwater recharge &#8211; requires information on soil hydraulic properties. The main objective of this study is the prediction of future groundwater recharge rates for the extent of Austria under changing climate conditions. To reach this goal, we use Machine Learning (ML) based soil hydraulic maps as a basis for the parameterization of the COntinuous SEmi-distributed RunOff model (COSERO).</p><p>For the spatial prediction of the soil parameters, XGBoost, a boosting ML-algorithm, was trained with soil hydraulic maps of the federal state of Lower Austria and available environmental raster datasets (e.g. climate data, digital elevation model, landcover etc.). Based on the Austrian wide available environmental covariates, the trained XGBoost model was then used to predict relevant soil hydraulic properties for the whole area of Austria (approx. 83&#160;900 km&#178;) at a target resolution of 1 x 1 km&#178;.</p><p>For our hydrological model set-up, we rescale the predicted soil hydraulic properties into the model parameter range and domain. After parameter optimization, i.e. in our case scaling the mean and thereby keeping the spatial patterns of the parameters, the conceptual rainfall-runoff model COSERO simulates spatially distributed discharge for the study area. We compare our model results to simulations of a model version using lumped soil parameters to assess the differences in the spatial distribution of groundwater recharge rates. Additionally, we analyze the quality of discharge simulations depending on the respective parameterization of the model. Overall, the results show an increased performance when using distributed soil hydraulic properties.</p><p>In summary, this study demonstrates the importance of considering the variability of soil information in a hydrological model framework and evaluates the suitability of implementing digital soil mapping products in groundwater recharge modeling.</p>
ZusammenfassungAngesichts der Klimawandelproblematik gewinnt auch die flächige Verfügbarkeit von bodenhydraulischen Informationen an Bedeutung. Diese Bodeninformationen bilden die Grundlage zur Modellierung hydrologischer Prozesse, speziell bei aktuellen Problemfeldern wie der Gefahrenausweisung von pluvialem Hochwasser. In Österreich gibt es derzeit kein Produkt, das die Bodendaten bundesweit flächig abbildet und gleichzeitig die hohe natürliche Variabilität der Bodeneigenschaften widerspiegelt. Ziel der vorliegenden Studie war es, auf Basis verfügbarer Daten relevante bodenhydraulische Parameter für die Gesamtfläche Österreichs abzuleiten und auch entsprechende Unsicherheiten anzugeben. Hierzu wurden zwei gängige Verfahren aus dem „Machine Learning“ (ML), XGBoost und FNN, getestet, um Zusammenhänge zwischen leicht messbaren bzw. flächig verfügbaren physio-geografischen sowie zusätzlichen Informationen aus Satellitenfernerkundung und den relevanten Bodenparametern zu entwickeln. Auf Basis der ML-Verfahren wurden die Bodenparameter Sand, Schluff, Ton und Humus flächig für ganz Österreich und für drei verschiedene Tiefenstufen auf einer Rasterbasis von 1 × 1 km2 abgeleitet. Die Ergebnisse stellen im direkten Vergleich mit dem derzeitig einzig österreichweit flächig verfügbaren Bodeninformationssystem eine deutliche Verbesserung dar. Die Regionalisierung der gesättigten hydraulischen Leitfähigkeit (ks) wurde indirekt – auf Grundlage der regionalisierten Bodenparameter und mithilfe von existierenden Pedotransfer Funktionen (PTFs) – und direkt – auf Basis vorhandener bodenhydraulischer Datensätze – getestet. Die Ableitung von ks ist nur mit großen Unsicherheiten möglich. Die erstellten Bodenkarten leisten einen wichtigen Beitrag zur Reduktion der vorhandenen Bodendatenlücken in Österreich und sollen als Grundlage für weitere Arbeiten zur Abschätzung der pluvialen Hochwassergefahr dienen.
<p>To assess future groundwater recharge rates in Austria under climate change conditions, detailed spatial soil information is required. &#160;Different data sources such as global soil maps (SoilGrids), regional soil maps of arable land (eBOD) and local soil profiles are available. However, they differ in scale and degree of data aggregation, as well as in spatial coverage.</p><p>Soil properties are characterized by a high spatial variability at all scales and it is well known that averaging will cause biases in the statistical relationships between different soil data sets, and between soil and landscape physio-geographical properties. Aiming for a best quality Austrian-wide soil map synthesizing all information, scale dependent multi-level relations between soil data bases are examined following two approaches:</p><p>Firstly, a linear relationship of soil variables at different scales is assumed. The statistical and geostatistical characteristics are analyzed at different aggregation levels to explore the scale-related behavior of our data. Secondly, machine learning algorithms (random forests and boosting methods) are applied to predict soil characteristics of existing regional soil maps by using all other available data sources as input features. Additional locally available variables such as elevation, slope, aspect, vegetation and climate data are evaluated for significance when predicting the missing soil information. &#160;</p><p>In summary, this study analyzes the statistical behavior and patterns of variability of soil properties at different scales and derives a modelling approach that can be used to predict regional soil properties from data sources spanning a range of different scales.</p>
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