Module 1: Hydrological Modeling with COSERO

Theoretical Framework, Spatial Discretization, and Data Sources

Mathew Herrnegger · mathew.herrnegger@boku.ac.at

Institute of Hydrology and Water Management (HyWa) BOKU University Vienna, Austria

LAWI301236 · Distributed Hydrological Modeling with COSERO

Introduction

Why Do We Need Hydrological Models?

Figure 1: Simplified visualization of processes and fluxes in the hydrological cycle(Kratzert et al., 2019)

Hydrological models serve as a objective link between our theoretical understanding of the water cycle and real-world applications. They provide the necessary basis for objective decision-making in water resources management and are important tools for scientific research.

By translating complex fluxes—such as precipitation, snowmelt, evapotranspiration, and subsurface flows—into quantifiable river discharge, models allow us to answer questions regarding safety, availability, and sustainability of water.

Domains of application include:

  • Flood Protection & Forecasting: Developing early warning systems and designing infrastructure (e.g., flood walls, retention basins) to protect life and property.
  • Hydropower: Design, but also optimizing reservoir operations and estimating potential energy production.
  • Drinking Water Provision: Ensuring sustainable supply and managing groundwater resources.
  • River Navigation: Predicting water levels to maintain navigable transport corridors.
  • Irrigation: Managing water allocation for agriculture, particularly in water-scarce regions.

The Modeling Challenge: A Non-Linear Transformation

The fundamental problem of rainfall-runoff modelling can be described as a non-linear transformation of driving meteorological inputs into catchment runoff. The model must effectively translate inputs like Precipitation, Temperature, and Potential Evapotranspiration into a discharge output.

This transformation is complex because the relationship between rainfall and runoff is not constant; it is a dynamic function \(f\) influenced by different factors:

  1. Catchment Characteristics: Static or slowly changing properties such as catchment shape, topography, vegetation cover, land use, soil properties, geology, and groundwater conditions.
  2. Hydrological Processes: The physical mechanisms governing water movement, including interception, depression storage, specific runoff generation processes (surface runoff, macropore flow, interflow, baseflow), and snow dynamics.
  3. Catchment Memory (System States): The role of antecedent conditions. The response to a rainfall event depends heavily on soil moisture patterns, existing snow cover, and the general season.
  4. Human Influences: Anthropogenic alterations that disrupt natural flow regimes, such as water extraction, inter-basin transfers, reservoir management, power plants, sealed surfaces, and river regulation.

To make things even more complicated, these relationships and dependencies are not constant and change over time.

NoteModule Overview

To address the complexities of the hydrological cycle, hydrological models like COSERO explicitly represents storage components, process interactions, and anthropogenic diversions. This module introduces distributed hydrological modeling using the CoseRo R interface, with a focus on Austrian catchments.

Learning Objectives:

  • Conceptualize: Understand the COSERO model structure and the distinction between lumped and distributed approaches.
  • Explore: Get an overvierw of the steps necessary to set up hydrological models and identify data sources for hydrological modeling in Austria and the world.
  • Apply: Get an overview of software and tools to initialize your first COSERO project.

The COSERO Model

Introduction and Historical Development

The COSERO model (COntinuous SEmi-distributed RunOff) is a continuous, (semi-)distributed, conceptual rainfall-runoff model developed at the Institute of Hydrology and Water Management (HyWa), formerly IWHW, at BOKU University Vienna, Austria. The model concept is similar to the HBV model (Bergström, 1992) and accounts for accumulation and melting of snow and glaciers, actual evapotranspiration, interception, soil water storage, separation of runoff into different flow components, and routing by means of a cascade of linear and non-linear reservoirs.

Over the years, many people have worked on COSERO, which evolved from a model structure originally developed for forecasting runoff of the river Enns in Austria (Nachtnebel et al., 1993). Subsequently, the model has undergone substantial improvements including enhanced snow modules, automatic parameter calibration, and modifications for real-time flood forecasting, distributed routing, or trans-catchment diversions.

Current development at BOKU-HyWa focuses on regionalization strategies and parameter optimization using spatially distributed catchment data—such as elevation, slope, aspect, land use, or soil—to estimate model parameters via a priori defined transfer functions (Feigl et al., 2020, 2022, 2026; Klotz et al., 2017b). This approach, similar to the Multiscale Parameter Regionalization (MPR) proposed by Samaniego et al. (2010), significantly reduces the number of parameters required for optimization and helps overcome scale dependency, while a second development avenue utilizes Machine-Learning based parameter fields (Zeitfogel et al., 2023; Zeitfogel et al., 2025).

Parallel to these scientific advancements, the model code has been technically modernized through OpenMP-based parallelization and dynamical array allocation to support high-resolution simulations of large regions, alongside architectural enhancements such as modularized subroutines, global variable definitions, and improved process representations for glacier melt and anthropogenic diversions.

TipCOSERO Handbook

The COSERO Handbook is the primary reference document for the model. It describes the model structure, all process equations, input/output file formats, and configuration options in detail. Refer to it whenever you need the mathematical formulation behind a process or want to understand what a specific parameter controls.

Applications

Since 1993, different versions of COSERO have been successfully applied in numerous scientific and commercial projects all over the world, including:

  • Flood Forecasting: Utilized in real-time flood warning systems in Austria and neighboring countries.
  • Climate Change Assessments: Applied to evaluate the impact of changing climatic conditions on water availability and hydropower production.
  • Water Balance Studies: Used to simulate the long-term water balance of various catchments to support water resources management.
  • Large-Sample Hydrology: Used for data-related research and baseline simulations.
  • Precipitation Analysis: Developing precipitation correction and adjustment methods, particularly in high-alpine environments.
Table 1: Selected publications using COSERO, organized by application category
Category Selected References
Climate Change Impact Assessment APCC (2014); Buttinger (2018); Ehrendorfer et al. (2024); Friedrichs-Manthey et al. (2024); Herrnegger et al. (2018b); Kiesel et al. (2020); Kling et al. (2012); Mehdi et al. (2021); Nachtnebel et al. (2011); Stanzel and Nachtnebel (2010); Stanzel et al. (2018); Stanzel and Kling (2018); Buttinger et al. (2018)
Flood Forecasting & Real-Time Applications Martin Santos et al. (2019); Martin Santos et al. (2020); Martin Santos et al. (2021); Nachtnebel and Kahl (2008); Nachtnebel et al. (2009a); Nachtnebel et al. (2009b); Nachtnebel et al. (2010); Nachtnebel et al. (2013); Pulka et al. (2021); Pulka (2021); Pulka et al. (2022); Stanzel et al. (2008); Wesemann et al. (2018b); Wesemann (2021)
Water Balance Studies Herrnegger and Nachtnebel (2011); Herrnegger and Nachtnebel (2012a); Herrnegger et al. (2020); Kling (2006); Zeitfogel et al. (2024b); Zeitfogel et al. (2025)
Large-Sample Hydrology & Data Kling et al. (2015); Klingler (2020); Klingler et al. (2021); Konold et al. (2025)
Precipitation Analysis & Correction Bica et al. (2011); Herrnegger and Nachtnebel (2012b); Herrnegger and Nachtnebel (2012c); Herrnegger (2013); Herrnegger et al. (2015); Herrnegger et al. (2018a); Maier et al. (2024); Maier et al. (2026); Omonge et al. (2022); Omonge et al. (2019); Pulka et al. (2023); Pulka et al. (2024)
Hydrological Modeling & Applications BMFLUW (2005); Eder et al. (2005); Enzinger (2009); Feiel (2018); Fuchs (1998); Guse et al. (2019); Herrnegger et al. (2020); Kling et al. (2006); Kling et al. (2014); Lerche (2022); Nachtnebel et al. (1993); Schößwendter (2018); Stanzel (2012); Wesemann et al. (2018b); Wesemann (2021)
Model Development & Parameterization Burgholzer (2017); Ehrendorfer (2022); Ehrendorfer and Herrnegger (2022); Ehrendorfer and Herrnegger (2023); Ehrendorfer et al. (2023); Frey and Holzmann (2015); Fuchs (1998); Haberl (2021); Kling (2002); Kling and Nachtnebel (2009); Klotz et al. (2015); Klotz et al. (2017a); Klotz et al. (2017b); Salamon (2020); Schulz et al. (2015); Stanzel (2012); Wesemann et al. (2018a); Zeitfogel et al. (2022); Zeitfogel et al. (2024a); Zeitfogel et al. (2024b); Zeitfogel et al. (2025)

COSERO is a flexible model able to simulate different hydrological systems across a wide range of scales and resolutions. Its spatial scalability—from lumped to semi-distributed and distributed setups—allows for applications ranging from small plot scales to large catchments covering thousands of square kilometers, while its temporal flexibility supports resolutions from 15-minute intervals for real-time flood forecasting to monthly time steps for long-term water balance studies. The model provides a comprehensive process representation, accounting for the accumulation and melting of snow and glaciers, actual evapotranspiration, interception, soil water storage, and routing via linear and non-linear reservoirs. Additionally, the model was also applied in an inverse modeling approach, utilizing runoff observations as input to calculate areal rainfall (Herrnegger, 2013; Herrnegger et al., 2015).

Model Concept: State-Space Approach

COSERO accounts for accumulation and melting of snow and glaciers, actual evapotranspiration from interception, snow and soil layer, storage of water in the soil, and separation of runoff into different runoff components (surface flow, interflow and baseflow) by means of a cascade of linear and non-linear reservoirs. The model is spatially distributed - all inputs, outputs and parameters have a spatial dimension.

COSERO is formulated in a state-space formulation with state transition functions:

\[ \dot{S}_t = f(\dot{S}_{t-1}, \dot{I}_t) \tag{1}\]

and output functions:

\[ \dot{O}_t = g(\dot{S}_{t-1}, \dot{I}_t) \tag{2}\]

where:

Table 2: Notation for state-space formulation
Symbol Units Description
\(\dot{I}_t\) mm/Δt Input
\(\dot{O}_t\) mm/Δt Output
\(\dot{S}_t\) mm System states
Δt - Model time step

The state transition function (Equation 1) describes how the state variables change from one time step to the next, based on the previous state and the current inputs. The output function (Equation 2) relates the state variables to the hydrological outputs of interest, such as streamflow, groundwater levels, or reservoir storage. State-space formulations are commonly used in hydrological modeling to represent the dynamic behavior of the hydrological cycle and the state variables typically represent different components of the water cycle, such as soil moisture, groundwater storage, snow accumulation, and surface water storage.

Model Structure

Figure 2: COSERO Model Structure showing modules, fluxes, states, and parameters

The COSERO model (Figure 2) integrates continuous hydrological processes through five interconnected modules: Snow, Glacier, Interception, Soil, and Routing. The schematic distinguishes between Input Fluxes (orange, e.g., \(P, T\)), System States (yellow, e.g., \(BW0\) soil moisture, \(SWW\) snow water equivalent), and Model Fluxes (blue/black arrows).

Hydrological Processes

Snow and Ice

  • Accumulation: Precipitation is partitioned into rain or snow using a linear transition between temperature thresholds SNOWTRT and RAINTRT. To account for sub-grid variability in alpine terrain, snowfall is distributed log-normally across internal elevation classes (controlled by NVAR).
  • Melt: Snowmelt follows a modified temperature-index approach where the melt factor varies seasonally between CTMIN (Dec 21) and CTMAX (Jun 21) to simulate albedo decay.
  • Glaciers: Glacier melt is calculated using a temperature-index method adjusted by GLAC_CT and considering long-term hourly radiation and initiates only after seasonal snow depletion.

Soil and Interception

  • Interception: A vegetation reservoir (capacity INTMAX) captures precipitation; excess water reaches the soil as throughfall.
  • Evapotranspiration (ETA): Total ETA sums evaporation from interception, snow sublimation, and soil moisture. Soil ETA is governed by potential evaporation corrected for slope (ETSLPCOR), vegetation (ETVEGCOR), and soil moisture (FKFAK).
  • Runoff Generation: The soil module (with parameters M, FK, PWP and state \(BW0\)) generates surface runoff (\(Q1\)) via a non-linear function of soil moisture (controlled by parameter BETA) and percolation (\(Q2\)) to subsurface storage (controlled by KBF) ).

Routing and Discharge Components

Total discharge (\(Q_{sim}\)) is the sum of three parallel flow components plus glacier melt:

  1. Surface Flow (\(QAB1\)): Fast response from reservoir \(BW1\) (recession TAB1). Outflow occurs only when storage exceeds the threshold H1, while vertical percolation to the interflow zone is controlled by TVS1.
  2. Interflow (\(QAB2\)): Intermediate response from reservoir \(BW2\) (recession TAB2). Outflow occurs when storage exceeds the threshold H2, with further percolation to the baseflow zone governed by TVS2.
  3. Baseflow (\(QAB3\)): Slow, delayed response from the deep groundwater reservoir \(BW3\) (recession TAB3), representing the long-term baseflow component.

These components are routed through a cascade of linear reservoirs (Zone Routing). The model also supports anthropogenic diversions with parameters Div_TONZ, QDIV_LT and QDIV_UT, allowing runoff redistribution for complex canal or hydropower systems (Wesemann et al., 2018b).

The following tables provide an overview of the system states, fluxes, and governing parameters.

Table 3: Important variables of the COSERO model. Flux variables represent sums over the time step; state variables give the water storage at the end of the time step.
Category Variable Type Description Unit
Input P Input Precipitation (sum over time step) mm
T Input Air temperature (average over time step) °C
ETP Input Potential evapotranspiration (sum over time step) mm
PZON Input Corrected precipitation (sum over time step) mm
JDAY — Julian day of the year starting on 22 December —
Precipitation PRAIN Flux Liquid precipitation / rainfall (after phase partitioning) mm
PSNOW Flux Solid precipitation / snow (after phase partitioning) mm
Interception BWI State Water stored in interception reservoir mm
ETAI Flux Actual evaporation from interception storage mm
PNETRAIN Flux Net-rainfall after interception, reaching the soil module mm
Snow & Ice SMELT Flux Actual snowmelt mm
SWW State Snow water equivalent mm
ETAS Flux Snow sublimation mm
ACCGLAC State Glacier ice water equivalent mm
BWLRGLAC State Water stored in glacier routing reservoir mm
MELTGLAC Flux Glacier melt mm
Soil BW0 State Water stored in soil reservoir mm
ETAG Flux Actual evapotranspiration from soil module mm
ETAT Flux Total actual evapotranspiration mm
Q1 Flux Fast runoff from soil module mm
Q2 Flux Percolation from soil module mm
QVS0 Flux Total runoff generation from soil module (Q1 + Q2) mm
Runoff Gen. BW1 State Water stored in surface flow reservoir mm
QAB1 Flux Fast / surface flow mm
QVS1 Flux Percolation to interflow reservoir mm
BW2 State Water stored in interflow reservoir mm
QAB2 Flux Interflow mm
QEX2 Flux Excess runoff when interflow reservoir is full mm
QVS2 Flux Percolation to baseflow reservoir mm
BW3 State Water stored in baseflow reservoir mm
QAB3 Flux Baseflow mm
QABzone Flux Total zone runoff mm
Routing Qaddinflow Input Optional: external inflow m³/s
Qreginflow Flux Optional: regression-based external inflow m³/s
QZU4zone Flux Inflow from upstream zones m³/s
BW4 State Water stored in zone routing reservoir m³
QAB4zone Flux Outflow from zone routing reservoir m³/s
QSIM Flux Simulated basin runoff m³/s

The following table lists typical COSERO parameters available for calibration, with their valid ranges, default values, and modification type (relchg = relative change factor applied to the spatial mean; abschg = absolute additive change).

Table 4: COSERO model parameters, ranges, and default values
Category Parameter Description Min Max Default Mod. type
Snow CTMAX Max snow melt factor on Jun 21 [mm/°C/d] 4.0 12.0 5.0 relchg
CTMIN Min snow melt factor on Dec 21 [mm/°C/d] 0.5 4.0 2.0 relchg
GLAC_CT Glacier melt factor adjustment 0.1 0.5 0.25 relchg
NVAR Variance for distributing new snowfall 0.1 2.5 1.5 relchg
RAINTRT Transition temperature for pure rain [°C] −1.0 4.0 3.0 abschg
SNOWTRT Transition temperature for pure snow [°C] −2.5 3.0 0.0 abschg
THRT Threshold temperature for snow melt [°C] −2.0 3.0 0.0 abschg
TVAR Std dev of air temperature within time step for snow melt [°C] 0.0 5.0 0.0 abschg
Soil BETA Runoff generation parameter (soil moisture function) 0.1 10.0 4.5 relchg
FK Field capacity (if FK=1 & PWP=0, M is the sole storage parameter) 0.08 0.42 1.0 relchg
KBF Recession constant baseflow [h] 1000 12000 3000 relchg
M Soil storage capacity [mm] 20 600 300 relchg
PWP Permanent wilting point (if PWP=0 & FK=1, M is the sole storage parameter) 0.03 0.12 0.0 relchg
Runoff H1 Outlet level surface flow reservoir [mm] 0.0 20.0 2.0 relchg
H2 Outlet level interflow reservoir [mm] 0.0 20.0 10.0 relchg
TAB1 Recession constant surface flow [h] 1 50 50 relchg
TAB2 Recession constant interflow [h] 25 300 500 relchg
TVS1 Percolation recession from surface flow reservoir [h] 5 200 100 relchg
TVS2 Percolation recession from interflow reservoir [h] 45 500 200 relchg
Groundwater TAB3 Recession constant baseflow reservoir [h] 500 8000 5000 relchg
Evapotranspiration ETSLPCOR ETP correction for slope and aspect 0.9 1.3 1.0 relchg
ETSYSCOR ETP systematic error correction factor 0.9 1.3 1.0 relchg
ETVEGCOR ETP correction for vegetation type 0.4 1.3 1.0 relchg
FKFAK Factor for actual ETA from ETP as function of soil moisture 0.3 0.9 0.7 relchg
Interception INTMAX Maximum interception storage capacity [mm] 0.5 6.0 0.0 relchg
Precipitation PCOR Overall precipitation correction factor 0.8 2.0 1.0 relchg
RAINCOR Rain-specific correction factor 0.8 2.0 1.0 relchg
SNOWCOR Snow-specific correction factor 0.8 2.0 1.0 relchg
Temperature TCOR Temperature correction constant [°C] 0.0 4.0 0.0 abschg
Routing TAB4 Recession constant subbasin routing [h] 0.05 5.0 1.0 relchg
TAB5 Time shift for QAB [h] 0.1 10.0 1.0 relchg

Spatial Discretization

Generally, hydrological models can be categorized by how they handle spatial variability. COSERO employs a flexible hierarchical structure that works between simple lumped approaches and fully distributed systems.

Spatial Discretization Approaches

Figure 3 illustrates the three primary classifications of hydrological models based on spatial representation:

  1. Lumped Models — Treat the entire catchment as a single, uniform unit.
    • Spatial Unit: Entire catchment.
    • Inputs and Parameters: One single value set applied globally.
    • Characteristics: Computationally highly efficient but ignores all spatial heterogeneity.
  2. Semi-Distributed Models — Divide the catchment into smaller, functionally unique units.
    • Spatial Unit: Sub-watersheds or Hydrological Response Units (HRUs) based on characteristics like elevation, land use, or geology.
    • Inputs and Parameters: Unique values assigned to each functional unit.
    • Characteristics: Balances process complexity with data requirements and computational demand.
  3. Distributed (Raster-based) Models — Divide the catchment into a regular geometric grid.
    • Spatial Unit: Discretisation on a raster basis (grid cells).
    • Inputs and Parameters: Spatially distributed values defined for every individual cell.
    • Characteristics: Provides high spatial resolution but requires significant computation and detailed data input.
Figure 3: Spatial discretization concepts: Lumped (top), HRU/Semi-distributed (middle), and Raster-based (bottom) (Google Gemini, 2026)

COSERO’s Hierarchical Structure

COSERO offers the flexibility to run in a fully distributed (gridded) mode, but also in a semi-distributed or lumped approach. It uses a hierarchical system to represent the river catchment, dividing it into Subbasins and Zones.

Subbasins (NB)

The catchment is first divided into several subbasins. For the outlet of each subbasin, the model computes a simulated discharge (\(Q_{sim}\)).

Modelling Zones (NZ/IZ)

To account for physical heterogeneity within a subbasin, a further subdivision into Model Zones (NZ/IZ) is performed. IZ reflects zones within a subbasin and NZ the zone ID within the total model. These zones act as the fundamental calculation units and can be defined using different concepts:

  • HRU Approach: Zones defined by overlaying (Sub-)catchment boundaries, Elevation zones, Soil information, or Land cover information.
  • Gridded Approach: With increasing computational power, a gridded approach has become standard for many COSERO applications (typically \(1 \times 1\) km or finer). Here, each grid cell acts as an individual Model Zone, allowing for a high-resolution representation of spatial heterogeneity.

Zones as Basic Modelling Units

Regardless of whether they are HRUs or grid cells, the zones (NZ) are the basic spatial modelling units. Each zone:

  1. Possesses a unique set of parameters.
  2. Receives specific meteorological input.
  3. Simulates all hydrological modules (Snow, Soil, etc.) independently.
  4. Computes a zone output.

Within the subbasin, the outflow of all zones is aggregated and, together with inflow from upstream subbasins, is routed to the basin outlet to form the subbasin runoff.

COSERO Parameter Dimensions

Model parameters are spatially distributed across the subbasin and have the dimensions defined by the hierarchy (Subbasin NB, Zone within a subbasin IZ). Some parameters also include a temporal dimension (e.g., maximum interceptions storage, which varies on a monthly basis).

Table 5: COSERO parameter dimensions and indices
Dimension Description
NB Index of Subbasin
IZ Index of Zone in subbasin
NZ Index of Zone in total model
NM Month index (1-12)
NC Vegetation / land cover class index
IKL Total number of snow classes per zone
IZONE Total number of zones per subbasin
NBASIN Total number of subbasins
NCLASS Total number of vegetation / land cover classes

Setting Up Hydrological Models

Establishing a hydrological model follows a systematic workflow involving four main stages (Figure 4).

  1. Model setup – spatial discretisation: Defining the catchment boundaries and subdividing the area into calculation units (subbasins, HRUs, or grid cells).
  2. Data collection & input generation: Gathering meteorological forcing data and static catchment properties. A core decision here is the choice of meteorological data source:
    • Point Data: Observations from stations (e.g., rain gauges or temperature data) must be interpolated to obtain spatial estimates for each model zone.
    • Gridded Data: Spatially continuous data products (e.g., radar-derived or reanalysis data) can be applied directly to the model grid, reducing preprocessing complexity.
  3. Model calibration & validation: Before calibration begins, base parameters must be estimated using a priori knowledge (e.g., from literature or physical properties). These are then iteratively adjusted so that simulated outputs match observed data (e.g., discharge). Before calibration, sensitivity analysis can help to narrow down the parameters and ranges to calibrate.
  4. Analysis and interpretation of simulations: Using the calibrated model to answer specific research or management questions.
Figure 4: Working steps to establish a hydrological model (Google Gemini, 2026)

Data Requirements and Data Sources

To execute these steps, the model generally requires three categories of data:

Austrian Data Sources 🏔️

For catchments in Austria, high-quality datasets are available for all three categories.

1. Meteorological Input

For meteorological input data in Austria, the an important source is the GeoSphere Austria Data Hub. This initiative is a exemplary and commendable effort: by making high-quality weather, climate, environmental, and geophysical datasets freely and openly accessible to researchers, public institutions, and commercial users alike, GeoSphere Austria sets a benchmark for open science and data-driven innovation.

For modelling purposes, we typically distinguish between daily and hourly meteorological products.

A. SPARTACUS v2.1 (Daily Gridded)

The Spatially Resolved Temperature and Precipitation Climatology for Austria is the standard dataset for daily modeling.

Attribute Details
Source GeoSphere Data Hub
DOI 10.60669/m6w8-s545
Resolution 1 km × 1 km; Daily
Parameters Precipitation (RR), Min/Max Temperature (TN, TX)

Note — Day definition and temporal alignment: SPARTACUS daily values do not follow the standard calendar day (00:00–00:00 CET). Precipitation (RR) is accumulated over 07:00–07:00 CET, while minimum and maximum temperature (TN, TX) are defined as the extremes of the 19:00–19:00 CET interval. Observed discharge from eHYD/HZB, by contrast, uses the standard 00:00–00:00 day. When processing SPARTACUS data with write_spartacus_precip() and write_spartacus_temp() in the CoseRo package, a temporal shift is applied by default (time_shift = TRUE) to harmonise all inputs to the 00:00–00:00 convention before writing the COSERO input files. This correction should always be applied when combining SPARTACUS with eHYD discharge records.

Mean temperature (\(TM\)) can be calculated as \(TM = \frac{TN + TX}{2}\), though the CoseRo package provides more sophisticated methods (e.g., the Dall’Amico approach) that account for the temperature distribution within the day.

B. INCA (15-Minute/Hourly Gridded)

The Integrated Nowcasting through Comprehensive Analysis (INCA) system is used for high-resolution, sub-daily modeling (e.g., flood forecasting). It combines station observations, remote sensing (radar), and numerical weather prediction models.

Attribute Details
Source GeoSphere Data Hub
DOI 10.60669/6akt-5p05
Resolution 1 km × 1 km; 15-Minutes (Precipitation) and Hourly
Projection MGI / Austria Lambert (EPSG: 31287)
Parameters Temperature, Precipitation, Wind, Global Radiation, Humidity

C. Station Data (Point)

For applications requiring raw station data (which must be interpolated), observations can be accessed via:

  • eHYD: ehyd.gv.at (Precipitation totals).
  • GeoSphere Data Hub: data.hub.geosphere.at (Comprehensive meteorological parameters).
  • Hydrographic Services of the Provincial States (Hydrographische Dienste der Bundesländer): An additional source for station data

Note — Sub-daily data: The publicly available eHYD datasets are limited to daily resolution. For applications requiring sub-daily (e.g., hourly) precipitation or temperature records — such as flood forecasting or urban hydrology — data must be requested directly via email.

2. Spatial Data: Hydrological Atlas of Austria (eHAO)

The eHAO offers thematic GIS layers representing the nation’s hydrological characteristics, such as soil types, land use, and water balance components.

Figure 5: The eHAO Web Interface
  • Format: Shapefile (.shp) download via layer interface.
  • Projection: MGI / Austria Lambert (EPSG: 31287).
  • Usage: Defining subbasins, HRUs, and parameterizing soil/land use properties.

3. Discharge/Validation Data: eHYD

The eHYD platform is the primary source for hydrographic observation data in Austria.

  • Access: Messstellen und Daten → Oberflächengewässer.
  • Data Types: Observed Discharge (Q), Water Level (H).
  • Usage: Validation of simulated discharge (\(Q_{sim}\)) against observed discharge (\(Q_{obs}\)).
  • Hydrographic Services of the Provincial States (Hydrographische Dienste der Bundesländer): An additional source for discharge data, particularly useful where eHYD coverage is incomplete or where access to specific gauging stations not available on eHYD is required.

Note — Sub-daily data: The eHYD platform provides discharge data at daily resolution. For applications requiring sub-daily (e.g., hourly or 15-minute) discharge records — such as flood forecasting or event-based modelling — data must be requested directly via email.

Global Data Sources 🌍

For applications outside of Austria or for large-scale studies, many global datasets for setting up hydrological models exist. Here is an incomplete list:

Table 6: Examples for global data sources for hydrological modeling parameters and inputs
Category Dataset Description Link
Discharge & Catchment GRDC Global Runoff Data Centre. River discharge data for over 10,000 stations worldwide. bafg.de
CAMELS Catchment Attributes and Meteorology for Large-sample Studies. Standardized datasets (meteo, discharge, soil, land use) for specific regions (USA, GB, AUS, BR, CL, etc.). NCAR
LamaH-CE Large-Sample Data for Hydrology for Central Europe. A CAMELS-like dataset covering the Danube and Rhine basins (over 850 catchments). Zenodo
Topography (DEM) HydroSHEDS Hydrological products based on SRTM (3 arc-sec), offering conditioned river networks, watershed boundaries, and drainage directions. hydrosheds.org
MERIT Hydro Multi-Error-Removed Improved-Terrain. A globally corrected DEM (~90m) specifically processed for hydrology (removal of tree canopy/noise). U-Tokyo
COP30 / TanDEM-X Copernicus GLO-30 and TanDEM-X. High-precision global DEMs at ~30m (1 arc-sec) and ~12m resolution respectively. ESA
Meteorology (Reanalysis) ERA5 Global atmospheric reanalysis from ECMWF. Hourly estimates of atmospheric variables on a ~30km grid (1950–present). ECMWF
ERA5-Land A downscaled version of ERA5 specifically for the land component. Provides hourly data at 9km resolution, better suited for catchment modeling. CDS
Precipitation (Satellite/Merged) MSWEP Multi-Source Weighted-Ensemble Precipitation. Merges gauge, satellite, and reanalysis data for high-quality global precip estimates (3-hourly, 0.1°). GloH2O
CHIRPS Climate Hazards Group InfraRed Precipitation with Station data. Rainfall data (daily, 0.05°) specifically designed for trend analysis and drought monitoring (50°S-50°N). CHG
GPM IMERG Integrated Multi-satellitE Retrievals for GPM. NASA’s high-resolution (0.1°, 30-min) satellite precipitation product. NASA
Soil & Land Cover HWSD Harmonized World Soil Database. A global soil raster database providing parameters (texture, bulk density, carbon) for modeling. FAO
SoilGrids Global soil information system providing prediction of soil properties at 250m resolution using machine learning. ISRIC
ESA WorldCover High-resolution (10m) global land cover map based on Sentinel-1 and Sentinel-2 data (versions 2020/2021). ESA

Software Environment & Tools

To bridge the gap between theoretical hydrological concepts and practical application, we utilize a specific software stack. The workflow integrates QGIS for spatial data processing, R for data preparation and analysis, and the COSERO hydrological model engine.

The Toolchain

  1. QGIS: Used for handling spatial data (catchment boundaries, DEMs, land use).
  2. COSERO: The core hydrological model (compiled Fortran executable).
  3. R & CoseRo: The interface for steering the model, processing inputs, and visualizing results.
WarningSystem Requirement

The core COSERO.exe model is a Windows-only executable. Therefore, a Windows operating system is required to run simulations. Users on macOS or Linux will need a virtual machine or emulation layer to execute the model core.

TipNew to R or RStudio?

If you need a refresher on R fundamentals — data types, vectors, data frames, dplyr, or ggplot — work through the R & RStudio Introduction supplementary resource before continuing.

The CoseRo Package

CoseRo (v0.9.0) provides a complete R interface for the COSERO hydrological model, covering the full modelling lifecycle — from downloading and preprocessing input data, through running simulations and reading outputs, to sensitivity analysis, parameter calibration, and interactive result exploration.

Core Capabilities

  1. Automated Model Execution: Run COSERO simulations programmatically from R scripts, with full control over simulation periods, spin-up, warm/cold start, and output type.
  2. Fast Output Reading: Efficiently read and parse 13+ COSERO output file formats, auto-detected by output type.
  3. Interactive Visualization: A modern Shiny web application with five analysis tabs (Run, Time Series, Seasonality, Statistics, Export).
  4. Parameter Optimization: DDS (fast, recommended) and SCE-UA (robust) algorithms for single- and multi-objective calibration.
  5. Sensitivity Analysis: Sobol-based global sensitivity analysis with parallel ensemble execution and dotty plot diagnostics.
  6. Data Download: Download SPARTACUS (RR, TN, TX) and WINFORE (ET₀) NetCDF files directly from the GeoSphere Austria data hub.
  7. Input Preprocessing: Convert gridded SPARTACUS/WINFORE climate data and eHYD discharge records to COSERO input format.

Installation

The package is hosted on GitHub. The recommended approach uses the remotes package; all core dependencies are installed automatically.

Show code 
install.packages("remotes")
remotes::install_github("Herrnegger/CoseRo")

Optional packages required for spatial preprocessing (SPARTACUS/WINFORE) and data download must be installed separately:

Show code 
# For SPARTACUS/WINFORE spatial preprocessing
install.packages(c("terra", "sf", "exactextractr", "future", "furrr"))

# For GeoSphere Austria data download
install.packages("httr")

# For SCE-UA optimisation
install.packages("rtop")

Quick Start

The fastest way to get started is to create the bundled Wildalpen example project and explore it in the Shiny app:

Show code 
library(CoseRo)

# Create a ready-to-run example project (Salza/Wildalpen catchment)
setup_cosero_project_example("C:/COSERO/Wildalpen")  # adjust path

# Launch the interactive app with data pre-loaded
launch_cosero_app("C:/COSERO/Wildalpen")

CoseRo Function Overview

The table below summarises all main functions grouped by task. Functions used in this course are covered in more detail in the relevant module.

Table 7: Main functions of the CoseRo R package
Group Function Description
Model Execution run_cosero() Execute a COSERO simulation with custom settings and warm/cold start
launch_cosero_app() Launch the interactive Shiny app
Project Setup setup_cosero_project_example() Create a ready-to-run example project (Wildalpen catchment)
setup_cosero_project() Create an empty COSERO project directory structure
Output Reading read_cosero_output() Read all COSERO output files (auto-detects OUTPUTTYPE 1–3)
read_cosero_parameters() Read the parameter file (para.txt)
get_subbasin_data() Extract discharge data for a specific subbasin
Single Run Metrics extract_run_metrics() Extract performance metrics from single-run statistics output
calculate_run_metrics() Calculate metrics directly from QSIM vs QOBS
Sensitivity Analysis load_parameter_bounds() Load parameter bounds from the bundled CSV
generate_sobol_samples() Generate Sobol quasi-random parameter sets
run_cosero_ensemble() Run ensemble simulations (sequential)
run_cosero_ensemble_parallel() Run ensemble simulations (parallel, recommended)
extract_ensemble_metrics() Extract metrics from ensemble statistics output
calculate_ensemble_metrics() Calculate metrics from ensemble QSIM/QOBS
calculate_sobol_indices() Calculate first-order and total-order Sobol indices
plot_sobol() Visualise Sobol sensitivity indices
plot_dotty() Parameter–output scatter plots (dotty plots)
plot_ensemble_uncertainty() Ensemble discharge uncertainty with observed overlay
plot_metric_distribution() Distribution of performance metrics across ensemble
extract_behavioral_runs() Filter behavioural runs by NSE/KGE/pBias criteria
Parameter Optimisation optimize_cosero_dds() DDS algorithm — fast, recommended for 3–10 parameters
optimize_cosero_sce() SCE-UA algorithm — robust global optimisation
create_optimization_bounds() Define parameter bounds for optimisation
plot_cosero_optimization() Plot optimisation convergence history
export_cosero_optimization() Export optimisation results and report
Input Preprocessing download_geosphere_data() Download SPARTACUS/WINFORE NetCDF files from GeoSphere Austria
write_spartacus_precip() Convert SPARTACUS precipitation to COSERO input format
write_spartacus_temp() Convert SPARTACUS Tmin/Tmax to COSERO Tmean input format
write_winfore_et0() Convert WINFORE ET₀ to COSERO input format
write_ehyd_qobs() Convert eHYD discharge CSV files to COSERO QOBS format

Preparation for Module 2

Before the next session, please ensure the following are in place:

  1. Install R and an IDE — RStudio (established) or Positron (modern alternative from Posit, recommended).
  2. Install QGIS (free, open-source).
  3. Install the CoseRo package as shown above.
  4. Verify access to the SPARTACUS (meteorological) and eHAO (spatial) data repositories.

What You Should Know

After completing this module you should understand the following concepts and be able to explain them in your own words. These points may also serve as a guide for your presentation.

  1. Why hydrological models? Articulate what a hydrological model is and what problems it is used to solve. Give at least two examples of practical applications (e.g., flood forecasting, climate impact assessment, water resources management).
  2. The COSERO model: Describe the state-space concept that underlies COSERO. Explain the role of state variables, fluxes, and parameters, and how the model transforms precipitation and temperature into discharge.
  3. Model structure: Identify the main hydrological processes represented in COSERO (snow accumulation/melt, soil moisture, surface and subsurface runoff, baseflow, evapotranspiration) and describe how they are connected. Explain what OUTPUTTYPE controls.
  4. Spatial discretization: Explain the hierarchical structure of basins, subbasins, and zones. Describe why zones — rather than subbasins — are the basic modelling unit, and how zone-level parameters carry spatial variability from landscape maps.
  5. Parameters: Distinguish between parameters that can be estimated from physical data (e.g., CTMAX from aspect/elevation, M from soil maps). Explain, why most parameters require calibration and explain why many parameters have no direct physical observable at the model scale.
  6. Data requirements: List the input files required to run COSERO and describe the purpose of each (defaults.txt, parameter file, meteorological inputs, QOBS file). Identify which data sources are used in Austria and which global alternatives exist.
  7. CoseRo toolchain: Navigate the CoseRo function overview table and identify which functions are relevant for model setup, execution, output reading, and sensitivity analysis. Set up the example project and run COSERO with a single command.

Document created: 2026-03-16

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