HydroPy (v1.0): a new global hydrology model written in Python

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components and their past evolution as well as potential future development under various scenarios. While GHMs have been part of the hydrologist's toolbox for several decades, the models are continuously being developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance, and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge; however, they can – at least to some extent – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similarly to MPI-HM and thus conclude the successful transition from MPI-HM to HydroPy.

Hydrology model establishment of pit lake: Extreme event rainfall data analysis

Analysis of water management in the pit lake is divided into two conditions, namely Continuous Events and Extreme Events. The former is an analysis of pit lake management related to the water filling in a pit lake that takes place continuously. Meanwhile, the later is the analysis of pit lake management related to the possibility of extreme conditions that will occur, including extreme rainfall. This study is focused only on the Extreme Event conditions. The Gumbel method is used to calculate the planned return period rainfall T concerning the prediction of extreme rainfall. Meanwhile, for a certain return period, rainfall intensity can be predicted using the Mononobe formula. Based on the result of calculation the Gumbel method, it shows that the planned rainfall for a return period of 10 years is 132.9 mm / day. Then based on the results of the calculation of rainfall intensity using the Mononobe formula, it is obtained that the intensity of rainfall for a return period of 10 years with a concentration-time of 5 minutes is 241.5 mm/hour, while the amount of rainfall intensity with a concentration-time of 300 minutes or 5 hours is 15.8 mm/hour.

HydroPy (v1.0): A new global hydrology model written in Python

Global hydrological models (GHMs) are a useful tool in the assessment of the land surface water balance. They are used to further the understanding of interactions between water balance components as well as their past evolution and potential future development under various scenarios. While GHMs are a part of the Hydrologist's toolbox since several decades, the models are continuously developed. In our study, we present the HydroPy model, a revised version of an established GHM, the Max-Planck Institute for Meteorology's Hydrology Model (MPI-HM). Being rewritten in Python, the new model requires much less effort in maintenance and due to its flexible infrastructure, new processes can be easily implemented. Besides providing a thorough documentation of the processes currently implemented in HydroPy, we demonstrate the skill of the model in simulating the land surface water balance. We find that evapotranspiration is reproduced realistically for the majority of the land surface but is underestimated in the tropics. The simulated river discharge correlates well with observations. Biases are evident for the annual accumulated discharge, however they can – at least to some part – be attributed to discrepancies between the meteorological model forcing data and the observations. Finally, we show that HydroPy performs very similar to MPI-HM and, thus, conclude the successful transition from MPI-HM to HydroPy.

Introducing HydroPy, an updated global hydrology model rewritten in Python

<p>Global hydrological models (GHM) are a useful tool to investigate the water cycle, to evaluate its sensitivity towards systematic changes, e.g. human impacts, and to project future conditions in river catchments for varying scenarios. They have been successfully applied for decades and there is still room for improvement.</p><p>Recently, we revised the Max Planck Institute for Meteorology’s Hydrology model (MPI-HM), which is an established GHM that was used in multiple case studies and inter-comparison projects. While still performing well, its source code (mainly Fortran77) has become increasingly difficult to maintain, thus hampering the implementation of new processes. For this reason, the model was rewritten from scratch based on the MPI-HM process formulations. The new model is mainly written in Python, thereby taking advantage of the highly optimized numpy and xarray libraries, and, hence, is aptly renamed to HydroPy. Using the original formulations, we make sure to preserve or even improve the old model’s skill while the switch to Python allows for much easier debugging and interactive model development.</p><p>In our presentation, we will evaluate the performance of the new HydroPy model and demonstrate its skill to simulate river discharge. Furthermore, we compare HydroPy to its predecessor MPI-HM and discuss the reasons of differences between their results.</p>