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hydro-model-xaj

What is hydro-model-xaj

Hydro-model-xaj is a python implementation for the XinAnJiang (XAJ) model, which is one of the most famous conceptual hydrological models, especially in Southern China.

Not an official version, just for learning (Because the objective condition of authors engineering level and urgent time, errors may exist)

How to run

Environment

Hydro-model-xaj is a Python console program (no graphic interface now). It is still developing, and we have not provided a pip or conda package for hydro-model-xaj yet, so please set up a Python environment for the code.

If you are new to python, please install miniconda or anaconda on your computer and config the environment.

Since you see hydro-model-xaj in GitHub, I think you have known a little about git and GitHub at least. Please install git on your computer and register your own GitHub account.

Then, fork hydro-model-xaj to your GitHub, and clone it to your computer. If you have forked it before, please update it from upstream as our previous version has some errors. Open your terminal, input：

# clone hydro-model-xaj, if you have cloned it, ignore this step 
$ git clone <address of hydro-model-xaj in your github>
# move to it
$ cd hydro-model-xaj
# if updating from upstream, pull the new version to local
$ git pull
# create python environment
$ conda env create -f environment.yml
# if conda is very slow, mamba can be an alternative:
# $ conda install -c conda-forge mamba
# $ mamba env create -f environment.yml
# activate it
$ conda activate xaj

Prepare data

To use your own data to run the model, we set a data interface, here is the convention:

  • All input data for models are three-dimensional NumPy array: [time, basin, variable], which means "time" series data for "variables" in "basins"
  • Data files should be .npy files with a JSON file that show the information of the data. We provide sample code in "test/test_data.py" to show how to process a .csv/.txt file to the required format.
  • To run the model, the dataset should be split into two parts: the training dataset (used for calibrating) and the testing dataset (used for evaluation). In the xxx directory, there must be four files: "basins_lump_p_pe_q_foldx_train.npy", "data_info_foldx_train.json", "basins_lump_p_pe_q_foldx_test.npy", and "data_info_foldx_test.json". (files' name cannot be changed; x is 0 if there is only one fold)

To run models in hydro-model-xaj, one need to prepare data in the required format.

We have provided sample data in the "example/example" directory. You can run the model with this data.

Run the model

Run the following code:

# you can change the algorithm parameters:
$ python calibrate_xaj.py --exp example --warmup_length 365 --model {\"name\":\"xaj_mz\",\"source_type\":\"sources\",\"source_book\":\"HF\"} --algorithm {\"name\":\"SCE_UA\",\"random_seed\":1234,\"rep\":5000,\"ngs\":20,\"kstop\":3,\"peps\":0.1,\"pcento\":0.1}
# for advices of hyper-parameters of sceua, please see the help comment of the function 'calibrate_xaj.py'
# python calibrate_xaj.py --exp <name of directory of the prepared data> --warmup_length <hydromodel need some warm-up period> --model <model function parameters> --algorithm <calibration algorithm parameters>

See the results

Run the following code:

$ python datapostprocess4calibrate.py --exp example

You will get two metrics files in the "example" directory: "basins_test_metrics_mean_all_cases.csv" and "basins_test_metrics_median_all_cases.csv". The first one is the mean metrics of the testing period -- one row means the mean metrics of all basins in a case, and the second one is the median metrics.

More details about the analysis could be seen in show_results.ipynb file. It is a jupyter notebook.

Now we only provide some simple statistics calculations.

How to make the sample data

In this part, we simply introduce how we prepare the sample data.

Here We provide an example for some basins in the CAMELS dataset, a very common used dataset for hydrological model evaluation.

You can download CAMELS according to this instruction.

Check if you have successfully downloaded and put it in the right place.

$ conda activate xaj
$ python
>>> import os
>>> from hydrodataset.camels import Camels
>>> camels = Camels(data_path=os.path.join("camels", "camels_us"), download=False, region="US")

if any error is raised, please see this instruction again.

Then, we provide a script to transform data organized like CAMELS to the required format, you can use it like this:

$ cd hydromodel/app
$ python datapreprocess4calibrate.py --camels_dir <name of camels_dir> --exp <name of directory of the prepared data> --calibrate_period <calibration period> --test_period <test period> --basin_id <basin id>
# such as: python datapreprocess4calibrate.py --camels_name camels_us --exp xxx --calibrate_period 1990-10-01 2000-10-01 --test_period 2000-10-01 2010-10-01 --basin_id 01439500 06885500 08104900 09510200

Then you can see some files in hydromodel/example/xxx directory.

Why does hydro-model-xaj exist

When we want to learn about the rainfall-runoff process and make forecasts for floods, etc. We often use classic hydrological models such as XAJ as a baseline because it is trusted by many engineers and researchers. However, after searching the website very few repositories could be found. One day I happened to start learning Python, so I decided to implement the model with Python. Previous commits for hydro-model-xaj have some errors, but now at least one executable version is provided.

Actually open-source science has brought a great impact on hydrological modeling. For example, SWAT and VIC are very popular now as they are public with great performance and readable documents; as more and more people use them, they become more stable and powerful. XAJ is a nice model used by many engineers for practical production. We need to inherit and develop it. I think hydro-model-xaj is a good start.

What are the main features

We basically implement the formula in this book -- 《流域水文模拟》

Other reference Chinese books：

More English references could be seen at the end of this README file.

The model mainly includes three parts:

For the first part, we use an evaporation coefficient K (ratio of potential evapotranspiration to reference crop evaporation generally from Allen, 1998) rather than Kc (the ratio of potential evapotranspiration to pan evaporation) because we often use potential evapotranspiration data from a system like GLDAS, NLDAS, etc. But it doesn't matter, when you use pan evaporation, just treat K as Kc.

For the second part, we provide multiple implementations, because, for this module, formulas in different books are a little different. One simplest version is chosen as a default setting. More details could be seen in the source code directly now. We provide four versions, two versions from two books.

For the third part -- routing module, we provide different ways: the default is a common way with recession constant ( CS) and lag time (L) shown in the figure; second (You can set the model's name as "xaj_mz" to use it) is a model from mizuRoute to generate unit hydrograph for surface runoff (Rs -> Qs) , as its parameters are easier to set, and we can optimize all parameters in a uniform way.

We provide two common calibration methods to optimize XAJ's parameters:

  • SCE-UA from spotpy
  • GA from DEAP: now only the method is used, but no completed case is provided yet. We will provide one soon.

Now the model is only for one computing element (typically, a headwater catchment). Soon we will provide calibration for multiple headwater catchments. To get a better simulation for large basins, a (semi-)distributed version may be needed, and it is not implemented yet. The following links may be useful:

Other implementations for XAJ:

How to contribute

If you want to add features for hydro-model-xaj, for example, write a distributed version for XAJ, please create a new git branch for your feature and send me a pull request.

If you find any problems in hydro-model-xaj, please post your questions on issues.

References

  • Allen, R.G., L. Pereira, D. Raes, and M. Smith, 1998. Crop Evapotranspiration, Food and Agriculture Organization of the United Nations, Rome, Italy. FAO publication 56. ISBN 92-5-104219-5. 290p.
  • Duan, Q., Sorooshian, S., and Gupta, V. (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28( 4), 1015– 1031, doi:10.1029/91WR02985.
  • François-Michel De Rainville, Félix-Antoine Fortin, Marc-André Gardner, Marc Parizeau, and Christian Gagné. 2012. DEAP: a python framework for evolutionary algorithms. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation (GECCO '12). Association for Computing Machinery, New York, NY, USA, 85–92. DOI:https://doi.org/10.1145/2330784.2330799
  • Houska T, Kraft P, Chamorro-Chavez A, Breuer L (2015) SPOTting Model Parameters Using a Ready-Made Python Package. PLoS ONE 10(12): e0145180. https://doi.org/10.1371/journal.pone.0145180
  • Mizukami, N., Clark, M. P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R. J., Markstrom, S. L., Hay, L. E., Woods, R., Arnold, J. R., and Brekke, L. D.: mizuRoute version 1: a river network routing tool for a continental domain water resources applications, Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016, 2016.
  • Zhao, R.J., Zhuang, Y. L., Fang, L. R., Liu, X. R., Zhang, Q. S. (ed) (1980) The Xinanjiang model, Hydrological Forecasting Proc., Oxford Symp., IAHS Publication, Wallingford, U.K.
  • Zhao, R.J., 1992. The xinanjiang model applied in China. J Hydrol 135 (1–4), 371–381.