The distance distribution dymamics (DDD) rainfall-runoff model has been developed with the aim of reducing the number of free, calibrated model parameters to a minimum. The subsurface and runoff dynamics do not use parameters estimated through calibration against runoff, but are estimated using Geographical Information Systems (GIS) and recession analysis. A GIS analysis determines the shape of parralel linear reservoirs from distance distributions, whereas a recession analysis estimate the celerity of subsurface water transport and hence the scale of the linear reservoirs. The model keeps track of the moisture input for 10 elevations zones of equal area, so the spatial distribution, accumulation and melt of snow is carried out independently for each elevation zone. Snow melt and evapotranspirations are estimated using an energybalance model, where each of the energy elements are estimated using proxy models. The DDD model has been used operationally at the Flood Forecasting Service at the Norwegian Water Resources and Energy Directorate since 2013.
The files located at this site should let you run the model for the sample catchment (88.4, a catchment located at the western part of Norway). There are two main R scripts: "Run_DDD_Modul_EB_Oct2018.r" and "DDD_Modul_EB_Oct2018_func.r", where the former calls on the latter (the main program). In addition there are many subroutines (stored in Functions.zip) which are loaded with the "Run.." script. In the "Run.." script you can set various controls, so that you can calibrate the model, run it from states, update etc. Not all of these controls are fully operational since the development of the DDD model is an ongoing project. Running the model (kal=0) and calibrating the model (kal=1) work fine.
###References
- Skaugen T. and C. Onof, 2014. A rainfall runoff model parameterized form GIS and runoff data. Hydrol. Process. 28, 4529-4542,DOI:10.1002/hyp.9968
- Skaugen, T., I. O. Peerebom and A. Nilsson, 2015. Use of a parsimonious rainfall-runoff model for predicting hydrological response in ungauged basins. Hydrol. Process. 29, 1999-2013, DOI:10.1002/hyp.10315
- Skaugen, T. and Z. Mengistu, 2016. Estimating catchment scale groundwater dynamics from recession analysis- enhanced constraining of hydrological models. Hydrol. Earth. Syst. Sci. 20, 4963-4981, doi: 10.5194/hess-20-4963-2016.
- Skaugen, T. and Weltzien, I. H., 2016. A model for the spatial distribution of snow water equivalent parameterised from the spatial variability of precipitation, The Cryosphere. 10, 1947-1963, doi:10.5194/tc-10_1947_2016
- Skaugen, T., H. Luijting, T. Saloranta, D. Vikhamar-Schuler and K. Müller, 2018. In search of operational snow model structures for the future - comparing four snowmodels for 17 catchments in Norway. Hydrology Research, 49.6, https://doi.org/10.2166/nh.2018.198