Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed
Abstract
:1. Introduction
- Only a few studies were conducted on large and complex Prairie watersheds,
- Only a lumped model or a distributed model was used independently, for hydrological forecasting study. Alternatively, in some cases, the multi-models were only a collection of lumped conceptual models,
- Identification of best hydrological model was usually based on historical meteorological or in some cases, deterministic weather forecast inputs. Evaluation and comparison of models based on raw and bias-corrected ensemble precipitation forecasts were not studied. As such, the objective of this research is designed to address these limitations and identify the best hydrological model from diverse multi-models for short- and medium-range flood forecasting in a Canadian Prairie watershed. In this study, four structurally varied hydrological models were set up in order to simulate and forecast inflows to the Shelmouth Reservoir, which is located in Upper Assiniboine River Basin. A mixture of two lumped, one distributed and one macroscale land surface models were used in this research. In forecast mode, bias-corrected precipitation from second-generation Global Ensemble Forecast System (GEFS v2) reforecasts was fed into the four models in order to evaluate the reliability, skill, and overall forecast performance of the ensemble reservoir inflows.
2. Materials
2.1. Study Area
2.2. Data
2.2.1. Ensemble Weather Forecasts
2.2.2. Observed Data
2.2.3. Reservoir Inflow
3. Methods
3.1. Hydrological Models
3.2. Calibration and Validation
3.3. Bias-Correction
3.4. Hindcast Simulation (Model Update and Forecast)
3.5. Ensemble Forecast Verification
3.5.1. Mean Continuous Rank Probability Score () and Skill Score ()
3.5.2. Reliability Diagram
3.5.3. Relative Operating Characteristics (ROC) and Skill Score (ROC Score)
4. Results
4.1. Calibration and Validation
4.2. Model Comparison in Forecast Mode
4.2.1. Overall Forecast Quality and Skill
4.2.2. Reliability
4.2.3. Hit and False Alarm Rate Distribution
5. Conclusions and Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Brief Description of the Calibration of Models
Appendix A.1. SNOW17
- (i)
- The mean area observed precipitation time series obtained by Theisen Polygon method
- (ii)
- The mean area observed temperature time series obtained by Theisen Polygon method
- (iii)
- The average elevation of the catchment
- (iv)
- The latitude of the centroid of the catchment
No | Parameters | Description | Unit | Ranges |
---|---|---|---|---|
1 | SCF | Snowfall correction factor | – | 0.4–1.6 |
2 | MFMAX | Maximum melt factor during non-rain periods considered to occur on June 21 | mm/6 h/°C | 0.5–2.0 |
3 | MFMIN | Minimum melt factor during non-rain periods considered to occur on December 21 | mm/6 h/°C | 0.05–0.5 |
4 | UADJ | The average wind function during rain-on-snow periods | mm/mb/°C | 0.03–0.2 |
5 | NMF | Maximum negative melt factor | mm/6 h/°C | 0.05–0.50 |
6 | MBASE | Base temperature for non-rain melt factor above which melt typically occurs | °C | 0–2.0 |
7 | PXTEMP1 | Lower Limit Temperature dividing tranistion from snow, if temp is less than or equal to pxtemp1, all precip is snow. Otherwise it is mixed linearly | °C | −2.0 to 0 |
7 | PXTEMP2 | Upper Limit Temperature dividing tranistion from snow, if temp is greater than or equal to pxtemp2, all precip is rain. Otherwise it is mixed linearly | °C | 1 to 3.0 |
8 | PLWHC | percent liquid water holding capacity of the snow pack | – | 0.02–0.3 |
9 | DAYGM | Daily melt at snow–soil interface | mm/day | 0–0.3 |
10 | TIPM | Antecedent snow temperature index | – | 0.1–0.2 |
Appendix A.2. SACSMA
- (i)
- Outflow (rain plus snowmelt) from SNOW17
- (ii)
- The catchment area of the basin
- (iii)
- Observed catchment outflow estimated by calculated reservoir inflow
No | Parameters | Description | Unit | Ranges |
---|---|---|---|---|
1 | UZTWM | Upper zone tension water maximum storage | [mm] | 1–150 |
2 | UZFWM | Upper zone free water maximum storage | [mm] | 1–150 |
3 | LZTWM | Lower zone tension water maximum storage | [mm] | 1–500 |
4 | LZFPM | Lower zone free water primary maximum storage | [mm] | 1–1000 |
5 | LZFSM | Lower zone free water supplemental maximum storage | [mm] | 1–1000 |
6 | ADIMP | Additional impervious area | [-] | 0.0–0.4 |
7 | UZK | Upper zone free water lateral depletion rate | [day−1] | 0.1–0.5 |
8 | LZPK | Lower zone primary free water depletion rate | [day−1] | 0.0001–0.025 |
9 | LZSK | Lower zone supplemental free water depletion rate | [day−1] | 0.01–0.25 |
10 | ZPERC | Maximum percolation rate | [-] | 1–250 |
11 | REXP | Exponent of the percolation equation [-] | [-] | 1–5.0 |
12 | PCTIM | Impervious fraction of the watershed area | [-] | 0.0–0.1 |
13 | PFREE | fraction percolating from upper to lower zone free water Storage | [-] | 0.0–0.6 |
14 | athorn | A constant for Thornthwaite’s equation | [-] | 0.1–0.3 |
15 | Rq | Routing Coefficient | [-] | 0.0–1 |
Appendix A.3. MACHBV
- (i)
- Outflow (rain plus snowmelt) from SNOW17
- (ii)
- The catchment area of the basin
- (iii)
- Observed catchment outflow estimated by calculated reservoir inflow
No | Parameters | Description | Unit | Ranges |
---|---|---|---|---|
1 | athorn | A constant for Thornthwaite’s equation | [-] | 0.1–0.3 |
2 | fc | Maximum soil box water content | [mm] | 50–800 |
3 | lp | Limit for potential evaporation | [mm/mm] | 0.1 × fc–0.9 × fc |
4 | beta | A non-linear parameter controlling runoff generation | [-] | 1–10 |
5 | K0 | Flow recession coefficient in an upper soil reservoir | [days] | 1–30 |
6 | lsuz | A threshold value used to control response routing on an upper soil reservoir | [mm] | 1–100 |
7 | K1 | Flow recession coefficient in an upper soil reservoir | [days] | 2.5–100 |
8 | cperc | A constant percolation rate parameter | [mm/day] | 0.01–6 |
9 | K2 | Flow recession coefficient in a lower soil reservoir | [days] | 20–1000 |
10 | maxbas | A triangle weighting function for modelling a channel routing routine | [days] | 1–20 |
11 | rcr | Rainfall correction factor | [-] | 0.5–1.5 |
12 | a1 | An exponent in relation between outflow and storage representing non-linearity of storage – discharge relationship of lower reservoir | [-] | 0.5–20 |
Appendix A.4. VIC
- (i)
- Average daily gridded interpolated precipitation data from the ground network,
- (ii)
- Daily gridded minimum, and maximum temperature data from ANUSPLIN,
- (iii)
- Average daily wind speed from the Global Environmental Multiscale (GEM) model.
- (i)
- Digital elevation model for SNOW elevation bands and flow direction computation
- (ii)
- Land cover data from Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6 data product
- (iii)
- Soil data from FAO’s Harmonized World Soil Database (HWSD) V 1.2
- (i)
- Snow: Rain-snow partitioning, snow accumulation, and melting are simulated at a sub-grid level using temperature index method lapsed through the Elevation (SNOW) bands.
- (ii)
- Evaporation: is simulated at each elevation band and land cover type using Penman-Monteith Approach.
No | Notation | Range | Unit | Definition |
---|---|---|---|---|
Soil parameters | ||||
1 | b | 10−5–0.4 | - | Variable infiltration curve parameter |
2 | Ds | 10−3–1 | - | Fraction of Dsmax where non-linear baseflow begins |
3 | Dm | 0.1–30 | mm/day | Maximum velocity of baseflow |
4 | Ws | 0.5–1 | - | Fraction of maximum soil moisture where non-linear baseflow occurs |
5 | s2 | 0.3–1.5 | m | Thickness of middle soil moisture layer |
6 | s3 | 0.3–1.5 | m | Thickness of bottom soil moisture layer |
Wetland parameters | ||||
7 | bmin_depth | 0.01–0.3 | m | Lake depth below which channel outflow is 0. |
8 | wfrac | 0.001–0.05 | - | Width of lake outlet, as a fraction of the lake perimeter |
9 | depth_in | 0.01–0.3 | m | Initial lake depth |
10 | rpercent | 0.1–1 | - | Fraction of grid cell runoff that enters lake (instead of going directly to channel network) |
11 | lake_depth | 0.1–1.5 | m | Maximum allowable depth of lake |
Routing parameters | ||||
12 | Vl | 0.5–3 | m/s | Flow/Wave velocity |
13 | Df | 200–4000 | m2/s | Flow diffusion |
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Calibration | Validation | |||||
---|---|---|---|---|---|---|
SACSMA | MACHBV | VIC | SACSMA | MACHBV | VIC | |
PFC | 0.180 | 0.174 | 0.247 | 0.234 | 0.231 | 0.270 |
KGE | 0.796 | 0.740 | 0.653 | 0.776 | 0.679 | 0.653 |
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Awol, F.S.; Coulibaly, P.; Tsanis, I.; Unduche, F. Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed. Water 2019, 11, 2201. https://doi.org/10.3390/w11112201
Awol FS, Coulibaly P, Tsanis I, Unduche F. Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed. Water. 2019; 11(11):2201. https://doi.org/10.3390/w11112201
Chicago/Turabian StyleAwol, Frezer Seid, Paulin Coulibaly, Ioannis Tsanis, and Fisaha Unduche. 2019. "Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed" Water 11, no. 11: 2201. https://doi.org/10.3390/w11112201
APA StyleAwol, F. S., Coulibaly, P., Tsanis, I., & Unduche, F. (2019). Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed. Water, 11(11), 2201. https://doi.org/10.3390/w11112201