Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Abstract
:1. Introduction
2. Rational and Contribution
3. Overview of Basic Watershed Processes and Streamflow Prediction
3.1. Streamflow Generation Processes
3.2. Streamflow Prediction
3.3. Basic Processes in Data-Driven Streamflow Prediction
4. Enhancing Streamflow Prediction Using a Physically Consistent and Domain-Aware Approach
4.1. Process Modeling Approach for Improved Streamflow Prediction in the Data-Driven Modeling Framework
4.1.1. Introducing Intermediate Variables
4.1.2. Combining Data-Driven and Process-Based Model Outputs
4.1.3. Residual Error Modeling
4.1.4. Simulated Streamflow as Input
4.1.5. Replacing Process-Based Model Modules
4.1.6. Model Calibration
Process-Based Model | DD Model | Study Region | Input Variables | Authors | Findings/Remarks |
---|---|---|---|---|---|
GR4J, modified IHACRES and TOPMODEL | ANN | France | Rainfall, streamflow, error | [88] | The hybrid model outperformed all the other models for 3-day forecasts. |
XAJ, SMAR, Tank model | ANN | China | Streamflow | [100] | The coupled modeling approach excelled over the individual models. |
HBV | ANN | Meuse River basin | Precipitation, streamflow | [121] | The conceptual data-driven model approach enhanced low-flow prediction. |
XAJ | ANN | China | Subbasin discharge | [101] | XAJ-ANN yielded more reasonable results. |
XAJ, TOPMODEL | AdaBoost | China | Streamflow | [104] | Enhanced low flow prediction performance was obtained. |
HEC-HMS | ANN | Taiwan | Precipitation, runoff | [132] | The hybrid model showed improved discharge prediction. |
SWAT | ANN | Canada | Streamflow, climate | [106] | SWAT-ANN outperformed the individual models. |
SWAT | ANN | USA | Baseflow, excess flow | [76] | Enhanced flow predictions in ungauged watersheds was achieved. |
GR4J | ANN | Australia | Streamflow, precipitation, GR4J simulated soil moisture index, PET | [77] | High peak flow performance was observed. |
HEC-HMS | SVM, ANN | Taiwan | Streamflow, precipitation | [97] | The HEC-HMS-SVR model provided the most accurate discharge. |
GR4J | LSTM, NARX | China | Error, precipitation, PET | [90] | GR4J with LSTM and NARX excelled for smaller catchments. |
HBV, GR4J, SIMHYD | MLR, Extra-Trees, LGB, XGB | Central Asia | Runoff, PET, climate | [133] | Acceptable results were achieved in ungauged regions. |
HBV | ANN, SVM | China | Climate, simulated runoff | [78] | HBV-ANN was more reliable and accurate for high-flow predictions. |
SWAT, VIC, BTOPMC | ANN | China | Streamflow | [111] | Enhanced low and peak flow prediction were achieved. |
NWM | New assessment tool | USA | * | [134] | The tool accurately predicted hydrographs across rising and recession phases, demonstrating exceptional performance. |
SAC-SMA | LSTM | USA | Residual error, streamflow, climate | [116] | The hybrid model enhanced the performance of SAC-SMA but struggled in catchments with prolonged low-flow conditions. |
HYMOD | ANN | USA | Streamflow, PET, climate | [99] | The hybrid model enhanced flow prediction. |
SWAT | ANN | Iraq | Residual error | [93] | SWAT-ANN surpassed the SWAT model. |
WEAP, GR2M | ANN | Ecuador | Streamflow, precipitation, PET | [83] | Enhanced peak flow forecasting with reliable performance across calibration and validation stages. |
SWAT, HEC-HMS | ANN | USA | Streamflow, precipitation | [98] | The hybrid model enhanced long-term forecasting and enabled SFP based on forecasted rainfall. |
GBHM | ANN | Thailand | Synthetic runoff, spatial inputs | [68] | Improved peak flow prediction and boosted model performance in data-scarce regions. |
EXP-HYDRO | P-RNN | USA | * | [124] | The method accurately inferred unobserved phenomena. |
SWAT | ANN | Iran | Residual error | [117] | SWAT-ANN performed better than SWAT. |
TOPMODEL | Boosting method | China | Precipitation, runoff | [135] | The ensemble approach performed better than TOPMODEL both in humid and semi-arid regions. |
HBV, NRECA | ANFIS, SVM, GMDH | Indonesia | Precipitation, streamflow | [84] | The hybrid model outperformed the hydrological models, with GMDH excelling in peak flow prediction. |
PRMS | LSTM | USA | Streamflow, climate | [21] | The hybrid model excelled, but its performance hinged on the process-based model’s performance. |
NWM | LSTM | USA | NWM outputs, catchment attributes, climate | [61] | LSTM improved NWM predictions. |
HBV | KNN, MLR, SOV, ANN, XGB, RF | Swiss | Residual error, streamflow, climate | [85] | HBV-XGB and HBV-RF performed best. |
WRF-Hydro | LSTM | Korea | Residual error, climate | [20] | WRF-Hydro-LSTM outperformed the individual models. |
SWAT | RF, ANN, GLM, gradient boosting, KNN, Cubist | India | Streamflow, climate | [110] | Ensemble streamflow-based prediction outperformed the other models. |
ABCD | SVR, GPR, Lasso, Ridge regression | India | ET, groundwater storage, soil moisture, precipitation | [79] | The hybrid model excelled beyond the process-based and data-driven models and maintained a reasonable water balance. |
HBV | RF, XGB | Swiss | Streamflow, climate | [136] | The hybrid model performed better. |
IHACRES, GR4J, MISD | MLP, SVM | Swiss | Runoff, climate | [32] | The process-based models’ performance increased by 19%. |
HBV | dPL | USA | * | [127] | Differentiable, physics-based models achieved performance comparable to LSTM models. |
XAJ | MCQRNN | China | Rainfall, observed and simulated flow | [109] | XAJ-MCQRNN outperformed MCQRNN on interval and point flood forecasts. |
EXP-HYDRO | Neural ODE | USA | * | [125] | Improved and interpretable results were obtained. |
VIC, CaMa-Flood | LSTM | Lancang–Mekong River | Climate, simulated streamflow | [107] | LSTM using hydrologic model output improved prediction. |
AWBM | NLR, ANN | USA | Runoff | [102] | Using ANN to route within the conceptual model improved predictions, especially with delayed runoff as input. |
UEB | LSTM | USA | Simulated snowmelt and rainfall, PET | [81] | The coupled approach outperformed benchmark models and yielded reasonable spatiotemporal recharge–discharge distribution. |
HEC-HMS | ELM, SVR, LSTM | Iran | Climate, observed and simulated discharge | [113] | HEC-HMS-LSTM outperformed all other models. |
HYMOD, SAC-SMA, VIC | LSTM | USA | Climate, watershed characteristics, simulated ET | [137] | Performance depended on the process-based model. |
NWM | LSTM | USA | Residual error, precipitation | [91] | LSTM significantly boosted NWM prediction accuracy, enhancing both temporal precision and streamflow volume. |
MISD | GMDH | Sweden | Climate, MISD model result | [119] | Including more metrological forcing enhanced hybrid model performance. |
PCR-GLOBWB | RF | Rhine basin | Climate, intermediate variables, error | [94] | RF error correction significantly enhanced SFP, with calibrated and uncalibrated error correction yielding equally accurate results. |
TOPMODEL | ARIMA, LSTM, Prophet | China | Residual error | [138] | The integrated approach enhanced flow prediction, with the Prophet model performing the best. |
HBV | RF | India, Nepal | Error, observed and simulated streamflow | [95] | HBV-RF prediction excelled over HBV, enhancing low-flow prediction. |
HBV | RF | India, Nepal | Error, intermediate variables, weather data | [96] | The HBV-RF model excelled in performance, especially when weather and simulated streamflow data were incorporated. |
SWAT | LSTM | China | Climate, intermediate variables | [139] | SWAT-LSTM excelled over SWAT and LSTM, demonstrating efficiency in poorly gauged watersheds. |
PCR-GLOBWB | RF | Global | Intermediate variables, static predictors, PCR-GLOBWB model inputs | [140] | Including hydrological model outputs improved SFP. |
HBV | LSTM | China | Climate, simulated streamflow | [131] | Combining HBV with LSTM enhanced prediction accuracy, where tight coupling was the more efficient approach. |
GR4J | CNN, LSTM | Australia | Intermediate variables, observed streamflow, climate data | [141] | The integrated model outperformed the individual models, particularly excelling in arid catchments. |
SWAT | LSTM | Malaysia | Precipitation, simulated streamflow | [118] | Coupling the calibrated SWAT model with LSTM improved SFP. |
EXP-HYDRO | ENN | USA | * | [122] | The EXP-HYDRO with ENN outperformed the conceptual model, yielding physically consistent results. |
SWAT+ | GRU | China | Climate, Residual error | [92] | SWAT+ glacier error corrected using GRU improved low- and peak-flow predictions. |
VIC, CaMa-Flood | RNN, LSTM | China | Climate, simulated flow | [115] | LSTM combined with the hydrologic model achieved superior performance. |
XAJ, SWAT | RF | USA | * | [120] | RF with XAJ enhanced prediction performance, while SWAT and RF-SWAT exhibited comparable accuracies. |
EXP-HYDRO | P-RNN | China | * | [126] | The physics-informed deep learning model surpassed EXP-HYDRO in permafrost-affected alpine catchments under climate change conditions. |
HBV | dPL | USA | * | [142] | For ungauged regions, differentiable physics-informed machine learning outperformed LSTM. Suitable for climate change assessment. |
NWM | ANN | USA | Streamflow, soil, land use, topography | [62] | The hybrid model improved the forecast reliability significantly. |
4.2. Hydrograph Separation and Analysis-Based Streamflow Prediction-Enhancing Techniques
4.2.1. Baseflow Separation
4.2.2. Identifying Flow Events
4.2.3. Hydrograph Segmenting
Methods | Input Variables | Study Region | Data-Driven Models | Authors | Finding/Remark |
---|---|---|---|---|---|
Data portioning into three groups based on low-, medium-, and high-flow events | Rainfall, temperature | USA | ANN | [11] | Satisfactory result for average event; unsatisfactory result for extreme events. |
Hydrograph decomposition, recession analysis | Effective rainfall, streamflow | USA | ANN | [154] | Decomposing a flow hydrograph enhanced prediction more effectively than preprocessing with a self-organizing map. |
Data portioning into low, medium, and high | Streamflow | Italy | ANN | [150] | Basic data partitioning yielded better predictions than models based on signal-processed data. |
Clustering into low and high flow, time-based and recursive baseflow separation | Precipitation, streamflow | Nepal, Italy, U.K. | ANN, modular approach | [26] | Hydrology-informed ANNs surpassed standard ANNs. |
Baseflow separation | Precipitation, streamflow | Nepal, Italy | ANN, modular approach | [156] | Domain knowledge made the ANN more accurate. |
Hydrograph decomposed into four: two in each rising and falling limb | Rainfall, streamflow | USA | ANN, ERGA | [145] | The approach yielded more accurate predictions than the conceptual model that was used. |
Baseflow separation (Bflow), decomposing into low and high flow | Precipitation, ET, infiltration depth, surface runoff | USA | ANN, modular approach | [56] | Management scenario assessment. |
Recursive baseflow separation | Climate, streamflow | USA | ELM, modular approach | [149] | The modular model did not enhance SFP. |
Baseflow separation (one-parameter digital filter and recursive digital filter) | Precipitation, streamflow | Morocco | ANN | [15] | Improved peak flow was found. The ANN using baseflow performed better than the signal processed-based model. |
Separating low and high flow | Streamflow | Iran | ANN | [151] | Enhanced high and low flow prediction were achieved. |
Recursive baseflow separation | Climate, PET, streamflow | USA | SVR, ANN, RF | [144] | Baseflow separation enhanced streamflow simulation accuracy. |
Baseflow separation using the Lyne–Hollick method | Streamflow | China | ANN, LSTM, SVM, Holt–Winter, GRU, ARIMA | [148] | The model prediction performance improved. |
Flow pattern recognition (five classes) | Streamflow | China | ANN, SVM | [155] | Models with flow classes outperformed the other models. |
Extreme events and monotonic rainfall–runoff relationshiop | Climate data | USA | LSTM | [14] | Model performance improved compared with LSTM. |
Clustering flow: baseflow, rising limb, falling limb | Rainfall, evaporation, surface soil moisture, streamflow | China | Deep Belief Networks (DBNs) | [153] | Improved peak values and higher accuracy. |
Process-based baseflow separation | Climate, irrigation, streamflow | China | Stepwise cluster | [13] | The hybrid model outperformed the conventional data-driven models. |
One-parameter recursive single-pass filter baseflow separation method | Climate, PET, streamflow | USA | ANN, LSTM | [25] | Improved low-flow prediction. |
5. Discussion and Future Direction
5.1. Research Gap
5.1.1. The Role of Hydrological Science
5.1.2. Model Interpretability and Transferability
5.1.3. Risk of Overfitting and Model Complexity
5.2. Promising Areas for Future Research
5.2.1. Emergence Physics-Wrapped Neural Networks
5.2.2. Applicability in Ungauged Regions and Assessment of Natural and Anthropogenic Factors
6. Summary and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Yifru, B.A.; Lim, K.J.; Lee, S. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review. Sustainability 2024, 16, 1376. https://doi.org/10.3390/su16041376
Yifru BA, Lim KJ, Lee S. Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review. Sustainability. 2024; 16(4):1376. https://doi.org/10.3390/su16041376
Chicago/Turabian StyleYifru, Bisrat Ayalew, Kyoung Jae Lim, and Seoro Lee. 2024. "Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review" Sustainability 16, no. 4: 1376. https://doi.org/10.3390/su16041376