scholarly journals Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

2019 ◽  
Vol 55 (12) ◽  
pp. 11344-11354 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Mathew Herrnegger ◽  
Alden K. Sampson ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ungauged Basins ◽  
Predictions In Ungauged Basins

scholarly journals Towards Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

2019 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
mathew herrnegger ◽  
Alden Keefe Sampson ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ungauged Basins ◽  
Predictions In Ungauged Basins

Runoff Predictions in Ungauged Basins Using Sequence-to-Sequence Models

2021 ◽  
pp. 126975
Author(s):  
Hanlin Yin ◽  
Zilong Guo ◽  
Xiuwei Zhang ◽  
Jiaojiao Chen ◽  
Yanning Zhang
Keyword(s):  
Ungauged Basins ◽  
Predictions In Ungauged Basins

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

scholarly journals Data-driven catchment classification: application to the PUB problem

2011 ◽  
Vol 8 (1) ◽  
pp. 391-427 ◽  
Author(s):  
M. Di Prinzio ◽  
A. Castellarin ◽  
E. Toth
Keyword(s):  
Cross Validation ◽  
Principal Component ◽  
Annual Runoff ◽  
Data Driven ◽  
Spatiotemporal Variability ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Streamflow Regime ◽  
Predictions In Ungauged Basins ◽  
Unsupervised Neural Networks

Abstract. Objective criteria for catchment classification are identified by the scientific community among the key research topics for improving the interpretation and representation of the spatiotemporal variability of streamflow. A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ~300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We qualitatively and quantitatively compare in the context of PUB (Prediction in Ungauged Basins) a reference classification, RC, with four alternative classifications, AC's. RC was identified by using indices of the streamflow regime as input to SOM, whereas AC's were identified on the basis of catchment descriptors that can be derived for ungauged basins. One AC directly adopts the available catchment descriptors as input to SOM. The remaining AC's are identified by applying SOM to two sets of derived variables obtained by applying Principal Component Analysis (PCA, second AC) and Canonical Correlation Analysis (CCA, third and fourth ACs) to the available catchment descriptors. First, we measure the similarity between each AC and RC. Second, we use AC's and RC to regionalize several streamflow indices and we compare AC's with RC in terms of accuracy of streamflow prediction. In particular, we perform an extensive cross-validation to quantify nationwide the accuracy of predictions in ungauged basins of mean annual runoff, mean annual flood, and flood quantiles associated with given exceedance probabilities. Results of the study show that CCA can significantly improve the effectiveness of SOM classifications for the PUB problem.

scholarly journals Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale

2012 ◽  
Vol 57 (2) ◽  
pp. 229-247 ◽  
Author(s):  
Johan Strömqvist ◽  
Berit Arheimer ◽  
Joel Dahné ◽  
Chantal Donnelly ◽  
Göran Lindström
Keyword(s):  
Ungauged Basins ◽  
National Scale ◽  
Set Up ◽  
Predictions In Ungauged Basins

scholarly journals Regionalization of hydrological model parameters using gradient boosting machine

2021 ◽  
Author(s):  
Zhihong Song ◽  
Jun Xia ◽  
Gangsheng Wang ◽  
Dunxian She ◽  
Chen Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydrological Model ◽  
Spatial Coherence ◽  
Gradient Boosting ◽  
Model Parameters ◽  
Explanatory Variables ◽  
Terrain Attributes ◽  
Gradient Boosting Machine ◽  
Stepwise Mlr ◽  
Predictions In Ungauged Basins

Abstract. Regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman-Monteith-Leuning (PML) equation into the Distributed Time-Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration process. We calibrated six key model parameters grid-by-grid across China using a multivariable calibration strategy, which incorporates spatiotemporal runoff and evapotranspiration (ET) datasets (0.25°, monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters, but performed better in humid than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at national scale, though the improvement is not significant pertaining to watershed streamflow validation due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters by using watershed properties in ungauged regions.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 10 (1) ◽  
pp. 449-485 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Low Flows ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. In a three-part paper we assess the performance of runoff predictions in ungauged basins in a comparative way. While Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation of hydrographs and hydrological extremes through a literature review, in this paper we assess prediction of a range of runoff signatures for a consistent dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-Kriging, a geostatistical interpolation method that accounts for the river network hierarchy. From the runoff timeseries, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrograph. The predictive performance is assessed by the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation mode. Results of the comparative assessment show that the geostatistical approach (Top-Kriging) generally outperforms the regionalised rainfall–runoff model. The predictive performance increases with catchment area for both methods and all signatures, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows is the most difficult to capture followed by the low flows. The relative predictive performance of the signatures depends on the selected performance measures. It is therefore essential to report performance in a consistent way by more than one performance measure.

The importance of geology when estimating catchment-scale streamflow characteristics: Application of a new technique for hydrologic similarity and regionalization

2021 ◽  
Author(s):  
Joseph Janssen ◽  
Ali Ameli
Keyword(s):  
Research Area ◽  
Scientific Understanding ◽  
Laboratory Scale ◽  
Hydrological Processes ◽  
Climate Change Scenarios ◽  
Large Set ◽  
Ungauged Basins ◽  
Catchment Scale ◽  
Hydrological Variable ◽  
Predictions In Ungauged Basins

<p>Expanding the scientific understanding of global hydrological processes is a key research area for hydrologists. Research in this area can allow hydrologists to make better predictions in ungauged basins and catchments under climate change scenarios. Though hydrological processes are largely understood at a laboratory-scale, catchment-scale processes are much more complex and unknown. Previous studies at the catchment-scale have shown catchment geology is largely irrelevant in determining components of streamflow. Laboratory-scale experiments, however, have revealed that this is unlikely. This contradiction indicates the current techniques for determining hydrological variable importance in the literature are insufficient. In this paper, we quantify the influence of the interaction amongst climatic, geological, and topographical features on a large set of hydrological signatures in snow-dominated regions across North America, using Stable Extrapolative Marginal Contribution Feature Importance. The preliminary results show that when we consider interaction effects among climatic and geophysical features, and remove the influence of correlation, geological features show considerable importance at the catchment scale. We contend that this study contributes to the scientific understanding of catchment-scale hydrological processes, especially in cold, ungauged basins.</p>

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