scholarly journals Estimating regional model parameters using spatial land cover information – implications for predictions in ungauged basins

2011 ◽  
Keyword(s):  
Land Cover ◽  
Regional Model ◽  
Model Parameters ◽  
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 Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 528 ◽  
Author(s):  
Santiago Narbondo ◽  
Angela Gorgoglione ◽  
Magdalena Crisci ◽  
Christian Chreties
Keyword(s):  
Weather Conditions ◽  
Climatic Conditions ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Physical Similarity ◽  
Tropical Regions ◽  
Runoff Prediction ◽  
Tropical Areas ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins

Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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 Evaluating and improving the Community Land Model's sensitivity to land cover

2018 ◽  
Vol 15 (15) ◽  
pp. 4731-4757 ◽  
Author(s):  
Ronny Meier ◽  
Edouard L. Davin ◽  
Quentin Lejeune ◽  
Mathias Hauser ◽  
Yan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
Soil Column ◽  
Plant Functional Type ◽  
Model Parameters ◽  
Functional Type ◽  
Land Surface Models ◽  
Surface Models ◽  
Open Land

Abstract. Modeling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions but have also highlighted the lack of agreement across different models. Recently, remote-sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing and in situ data sources. To extract the local sensitivity to land cover, we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing subgrid model output to local observations to improve current land surface models' ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate.

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 Fourier Landscape Pattern Indices for Predicting South Carolina Watershed Fecal Coliform

2017 ◽  
Vol 10 (1) ◽  
pp. 20-34
Author(s):  
Alvin Spivey ◽  
Anthony Vodacek
Keyword(s):  
Land Use ◽  
South Carolina ◽  
Land Cover ◽  
Landscape Pattern ◽  
Fecal Coliform ◽  
Model Parameters ◽  
Residential Areas ◽  
Ecological Processes ◽  
Bare Rock ◽  
Decadal Scale

AbstractExtending the Landscape Pattern Metric (LPM) model analysis in Smith et al. (2001) into a LPM decision model, decadal scale prediction of fecal coliform compromised South Carolina watersheds is developed. The model’s parameter variability identifies the greatest contributors to a compromised watershed’s prediction. The complete set of model parameters include Land Cover Land Use (LCLU) & slope,along stream proportion, Fourier Metric of Fragmentation (FMF), Fourier Metric of Proportion (FMP), and Least Squares Fourier Transform Fractal Dimension (LsFT). The 1992 National Land Cover Data (NLCD) Land Cover Land Use (LCLU) within fecal coliform compromised watersheds is used to train the model parameters, and the 2001 NLCD LCLU is used to test the LPM model. The most significant model parameters arealong stream bare rock LsFT,FMF between urban/recreational grasses and evergreen forests, andFMF between deciduous forests and high density residential areas. These metrics contribute significantly more than the bestproportiondescriptor:proportion of urban/recreational grasses. In training, the proposed model correctly identified 92 % of the compromised watersheds; while the Smith et al. (2001) model 94 % of the compromised watersheds were correctly identified. This study reveals the ability of Fourier metrics to interpret ecological processes, and the need for more appropriate landscape level models.

scholarly journals Selection of catchment descriptors for the physical similarity approach. Part II: Application

2013 ◽  
Vol 8 (No. 4) ◽  
pp. 186-194
Author(s):  
M. Heřmanovský ◽  
P. Pech
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Model Parameters ◽  
Physical Similarity ◽  
Model Parameter ◽  
Soil Moisture Accounting ◽  
Estimated Parameters ◽  
Selection Of

This paper demonstrates an application of the previously published method for selection of optimal catchment descriptors, according to which similar catchments can be identified for the purpose of estimation of the Sacramento – Soil Moisture Accounting (SAC-SMA) model parameters for a set of tested catchments, based on the physical similarity approach. For the purpose of the analysis, the following data from the Model Parameter Estimation Experiment (MOPEX) project were taken: a priori model parameter sets used as reference values for comparison with the newly estimated parameters, and catchment descriptors of four categories (climatic descriptors, soil properties, land cover and catchment morphology). The inverse clustering method, with Andrews’ curves for a homogeneity check, was used for the catchment grouping process. The optimal catchment descriptors were selected on the basis of two criteria, one comparing different subsets of catchment descriptors of the same size (MIN), the other one evaluating the improvement after addition of another catchment descriptor (MAX). The results suggest that the proposed method and the two criteria used may lead to the selection of a subset of conditionally optimal catchment descriptors from a broader set of them. As expected, the quality of the resulting subset of optimal catchment descriptors is mainly dependent on the number and type of the descriptors in the broader set. In the presented case study, six to seven catchment descriptors (two climatic, two soil and at least two land-cover descriptors) were identified as optimal for regionalisation of the SAC-SMA model parameters for a set of MOPEX catchments.

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