scholarly journals Explicit simulations of stream networks to guide hydrological modelling in ungauged basins

2010 ◽  
Vol 7 (1) ◽  
pp. 847-877 ◽  
Author(s):  
S. Stoll ◽  
M. Weiler
Keyword(s):  
Regional Climate ◽  
Model Development ◽  
Hydrological Modelling ◽  
Distributed Data ◽  
Ungauged Basins ◽  
Stream Networks ◽  
Stream Network ◽  
Network Modelling ◽  
Discharge Data ◽  
Parameter Evaluation

Abstract. Rainfall-runoff modelling in ungauged basins is still one of the greatest challenges in recent hydrological research. The lack of discharge data necessitates the establishment of new innovative approaches to guide hydrological modelling in ungauged basins. Besides the transfer of calibrated parameters from similar gauged catchments, the application of distributed data as a hydrological response in addition to discharge seems to be promising. A new approach for model and parameter evaluation based on explicit simulation of the spatial stream network was tested in four different catchments in Germany. In a first step, spatial explicit modelling of stream networks was performed using a simplified version of the process-based model Hill-Vi together with regional climate normals. The simulated networks were compared to mapped stream networks and their degree of spatial agreement was evaluated. Significant differences between good and poor simulations could be distinguished and the corresponding parameter sets relate well with the hydrogeological properties of the catchments. The optimized parameters were subsequently used to simulate daily discharge using an observed time series of precipitation and air temperature. The performance was evaluated against observed discharge and water balance. This approach shows some promising results but also some limitations. Although the model's parsimonious model structure should to be further improved regarding discharge recession and evapotranspiration, the performance was similar to the regionalisation methods. Stream network modelling, which has minimal data requirements, seems to be a reasonable alternative for model development and parameter evaluation in ungauged basins.

scholarly journals Explicit simulations of stream networks to guide hydrological modelling in ungauged basins

2010 ◽  
Vol 14 (8) ◽  
pp. 1435-1448 ◽  
Author(s):  
S. Stoll ◽  
M. Weiler
Keyword(s):  
Regional Climate ◽  
Model Development ◽  
Hydrological Modelling ◽  
Distributed Data ◽  
Ungauged Basins ◽  
Stream Networks ◽  
Stream Network ◽  
Network Modelling ◽  
Discharge Data ◽  
Parsimonious Model

Abstract. Rainfall-runoff modelling in ungauged basins is still one of the greatest challenges in hydrological research. The lack of discharge data necessitates the establishment of new innovative approaches to guide hydrological modelling in ungauged basins. Besides the transfer of calibrated parameters from similar gauged catchments, the application of distributed data as a hydrological response in addition to discharge seems to be promising. A new approach to guide hydrological modelling based on explicit simulation of the spatial stream network was tested in four different catchments in Germany. In a first step we used a simplified version of the process-based model Hill-Vi together with regional climate normals to simulate stream networks. The calculation of gravity driven lateral subsurface and groundwater flow is used to identify patterns of stream cells, which were compared to reference stream networks and their degree of spatial agreement was evaluated. Significant differences between good and poor simulations could be distinguished and the corresponding parameter sets relate well with the hydrogeological properties of the catchments. The optimized parameters were subsequently used to simulate daily discharge using an observed time series of precipitation and air temperature. The performance was evaluated against observed discharge and water balance. This approach shows some promising results but also some limitations. Although the model's parsimonious model structure could be further improved regarding discharge recession and evapotranspiration, the performance was similar to regionalisation methods. Stream network modelling, which has minimal data requirements, seems to be a reasonable alternative for model development and parameter evaluation in ungauged basins.

scholarly journals Geomorphometry-based modelling of discharge series in ungauged basins – Robustness regarding DEM sources

2020 ◽  
Vol 383 ◽  
pp. 129-134 ◽  
Author(s):  
Stéphane Ecrepont ◽  
Christophe Cudennec
Keyword(s):  
Transfer Functions ◽  
Hydrological Modelling ◽  
Ungauged Basins ◽  
Stream Networks ◽  
Ungauged Basin ◽  
Headwater Basins

Abstract. The sensitivity of a geomorphology-based hydrological modelling is evaluated according to four DEMs from 5 to 50 m resolution in Brittany, France. A set of 8 basins (5–565.7 km2) is used in a pseudo-ungauged context to explore the potential of Prediction in Ungauged Basin (PUB). The results show that despite slight differences on the stream networks extracted from DEMs and associated transfer functions, a coarse-worldwide DEM such as SRTM (25 m) supported similar performances than the finer French DEM (5 m) based on three validation indices. Finer DEMs may be useful only on headwater basins to gain a marginal performance.

scholarly journals Can the heterogeneity in stream dissolved organic carbon be explained by contributing landscape elements?

2014 ◽  
Vol 11 (4) ◽  
pp. 1199-1213 ◽  
Author(s):  
A. M. Ågren ◽  
I. Buffam ◽  
D. M. Cooper ◽  
T. Tiwari ◽  
C. D. Evans ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Model Development ◽  
Field Measurements ◽  
Mixing Model ◽  
Residual Analysis ◽  
Residence Times ◽  
Stream Network ◽  
Landscape Elements ◽  
Modelling Studies

Abstract. The controls on stream dissolved organic carbon (DOC) concentrations were investigated in a 68 km2 catchment by applying a landscape-mixing model to test if downstream concentrations could be predicted from contributing landscape elements. The landscape-mixing model reproduced the DOC concentration well throughout the stream network during times of high and intermediate discharge. The landscape-mixing model approach is conceptually simple and easy to apply, requiring relatively few field measurements and minimal parameterisation. Our interpretation is that the higher degree of hydrological connectivity during high flows, combined with shorter stream residence times, increased the predictive power of this whole watershed-based mixing model. The model was also useful for providing a baseline for residual analysis, which highlighted areas for further conceptual model development. The residual analysis indicated areas of the stream network that were not well represented by simple mixing of headwaters, as well as flow conditions during which simple mixing based on headwater watershed characteristics did not apply. Specifically, we found that during periods of baseflow the larger valley streams had much lower DOC concentrations than would be predicted by simple mixing. Longer stream residence times during baseflow and changing hydrological flow paths were suggested as potential reasons for this pattern. This study highlights how a simple landscape-mixing model can be used for predictions as well as providing a baseline for residual analysis, which suggest potential mechanisms to be further explored using more focused field and process-based modelling studies.

Spatial scale effects on model parameter estimation and predictive uncertainty in ungauged basins

2012 ◽  
Vol 44 (3) ◽  
pp. 441-453 ◽  
Author(s):  
Denis A. Hughes ◽  
Evison Kapangaziwiri ◽  
Jane Tanner
Keyword(s):  
Parameter Estimation ◽  
Spatial Scale ◽  
Scale Effects ◽  
Physical Property ◽  
Hydrological Modelling ◽  
Ungauged Basins ◽  
Estimation Equations ◽  
Property Data ◽  
Physical Property Data ◽  
Parameter Values

The most appropriate scale to use for hydrological modelling depends on the model structure, the purpose of the results and the resolution of available data used to quantify parameter values and provide the climatic forcing. There is little consensus amongst the community of model users on the appropriate model complexity and number of model parameters that are needed for satisfactory simulations. These issues are not independent of modelling scale, the methods used to quantify parameter values, nor the purpose of use of the simulations. This paper reports on an investigation of spatial scale effects on the application of an approach to quantify the parameter values (with uncertainty) of a rainfall-runoff model with a relatively large number of parameters. The quantification approach uses estimation equations based on physical property data and is applicable to gauged and ungauged basins. Within South Africa the physical property data are available at a finer spatial resolution than is typically used for hydrological modelling. The results suggest that reducing the model spatial scale offers some advantages. Potential disadvantages are related to the need for some subjective interpretation of the available physical property data, as well as inconsistencies in some of the parameter estimation equations.

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>.  When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

scholarly journals Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals

2020 ◽  
Vol 10 (24) ◽  
pp. 9005
Author(s):  
Chien-Cheng Lee ◽  
Zhongjian Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Recognition Accuracy ◽  
Signal Interference ◽  
Stream Networks ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Stream Network ◽  
Value Decomposition

Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.

scholarly journals Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 32 ◽  
Author(s):  
Nag ◽  
Biswal
Keyword(s):  
Model Performance ◽  
Data Availability ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Study Region ◽  
Calibration Free ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Free Dynamic

Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological information from gauged basins to ungauged basins. As a consequence, regionalization-based FDC predictions are not very reliable where discharge data are scarce quantitatively and/or qualitatively. In such a scenario, it is perhaps more meaningful to use a calibration-free rainfall‒runoff model that can exploit easily available meteorological information to predict FDCs in ungauged basins. This hypothesis is tested in this study by comparing a well-known regionalization-based model, the inverse distance weighting (IDW) model, with the recently proposed calibration-free dynamic Budyko model (DB) in a region where discharge observations are not only insufficient quantitatively but also show apparent signs of observational errors. The DB model markedly outperformed the IDW model in the study region. Furthermore, the IDW model’s performance sharply declined when we randomly removed discharge gauging stations to test the model in a variety of data availability scenarios. The analysis here also throws some light on how errors in observational datasets and drainage area influence model performance and thus provides a better picture of the relative strengths of the two models. Overall, the results of this study support the notion that a calibration-free rainfall‒runoff model can be chosen to predict FDCs in discharge data-scarce regions. On a philosophical note, our study highlights the importance of process understanding for the development of meaningful hydrological models.

scholarly journals Beaver dam capacity of Canada’s boreal plain in response to environmental change

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nichole-Lynn Stoll ◽  
Cherie J. Westbrook
Keyword(s):  
Environmental Conditions ◽  
Environmental Changes ◽  
Water Cycle ◽  
Stream Networks ◽  
Stream Network ◽  
Beaver Dam ◽  
Riding Mountain National Park ◽  
Induced Changes ◽  
Response To Environmental Change ◽  
Beaver Dams

Abstract Environmental changes are altering the water cycle of Canada’s boreal plain. Beaver dams are well known for increasing water storage and slowing flow through stream networks. For these reasons beavers are increasingly being included in climate change adaptation strategies. But, little work focuses on how environmental changes will affect dam building capacity along stream networks. Here we estimate the capacity of the stream network in Riding Mountain National Park, Manitoba, Canada to support beaver dams under changing environmental conditions using a modelling approach. We show that at capacity, the park’s stream network can support 24,690 beaver dams and hold between 8.2 and 12.8 million m3 of water in beaver ponds. Between 1991 and 2016 the park’s vegetation composition shifted to less preferred beaver forage, which led to a 13% decrease in maximum dam capacity. We also found that dam capacity is sensitive to the size of regularly-occurring floods—doubling the 2-year flood reduces the park’s dam capacity by 21%. The results show that the potential for beaver to offset some expected climatic-induced changes to the boreal water cycle is more complex than previously thought, as there is a feedback wherein dam capacity can be reduced by changing environmental conditions.

Flowering time control: gene network modelling and the link to quantitative genetics

2005 ◽  
Vol 56 (9) ◽  
pp. 919 ◽  
Author(s):  
Stephen M. Welch ◽  
Zhanshan Dong ◽  
Judith L. Roe ◽  
Sanjoy Das
Keyword(s):  
Flowering Time ◽  
Quantitative Genetics ◽  
Model Development ◽  
Genetic Network ◽  
Ideal Theory ◽  
Emergent Properties ◽  
Network Modelling ◽  
Crop Models ◽  
Time Control ◽  
Flowering Time Control

Flowering is a key stage in plant development that initiates grain production and is vulnerable to stress. The genes controlling flowering time in the model plant Arabidopsis thaliana are reviewed. Interactions between these genes have been described previously by qualitative network diagrams. We mathematically relate environmentally dependent transcription, RNA processing, translation, and protein–protein interaction rates to resultant phenotypes. We have developed models (reported elsewhere) based on these concepts that simulate flowering times for novel A. thaliana genotype–environment combinations. Here we draw 12 contrasts between genetic network (GN) models of this type and quantitative genetics (QG), showing that both have equal contributions to make to an ideal theory. Physiological dominance and additivity are examined as emergent properties in the context of feed-forwards networks, an instance of which is the signal-integration portion of the A. thaliana flowering time network. Additivity is seen to be a complex, multi-gene property with contributions from mass balance in transcript production, the feed-forwards structure itself, and downstream promoter reaction thermodynamics. Higher level emergent properties are exemplified by critical short daylength (CSDL), which we relate to gene expression dynamics in rice (Oryza sativa). Next to be discussed are synergies between QG and GN relating to the quantitative trait locus (QTL) mapping of model coefficients. This suggests a new verification test useful in GN model development and in identifying needed updates to existing crop models. Finally, the utility of simple models is evinced by 80 years of QG theory and mathematical ecology.

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