Value of multi-source dataset for hydrological catchment modeling 

2021 ◽  
Author(s):  
Carolina Natel de Moura ◽  
João Marcos Carvalho ◽  
Jan Seibert
Keyword(s):  
Large Scale ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Short Term Memory ◽  
Ground Level ◽  
Added Value ◽  
Quality Analysis ◽  
Level Gauge ◽  
Streamflow Prediction ◽  
Robust Hypothesis Testing

<p>Global meteorological and hydrological datasets have become increasingly available in the past few decades, marked by an increase in the number of large datasets, often including hundreds of catchments. These data sets bring two main advantages: the ability to perform hydrological modeling over a large number of catchments located in different hydroclimate characteristics, - which leads us to more robust hypothesis testing, and the ability to address the uncertainties related to the hydrological model input data. However, the full added value to hydrological modeling is not yet fully understood. The main questions surrounding the use of multi-source and large-scale datasets are related to how much value these datasets add to the performance of hydrological models. How different are these datasets, how accurate are they, and whether their use results in similar or rather different hydrological simulations? Other questions are how can we better combine them for improved predictions, and what is the average uncertainty of the input datasets in hydrological modeling? We aimed here to investigate better those issues using Brazilian catchments as study cases. The Brazilian hydrometeorological network has several issues to overcome, such as an undistributed spatial network resulting in data-scarce areas, a large amount of missing data, and the lack of standardized and transparent quality analysis. In this study, we used a national hydrometeorological dataset (CAMELS-BR) along with other several global forecast and reanalysis meteorological datasets, such as the CFSv2 and ECMWF, for the streamflow prediction using the data-driven model Long-Short Term Memory (LSTM). Initial results indicate that calibrating a recurrent neural network is clearly depending on the data source. Moreover, the tested global meteorological products are found to be suitable for hydrological modeling. The combination of different data sources in the hydrological model seems to be beneficial, especially in those areas where ground-level gauge stations are scarce.</p>

scholarly journals Development of the Community Water Model (CWatM v1.04) A high-resolution hydrological model for global and regional assessment of integrated water resources management

2019 ◽  
Author(s):  
Peter Burek ◽  
Yusuke Satoh ◽  
Taher Kahil ◽  
Ting Tang ◽  
Peter Greve ◽  
...  
Keyword(s):  
Water Resources ◽  
Open Source ◽  
Water Use ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Integrated Water Resources Management ◽  
Water Model ◽  
Management Options ◽  
Community Water

Abstract. We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arc min to 30 arc sec at daily time steps. CWatM is open-source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes, but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture.

Dynamic Task-Scheduling Based Parallel Processing on Watershed Distributed Eco-Hydrological Model

2014 ◽  
Vol 556-562 ◽  
pp. 3492-3495
Author(s):  
Yao Li ◽  
Da Lin Jiang
Keyword(s):  
Parallel Processing ◽  
Task Scheduling ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Hydrological Simulation ◽  
Dynamic Task ◽  
Walnut Gulch ◽  
Simulation Task ◽  
Dynamic Task Scheduling

Watershed distributed eco-hydrological model is an important tool in the field of global change research. Due to the complexity of eco-hydrological model, watershed distributed eco-hydrological simulation requires large amounts of computations. The compution overhead turns to be a big prolem for those basin areas. Another challenge is that the traditional sequential computation techniques cannot meet the requirements of watershed eco-hydrological model, which highly limits the application of watershed distributed eco-hydrological model in large scale areas. This paper proposed a dynamic task-scheduling based parallel processing method for eco-hydrological model. The whole simulation task are firstly decoupled into independent grid based parallel processing tasks based on the relation of upstream and downstream sequence. Then a dynamic task-tree was built up according to the dependency of each cell in the watershed, which can generate dynamic task scheduling sequence. Following the task scheduling sequence, PBS task scheduler submitted workloads, realizing parallel calculation. This approach was applied in the watershed of Walnut Gulch watershed in Arizona, USA. The result showed that this method can highly improves the efficiency of watershed eco-hydrological modeling almost 6 times compared to that of the traditional sequential eco-hydrological modeling. Therefore, this approach can effectively promote the applications of watershed eco-hydrological model.

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2015 ◽  
Vol 12 (10) ◽  
pp. 10559-10601 ◽  
Author(s):  
P. Lopez Lopez ◽  
N. Wanders ◽  
J. Schellekens ◽  
L. J. Renzullo ◽  
E. H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Added Value ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tuned river models. A possible solution to the problem may be to drive the coarse resolution models with locally available high spatial resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee river basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (from 0.5° downscaled to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high resolution gauging station based gridded dataset (0.05°). Downscaled satellite derived soil moisture (from approx. 0.5° downscaled to 0.08° resolution) from AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can help to advance this research.

scholarly journals Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling

2019 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Guy Shalev ◽  
Günter Klambauer ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Large Scale ◽  
Hydrological Modeling ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Hydrological Models ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a big data paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.

scholarly journals On the Suitability of GCM Runoff Fields for River Discharge Modeling: A Case Study Using Model Output from HadGEM2 and ECHAM5

2012 ◽  
Vol 13 (1) ◽  
pp. 140-154 ◽  
Author(s):  
F. C. Sperna Weiland ◽  
L. P. H. van Beek ◽  
J. C. J. Kwadijk ◽  
M. F. P. Bierkens
Keyword(s):  
Land Surface ◽  
River Discharge ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Subsurface Runoff ◽  
Discharge Modeling ◽  
Annual Discharge

Abstract The representation of hydrological processes in land surface schemes (LSSs) has recently been improved. In this study, the usability of GCM runoff for river discharge modeling is evaluated by validating the mean, timing, and amplitude of the modeled annual discharge cycles against observations. River discharge was calculated for six large rivers using runoff, precipitation, and actual evaporation from the GCMs ECHAM5 and Hadley Centre Global Environmental Model version 2 (HadGEM2). Four methods were applied: 1) accumulation of GCM runoff, 2) routing of GCM runoff, 3) routing of GCM runoff combined with temporal storage of subsurface runoff, and 4) offline hydrological modeling with the global distributed hydrological model PCRaster Global Water Balance (PCR-GLOBWB) using meteorological data from the GCMs as forcing. The quality of discharge generated by all four methods is highly influenced by the quality of the GCM data. In small catchments, the methods that include runoff routing perform equally well, although offline modeling with PRC-GLOBWB outperforms the other methods for ECHAM5 data. For larger catchments, routing introduces realistic travel times, decreased day-to-day variability, and it reduces extremes. Complexity of the LSS of both GCMs is comparable to the complexity of the hydrological model. However, in HadGEM2 the absence of subgrid variability for saturated hydraulic conductivity results in a large subsurface runoff flux and a low seasonal variability in the annual discharge cycle. The analysis of these two GCMs shows that when LSSs are tuned to reproduce realistic water partitioning at the grid scale and a routing scheme is also included, discharge variability and change derived from GCM runoff could be as useful as changes derived from runoff obtained from offline simulations using large-scale hydrological models.

scholarly journals Hydrological Modeling in Agricultural Intensive Watershed: The Case of Upper East Fork White River, USA

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 137
Author(s):  
George Bariamis ◽  
Evangelos Baltas
Keyword(s):  
Land Cover ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Groundwater Resources ◽  
Model Parameters ◽  
Agricultural Field ◽  
Streamflow Prediction ◽  
Model Simulations ◽  
White River ◽  
Model Setup

Identifying the core hydrological processes of catchments is a critical step for operative hydrological modeling. This study attempts to assess the long-term alterations in streamflow in three adjacent catchments of Upper East Fork White River, Indiana USA, by employing the SWAT hydrological model. The model simulations are spanning from 1980 up to 2015 and distributed in three configurations periods to identify monthly alterations in streamflow. For this purpose, water abstraction, land use, tillage, and agricultural field drainage practices have been incorporated in the model to provide accurate data input. The model setup also integrates spatially disaggregated sectorial water use data from surface and groundwater resources integrating the significant increases of water abstractions mainly for agricultural and public water supply purposes. The land cover of the study area is governed by rotating crops, while agricultural practices and tile drainage are crucial model parameters affecting the regional hydrological balance. Streamflow prediction is based on the SUFI-2 algorithm and the SWAT-CUP interface has been used for the monthly calibration and validation phases of the model. The evaluation of model simulations indicate a progressively sufficient hydrological model setup for all configuration periods with NSE (0.87, 0.88, and 0.88) and PBIAS (14%, −7%, and −2.8%) model evaluation values at the Seymour outlet. Surface runoff/precipitation as well as percolation/precipitation ratios have been used as indicators to identify trends to wetter conditions. Model outputs for the upstream areas, are successful predictions for streamflow assessment studies to test future implications of land cover and climate change.

scholarly journals Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

2021 ◽  
Vol 25 (3) ◽  
pp. 1189-1209
Author(s):  
Marc Girons Lopez ◽  
Louise Crochemore ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Hydrological Model ◽  
Temporal Variations ◽  
Forecast Skill ◽  
Added Value ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
The Future ◽  
Spatio Temporal ◽  
Forecasting And Warning

Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the ensemble streamflow prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39 493 catchments in Sweden, understand their spatio-temporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatio-temporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slow-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in seven unique regions with similar hydrological behaviour. Overall, these results contribute to identifying in which areas and seasons and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service, but also, most importantly, for guiding decision-making in critical services such as hydropower management and risk reduction.

scholarly journals Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

2020 ◽  
Author(s):  
Marc Girons Lopez ◽  
Louise Crochemore ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Hydrological Model ◽  
Forecast Skill ◽  
Added Value ◽  
Spatiotemporal Variations ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
The Future ◽  
Ensemble Streamflow Prediction ◽  
Forecasting And Warning

Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the Ensemble Streamflow Prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39,493 catchments in Sweden, understand their spatiotemporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatiotemporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly-regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slowly-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in 7 unique regions with similar hydrological behaviour. Overall, these results contribute to identify in which areas, seasons, and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service but, most importantly, to guide decision-making in critical services such as hydropower management and risk reduction.

scholarly journals Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management

2020 ◽  
Vol 13 (7) ◽  
pp. 3267-3298 ◽  
Author(s):  
Peter Burek ◽  
Yusuke Satoh ◽  
Taher Kahil ◽  
Ting Tang ◽  
Peter Greve ◽  
...  
Keyword(s):  
Water Resources ◽  
Open Source ◽  
Water Use ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Integrated Water Resources Management ◽  
Water Model ◽  
Management Options ◽  
Community Water

Abstract. We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arcmin to 30 arcsec at daily time steps. CWatM is open source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows for dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of-the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture.

scholarly journals NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

2021 ◽  
Vol 11 (10) ◽  
pp. 4426
Author(s):  
Chunyan Ma ◽  
Ji Fan ◽  
Jinghao Yao ◽  
Tao Zhang
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Evaluation Criteria ◽  
Image Data ◽  
Basketball Player ◽  
Basketball Players ◽  
Convolutional Network ◽  
Atomic Actions ◽  
New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

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