Large-scale automatic generation of hydrological models from airborne TEM data and boreholes

Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ([1]).Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library ([3]). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters ([2]). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.More information about our modelling framework is at https://lue.computationalgeography.org.References[1] M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923&#8211;4947, 2015. [2] K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted. [3] H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.

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Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

Water ◽

10.3390/w14010110 ◽

2022 ◽

Vol 14 (1) ◽

pp. 110

Author(s):

Raphael Schneider ◽

Simon Stisen ◽

Anker Lajer Højberg

Keyword(s):

Large Scale ◽

Agricultural Land ◽

Hydrological Cycle ◽

Drainage System ◽

Water Model ◽

Generation Process ◽

Hydrological Models ◽

Drainage Patterns ◽

Drain Flow ◽

Set Up

About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km2) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.

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Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-8375-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 8375-8424 ◽

Cited By ~ 3

Author(s):

A. F. Van Loon ◽

M. H. J. Van Huijgevoort ◽

H. A. J. Van Lanen

Keyword(s):

Large Scale ◽

Dry Season ◽

Hydrological Drought ◽

Hydrological Models ◽

Drought Characteristics ◽

Snow Season ◽

Scale Models ◽

Ensemble Mean ◽

Semi Arid

Abstract. Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is: how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that were part of the model intercomparison project of WATCH (WaterMIP). For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity), drought propagation features (pooling, attenuation, lag, lengthening), and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought). Drought characteristics simulated by large-scale models clearly reflected drought propagation, i.e. drought events became less and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having less and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an underestimation of wet-to-dry-season droughts and snow-related droughts. Furthermore, almost no composite droughts were simulated for slowly responding areas, while many multi-year drought events were expected in these systems. We conclude that drought propagation processes are reasonably well reproduced by the ensemble mean of large-scale models in contrasting catchments in Europe and that some challenges remain in catchments with cold and semi-arid climates and catchments with large storage in aquifers or lakes. Improvement of drought simulation in large-scale models should focus on a better representation of hydrological processes that are important for drought development, such as evapotranspiration, snow accumulation and melt, and especially storage. Besides the more explicit inclusion of storage (e.g. aquifers) in large-scale models, also parametrisation of storage processes requires attention, for example through a global scale dataset on aquifer characteristics.

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The importance of vegetation to understand terrestrial water storage variations

10.5194/hess-2021-394 ◽

2021 ◽

Author(s):

Tina Trautmann ◽

Sujan Koirala ◽

Nuno Carvalhais ◽

Andreas Güntner ◽

Martin Jung

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Water Storage ◽

Model Parameters ◽

Intermediate Water ◽

Model Structure ◽

Hydrological Models ◽

Terrestrial Water Storage ◽

Terrestrial Water

Abstract. So far, various studies aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way how vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including vegetation on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment with vegetation parameters varying in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but including vegetation data led to even better performance and more physically plausible parameter values. Largest improvements regarding TWS and ET were seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of snow and different soil water storage components to the TWS variations, with the dominance of an intermediate water pool that interacts with the fast plant accessible soil moisture and the delayed water storage. The findings indicate the important role of deeper moisture storages as well as groundwater-soil moisture-vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation-water interactions.

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Are large scale models useful? A case of nested model domains for assessing current and future stream runoff and sediments

10.5194/egusphere-egu21-8852 ◽

2021 ◽

Author(s):

Alena Bartosova ◽

Berit Arheimer ◽

Alban de Lavenne ◽

René Capell ◽

Johan Strömqvist

Keyword(s):

Climate Change ◽

Spatial Patterns ◽

Large Scale ◽

Aridity Index ◽

Global Climate Models ◽

Hydrological Models ◽

Nested Model ◽

Nested Models ◽

Changing Climate ◽

Scale Models

Continental and global dynamic hydrological models have emerged recently as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE). We present and compare historical simulations of runoff, soil moisture, aridity, and sediment concentrations for three nested model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on hydrological variables from changing climate were then assessed using the global and continental HYPE applications with ensembles based on 3 CMIP5 global climate models (GCMs).Simulated historical sediment concentrations varied considerably among the nested models in spatial patterns while runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Output variables that were calibrated, e.g. runoff, were shown to result in more reliable and consistent projected changes among the different model scales than derived variables such as the actual aridity index. The comparison was carried out for ensemble averages as well as individual GCMs to illustrate the variability and the need for robust assessments.Global hydrological models are shown to be valuable tools for e.g. first screenings of climate change effects and detection of spatial patterns and can be useful to provide information on current and future hydrological states at various domains. The challenge is (1) in deciding when we should use the large-scale models and (2) in interpreting the results, considering the uncertainty of the model results and quality of data especially at the global scale. Comparison across nested domains demonstrates the significance of scale which needs to be considered when interpreting the impacts alongside with model performance.Bartosova et al, 2021: Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Accepted to Special Issue Journal of Hydraulic Engineering.

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Emergency fault affected wide-area automatic generation control via large-scale deep reinforcement learning

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104500 ◽

2021 ◽

Vol 106 ◽

pp. 104500

Author(s):

Jiawen Li ◽

Tao Yu ◽

Xiaoshun Zhang

Keyword(s):

Reinforcement Learning ◽

Large Scale ◽

Automatic Generation ◽

Wide Area ◽

Automatic Generation Control ◽

Generation Control

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A hydrometeorological model intercomparison as a tool to quantify the forecast uncertainty in a medium size basin

Natural Hazards and Earth System Science ◽

10.5194/nhess-8-819-2008 ◽

2008 ◽

Vol 8 (4) ◽

pp. 819-838 ◽

Cited By ~ 10

Author(s):

A. Amengual ◽

T. Diomede ◽

C. Marsigli ◽

A. Martín ◽

A. Morgillo ◽

...

Keyword(s):

Large Scale ◽

Mesoscale Model ◽

Northern Italy ◽

Precipitation Event ◽

Medium Size ◽

Hydrological Models ◽

Model Intercomparison ◽

Mesoscale Models ◽

Prediction And Prevention ◽

The One

Abstract. In the framework of AMPHORE, an INTERREG III B EU project devoted to the hydrometeorological modeling study of heavy precipitation episodes resulting in flood events and the improvement of the operational hydrometeorological forecasts for the prediction and prevention of flood risks in the Western Mediterranean area, a hydrometeorological model intercomparison has been carried out, in order to estimate the uncertainties associated with the discharge predictions. The analysis is performed for an intense precipitation event selected as a case study within the project, which affected northern Italy and caused a flood event in the upper Reno river basin, a medium size catchment in the Emilia-Romagna Region. Two different hydrological models have been implemented over the basin: HEC-HMS and TOPKAPI which are driven in two ways. Firstly, stream-flow simulations obtained by using precipitation observations as input data are evaluated, in order to be aware of the performance of the two hydrological models. Secondly, the rainfall-runoff models have been forced with rainfall forecast fields provided by mesoscale atmospheric model simulations in order to evaluate the reliability of the discharge forecasts resulting by the one-way coupling. The quantitative precipitation forecasts (QPFs) are provided by the numerical mesoscale models COSMO and MM5. Furthermore, different configurations of COSMO and MM5 have been adopted, trying to improve the description of the phenomena determining the precipitation amounts. In particular, the impacts of using different initial and boundary conditions, different mesoscale models and of increasing the horizontal model resolutions are investigated. The accuracy of QPFs is assessed in a threefold procedure. First, these are checked against the observed spatial rainfall accumulations over northern Italy. Second, the spatial and temporal simulated distributions are also examined over the catchment of interest. And finally, the discharge simulations resulting from the one-way coupling with HEC-HMS and TOPKAPI are evaluated against the rain-gauge driven simulated flows, thus employing the hydrological models as a validation tool. The different scenarios of the simulated river flows – provided by an independent implementation of the two hydrological models each one forced with both COSMO and MM5 – enable a quantification of the uncertainties of the precipitation outputs, and therefore, of the discharge simulations. Results permit to highlight some hydrological and meteorological modeling factors which could help to enhance the hydrometeorological modeling of such hazardous events. Main conclusions are: (1) deficiencies in precipitation forecasts have a major impact on flood forecasts; (2) large-scale shift errors in precipitation patterns are not improved by only enhancing the mesoscale model resolution; and (3) weak differences in flood forecasting performance are found by using either a distributed continuous or a semi-distributed event-based hydrological model for this catchment.

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Effects of climate model radiation, humidity and wind estimates on hydrological simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-16-305-2012 ◽

2012 ◽

Vol 16 (2) ◽

pp. 305-318 ◽

Cited By ~ 62

Author(s):

I. Haddeland ◽

J. Heinke ◽

F. Voß ◽

S. Eisner ◽

C. Chen ◽

...

Keyword(s):

Bias Correction ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Correction Method ◽

Hydrological Models ◽

Temperature And Precipitation ◽

Effects Of Radiation ◽

Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

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