The emergence of community models in hydrology

2020 ◽  
Author(s):  
Nans Addor ◽  
Martyn P. Clark ◽  
Brian Henn
Keyword(s):  
Model Development ◽  
Water Dynamics ◽  
Model Structure ◽  
Hydrological Models ◽  
Single Model ◽  
Cross Model ◽  
Structural Error ◽  
Terrestrial Water ◽  
Parameter Values ◽  
Single Set

<p>Hydrological models (HMs) are essential tools to explore terrestrial water dynamics and to anticipate future hydrological events. Since their inception, HMs have been developed in parallel by different institutions. There is now a plethora of HMs, yet a relative absence of cross-model developments (code is almost never portable between models) and of guidance on model selection (modellers typically stick to the model they are most familiar with). Furthermore, traditional HMs, developed over the last decades by successive code additions, are rarely adapted to modern hydrological challenges, principally because they lack modularity. These HMs typically rely on a single model structure (most processes are simulated by a single set of equations), which make it difficult to i) understand differences between models, ii) run a large ensemble of models, iii) capture the spatial variability of hydrological processes and iv) develop and improve hydrological models in a coordinated fashion across the community.</p><p>These limitations can be overcome by modular modelling frameworks (MMFs), which are master templates for model generation. MMFs offer several options for each important modelling decision. They also allow users to add functionalities when they are required, by loading libraries developed and maintained by the community. This presentation uses FUSE (Framework for Understanding Structural Error) as an example of MMF for hydrology. FUSE enables the generation of a myriad of conceptual HMs by recombining elements from four commonly-used models. This presentation will summarize the development of FUSE version 2 (FUSE2), which was created with users in mind and significantly increases the usability and range of applicability of the original FUSE. In FUSE2, NetCDF output files contain a detailed description of the modelling decisions (e.g., selected modules, numerical scheme, parameter values), which improves reproducibility. FUSE2 also makes code re-usable, as modules can be used across the community and are not limited to a single model structure. After decades of siloed model development, we argue that MMFs are essential to develop and improve hydrological models in a coordinated fashion across the community.</p>

Stepwise improvement of hydrological models using satellite-based evaporation and total water storage estimations

2021 ◽  
Author(s):  
Markus Hrachowitz ◽  
Petra Hulsman ◽  
Hubert Savenije
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Model Development ◽  
Water Storage ◽  
Spatiotemporal Variability ◽  
Model Structure ◽  
Hydrological Models ◽  
Total Water ◽  
Spatiotemporal Model ◽  
Multiple Variables

<p>Hydrological models are often calibrated with respect to flow observations at the basin outlet. As a result, flow predictions may seem reliable but this is not necessarily the case for the spatiotemporal variability of system-internal processes, especially in large river basins. Satellite observations contain valuable information not only for poorly gauged basins with limited ground observations and spatiotemporal model calibration, but also for stepwise model development. This study explored the value of satellite observations to improve our understanding of hydrological processes through stepwise model structure adaption and to calibrate models both temporally and spatially. More specifically, satellite-based evaporation and total water storage anomaly observations were used to diagnose model deficiencies and to subsequently improve the hydrological model structure and the selection of feasible parameter sets. A distributed, process based hydrological model was developed for the Luangwa river basin in Zambia and calibrated with respect to discharge as benchmark. This model was modified stepwise by testing five alternative hypotheses related to the process of upwelling groundwater in wetlands, which was assumed to be negligible in the benchmark model, and the spatial discretization of the groundwater reservoir. Each model hypothesis was calibrated with respect to 1) discharge and 2) multiple variables simultaneously including discharge and the spatiotemporal variability in the evaporation and total water storage anomalies. The benchmark model calibrated with respect to discharge reproduced this variable well, as also the basin-averaged evaporation and total water storage anomalies. However, the evaporation in wetland dominated areas and the spatial variability in the evaporation and total water storage anomalies were poorly modelled. The model improved the most when introducing upwelling groundwater flow from a distributed groundwater reservoir and calibrating it with respect to multiple variables simultaneously. This study showed satellite-based evaporation and total water storage anomaly observations provide valuable information for improved understanding of hydrological processes through stepwise model development and spatiotemporal model calibration.</p>

scholarly journals The importance of vegetation to understand terrestrial water storage variations

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.

scholarly journals Do land parameters matter in large-scale terrestrial water dynamics? – Toward new paradigms in modelling strategies

2013 ◽  
Vol 10 (11) ◽  
pp. 13191-13229 ◽  
Author(s):  
L. Gudmundsson ◽  
S. I. Seneviratne
Keyword(s):  
Water Resources ◽  
Large Scale ◽  
River Flow ◽  
Current Model ◽  
Model Development ◽  
Process Models ◽  
Water Dynamics ◽  
Small Scale ◽  
Atmospheric Forcing ◽  
Terrestrial Water

Abstract. Large-scale variations of terrestrial water storages and fluxes are key aspects in the Earth system, as they control ecosystem processes, feed back on weather and climate, and form the basis for water resources management. However, relevant observations are limited and process models used for estimation are highly uncertain. These models rely on approximations of terrestrial processes as well as on location-specific parameters (e.g.;soil types, topography) to translate atmospheric forcing (e.g.;precipitation, net radiation) into terrestrial water variables (e.g.;soil moisture, river flow). To date it is unclear which processes and parameters should be included to model terrestrial water systems on regional to global scales. Using a data driven approach we show, that skillful estimates of monthly water dynamics in Europe can be derived from information on atmospheric drivers alone and that the inclusion of land parameters does not improve the estimate. The results highlight that substantial parts of terrestrial water dynamics are controlled by atmospheric forcing, which dominates over land parameters. This is not reflected in current model developments, which are striving at incorporating an increasing number of small scale processes and related parameters. Our results thus point at major potential for theory and model development, with important implications for water resources modelling, seasonal forecasting and climate change projections.

scholarly journals History, Colonialism, and Archival Methods in Socio-Hydrological Scholarship: A Case Study of the Boerasirie Conservancy in British Guiana

World ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 205-215
Author(s):  
Joshua Mullenite
Keyword(s):  
Environmental History ◽  
Cross Section ◽  
Science And Technology Studies ◽  
Model Development ◽  
Human Geography ◽  
Hydrological Models ◽  
British Guiana ◽  
Current Role

In this article, I review a cross-section of research in socio-hydrology from across disciplines in order to better understand the current role of historical-archival analysis in the development of socio-hydrological scholarship. I argue that despite its widespread use in environmental history, science and technology studies, anthropology, and human geography, archival methods are currently underutilized in socio-hydrological scholarship more broadly, particularly in the development of socio-hydrological models. Drawing on archival research conducted in relation to the socio-hydrology of coastal Guyana, I demonstrate the ways in which such scholarship can be readily incorporated into model development.

scholarly journals Rainfall-induced release of microbes from manure: model development, parameter estimation, and uncertainty evaluation on small plots

2016 ◽  
Vol 14 (3) ◽  
pp. 443-459 ◽  
Author(s):  
Keewook Kim ◽  
Gene Whelan ◽  
Marirosa Molina ◽  
S. Thomas Purucker ◽  
Yakov Pachepsky ◽  
...  
Keyword(s):  
Model Development ◽  
Fecal Coliforms ◽  
Time Varying ◽  
Rainfall Runoff ◽  
Swine Slurry ◽  
Four Seasons ◽  
Release Curve ◽  
Parameter Values ◽  
Runoff Events ◽  
Parameter Distributions

A series of simulated rainfall-runoff experiments with applications of different manure types (cattle solid pats, poultry dry litter, swine slurry) was conducted across four seasons on a field containing 36 plots (0.75 × 2 m each), resulting in 144 rainfall-runoff events. Simulating time-varying release of Escherichia coli, enterococci, and fecal coliforms from manures applied at typical agronomic rates evaluated the efficacy of the Bradford–Schijven model modified by adding terms for release efficiency and transportation loss. Two complementary, parallel approaches were used to calibrate the model and estimate microbial release parameters. The first was a four-step sequential procedure using the inverse model PEST, which provides appropriate initial parameter values. The second utilized a PEST/bootstrap procedure to estimate average parameters across plots, manure age, and microbe, and to provide parameter distributions. The experiment determined that manure age, microbe, and season had no clear relationship to the release curve. Cattle solid pats released microbes at a different, slower rate than did poultry dry litter or swine slurry, which had very similar release patterns. These findings were consistent with other published results for both bench- and field-scale, suggesting the modified Bradford–Schijven model can be applied to microbial release from manure.

Plants, vital players in the terrestrial water cycle

2021 ◽  
Author(s):  
Marie-Claire ten Veldhuis ◽  
Tom van den Berg ◽  
Martine van der Ploeg ◽  
Elias Kaiser ◽  
Satadal Dutta ◽  
...  
Keyword(s):  
Water Transport ◽  
Water Cycle ◽  
Turgor Pressure ◽  
Water Dynamics ◽  
Plant Water ◽  
Flow Monitoring ◽  
Vital Functions ◽  
Main Challenge ◽  
Macro Scale ◽  
Terrestrial Water

<p>Plant transpiration accounts for about half of all terrestrial evaporation (Jasechko et al., 2013). Plants need water for many vital functions including nutrient uptake, growth, maintenance of cell turgor pressure and leaf cooling. Due to the regulation of water transport by stomata in the leaves, plants lose 97% of the water they take via their roots, to the atmosphere. They can be viewed as transpiration-powered pumps on the interface between the soil and atmosphere.</p><p>Measuring plant-water dynamics is essential to gain better insight into their role in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for transpiration measurement and highlight several promising innovations for monitoring plant-water relations. Some of the techniques we will cover include stomata imaging by microscopy, gas exchange for stomatal conductance and transpiration monitoring, thermometry for water stress detection, sap flow monitoring, hyperspectral imaging, ultrasound spectroscopy, accelerometry, scintillometry and satellite-remote sensing.</p><p>Outlook: To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques is required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly solve for spatial heterogeneity as well as temporal variability, dense network sampling is needed.</p><p>In PLANTENNA (https://www.4tu.nl/plantenna/en/) a team of electronics, precision and microsystems engineers together with plant and environmental scientists develop and implement innovative (3D-)sensor networks that measure plant and environmental parameters at high resolution and low cost. Our main challenge for in-situ sensor autonomy (“plug and forget”) is energy: we want the sensor nodes to be hyper-efficient and rely fully on (miniaturised) energy-harvesting.</p><p><strong>REFERENCES: </strong></p><p>Jasechko, S., Sharp, Z. D., Gibson, J. J., Birks, S. J., Yi, Y., & Fawcett, P. J. (2013). Terrestrial water fluxes dominated by transpiration. Nature, 496(7445), 347-350.<br>Plantenna: "Internet of Plants". (n.d.). https://www.4tu.nl/plantenna/en/</p><p> </p>

scholarly journals Benchmarking and Process Diagnostics of Land Models

2018 ◽  
Vol 19 (11) ◽  
pp. 1835-1852 ◽  
Author(s):  
Grey S. Nearing ◽  
Benjamin L. Ruddell ◽  
Martyn P. Clark ◽  
Bart Nijssen ◽  
Christa Peters-Lidard
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Process Diagnostics ◽  
Surface Energy Fluxes ◽  
Structural Error ◽  
Intercomparison Project ◽  
Parameter Values ◽  
Process Level

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

Temperature Forecasting System Using Fuzzy Mathematical Model

2018 ◽  
Vol 9 (3) ◽  
pp. 48-57
Author(s):  
Abdel Karim M. Baareh
Keyword(s):  
Fuzzy Logic ◽  
Human Life ◽  
Model Development ◽  
Logic Model ◽  
Model Structure ◽  
Weather Underground ◽  
Temperature Forecasting ◽  
Forecasting System ◽  
Fuzzy Logic Technique ◽  
Mumbai City

Temperature study and model development related to estimation is an essential and important task not only for a human life but also for animal life, agriculture, tourism, water reservation and evaporation, and many other fields. Regression is considered a dominant prediction model which is heavily used in forecasting in spite of the difficulties related to the number of available measurements, the order of the model and the nonlinearity of the data. In this article, the purpose is to use a nonlinear model structure to forecast the temperature at the airport of Mumbai city in India using the fuzzy logic technique. The datasets were collected for twelve months period starting from 1st of January 2009 to 31st of December at a weather underground in India. The datasets were divided into two parts, 288 days (80%) of the data for training and the remaining 72 days (20%) for testing. The results obtained and the error calculated using the fuzzy logic model were satisfactory.

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

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