Is the groundwater reservoir linear? Learning from data in hydrological modelling

Abstract. Although catchment behaviour during recession periods is better identifiable than in other periods, the representation of hydrograph recession is often weak in hydrological simulations. Among the various aspects that influence model performance during low flows, in this paper we concentrate on those more inherently related to the modelling, such as the development of a suitable model conceptualization, and the choice of an appropriate calibration strategy. In this context we develop a methodology where the calibration procedure is combined with an iterative process of model improvement, to obtain an optimal model configuration that performs well both during low flows and high flows. The methodology starts by calculating a synthetic master recession curve that represents the long-term recession of a given catchment. Subsequently, using a simple reservoir model, we determine the storage-discharge relation that simulates the slow hydrograph component. This relation is determined without making any a-priori assumption on its form and is inferred from discharge data available through an iterative process. Next, high flow related parameters are recalibrated separately, to avoid that the simulation of low discharges is neglected in favour of a higher performance in simulating peak discharges. This methodology is applied on several catchments in Luxembourg, and as a result we determined that in all catchments except one (where human interference is high) within the chosen model structure a linear reservoir describes best the observed groundwater behaviour. This result is used to trigger a discussion as to the general suitability of the use of a linear groundwater reservoir in hydrological modelling.

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Is the groundwater reservoir linear? Learning from data in hydrological modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-2-1717-2005 ◽

2005 ◽

Vol 2 (4) ◽

pp. 1717-1755 ◽

Cited By ~ 4

Author(s):

F. Fenicia ◽

H. H. G. Savenije ◽

P. Matgen ◽

L. Pfister

Keyword(s):

A Priori ◽

Model Performance ◽

Flow Simulation ◽

Calibration Procedure ◽

Low Flow ◽

Low Flows ◽

Learning From Data ◽

Recession Curve ◽

Master Recession Curve ◽

High Flows

Abstract. Although catchment behaviour during recession periods appears to be better identifiable than in other periods, the representation of hydrograph recession is often weak in hydrological simulations. Reason lies in the various sources of uncertainty that affect hydrological simulations, and in particular in the inherent uncertainty concerning model conceptualizations, when they are based on an a-priori representation of the natural system. When flawed conceptualizations combine with calibration strategies that favour an accurate representation of peak flows, model structural inadequacies manifest themselves in a biased representation of other aspects of the simulation, such as flow recession and low flows. In this paper we try to reach good model performance in low flow simulation and make use of a flexible model structure that can adapt to match the observed discharge behaviour during recession periods. Moreover, we adopt a step-wise calibration procedure where we try to avoid that the simulation of low flows is neglected in favour of other hydrograph characteristics. The model used is designed to reproduce specific hydrograph characteristics and is composed of four reservoirs: an interception reservoir, an unsaturated soil reservoir, a fast reacting reservoir, and a slow reacting reservoir. The slow reacting reservoir conceptualises the processes that lead to the generation of the slow hydrograph component, and is characterized by a storage-discharge relation that is not determined a-priori, but is derived from the observations following a ``top-down'' approach. The procedure used to determine this relation starts by calculating a synthetic master recession curve that represents the long-term recession of the catchment. Next, a calibration procedure follows to force the outflow from the slow reacting reservoir to match the master recession curve. Low flows and high flows related parameters are calibrated in separate stages because we consider them to be related to different processes, which can be identified separately. This way we avoid that the simulation of low discharges is neglected in favour of a higher performance in simulating peak discharges. We have applied this analysis to several catchments in Luxembourg, and in each case we have determined which form (linear or non linear) of the storage-discharge relationship best describes the slow reacting reservoir. We conclude that in all catchments except one (where human interference is high) a linear relation applies.

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Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

Hydrology and Earth System Sciences ◽

10.5194/hess-24-3331-2020 ◽

2020 ◽

Vol 24 (6) ◽

pp. 3331-3359 ◽

Cited By ~ 1

Author(s):

Petra Hulsman ◽

Hessel C. Winsemius ◽

Claire I. Michailovsky ◽

Hubert H. G. Savenije ◽

Markus Hrachowitz

Keyword(s):

Cross Section ◽

Hydrological Model ◽

Model Performance ◽

Hydrological Modelling ◽

Google Earth ◽

Water Levels ◽

Discharge Data ◽

Model Parameter ◽

Virtual Station ◽

River Cross Section

Abstract. Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall–runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash–Sutcliffe efficiency of ENS,Q=0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95=0.61–0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q=-1.4 (ENS,Q,5/95=-2.3–0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95=-2.9–0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95=-2.6–0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler–Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q=0.60 (ENS,Q,5/95=-0.31–0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins.

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An application of the HBV model to the Tamor Basin in Eastern Nepal

Journal of Hydrology and Meteorology ◽

10.3126/jhm.v7i1.5616 ◽

1970 ◽

Vol 7 (1) ◽

pp. 49-58 ◽

Cited By ~ 5

Author(s):

S Normand ◽

M Konz ◽

J Merz

Keyword(s):

Monsoon Season ◽

Model Performance ◽

Discharge Data ◽

Ice Melt ◽

Low Flows ◽

High Flows ◽

Hbv Model ◽

Gauged Basins ◽

Two Parameter ◽

Precipitation Events

The semi-distributed, conceptual hydrological model HBV was applied to Tamor Nadi in order to estimate runoff at Tapethok, Taplejung, in Eastern Nepal. As there was no discharge data available for this particular location, the model was first calibrated and validated for the bigger, gauged basins at Mulghat and Majithar. However due to its structure HBV shows difficulties in modelling low and high flows correctly at the same time. Therefore two parameter sets were produced: one with focus on the model performance during low flows and the second one, on high flows. Those parameters were then applied to the basin at Tapethok. Generally HBV was able to correctly simulate low flows except for some sharp peaks due to isolated precipitation events. However, pre-monsoon discharge was overestimated while the runoff of the monsoon season were most of the time underestimated. The main reasons for this situation are: (1) HBV generates runoff from one single groundwater reservoir for the entire catchment, leading to sharp peaks with a rapid recession and therefore exaggerated reactions on precipitation during dry season; (2) during pre-monsoon snow and ice melt gain in importance and add to the mentioned problem; (3) due to the simplified representation of storages in the model structure the catchment area drains too quickly. Keywords: Tamor BasinDOI: http://dx.doi.org/10.3126/jhm.v7i1.5616 JHM 2010; 7(1): 49-58

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Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

10.26434/chemrxiv.9873599.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

Michael Withnall ◽

Edvard Lindelöf ◽

Ola Engkvist ◽

Hongming Chen

Keyword(s):

Message Passing ◽

State Of The Art ◽

A Priori ◽

Model Performance ◽

Learning Approaches ◽

Physical Chemical ◽

Chemical Descriptor ◽

Derived Properties ◽

Memory Schemes ◽

Hyperparameter Selection

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

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Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales

Water ◽

10.3390/w12051279 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1279

Author(s):

Tyler Madsen ◽

Kristie Franz ◽

Terri Hogue

Keyword(s):

Local Population ◽

Model Performance ◽

Forecast Model ◽

Hydrologic Model ◽

Laboratory Research ◽

Discharge Data ◽

Distributed Hydrologic Model ◽

Verification Period ◽

Silver Spring

Demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. In the current study, the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (NOAA/NWS, Silver Spring, MD, USA) was used to explore the accuracy of a distributed hydrologic model to simulate discharge at watershed scales ranging from 20 to 2500 km2. The model was calibrated and validated using observed discharge data at the basin outlets, and discharge at uncalibrated subbasin locations was evaluated. Two precipitation products with nominal spatial resolutions of 12.5 km and 4 km were tested to characterize the role of input resolution on the discharge simulations. In general, model performance decreased as basin size decreased. When sub-basin area was less than 250 km2 or 20–40% of the total watershed area, model performance dropped below the defined acceptable levels. Simulations forced with the lower resolution precipitation product had better model evaluation statistics; for example, the Nash–Sutcliffe efficiency (NSE) scores ranged from 0.50 to 0.67 for the verification period for basin outlets, compared to scores that ranged from 0.33 to 0.52 for the higher spatial resolution forcing.

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The Influence of Flow Regime on Ecological Quality, Bird Diversity, and Shellfish Fisheries in a Lowland Mediterranean River and Its Coastal Area

Water ◽

10.3390/w11050918 ◽

2019 ◽

Vol 11 (5) ◽

pp. 918 ◽

Cited By ~ 6

Author(s):

Oscar Belmar ◽

Carles Ibáñez ◽

Ana Forner ◽

Nuno Caiola

Keyword(s):

Aquatic Vegetation ◽

Environmental Flows ◽

Ecological Status ◽

Model Performance ◽

Habitat Characteristics ◽

Bird Diversity ◽

Ecological Quality ◽

Anthropogenic Pressures ◽

Discharge Data ◽

Mantis Shrimp

Designing environmental flows in lowland river sections and estuaries is a challenge for researchers and managers, given their complexity and their importance, both for nature conservation and economy. The Ebro River and its delta belong to a Mediterranean area with marked anthropogenic pressures. This study presents an assessment of the relationships between mean flows (discharges) computed at different time scales and (i) ecological quality based on fish populations in the lower Ebro, (ii) bird populations, and (iii) two shellfish fishery species of socioeconomic importance (prawn, or Penaeus kerathurus, and mantis shrimp, or Squilla mantis). Daily discharge data from 2000 to 2015 were used for analyses. Mean annual discharge was able to explain the variation in fish-based ecological quality, and model performance increased when aquatic vegetation was incorporated. Our results indicate that a good ecological status cannot be reached only through changes on discharge, and that habitat characteristics, such as the coverage of macrophytes, must be taken into account. In addition, among the different bird groups identified in our study area, predators were related to river discharge. This was likely due to its influence on available resources. Finally, prawn and mantis shrimp productivity were influenced up to a certain degree by discharge and physicochemical variables, as inputs from rivers constitute major sources of nutrients in oligotrophic environments such as the Mediterranean Sea. Such outcomes allowed revisiting the environmental flow regimes designed for the study area, which provides information for water management in this or in other similar Mediterranean zones.

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Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

10.5194/egusphere-egu21-8782 ◽

2021 ◽

Author(s):

Moctar Dembélé ◽

Bettina Schaefli ◽

Grégoire Mariéthoz

Keyword(s):

West Africa ◽

Large Scale ◽

Hydrological Model ◽

Meteorological Data ◽

Model Performance ◽

Hydrologic Model ◽

Hydrological Modelling ◽

Climate Change Scenarios ◽

Depth Analysis ◽

Input Dataset

The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Demb&#233;l&#233; et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.Demb&#233;l&#233;, M., Schaefli, B., van de Giesen, N., & Mari&#233;thoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020

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Influence of Hydrological Model Selection on Simulation of Moderate and Extreme Flow Events: A Case Study of the Blue Nile Basin

Advances in Meteorology ◽

10.1155/2016/7148326 ◽

2016 ◽

Vol 2016 ◽

pp. 1-28 ◽

Cited By ~ 13

Author(s):

Charles Onyutha

Keyword(s):

Model Selection ◽

Extreme Events ◽

Goodness Of Fit ◽

Model Performance ◽

Flow Conditions ◽

Nile Basin ◽

Blue Nile ◽

Low Flows ◽

Blue Nile Basin ◽

Extreme Flow

Five hydrological models were applied based on data from the Blue Nile Basin. Optimal parameters of each model were obtained by automatic calibration. Model performance was tested under both moderate and extreme flow conditions. Extreme events for the model performance evaluation were extracted based on seven criteria. Apart from graphical techniques, there were nine statistical “goodness-of-fit” metrics used to judge the model performance. It was found that whereas the influence of model selection may be minimal in the simulation of normal flow events, it can lead to large under- and/or overestimations of extreme events. Besides, the selection of the best model for extreme events may be influenced by the choice of the statistical “goodness-of-fit” measures as well as the criteria for extraction of high and low flows. It was noted that the use of overall water-balance-based objective function not only is suitable for moderate flow conditions but also influences the models to perform better for high flows than low flows. Thus, the choice of a particular model is recommended to be made on a case by case basis with respect to the objectives of the modeling as well as the results from evaluation of the intermodel differences.

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Hydrological modelling and anthropogenic water use

10.5194/egusphere-egu21-13186 ◽

2021 ◽

Author(s):

Ponnambalam Rameshwaran ◽

Ali Rudd ◽

Vicky Bell ◽

Matt Brown ◽

Helen Davies ◽

...

Keyword(s):

Climate Change ◽

Water Use ◽

Hydrological Model ◽

Model Performance ◽

River Flows ◽

Discharge Data ◽

New Approach ◽

Water Demands ◽

The Impact ◽

Grid Based

Despite Britain&#8217;s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with &#8216;Hands off Flow&#8217; conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.

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Global BROOK90: validation, uncertainties, current progress and future outline.

10.5194/egusphere-egu21-742 ◽

2021 ◽

Author(s):

Ivan Vorobevskii ◽

Rico Kronenberg

Keyword(s):

Water Balance ◽

Model Performance ◽

Low Flow ◽

Easy Access ◽

Climate Projections ◽

Seasonal Forecasts ◽

Water Balance Components ◽

Discharge Data ◽

Small Catchment ◽

Wide Range

&#8216;Just drop a catchment and receive reasonable model output&#8217; &#8211; still stays as motto and main idea of the &#8216;Global BROOK90&#8217; project. The open-source R-package is build-up on global land cover, soil, topographical, meteorological datasets and the lumped hydrological model as a core to simulate water balance components on HRU scale all over the world in an automatic mode. First introduced in EGU2020 and followed by GitHub code release including an publication of methodology with few examples we want to continue with the insights on the current state and highlight the future steps of the project.A global validation of discharge and evapotranspiration components of the model showed promising results. We used 190 small (median size of 64 km2) catchments and FLUXNET data which represent a wide range of relief, vegetation and soil types within various climate zones. The model performance was evaluated with NSE, KGE, KGESS and MAE. In more than 75 % of the cases the framework performed better than the mean of the observed discharge. On a temporal scale the performance is significantly better on a monthly vs daily scale. Cluster analysis revealed that some of the site characteristics have a significant influence on the performance. Additionally, it was found that Global BROOK90 outperforms GloFAS ERA5 discharge reanalysis (for the category with smallest catchments).A cross-combination of three different BROOK90 setups and three forcing datasets was set up to reveal uncertainties of the Global BROOK90 package using a small catchment in Germany as a case study. Going from local to regional and finally global scale we compared mixtures of model parameterization schemes (original calibrated BROOK90, EXTRUSO and Global BROOK90) and meteorological datasets (local gauges, RaKlida and ERA5). Besides high model performances for a local dataset plus a calibrated model and weaker results for ERA5 and the Global BROOK90, it was found that the ERA5 dataset is still able to provide good results when combined with a regional and local parameterization. On the other side, the combination of a global parameterization with local and regional forcings gives still adequate, but much worse results. Furthermore, a hydrograph separation revealed that the Global BROOK90 parameterization as well as ERA5 discharge data perform weaker especially within low flow periods.Currently, some new features are added to the original package. First, with the recent release of the ERA5 extension, historical simulations with the package now are expanded to 1950-2021 period. Additionally, an alternative climate reanalysis dataset is included in the framework (Merra-2, 0.5x0.625-degree spatial resolution, starting from 1980). A preliminary validation shows insignificant differences between both meteorological datasets with respect to the discharge based model performance.Further upgrades of the framework will include the following core milestones: recognition of forecast and climate projections and parameter optimization features. In the nearest future we plan to utilize full power of the Climate Data Store for easy access to seasonal forecasts (i.e. ECMWF, DWD, NCEP) as well as climate projections (CMIP5) to extend the package&#8217;s scope to predict near and far future water balance components.

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