Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain

2020 ◽  
Author(s):  
Simon Stisen ◽  
Raphael Schneider ◽  
Anker Lajer Højberg
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Large Scale ◽  
Agricultural Land ◽  
Water Cycle ◽  
Regional Scale ◽  
Drainage System ◽  
Water Model ◽  
Hydrological Models ◽  
Drain Flow

<p>About half of the Danish agricultural land is artificially drained to make land arable and increase crop yield. Those artificial drains, mostly in the form on tile drains, have a significant effect on the groundwater flow patterns and the whole water cycle. Consequently, the drainage system must also be represented in hydrological models that are used to understand and simulate, for example, recharge patterns, groundwater flow paths, or the transport and retention of nutrients. However, representation of drain in regional- and large-scale hydrological models is challenging due to i) issues with scale, ii) a lack of data on the distribution of the drain network, and iii) a lack of direct observations of drain flow. This calls for more indirect methods to inform such models.</p><p>We assume that drain flow leaves a signal in certain hydrograph signatures, as it impacts the generation of streamflow. Based on a dataset of observed discharge covering all of Denmark, and simulation results from regional-scale hydrological models, we use machine learning regressors to shed light on possible correlations between hydrograph signatures and artificial drainage. Building up on this step, we run a series of calibration exercises on a hydrological model of the agriculturally dominated Norsminde catchment, Denmark (~100 km<sup>2</sup>). The model is set up in the DHI MIKE SHE software, as distributed coupled groundwater-surface water models with a grid size of 100 m. The different calibration exercises differed in the objective functions used: either we only use conventional stream flow metrics (KGE), or also include hydrograph signatures that showed sensitive towards drain flow in our regression analysis. We then evaluate the results from the different calibration exercises, in terms of how well the model reproduces directly observed drain flow, and spatial drainage patterns.</p><p>Despite including hydrologic signatures in the calibration process, the representation of drain flow in large-scale models remains challenging. Eventually, the insight gained from this and similar studies will be incorporated in the National Water Resources Model for Denmark, to help improving national targeted regulation of nitrate application through fertilizers.</p>

scholarly journals Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

Water ◽  
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.

scholarly journals Common problematic aspects of coupling hydrological models with groundwater flow models on the river catchment scale

2006 ◽  
Vol 9 ◽  
pp. 63-71 ◽  
Author(s):  
R. Barthel
Keyword(s):  
Groundwater Flow ◽  
Groundwater Recharge ◽  
Large Scale ◽  
Regional Scale ◽  
Integrated Water Resources Management ◽  
Model Coupling ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Common Definition ◽  
Definition Of

Abstract. Model coupling requires a thorough conceptualisation of the coupling strategy, including an exact definition of the individual model domains, the "transboundary" processes and the exchange parameters. It is shown here that in the case of coupling groundwater flow and hydrological models – in particular on the regional scale – it is very important to find a common definition and scale-appropriate process description of groundwater recharge and baseflow (or "groundwater runoff/discharge") in order to achieve a meaningful representation of the processes that link the unsaturated and saturated zones and the river network. As such, integration by means of coupling established disciplinary models is problematic given that in such models, processes are defined from a purpose-oriented, disciplinary perspective and are therefore not necessarily consistent with definitions of the same process in the model concepts of other disciplines. This article contains a general introduction to the requirements and challenges of model coupling in Integrated Water Resources Management including a definition of the most relevant technical terms, a short description of the commonly used approach of model coupling and finally a detailed consideration of the role of groundwater recharge and baseflow in coupling groundwater models with hydrological models. The conclusions summarize the most relevant problems rather than giving practical solutions. This paper aims to point out that working on a large scale in an integrated context requires rethinking traditional disciplinary workflows and encouraging communication between the different disciplines involved. It is worth noting that the aspects discussed here are mainly viewed from a groundwater perspective, which reflects the author's background.

Vulnerability of coastal areas to increased aquifer saturation due to climate change

2021 ◽  
Author(s):  
Alexandre Gauvain ◽  
Ronan Abhervé ◽  
Jean-Raynald de Dreuzy ◽  
Luc Aquilina ◽  
Frédéric Gresselin
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Large Scale ◽  
Regional Scale ◽  
Drainage System ◽  
Hydrological Regime ◽  
Coastal Areas ◽  
Western Coast ◽  
Study Sites ◽  
The Impact

<p>Like in other relatively flat coastal areas, flooding by aquifer overflow is a recurring problem on the western coast of Normandy (France). Threats are expected to be enhanced by the rise of the sea level and to have critical consequences on the future development and management of the territory. The delineation of the increased saturation areas is a required step to assess the impact of climate change locally. Preliminary models showed that vulnerability does not result only from the sea side but also from the continental side through the modifications of the hydrological regime.</p><p>We investigate the processes controlling these coastal flooding phenomena by using hydrogeological models calibrated at large scale with an innovative method reproducing the hydrographic network. Reference study sites selected for their proven sensitivity to flooding have been used to validate the methodology and determine the influence of the different geomorphological configurations frequently encountered along the coastal line.</p><p>Hydrogeological models show that the rise of the sea level induces an irregular increase in coastal aquifer saturations extending up to several kilometers inland. Back-littoral channels traditionally used as a large-scale drainage system against high tides limits the propagation of aquifer saturation upstream, provided that channels are not dominantly under maritime influence. High seepage fed by increased recharge occurring in climatic extremes may extend the vulnerable areas and further limit the effectiveness of the drainage system. Local configurations are investigated to categorize the influence of the local geological and geomorphological structures and upscale it at the regional scale.</p>

scholarly journals Characterization of Surface Evidence of Groundwater Flow Systems in Continental Mexico

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2459
Author(s):  
Alessia Kachadourian-Marras ◽  
Margarita M. Alconada-Magliano ◽  
José Joel Carrillo-Rivera ◽  
Edgar Mendoza ◽  
Felipe Herrerías-Azcue ◽  
...  
Keyword(s):  
Groundwater Flow ◽  
Water Cycle ◽  
Regional Scale ◽  
Ground Surface ◽  
The Public ◽  
Groundwater Flow Systems ◽  
Human Needs ◽  
Flow Systems ◽  
Vegetation And Climate

The dynamics of the underground part of the water cycle greatly influence the features and characteristics of the Earth’s surface. Using Tóth’s theory of groundwater flow systems, surface indicators in Mexico were analyzed to understand the systemic connection between groundwater and the geological framework, relief, soil, water bodies, vegetation, and climate. Recharge and discharge zones of regional groundwater flow systems were identified from evidence on the ground surface. A systematic hydrogeological analysis was made of regional surface indicators, published in official, freely accessible cartographic information at scales of 1:250,000 and 1:1,000,000. From this analysis, six maps of Mexico were generated, titled “Permanent water on the surface”, “Groundwater depth”, “Hydrogeological association of soils”, “Hydrogeological association of vegetation and land use”, “Hydrogeological association of topoforms”, and “Superficial evidence of the presence of groundwater flow systems”. Mexico’s hydrogeological features were produced. The results show that 30% of Mexico is considered to be discharge zones of groundwater flow systems (regional, intermediate, and recharge). Natural recharge processes occur naturally in 57% of the country. This work is the first holistic analysis of groundwater in Mexico carried out at a national–regional scale using only the official information available to the public. These results can be used as the basis for more detailed studies on groundwater and its interaction with the environment, as well as for the development of integrative planning tools to ensure the sustainability of ecosystems and satisfy human needs.

The performance of LSTM models from basin to continental scales

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Günter Klambauer ◽  
Grey Nearing ◽  
Sepp Hochreiter
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Regional Scale ◽  
Data Driven ◽  
Hydrological Models ◽  
Short Term ◽  
Term Memory ◽  
Basin Scale ◽  
Long Short Term Memory

<p>Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used. </p><p>One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.</p><p>The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.</p><p>Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).</p><p>LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.  </p><p>However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale. </p><p>In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.</p><p>References:</p><p>Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. </p><p>Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a. </p><p>Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.</p>

scholarly journals Storage cascade vs. MODFLOW for the modelling of groundwater flow in the context of the calibration of a hydrological model in the Ammer catchment

2006 ◽  
Vol 9 ◽  
pp. 101-108 ◽  
Author(s):  
V. Rojanschi ◽  
J. Wolf ◽  
R. Barthel
Keyword(s):  
Groundwater Flow ◽  
Flow Model ◽  
Water Cycle ◽  
Hydrological Models ◽  
Advantages And Disadvantages ◽  
Spatially Distributed ◽  
Distributed Process ◽  
Glue Method ◽  
Model Structures ◽  
Data Requirements

Abstract. Hydrological models are the decisive tools to evaluate the effect of global change upon the water cycle. But the applied hydrological models have to be a trade-off between their degree of complexity and manageable structures and data requirements. This paper compares the advantages and disadvantages of integrating a spatially-distributed process-based groundwater flow model in the context of the calibration of a catchment runoff concentration model. The multi-objective optimisation and the GLUE method are used to analyse the performance and the parameter identifiability of both model structures.

scholarly journals RECOG RL01: Correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

2020 ◽  
Author(s):  
Simon Deggim ◽  
Annette Eicker ◽  
Lennart Schawohl ◽  
Helena Gerdener ◽  
Kerstin Schulze ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Regional Scale ◽  
Water Storage ◽  
Water Volume ◽  
Optical Remote Sensing ◽  
Hydrological Models ◽  
Total Water ◽  
Satellite Mission ◽  
The Impact

Abstract. Observations of changes in terrestrial water storage obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighboring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes & reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a re-location of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction RECOG-EQ includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above 9 Mw. We can show that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, even in neighboring river basins, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available as open access via the Pangea database (RECOG-LR: Deggim et al. (2020a) https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al. (2020b, under revision), https://doi.pangaea.de/10.1594/PANGAEA.921923).

scholarly journals THE AMELIORATIVE CONDITION OF THE IRRIGATED LIGHT SEROZEM OF THE TURKESTAN REGION

2020 ◽  
Vol 17 (36) ◽  
pp. 920-933
Author(s):  
Samat I TANIRBERGENOV ◽  
Beibut U SULEIMENOV ◽  
Dragan CAKMAK ◽  
Elmira SALJNIKOV ◽  
Zhassulan SMANOV
Keyword(s):  
Large Scale ◽  
Agricultural Land ◽  
Saline Soil ◽  
Drainage System ◽  
Soil Salinization ◽  
General Level ◽  
Saline Soils ◽  
Vertical Drainage ◽  
Improper Use ◽  
Autumn And Winter

The relevance of the study is conditioned by the fact that the large-scale irrigation of cotton fields in arid and desert areas of the Turkestan region inevitably leads to the processes of soil salinization. Salinity is a global problem for humanity. Soil salinization is associated with drainage problems, improper use of water resources, growing demand for agricultural products, which leads to increased pressure on agricultural land. In this regard, this paper is directed at investigating the soil salinity of the irrigated light serozem in a cotton farm of Southern Kazakhstan (now Turkestan region) under the vertical drainage, which would provide the necessary background for the reconstruction of the collection-drainage system of the whole region, thus contributing to the increasing the net yield and the quality of the row cotton, as well as preventing soil deterioration. The leading method for studying the issues of the article was the dispersion method, according to which the salinity of soils was determined by seasons. The main objectives were studying the dynamics of salts changes seasonally and timely under the vertical drainage and studying the spatial distribution of salts in the cotton-based farm. The results showed that in 2014 there was recorded a positive dynamic of changes compared to 2012. In spring 2014, the area under medium saline soil in the 0-20 cm layer decreased from 79.5 to 57.7 %; the weakly saline soil area increased from 20.5 to 34.6 %. In the autumn and winter periods, the area of strongly saline soils decreased from 25.6 to 14.1 %. The area of non-saline soils was recorded at 7.7 %. The results showed that changes in the amount of the ions, both vertically and seasonally, occur with the transport of salts along with soil profile under the influence of temperature gradients and the level of groundwater, i.e., in spring from up to down, and in autumn and winter, contrary from down to up. The theoretical and practical value of the study lies in the fact that the material for improving, preventing the salinization of soils will lead to an increase in the general level of ecological safety of the region and country in general.

scholarly journals RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes

2021 ◽  
Vol 13 (5) ◽  
pp. 2227-2244
Author(s):  
Simon Deggim ◽  
Annette Eicker ◽  
Lennart Schawohl ◽  
Helena Gerdener ◽  
Kerstin Schulze ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Regional Scale ◽  
Water Storage ◽  
Water Volume ◽  
Optical Remote Sensing ◽  
Hydrological Models ◽  
Total Water ◽  
Lakes And Reservoirs ◽  
The Impact

Abstract. Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).

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