scholarly journals Challenges in developing a global gradient-based groundwater model (G<sup>3</sup>M v1.0) for the integration into a global hydrological model

2019 ◽  
Vol 12 (6) ◽  
pp. 2401-2418 ◽  
Author(s):  
Robert Reinecke ◽  
Laura Foglia ◽  
Steffen Mehl ◽  
Tim Trautmann ◽  
Denise Cáceres ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Groundwater Modeling ◽  
Global Scale ◽  
Sensitivity Analyses ◽  
Scale Model ◽  
Hydrological Models ◽  
Model Framework ◽  
Gradient Based

Abstract. In global hydrological models, groundwater (GW) is typically represented by a bucket-like linear groundwater reservoir. Reservoir models, however, (1) can only simulate GW discharge to surface water (SW) bodies but not recharge from SW to GW, (2) provide no information on the location of the GW table, and (3) assume that there is no GW flow among grid cells. This may lead, for example, to an underestimation of groundwater resources in semiarid areas where GW is often replenished by SW or to an underestimation of evapotranspiration where the GW table is close to the land surface. To overcome these limitations, it is necessary to replace the reservoir model in global hydrological models with a hydraulic head gradient-based GW flow model. We present G3M, a new global gradient-based GW model with a spatial resolution of 5′ (arcminutes), which is to be integrated into the 0.5∘ WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables in-memory coupling to WGHM while keeping overall runtime relatively low, which allows sensitivity analyses, calibration, and data assimilation. This paper presents the G3M concept and model design decisions that are specific to the large grid size required for a global-scale model. Model results under steady-state naturalized conditions, i.e., neglecting GW abstractions, are shown. Simulated hydraulic heads show better agreement to observations around the world compared to the model output of de Graaf et al. (2015). Locations of simulated SW recharge to GW are found, as is expected, in dry and mountainous regions but areal extent of SW recharge may be underestimated. Globally, GW discharge to rivers is by far the dominant flow component such that lateral GW flows only become a large fraction of total diffuse and focused recharge in the case of losing rivers, some mountainous areas, and some areas with very low GW recharge. A strong sensitivity of simulated hydraulic heads to the spatial resolution of the model and the related choice of the water table elevation of surface water bodies was found. We suggest to investigate how global-scale groundwater modeling at 5′ spatial resolution can benefit from more highly resolved land surface elevation data.

Analyzing the propagation of drought through water storages using global scale GRACE-based data assimilation

2021 ◽  
Author(s):  
Helena Gerdener ◽  
Kerstin Schulze ◽  
Olga Engels ◽  
Jürgen Kusche ◽  
Hannes Müller Schmied ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Hydrological Model ◽  
Crop Yields ◽  
Global Scale ◽  
Hydrological Drought ◽  
Time Lags ◽  
Satellite Mission

&lt;p&gt;The frequency and severity of drought increase in many regions of the world, which emphasizes the need for sufficient research to better monitor and trigger management plans. An important role hereby plays hydrological drought, because it affects water supply and crop yields that are necessary to ensure food security. Typically, hydrological drought detection is based on in-situ observations of fluxes or storages at the surface. However, this neglects the fact that drought might occur in multiple storages with different timing and severity.&amp;#160; The use of subsurface storage, e.g. groundwater, is rare because the available in-situ well level monitoring is irregularly distributed in space and time and access might be restricted, for example due to national security reasons or problems in converting them to storage estimates.&lt;/p&gt;&lt;p&gt;The satellite mission Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO offer a great possibility to observe the total water storage, i.e. the sum of surface and subsurface storages, on a global scale from space. However, GRACE is restricted to monthly data on a spatial resolution of about 300 km and the vertical sum of the storages. Hydrological models present another possibility to derive global storage information with a finer spatial (~50km), temporal and vertical resolution than GRACE but they do not perfectly represent the reality because they are underlying assumptions and are affected by uncertainty of forcing data. Therefore, to enable downscaling of GRACE while improving the models realism, the GRACE measurements are assimilated into a hydrological model.&lt;/p&gt;&lt;p&gt;In previous works we used a framework that assimilates GRACE into the WaterGAP Global Hydrological Model (WGHM) regionally or basin-wise. In this work we present a new framework that globally assimilates GRACE on a 4 degree grid with full uncertainty information from 2003 to 2018. The framework enables to assimilate about 95% of the global WGHM land surface except Greenland. With regard to vertical and spatial resolution the performance of model, observation and assimilation is compared. Global GRACE based drought indicators are applied and its development in the different compartments of surface water, soil and groundwater is analyzed to identify new insights into the propagation of drought. We expect that by including GRACE we derive new information especially for groundwater droughts, which might reveal time lags and a different severity as compared to surface water droughts for some regions.&lt;/p&gt;

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

scholarly journals Beyond the bucket – Developing a global gradient-based groundwater model (G<sup>3</sup>M v1.0) for a global hydrological model from scratch

2018 ◽  
Author(s):  
Robert Reinecke ◽  
Laura Foglia ◽  
Steffen Mehl ◽  
Tim Trautmann ◽  
Denise Cáceres ◽  
...  
Keyword(s):  
Steady State ◽  
Capillary Rise ◽  
Hydrological Models ◽  
Grid Cells ◽  
Reservoir Model ◽  
Model Framework ◽  
Water Flows ◽  
Macro Scale ◽  
Gradient Based ◽  
The Impact

Abstract. To quantify water flows between groundwater (GW) and surface water (SW) as well as the impact of capillary rise on evapotranspiration by global hydrological models (GHMs), it is necessary to replace the bucket-like linear GW reservoir model typical for hydrological models with a fully integrated gradient-based GW flow model. Linear reservoir models can only simulate GW discharge to SW bodies, provide no information on the location of the GW table and assume that there is no GW flow among grid cells. A gradient-based GW model simulates not only GW storage but also hydraulic head, which together with information on SW table elevation enables the quantification of water flows from GW to SW and vice versa. In addition, hydraulic heads are the basis for calculating lateral GW flow among grid cells and capillary rise. G3M is a new global gradient-based GW model with a spatial resolution of 5' that will replace the current linear GW reservoir in the 0.5° WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables in-memory coupling to WGHM while keeping overall runtime relatively low, allowing sensitivity analyses and data assimilation. This paper presents the G3M concept and specific model design decisions together with results under steady-state naturalized conditions, i.e. neglecting GW abstractions. Cell-specific conductances of river beds, which govern GW-SW interaction, were determined based on the 30'' steady-state water table computed by Fan et al. (2013). Together with an appropriate choice for the effective elevation of the SW table within each grid cell, this enables a reasonable simulation of drainage from GW to SW such that, in contrast to the GW model of de Graaf et al. (2015, 2017), no additional drainage based on externally provided values for GW storage above the floodplain is required in G3M. Comparison of simulated hydraulic heads to observations around the world shows better agreement than de Graaf et al. (2015). In addition, G3M output is compared to the output of two established macro-scale models for the Central Valley, California, and the continental United States, respectively. As expected, depth to GW table is highest in mountainous and lowest in flat regions. A first analysis of losing and gaining rivers and lakes/wetlands indicates that GW discharge to rivers is by far the dominant flow, draining diffuse GW recharge, such that lateral flows only become a large fraction of total diffuse and focused recharge in case of losing rivers and some areas with very low GW recharge. G3M does not represent losing rivers in some dry regions. This study presents the first steps towards replacing the linear GW reservoir model in a GHM while improving on recent efforts, demonstrating the feasibility of the approach and the robustness of the newly developed framework.

scholarly journals Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

2019 ◽  
Vol 12 (7) ◽  
pp. 3017-3043 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global–regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

scholarly journals Improving climate model accuracy by exploring parameter space with an O(10<sup>5</sup>) member ensemble and emulator

2018 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Climate Model Simulations ◽  
Perturbed Physics

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental mid-latitudes. This work, using the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multi-phased parameter refinement approach, particularly over northwestern United States(NWUS). Each phase consists of 1) creating a perturbed physics ensemble with the coupled global – regional atmospheric model, 2) building statistical emulators that estimate climate metrics as functions of parameter values, 3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, and land surface parameters that affect plant photosynthesis, transpiration and evaporation, but also suggest that this iterative approach to perturbed physics has an important role to play in the evolution of physical parameterizations.

scholarly journals A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4576
Author(s):  
Yueming Duan ◽  
Wenyi Zhang ◽  
Peng Huang ◽  
Guojin He ◽  
Hongxiang Guo
Keyword(s):  
Neural Network ◽  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
The Other ◽  
Water Extraction ◽  
Mapping Accuracy ◽  
Multi Scale ◽  
Land Surface Water

Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

scholarly journals Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) Version 1.0

2018 ◽  
Author(s):  
Gautam Bisht ◽  
William J. Riley ◽  
Glenn E. Hammond ◽  
David M. Lorenzetti
Keyword(s):  
Land Surface ◽  
Flow Model ◽  
Global Scale ◽  
Scale Model ◽  
Benchmark Problems ◽  
Saturated Flow ◽  
Hydrologic Processes ◽  
Climate Change Effects ◽  
Computational Performance ◽  
Surface Models

Abstract. Improving global-scale model representations of coupled surface and groundwater hydrology is important for accurately simulating terrestrial processes and predicting climate change effects on water resources. Most existing land surface models, including the default E3SM Land Model (ELMv0), which we modify here, routinely employ different formulations for water transport in the vadose and pheratic zones. In this work, we developed the Variably Saturated Flow Model (VSFM) in ELMv1 to unify the treatment of soil hydrologic processes in the unsaturated and saturated zones. VSFM was tested on three benchmark problems and results were evaluated against observations and an existing benchmark model (PFLOTRAN). The ELMv1-VSFM's subsurface drainage parameter, fd, was calibrated to match an observationally-constrained and spatially-explicit global water table depth (WTD) product. An optimal fd was obtained for 79 % of global 1.90 × 2.50 gridcells, while the remaining 21 % of global gridcells had predicted WTD deeper than the observationally-constrained estimate. Comparison with predictions using the default fd value demonstrated that calibration significantly improved prediction, primarily by allowing much deeper WTDs. Model evaluation using the International Land Model Benchmarking package (ILAMB) showed that improvements in WTD predictions did not degrade model skill for any other metrics. We evaluated the computational performance of the VSFM model and found that the model is about 30 % more expensive than the default ELMv0 with an optimal processor layout.

scholarly journals GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models

2021 ◽  
Vol 14 (12) ◽  
pp. 7545-7571
Author(s):  
Tom Gleeson ◽  
Thorsten Wagener ◽  
Petra Döll ◽  
Samuel C. Zipper ◽  
Charles West ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Large Scale ◽  
Groundwater Resources ◽  
Regional Scale ◽  
Global Scale ◽  
Expert Elicitation ◽  
Data Availability ◽  
Scale Model ◽  
Scale Models

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.

scholarly journals On the use of high resolution satellite imagery to estimate irrigation volumes and its impact in land surface modeling

2019 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Stéphanie Faroux ◽  
Aurore Brut ◽  
Gilles Boulet
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Global Scale ◽  
Hydrological Models ◽  
Irrigation Scheme ◽  
Available Water Content ◽  
Irrigation Practices

Abstract. Irrigation is a major issue for water resources management agencies as it is the main component of human fresh water consumption. However, irrigation can be monitored at plot scale but not at larger scales, i.e. from river basin to global scale. Hence, simulating the irrigation process in models is of great interest, not only to forecast the water availability, but also to provide realistic lower boundary conditions for atmosphere and climate models. This process is relatively well represented in agronomical or agro-hydrological models, designed for crop and water management at the plot scale. But this kind of model is not adapted for water management at the basin scale or even larger scale, due to their complexity. Land Surface Models (LSMs) are used for this purpose. However, irrigation is not well represented in LSMs. These models use basic decision rules to estimate irrigation volumes. Most of the time, it only consists in triggering an irrigation event when the soil moisture in the root zone drops below a fixed threshold. This threshold is unique at global scale, being independent of the crop type or the common irrigation practices in the simulated area. Then an irrigation amount is applied based on the volume needed to replenish the soil reservoir to a fixed level. There is no consideration about actual agricultural practices. These simple irrigation schemes do not have the flexibility needed to adapt to the wide variety of crops and irrigation practices encountered at large scales. The present study aims at developing and evaluating an irrigation scheme very similar to the one used in agronomical or agro-hydrological models for the SURFEX-ISBA LSM developed by Meteo-France. Particularly, it allows adapting the triggering threshold spatially and temporally and relating it to the actual phenology of the crop and to the irrigation practices. But increasing the flexibility of a model also means that it needs more input information to constrain it. High-resolution remote sensing products, like those derived from Sentinel-2, can provide part of this information spatially. This study thus presents a method to determine irrigation parameters, and particularly the triggering soil moisture threshold, from high-resolution remotely sensed leaf area index. This method is compared to three other experiments: a reference simulation with the current irrigation scheme of SURFEX-ISBA, a second experiment designed to show the contribution of remotely sensed irrigation period determination in the current scheme and a third which uses a single threshold over the season. The comparison is done on several maize plots in southwestern France. The results show that the method using remote sensing to modulate the triggering soil moisture threshold shows the best performances in estimating annual irrigation volumes. Indeed, it shows a bias around 10 mm per year and a RMSE around 30 mm whereas the standard scheme shows a bias around 50 mm per year and a RMSE around 60 mm. The sensitivity to the estimation of the soil maximal available water content is then performed. It shows that all the experiments are very sensitive when the maximal available water content in the soil is low. Finally, the impact on evapotranspiration is evaluated. It shows small differences between experiments and with the measured evapotranspiration. This study thus shows the potential of using high resolution remote sensing products to improve the irrigation simulation in LSMs. Indeed, it allows increasing the realism of the irrigation scheme while keeping it generic enough to simulate at regional to global scale.

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