scholarly journals Estimating High-Resolution Groundwater Storage from GRACE: A Random Forest Approach

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 63 ◽  
Author(s):  
Md Rahaman ◽  
Balbhadra Thakur ◽  
Ajay Kalra ◽  
Ruopu Li ◽  
Pankaj Maheshwari
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Land Surface ◽  
Human Impacts ◽  
Pearson Correlation ◽  
Land Surface Model ◽  
High Plains ◽  
Surface Model ◽  
Groundwater Storage ◽  
Grace Data

Gravity Recovery and Climate Experiment (GRACE) data have become a widely used global dataset for evaluating the variability in groundwater storage for the different major aquifers. Moreover, the application of GRACE has been constrained to the local scale due to lower spatial resolution. The current study proposes Random Forest (RF), a recently developed unsupervised machine learning method, to downscale a GRACE-derived groundwater storage anomaly (GWSA) from 1° × 1° to 0.25° × 0.25° in the Northern High Plains aquifer. The RF algorithm integrated GRACE to other satellite-based geospatial and hydro-climatological variables, obtained from the Noah land surface model, to generate a high-resolution GWSA map for the period 2009 to 2016. This RF approach replicates local groundwater variability (the combined effect of climatic and human impacts) with acceptable Pearson correlation (0.58 ~ 0.84), percentage bias (−14.67 ~ 2.85), root mean square error (15.53 ~ 46.69 mm), and Nash-Sutcliffe efficiency (0.58 ~ 0.84). This developed RF model has significant potential to generate finer scale GWSA maps for managing groundwater at both local and regional scales, especially for areas with sparse groundwater monitoring wells.

scholarly journals A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model

2016 ◽  
Vol 52 (4) ◽  
pp. 950-964 ◽  
Author(s):  
Alan D. Snow ◽  
Scott D. Christensen ◽  
Nathan R. Swain ◽  
E. James Nelson ◽  
Daniel P. Ames ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
National Scale ◽  
Forecast System ◽  
Hydrologic Forecast

scholarly journals ORCHIDEE-ROUTING: A new river routing scheme using a high resolution hydrological database

2018 ◽  
Author(s):  
Trung Nguyen-Quang ◽  
Jan Polcher ◽  
Agnès Ducharne ◽  
Thomas Arsouze ◽  
Xudong Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Flow ◽  
Anthropogenic Impacts ◽  
Computational Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Routing Scheme ◽  
Wide Range ◽  
River Routing

Abstract. This study presents a revised river routing scheme (RRS) for the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model. The revision is carried out to benefit from the high resolution topography provided the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), processed to a resolution of approximately 1 kilometer. The RRS scheme of the ORCHIDEE uses a unit-to-unit routing concept which allows to preserve as much of the hydrological information of the HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasted size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the revised scheme is evaluated against observations at both monthly and daily timescales. The new RRS captures satisfactorily the seasonal variability of river discharges, although important biases come from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show promising performances of this high resolution RRS for replicating river flow variation at various frequencies. Eventually, this RRS is well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation).

scholarly journals Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles

2018 ◽  
Vol 22 (7) ◽  
pp. 3863-3882 ◽  
Author(s):  
Fuxing Wang ◽  
Jan Polcher ◽  
Philippe Peylin ◽  
Vladislav Bastrikov
Keyword(s):  
Iberian Peninsula ◽  
Land Surface ◽  
Measurement Errors ◽  
River Discharge ◽  
Water Footprint ◽  
Human Impacts ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Balance Model ◽  
Surface Model

Abstract. River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The “model systematic errors” and “human impacts” (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30–150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The “correction” increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P−E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

scholarly journals ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database

2018 ◽  
Vol 11 (12) ◽  
pp. 4965-4985 ◽  
Author(s):  
Trung Nguyen-Quang ◽  
Jan Polcher ◽  
Agnès Ducharne ◽  
Thomas Arsouze ◽  
Xudong Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
River Flow ◽  
Anthropogenic Impacts ◽  
Computational Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Routing Scheme ◽  
Wide Range ◽  
River Routing

Abstract. The river routing scheme (RRS) in the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model is a valuable tool for closing the water cycle in a coupled environment and for validating the model performance. This study presents a revision of the RRS of the ORCHIDEE model that aims to benefit from the high-resolution topography provided by the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), which is processed to a resolution of approximately 1 km. Adapting a new algorithm to construct river networks, the new RRS in ORCHIDEE allows for the preservation of as much of the hydrological information from HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasting size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, in order to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the new scheme is evaluated against observations at both monthly and daily timescales. The new RRS satisfactorily captures the seasonal variability of river discharge, although important biases stem from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show the promising performance of this high-resolution RRS with respect to replicating river flow variation at various frequencies. Furthermore, this RRS may also eventually be well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation).

scholarly journals Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic Games

2011 ◽  
Vol 12 (4) ◽  
pp. 508-530 ◽  
Author(s):  
Natacha B. Bernier ◽  
Stéphane Bélair ◽  
Bernard Bilodeau ◽  
Linying Tong
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Near Surface ◽  
Elevation Changes ◽  
Forecast Models ◽  
Operational Models ◽  
Paralympic Games

Abstract A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

scholarly journals Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

2019 ◽  
Vol 20 (5) ◽  
pp. 793-819 ◽  
Author(s):  
Joseph A. Santanello Jr. ◽  
Patricia Lawston ◽  
Sujay Kumar ◽  
Eli Dennis
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Coupled Models ◽  
Wide Range ◽  
Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

An investigation into the influence of high-resolution land-surface heterogeneity on atmospheric dynamics

2020 ◽  
Author(s):  
Jason Simon ◽  
Khaled Ghannam ◽  
Gabriel Katul ◽  
Paul Dirmeyer ◽  
Kirsten Findell ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Planetary Boundary Layer ◽  
Land Surface ◽  
Climate Modeling ◽  
Land Surface Model ◽  
Surface Heterogeneity ◽  
Atmospheric Dynamics ◽  
Surface Model ◽  
Land Surface Heterogeneity

<p>Land-surface heterogeneity is known to play an important role in land surface hydrology and thus the boundary conditions for numerical weather prediction (NWP) and climate modeling. For this reason, there have been considerable efforts over the past two decades to improve its representation in large scale models. However, to date, the inclusion of sub-grid heterogeneity in modeling land-atmosphere interactions in regional and global models has been limited to sub-grid spatial means and thus have almost entirely disregarded its multi-scale impact on the simulated atmospheric dynamics. To begin to address this challenge, here we use large-eddy simulations (LES) coupled to a land-surface model to gain a more complete understanding of its role in the coupled land-atmosphere system. In this work, we illustrate its impact over the Southern Great Plains (SGP) site in the United States and present a path forward for using these modeling experiments to guide the development of a complementary coupling parameterization within climate models.</p><p>More specifically, over the SGP site, we use high-resolution LES to investigate the impact of SGS land heterogeneity under different atmospheric and surface conditions to inform the development of land-surface and planetary boundary layer (PBL) parameterizations for coarser, operational-scale weather and climate modeling efforts. The experiment methodology uses a high-resolution land-surface model (WRF-Hydro), spun-up over multiple years using reanalysis data, which is then coupled to the Weather Research and Forecasting (WRF) model for high-resolution LES. Cases are considered using both the fully heterogeneous land model as well as using a homogeneous surface with domain-averaged flux values at all grid points, allowing the dynamical effects of land-surface heterogeneity on the atmosphere to be isolated, and the land/atmospheric conditions under which land-surface heterogeneity plays a role to be studied. Results are evaluated primarily by the differences in the development of the planetary boundary layer and the extent, duration and intensity of developing rainfall events.</p>

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