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

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).

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

10.5194/gmd-2018-57 ◽

2018 ◽

Cited By ~ 1

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).

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River parametrisation of the JULES land surface model for improved runoff routing at the global scale.

10.5194/egusphere-egu21-3444 ◽

2021 ◽

Author(s):

Aristeidis Koutroulis ◽

Manolis Grillakis ◽

Camilla Mathison ◽

Eleanor Burke

Keyword(s):

Spatial Resolution ◽

Land Surface ◽

River Flow ◽

Land Surface Model ◽

Flow Simulation ◽

Surface Model ◽

Routing Scheme ◽

Basin Scale ◽

River Flow Simulation ◽

River Routing

The JULES land surface model has a wide ranging application in studying different processes of the earth system including hydrological modeling [1]. Our aim is to tune the existing configuration of the global river routing scheme at 0.5o spatial resolution [2] and improve river flow simulation performance at finer temporal scales. To do so, we develop a factorial experiment of varying effective river velocity and meander coefficient, components of the Total Runoff Integrating Pathways (TRIP) river routing scheme. We test and adjust best performing configurations at the basin scale based on observations from GRDC 230 stations that exhibiting a variety of hydroclimatic and physiographic conditions. The analysis was focused on watersheds of near-natural conditions [3] to avoid potential influences of human management on river flow. The HydroATLAS database [4] was employed to identify basin scale descriptive hydro-environmental indicators that could be associated with the components of the TRIP. These indicators summarize hydrologic and physiographic characteristics of the drainage area of each flow gauge. For each basin we select the best performing set of TRIP parameters per basin resulting to the optimal efficiency of river flow simulation based on the Nash&#8211;Sutcliffe and Kling&#8211;Gupta efficiency metrics. We find that better performance is driven predominantly by characteristics related to the stream gradient and terrain slope. These indicators can serve as descriptors for extrapolating the adjustment of TRIP parameters for global land configurations at 0.5o spatial resolution using regression models.&#160;[1] Papadimitriou et al 2017, Hydrol. Earth Syst. Sci., 21, 4379&#8211;4401[2] Falloon et al 2007. Hadley Centre Tech. Note 72, 42 pp.[3] Fang Zhao et al 2017 Environ. Res. Lett. 12 075003[4] Linke et al 2019, Scientific Data 6: 283.

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Evaluating LSM-Based Water Budgets over a West African Basin Assisted with a River Routing Scheme

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0012.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2331-2346 ◽

Cited By ~ 11

Author(s):

Augusto C. V. Getirana ◽

Aaron Boone ◽

Christophe Peugeot

Keyword(s):

Deep Water ◽

Land Surface ◽

Land Surface Model ◽

Base Flow ◽

Water Infiltration ◽

Surface Model ◽

Daily Streamflow ◽

Routing Scheme ◽

Multidisciplinary Analysis ◽

River Routing

Abstract Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Ouémé River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum–Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005–08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five gauges within the basin. Results demonstrate that the RRS simulates streamflow at all gauges with relative errors varying from −20% to 3% and Nash–Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the subbasin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated streamflows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0186.1 ◽

2019 ◽

Vol 20 (5) ◽

pp. 793-819 ◽

Cited By ~ 12

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.

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Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

10.5194/gmd-2021-205 ◽

2021 ◽

Author(s):

Evan Baker ◽

Anna Harper ◽

Daniel Williamson ◽

Peter Challenor

Keyword(s):

High Resolution ◽

Land Surface ◽

Global Climate ◽

Computational Cost ◽

Land Surface Model ◽

Parameter Tuning ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.

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Rethinking large scale river routing by leveraging a field-scale resolving land surface model

10.5194/egusphere-egu2020-17335 ◽

2020 ◽

Author(s):

Nathaniel Chaney ◽

Noemi Vergopolan ◽

Colby Fisher

Keyword(s):

Land Surface ◽

Large Scale ◽

Land Surface Model ◽

Added Value ◽

Surface Model ◽

Earth System ◽

Field Scale ◽

Routing Scheme ◽

Earth System Models ◽

River Routing

Over the past decade there has been important progress towards modeling the water, energy, and carbon cycles at field scales (10-100 meter) over continental extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid hydrologic response units (HRUs); these HRUs are defined via cluster analysis of available field-scale environmental datasets (e.g., elevation). However, until now, there has yet to be complementary advances in river routing schemes that are able to fully harness HydroBlocks&#8217; approach to sub-grid heterogeneity, thus limiting the added value of field-scale resolving land surface models (e.g., riparian zone dynamics, irrigation from surface water, and interactive floodplains). In this presentation, we will introduce a novel large scale river routing scheme that leverages the modeled field-scale heterogeneity in HydroBlocks through more realistic sub-grid stream network topologies, reach-based river routing, and the simulation of floodplain dynamics.The primary features of the novel river routing scheme include: 1) each macroscale grid cell is assigned its own river network delineated from field-scale DEMs; 2) similar sub-grid reaches (e.g., Shreve order) are grouped/clustered to ensure computational tractability; 3) the fine-scale inlet/outlet reaches of the macroscale grid cells are linked to assemble the continental river networks; 4) river dynamics are solved at the reach-level via an implicit solution of the Kinematic wave with floodplain dynamics; 5) two way connectivity is established between each cell&#8217;s sub-grid HRUs and the river network. The resulting routing scheme is able to effectively represent sub-100 meter-delineated stream networks within Earth system models with relatively minor increases in computation with respect to existing approaches. To illustrate the scheme&#8217;s novelty when coupled to the HydroBlocks land surface model, we will present simulation results over the Yellowstone river in the United States between 2002 and 2018. We will show the added value of the scheme when compared to existing approaches with regards to floodplain dynamics, water management, and riparian corridors. Furthermore, we will present results regarding the scheme&#8217;s computational tractability to ensure the feasibility of its use within Earth system models. Finally, we will discuss the potential of this approach to enhance flood and drought monitoring tools, numerical weather prediction, and climate models.

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

JAWRA Journal of the American Water Resources Association ◽

10.1111/1752-1688.12434 ◽

2016 ◽

Vol 52 (4) ◽

pp. 950-964 ◽

Cited By ~ 22

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

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A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

10.5194/egusphere-egu21-3776 ◽

2021 ◽

Author(s):

Yifan Cheng ◽

Andrew Newman ◽

Sean Swenson ◽

David Lawrence ◽

Anthony Craig ◽

...

Keyword(s):

High Resolution ◽

River Basin ◽

Land Surface ◽

Computational Cost ◽

Reanalysis Data ◽

Freshwater Ecosystems ◽

System Model ◽

Surface Model ◽

Yukon River ◽

Yukon River Basin

Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24th degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.

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Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

Open Geosciences ◽

10.2478/v10085-009-0043-2 ◽

2010 ◽

Vol 2 (2) ◽

Cited By ~ 4

Author(s):

Diandong Ren

Keyword(s):

Soil Moisture ◽

Soil Temperature ◽

Land Surface ◽

Surface Soil ◽

Land Surface Model ◽

Initial Guess ◽

Surface Model ◽

Soil Moisture Condition ◽

Wide Range ◽

Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

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Continental Runoff into the Oceans (1950–2008)

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0183.1 ◽

2015 ◽

Vol 16 (4) ◽

pp. 1502-1520 ◽

Cited By ~ 24

Author(s):

Elizabeth A. Clark ◽

Justin Sheffield ◽

Michelle T. H. van Vliet ◽

Bart Nijssen ◽

Dennis P. Lettenmaier

Keyword(s):

South America ◽

Land Surface ◽

Water Cycle ◽

Land Surface Model ◽

Hydrologic Model ◽

Surface Model ◽

Infiltration Capacity ◽

Continental Scale ◽

Wide Range ◽

Continental Runoff

Abstract A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world’s oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world’s oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia–Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia–Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa.

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