scholarly journals HydroBlocks: a field-scale resolving land surface model for application over continental extents

2016 ◽  
Vol 30 (20) ◽  
pp. 3543-3559 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Peter Metcalfe ◽  
Eric F. Wood
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Field Scale

Surface soil moisture data assimilation for irrigation amounts and timing estimation in semi-arid regions

2021 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Saïd Khabba ◽  
Jamal Ezzahar ◽  
Olivier Merlin
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
In Situ Data ◽  
Statistical Metrics

<p>Irrigation is the largest consumer of water in the world, with more than 70% of the world's fresh water dedicated to agriculture. In this context, we developed and evaluated a new method to predict daily to seasonal irrigation timing and amounts at the field scale using surface soil moisture (SSM) data assimilated into a simple  land surface model through a particle filter technique. The method is first tested using in situ SSM before using SSM products retrieved from Sentinel-1. Data collected on different wheat fields grown  in Morocco, for both flood and drip irrigation techniques, are used to assess the performance of the proposed method. With in situ data, the results are good. Seasonal amounts are retrieved with R > 0.98, RMSE <42 mm and bias<2 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation where more than 70% of the irrigation events are detected with a time difference from actual irrigation events shorter than 4 days, when assimilating SSM observation every 6 days to mimics Sentinel-1 revisit time. Over the drip irrigated fields, the statistical metrics are R = 0.70, RMSE =28.5 mm and bias= -0.24 mm for irrigation amounts cumulated over 15 days. The approach is then evaluated using SSM products derived from Sentinel-1 data; statistical metrics are R= 0.64, RMSE= 28.78 mm and bias = 1.99 mm for irrigation amounts cumulated over 15 days. In addition to irrigated fields, the applicationof the developed methodover rainfed fieldsdid not detect any irrigation. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.</p>

scholarly journals Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

2021 ◽  
Vol 25 (5) ◽  
pp. 2445-2458
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Soil Physics ◽  
Field Scale

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

Improving soil moisture predictions from a land surface model by optimisation of pedotransfer functions.

2021 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Richard Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Pedotransfer Functions ◽  
Small Scale ◽  
Surface Model ◽  
Land Surface Models ◽  
Field Scale ◽  
Surface Models

<p>Accurate soil moisture predictions from land surface models are important in hydrological, ecological and agricultural applications. Despite increasing availability of wide area soil moisture measurements, few studies have combined soil moisture predictions from models with in-situ observations beyond the point scale. This work uses the LAVENDAR data assimilation framework to markedly improve soil moisture estimates from the JULES land surface model using field scale Cosmic Ray Neutron sensor observations from the UKCEH COSMOS-UK network. Rather than directly updating modelled soil moisture estimates towards measured values, we optimize constants in the underlying pedotransfer functions (PTF) which relate soil texture to soil hydraulics parameters. In this way we generate a single set of newly calibrated PTFs based on field scale observations from a number of UK sites with different soil types. We demonstrate that calibrating PTFs in this way can improve the performance of JULES. Further, we suggest that calibrating PTFs for the soils on which they are to be used and at the scales at which land surface models are applied (rather than on small-scale soil samples) will ultimately improve the performance of land surface models, potentially leading to improvements in flood, drought and climate projections.</p>

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

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

<p>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’ 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.</p><p>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’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’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’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.</p>

scholarly journals Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

2021 ◽  
Vol 3 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Added Value ◽  
Surface Model ◽  
Model Parameters ◽  
Validation Period ◽  
Field Scale ◽  
Mean Square Errors

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.

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