scholarly journals Technical Note: Reducing the spin-up time of integrated surface water–groundwater models

2014 ◽  
Vol 18 (12) ◽  
pp. 5169-5179 ◽  
Author(s):  
H. Ajami ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
S. Stisen
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Initial Conditions ◽  
Hybrid Approach ◽  
Land Surface Model ◽  
Technical Note ◽  
Surface Model ◽  
Depth To Water Table ◽  
Computationally Intensive ◽  
Semi Arid Region

Abstract. One of the main challenges in the application of coupled or integrated hydrologic models is specifying a catchment's initial conditions in terms of soil moisture and depth-to-water table (DTWT) distributions. One approach to reducing uncertainty in model initialization is to run the model recursively using either a single year or multiple years of forcing data until the system equilibrates with respect to state and diagnostic variables. However, such "spin-up" approaches often require many years of simulations, making them computationally intensive. In this study, a new hybrid approach was developed to reduce the computational burden of the spin-up procedure by using a combination of model simulations and an empirical DTWT function. The methodology is examined across two distinct catchments located in a temperate region of Denmark and a semi-arid region of Australia. Our results illustrate that the hybrid approach reduced the spin-up period required for an integrated groundwater–surface water–land surface model (ParFlow.CLM) by up to 50%. To generalize results to different climate and catchment conditions, we outline a methodology that is applicable to other coupled or integrated modeling frameworks when initialization from an equilibrium state is required.

scholarly journals Technical Note: Reducing the spin-up time of integrated surface water–groundwater models

2014 ◽  
Vol 11 (6) ◽  
pp. 6969-6992
Author(s):  
H. Ajami ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
S. Stisen
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Initial Conditions ◽  
Hybrid Approach ◽  
Land Surface Model ◽  
Technical Note ◽  
Integrated Modelling ◽  
Surface Model ◽  
Catchment Scale ◽  
Time Required

Abstract. One of the main challenges in catchment scale application of coupled/integrated hydrologic models is specifying a catchment's initial conditions in terms of soil moisture and depth to water table (DTWT) distributions. One approach to reduce uncertainty in model initialization is to run the model recursively using a single or multiple years of forcing data until the system equilibrates with respect to state and diagnostic variables. However, such "spin-up" approaches often require many years of simulations, making them computationally intensive. In this study, a new hybrid approach was developed to reduce the computational burden of spin-up time for an integrated groundwater-surface water-land surface model (ParFlow.CLM) by using a combination of ParFlow.CLM simulations and an empirical DTWT function. The methodology is examined in two catchments located in the temperate and semi-arid regions of Denmark and Australia respectively. Our results illustrate that the hybrid approach reduced the spin-up time required by ParFlow.CLM by up to 50%, and we outline a methodology that is applicable to other coupled/integrated modelling frameworks when initialization from equilibrium state is required.

scholarly journals Variational Assimilation of Land Surface Temperature within the ORCHIDEE Land Surface Model Version 1.2.6

2016 ◽  
Author(s):  
H. S. Benavides Pinjosovsky ◽  
S. Thiria ◽  
C. Ottlé ◽  
J. Brajard ◽  
F. Badran ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Adjoint Model ◽  
Variational Assimilation ◽  
Internal Parameters

Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software denoted YAO was used as a framework to implement a 4D-VAR assimilation method. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the eleven most influent internal parameters of SECHIBA or of the initial conditions of the soil water content by observing the land surface temperature measured in situ or as it could be observed by remote sensing as brightness temperature. The paper presents the fundamental principles of the 4D-Var assimilation, the semi-generator software YAO and some experiments showing the accuracy of the adjoint code distributed. In addition, a distributed version is available when only the land surface temperature is observed.

scholarly journals Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins

2020 ◽  
Author(s):  
Yifan Zhou ◽  
Benjamin F. Zaitchik ◽  
Sujay V. Kumar ◽  
Kristi R. Arsenault ◽  
Mir A. Matin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Land Surface ◽  
Root Zone ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Earth Observing System ◽  
South And Southeast Asia ◽  
Forecasting System ◽  
Land Information System

Abstract. South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

scholarly journals Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

2020 ◽  
Vol 24 (3) ◽  
pp. 1073-1079
Author(s):  
Amirhossein Mazrooei ◽  
Arumugam Sankarasubramanian ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Technical Note ◽  
Surface Model ◽  
Lead Times ◽  
Southeastern Us ◽  
Agricultural Planning ◽  
Model Initialization

Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.

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.

scholarly journals Technical Note: Evaluation of the Skill in Monthly-to-Seasonal Soil Moisture Forecasting Based on SMAP Satellite Observations over the Southeast US

2019 ◽  
Author(s):  
Amirhossein Mazrooei ◽  
Venkat Lakshmi ◽  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Technical Note ◽  
Surface Model ◽  
Lead Times ◽  
Agricultural Planning ◽  
Southeast Us

Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly to seasonal (M2S) top layer SM forecasts by forcing 1–3 month ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the Southeast US (SEUS) and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-months (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1–3 month lead times respectively. As a case study, the evaluation of the issued forecasts based on the drought indexes monitored during the 2007 historical drought over the SEUS also indicate promising skill in monthly SM forecasting to support agricultural planning and water management for such natural hazards.

scholarly journals Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins

2021 ◽  
Vol 25 (1) ◽  
pp. 41-61
Author(s):  
Yifan Zhou ◽  
Benjamin F. Zaitchik ◽  
Sujay V. Kumar ◽  
Kristi R. Arsenault ◽  
Mir A. Matin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Land Surface ◽  
Root Zone ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Earth Observing System ◽  
South And Southeast Asia ◽  
Forecasting System ◽  
Land Information System

Abstract. South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end users across the region. This system applies the Noah-Multiparameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5 km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made 1 to 2 months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

scholarly journals Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange

2016 ◽  
Vol 13 (14) ◽  
pp. 4271-4277 ◽  
Author(s):  
Joshua B. Fisher ◽  
Munish Sikka ◽  
Deborah N. Huntzinger ◽  
Christopher Schwalm ◽  
Junjie Liu
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Technical Note ◽  
User Preferences ◽  
Surface Model ◽  
Co2 Fluxes ◽  
Ensemble Mean ◽  
Hourly Output ◽  
Biosphere Model

Abstract. The land surface provides a boundary condition to atmospheric forward and flux inversion models. These models require prior estimates of CO2 fluxes at relatively high temporal resolutions (e.g., 3-hourly) because of the high frequency of atmospheric mixing and wind heterogeneity. However, land surface model CO2 fluxes are often provided at monthly time steps, typically because the land surface modeling community focuses more on time steps associated with plant phenology (e.g., seasonal) than on sub-daily phenomena. Here, we describe a new dataset created from 15 global land surface models and 4 ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), temporally downscaled from monthly to 3-hourly output. We provide 3-hourly output for each individual model over 7 years (2004–2010), as well as an ensemble mean, a weighted ensemble mean, and the multi-model standard deviation. Output is provided in three different spatial resolutions for user preferences: 0.5°  ×  0.5°, 2.0°  ×  2.5°, and 4.0°  ×  5.0° (latitude  ×  longitude). These data are publicly available from doi:10.3334/ORNLDAAC/1315.

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