scholarly journals Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1367 ◽  
Author(s):  
Tiago Ramos ◽  
Lucian Simionesei ◽  
Ana Oliveira ◽  
Hanaa Darouich ◽  
Ramiro Neves
Keyword(s):  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Soil Water Balance ◽  
Landsat 8 ◽  
State Variables ◽  
Area Index ◽  
Final Model ◽  
The Impact

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

Impacts of wildfires on the 2020 floods in southeast Australia: A vegetation data-assimilation case study  using the NASA coupled LIS/WRF-Hydro system

2021 ◽  
Author(s):  
Timothy Lahmers ◽  
Sujay Kumar ◽  
Aubrey Dugger ◽  
David Gochis ◽  
Joseph Santanello
Keyword(s):  
Data Assimilation ◽  
Hydrological Modeling ◽  
New South ◽  
New South Wales ◽  
Extreme Rainfall ◽  
Surface Energy Budget ◽  
Area Index ◽  
South Wales ◽  
Land Information System ◽  
The Impact

<div> <p>In late 2019 widespread wildfires impacted much of the New South Wales province in south east Australia, and this loss of vegetation contributed to increased surface runoff and consequently major flooding caused by extreme rainfall by early 2020. The recently developed NASA LIS/WRF-Hydro system enables the data assimilation (DA) capabilities of the NASA Land Information System (LIS) and the surface hydrological modeling capabilities of the WRF-Hydro model to be combined in a single model architecture. Combining the DA capabilities of the LIS system with WRF-Hydro, which has been used for both research and operational hydrologic simulations, we investigate the impacts of vegetation DA on the simulated floods in several basins across New South Wales, with varying degrees of burn severity from the 2019 fires. We also consider the impacts of the wildfires, as realized through vegetation DA on water partitioning and the surface energy budget, which both have implications for L-A interactions. For DA, we utilize the leaf area index retrievals from MODIS and vegetation optical depth from SMAP. For the present study, we will quantify the impact of the changes to the landscape brought about by the wildfires on hydrologic response, including flood severity, which would not be possible without the DA capabilities of the LIS/WRF-Hydro system.</p> </div>

Validate the DSSAT Model on Winter Wheat under Conservation Tillage Treatments in the West Henan, China

2014 ◽  
Vol 522-524 ◽  
pp. 699-708 ◽  
Author(s):  
Xiang Hui Lu ◽  
Hua Bai ◽  
Hui Ying Liu
Keyword(s):  
Water Balance ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Soil Water ◽  
Leaf Area ◽  
Conservation Agriculture ◽  
Soil Water Balance ◽  
The West ◽  
Area Index ◽  
Straw Mulching

Crop growth simulation models can be useful in evaluating the impacts of different tillage and residue management operations on the changes in land productivity and soil-water balance components. They offer a potentially valuable set of tools for examining questions related to performance of conservation agriculture. This can be both to improve our understanding or conceptualization of processes and to improve quantitative predictions for use by agronomists, growers, policy makers or others. We applied the new Decision Support System for Agro-technology Transfer (DSSAT) version 4.5, an improved crop growth simulation model, to three conservation agriculture treatments and one conventional tillage treatment data from a field-scale study in west Henan region of China to predict winter-wheat yield, leaf area index and soil-water balance. The sites average annual precipitation is 632mm and it had a winter wheat-fallow-winter wheat rotation. There winter wheat planting in October and harvesting in next year June. The model was calibrated using 2005-2006 winter-wheat crop data from field experiments of the four treatments. The treatments were: (1) decreased tillage (DT): mulching of 10-15cm height straw and one ploughing operation to 25cm depth on July 1st; (2) zero tillage (ZT): zero tillage with 35-40cm height straw mulching; (3) subsoiling (SS): 35-40cm height straw mulching and subsoil to 40cm depth on July 1st; (4) conventional tillage (CT): 10-15cm height straw mulching and two ploughing operations 20cm deep on July 1st and October 1st. The DSSAT satisfactorily simulated the four treatments variations in winter-wheat yield, leaf area index and soil-water balance. There was better agreement between observed and predicted yields (the error absolute values were less than 3.95% and the error mean absolute values were less than 2.78%). The mean value of root mean square errors (RMSE) for simulated leaf area index (LAI) and soil water storage were 0.41cm2·cm-2 and 0.08cm3·cm-3 for DT, ZT, SS and CT, treatment respectively. The predicted water use efficiency for the four treatments were 15.85, 15.40, 16.58 and 15.81kg·mm-1·ha-1, respectively. These values were close to the values calculated from field measured data (16.82, 14.44, 16.86 and 15.66kg·mm-1·ha-1, respectively). Although the analysis results show us that the DSSAT V4.5 is well suited for simulating winter-wheat growth in the West Henan region of China, these results are preliminary and based on only one year of experimental data and four treatments and further long-term analyses need to be carried out for improving the understanding of the conservation agriculture cropping systems in the west Henan region of China.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2010 ◽  
Vol 14 (10) ◽  
pp. 2099-2120 ◽  
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green leaf area index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on complete or near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2008 ◽  
Vol 5 (2) ◽  
pp. 579-648
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green Leaf Area Index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

scholarly journals Soil water dynamics and evapotranspiration of forage cactus clones under rainfed conditions

2015 ◽  
Vol 50 (7) ◽  
pp. 515-525 ◽  
Author(s):  
Thieres George Freire da Silva ◽  
Jorge Torres Araújo Primo ◽  
Magna Soelma Beserra de Moura ◽  
Sérvulo Mercier Siqueira e Silva ◽  
José Edson Florentino de Morais ◽  
...  
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Water Consumption ◽  
Water Availability ◽  
Crop Growth ◽  
Water Dynamics ◽  
Soil Water Balance ◽  
Soil Water Availability ◽  
Soil Water Dynamics ◽  
Area Index

Abstract: The objective of this work was to evaluate soil water dynamics in areas cultivated with forage cactus clones and to determine how environmental conditions and crop growth affect evapotranspiration. The study was conducted in the municipality of Serra Talhada, in the state of Pernambuco, Brazil. Crop growth was monitored through changes in the cladode area index (CAI) and through the soil cover fraction, calculated at the end of the cycle. Real evapotranspiration (ET) of the three evaluated clones was obtained as the residual term in the soil water balance method. No difference was observed between soil water balance components, even though the evaluated clones were of different genus and had different CAI increments. Accumulated ET was of 1,173 mm during the 499 days of the experiment, resulting in daily average of 2.35 mm. The CAI increases the water consumption of the Orelha de Elefante Mexicana clone. In dry conditions, the water consumption of the Miúda clone responds more slowly to variation in soil water availability. The lower evolution of the CAI of the IPA Sertânia clone, during the rainy season, leads to a higher contribution of the evaporation component in ET. The atmospheric demand controls the ET of clones only when there is higher soil water availability; in this condition, the water consumption of the Miúda clone decreases more rapidly with the increase of atmospheric demand.

scholarly journals The impact of climate change on soil water balance and agricultural production sustainability in Mediterranean part of Bosnia and Herzegovina

2020 ◽  
Vol 53 (1) ◽  
pp. 125
Author(s):  
Melisa Ljusa ◽  
Hamid Custovic ◽  
Sabina Hodzic
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Bosnia And Herzegovina ◽  
Calculated Data ◽  
Vegetation Period ◽  
Soil Water Balance ◽  
Monthly Precipitation ◽  
The World ◽  
The Impact

<p>The world agriculture uses about 70% of the world water resources in irrigation. The concern over the sustainability of water use as demand for agricultural, industrial, and domestic uses continues to increase. Conflicts between particular sectors result in tensions, which sometimes lead to “water wars” in different parts of the world. It is the reason why many national and international organizations are putting the water quantity and quality questions on the top of the world’s open questions/problems. The main aim of this paper is to present soil water balance of the Mediterranean region of Bosnia and Herzegovina, prepared for a long-term time series for two locations (Trebinje and Mostar) annually and during the vegetation period. The mean long-term data has been used as a base for future predicted calculation. The predicted PET was based on an increase in air temperature by 2°C and predicted decrease in precipitation by 25%. With so predicted calculated data of monthly PET and monthly precipitation the predicted soil water balance was done.</p>

scholarly journals Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

2014 ◽  
Vol 15 (3) ◽  
pp. 1117-1134 ◽  
Author(s):  
Eunjin Han ◽  
Wade T. Crow ◽  
Thomas Holmes ◽  
John Bolten
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Agricultural Drought ◽  
Soil Water Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
System Component

Abstract Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

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