Comparative study of two crop yield simulation models

Xevi, Emmanuel; Gilley, James R.; Feyen, Jan

doi:10.1016/0378-3774(95)01218-4

Cited by 57 publications

(30 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This seems to be of minor concern in conventional thinking but inevitably the veracity of the input data will be reflected in the end. According to Xevi et al (1996) proper evaluation of the water balance in the unsaturated zone depends strongly on the appropriate characterisation of the soil hydraulic functions. Therefore, proper definition of the soil hydraulic parameters under field conditions should be taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach

Ines

Droogers

2002

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

The practical application of simulation models in the field is sometimes hindered by the difficulty of deriving the soil hydraulic properties of the study area. The procedure so-called inverse modelling has been investigated in many studies to address the problem where most of the studies were limited to hypothetical soil profile and soil core samples in the laboratory. Often, the numerical approach called forwardbackward simulation is employed to generate synthetic data then added with random errors to mimic the real-world condition. Inverse modelling is used to backtrack the expected values of the parameters. This study explored the potential of a Genetic Algorithm (GA) to estimate inversely the soil hydraulic functions in the unsaturated zone. Lysimeter data from a wheat experiment in India were used in the analysis. Two cases were considered: (1) a numerical case where the forward-backward approach was employed and (2) the experimental case where the real data from the lysimeter experiment were used. Concurrently, the use of soil water, evapotranspiration (ET) and the combination of both were investigated as criteria in the inverse modelling. Results showed that using soil water as a criterion provides more accurate parameter estimates than using ET. However, from a practical point of view, ET is more attractive as it can be obtained with reasonable accuracy on a regional scale from remote sensing observations. The experimental study proved that the forward-backward approach does not take into account the effects of model errors. The formulation of the problem is found to be critical for a successful parameter estimation. The sensitivity of parameters to the objective function and their zone of influence in the soil column are major determinants in the solution. Generally, their effects sometimes lead to non-uniqueness in the solution but to some extent are partly handled by GA. Overall, it was concluded that the GA approach is promising to the inverse problem in the unsaturated zone.

show abstract

Section: Introductionmentioning

confidence: 99%

Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach

Ines

Droogers

2002

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…O CRM representa uma medida da tendência do modelo em superestimar ou subestimar os valores observados, sendo o valor zero indicativo de condição ótima (Loague e Green, 1991;Xevi et al, 1996). Valores positivos indicam tendência de superestimação do modelo enquanto valores negativos indicam tendência de subestimação do modelo (Loague e Green, 1991;Xevi et al, 1996).…”

Section: Methodsunclassified

“…Valores positivos indicam tendência de superestimação do modelo enquanto valores negativos indicam tendência de subestimação do modelo (Loague e Green, 1991;Xevi et al, 1996).…”

Section: Methodsunclassified

See 1 more Smart Citation

Parametrização de modelos de mineralização do nitrogênio orgânico em solo tratado com água residuária da suinocultura

et al. 2010

View full text Add to dashboard Cite

RESUMOPouco se sabe a respeito da dinâmica da mineralização do nitrogênio orgânico contido nos dejetos de suínos, tornando-se necessário a adequação de modelos para sua predição. O objetivo do presente trabalho foi parametrizar e avaliar modelos de mineralização do nitrogênio orgânico em solo tratado com água residuária da suinocultura (ARS), sob diferentes temperaturas e conteúdos de água. Amostras de 57,3 cm 3 de Latossolo VermelhoAmarelo distrófico foram misturadas com ARS, numa dose de aplicação de 400 kg ha -1 de nitrogênio, colocadas em recipientes plásticos e incubadas sob quatro diferentes temperaturas (15, 20, 25, 35ºC) e conteúdos de água correspondentes às tensões de 10, 30, 200 e 1500 kPa. Aos 3, 6, 12, 24, 48, 96 dias de incubação, foram retiradas amostras do solo incubado para quantificação das concentrações de amônio e nitrato. A partir dos valores de nitrogênio orgânico mineralizado, obtidos ao longo de diferentes tempos de incubação, foram determinados os parâmetros dos modelos de mineralização do nitrogênio no solo. O valor da concentração de nitrogênio potencialmente mineralizável (N 0 ) em solo com aplicação de ARS foi 2,5 vezes superior em relação à concentração obtida em solo sem aplicação de ARS. A constante de mineralização (k) em solo com ARS foi sempre maior em relação ao solo sem ARS. Houve tendência do modelo exponencial simples subestimar os valores da concentração de nitrogênio mineralizado. Na maioria das situações, o modelo potencial foi mais eficiente que o modelo exponencial simples para predizer a mineralização do nitrogênio orgânico.Palavras-chave: dejeto de suínos; disposição de residuos; nitrato; amônio. Parametrization of organic nitrogen mineralization models in soil treated with swine raising wastewater ABSTRACTLittle is known about the mineralization dynamic of organic nitrogen contained in swine manure, so models need to be adjusted for its prediction. The objective of the present study was to parameterize and assess models of organic nitrogen mineralization in soil treated with swine raising wastewater (SRW) at different temperatures and water contents. Samples of 57.3 cm 3 of dystrophic Red-Yellow Latosol were mixed with SRW at the application dose of 400 kg ha -1 nitrogen, placed in plastic cups and incubated at four different temperatures (15, 20, 25 and 35°C) and water contents corresponding to tensions of 10, 30, 200 and 1500 kPa. Samples were removed from the incubated soil after 3, 6, 12, 24, 48 and 96 days to quantify the ammonium and nitrate concentrations. The parameters of the soil organic nitrogen

show abstract

“…In semiarid regions, E may account for more than half of ET [Huxman et al, 2005], while soil moisture is the main source of water for T in the entire root zone. This indicates that ET significantly influences the water cycle at the near surface and in the root zone [Xevi et al, 1996], but it has not been considered yet in scaling down RS soil moisture products. Various approaches have been developed for estimating spatially distributed ET such as Simplified Surface Energy Balance Index (S-SEBI) [Roerink et al, 2000], Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC) [Allen et al, 2007], Surface Energy Balance Algorithm for Land (SEBAL) [Bastiaanssen et al, 1998[Bastiaanssen et al, , 2005, and Surface Heat Budget of the Arctic Ocean (SHEBA) [Su, 2002], among others.…”

Section: Introductionmentioning

confidence: 99%

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

Shin

Mohanty

2013

Water Resour. Res.

View full text Add to dashboard Cite

[1] Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: À0.073 to À0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m Â 800 m) downscaled by this approach matched well (R: 0.724 to À0.914, MBE: À0.203 to À0.169 for the LW 13; R: 0.343-0.865, MBE: À0.165 to À0.122 for the LW 21) with the in situ local scale soil moisture measurements during Southern Great Plains Experiment 1997 (SGP97). The good correspondence of observed soil water characteristics (h) functions (from the soil core samples) and genetic algorithm (GA) searched soil parameters at the LW 13 and 21 sites demonstrated the robustness of the algorithm. Although the algorithm is tested under limited conditions at field scale, this approach improves the availability of remotely sensed soil moisture product at finer resolution for various land surface and hydrological model applications.Citation: Shin, Y., and B. P. Mohanty (2013), Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications, Water Resour. Res., 49,

show abstract

Comparative study of two crop yield simulation models

Cited by 57 publications

References 14 publications

Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach

Inverse modelling in estimating soil hydraulic functions: a Genetic Algorithm approach

Parametrização de modelos de mineralização do nitrogênio orgânico em solo tratado com água residuária da suinocultura

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

Contact Info

Product

Resources

About