RZWQM: Simulating the effects of management on water quality and crop production

Hanson, Jon D.; Ahuja, Lajpat R.; Shaffer, M. J.; Rojas, K. W.; DeCoursey, Donn G.; Farahani, Hamid J.; Johnson, Katherine Wilts

doi:10.1016/s0308-521x(98)00002-x

Cited by 99 publications

(45 citation statements)

References 44 publications

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“…3, NT exhibits a pure nugget effect, indicating that bulk density varies randomly during the year by about 0.045 Mg m Despite the difficulties of fully representing the interactions between tillage operations, soil properties and environmental conditions during tillage (Alberts et al, 1995), there are empirical modelling approaches in use to relate bulk density to tillage operations with different tillage implements (Williams et al, 1984;Chen et al, 1998). The approach of Williams et al (1984) was originally developed for the EPIC model and is similarly implemented in the WEPP (Alberts et al, 1995) and RZWQM (Hanson et al, 1998) models.…”

Section: Soil Bulk Densitymentioning

confidence: 99%

“…For deeper layers (N0.1 m soil depth), only a slight reconsolidation due to rainfall (Rousseva et al, 1998) or even no effects were found (Karunatilake and Van Es, 2002). For the uppermost soil layer one of the most frequently used equations, originally developed by Onstad et al (1984), is implemented in the EPIC (Williams et al, 1984), WEPP (Alberts et al, 1995) and RZWQM (Hanson et al, 1998) models:…”

Section: Soil Bulk Densitymentioning

confidence: 99%

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Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments—A review

Fiener

Auerswald

Oost

2011

Earth-Science Reviews

123

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a b s t r a c t a r t i c l e i n f oSurface runoff and associated erosion processes adversely affect soil and surface water quality. There is increasing evidence that a sound understanding of spatial-temporal dynamics of land use and management are crucial to understanding surface runoff processes and underpinning mitigation strategies. In this review, we synthesise the effects of (1) temporal patterns of land management of individual fields, and (2) spatio-temporal interaction of several fields within catchments by applying semivariance analysis, which allows the extent and range of the different patterns to be compared. Consistent effects of management on the temporal dynamics of surface runoff of individual fields can be identified, some of which have been incorporated into small-scale hydrological models. In contrast, the effects of patchiness, the spatial organisation of patches with different soil hydrological properties, and the effects of linear landscape structures are less well understood and are rarely incorporated in models. The main challenge for quantifying these effects arises from temporal changes within individual patches, where the largest contrasts usually occur in mid-summer and cause a seasonally varying effect of patchiness on the overall catchment response. Some studies indicate that increasing agricultural patchiness, due to decreasing field sizes, reduces the catchment-scale response to rainfall, especially in cases of Hortonian runoff. Linear structures associated with patchiness of fields (e.g. field borders, ditches, and ephemeral gullies) may either increase or decrease the hydraulic connectivity within a catchment. The largest gap in research relates to the effects and temporal variation of patch interaction, the influence of the spatial organisation of patches and the interaction with linear structures. In view of the substantial changes in the structure of agricultural landscapes occurring throughout the world, it is necessary to improve our knowledge of the influence of patchiness and connectivity, and to implement this knowledge in new modelling tools.

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Section: Soil Bulk Densitymentioning

confidence: 99%

Section: Soil Bulk Densitymentioning

confidence: 99%

Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments—A review

Fiener

Auerswald

Oost

2011

Earth-Science Reviews

123

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“…Simulation models that integrate the effects of management on crop growth and leaching of chemicals below the root zone, on the other hand, provide a means of supplementing information gained from field experiment (Singh and Singh, 1996). There are numbers of models dealing with plant growth, water flow, and agricultural chemical movement, including GLEAMS (Leonard et al, 1987), LEACHM (Hutson and Wagenet, 1992), UNSATCHEM (Simunek et al, 1996), RZWQM (Hanson et al, 1998), SWAGMAN (Khan et al, 2003), and SALTMED (Ragab et al, 2005). Combining with a 3-year field experiment, Forkutsa et al (2009a,b) applied the soil-water model Hydrus-1D to understand the effects of water management strategies on soil salinity under cotton irrigated with shallow groundwater.…”

Section: Introductionmentioning

confidence: 99%

Evaluating salinity distribution in soil irrigated with saline water in arid regions of northwest China

Chen

Hou

et al. 2010

Agricultural Water Management

148

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a b s t r a c tIn arid and semi-arid regions, salinity is a serious and chronic problem for agriculture. A 3-year field experiment in the arid environment of Xinjiang, northwest China, was conducted to study the salinity change in soil resulting from deficit irrigation of cotton with non-saline, moderate saline and high saline water. The salinity profile distribution was also evaluated by an integrated water, salinity, and nitrogen model, ENVIRO-GRO. The simulated and observed salinity distributions matched well. Results indicated that after 3 years of cotton production, the average salinity in the 1.0-m soil profile was 336% and 547% of the original soil profile, respectively, for moderate saline and high saline water irrigation. If the practices continued, the average soil salinity (EC e ) in the 1.0-m soil profile would approach a steady level of 1.7, 10.8, and 14.7 dS m −1 , respectively, for the treatments receiving irrigation waters of 0.33, 3.62, and 6.71 dS m −1 . It was concluded that deficit irrigation of saline water in this region was not sustainable. Model simulation showed that a big flood irrigation after harvest can significantly reduce the salt accumulation in the soil profile, and that this practice was much more efficient for salinity control than applying the same extra amount of water during the growing season.

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“…The inter-pool transfer coefficients were slightly adjusted based on those calibrated by Ma et al (2007a) and Thorp et al (2007), including adjusting the slow residue to intermediate SOM to 0.2, the fast residue to fast SOM to 0.5, the fast SOM to intermediate SOM to 0.5, and the intermediate SOM to slow SOM to 0.7. The adjustment to the intermediate SOM to slow SOM transfer coefficient aided in simulating N mineralization rates (Hanson et al, 1998).…”

Section: Model Initializationmentioning

confidence: 99%

Effects of Subsurface Drainage Systems on Water and Nitrogen Footprints Simulated with RZWQM2

Craft¹,

Helmers²,

Malone³

et al. 2018

Transactions of the ASABE

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RZWQM: Simulating the effects of management on water quality and crop production

Cited by 99 publications

References 44 publications

Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments—A review

Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments—A review

Evaluating salinity distribution in soil irrigated with saline water in arid regions of northwest China

Effects of Subsurface Drainage Systems on Water and Nitrogen Footprints Simulated with RZWQM2

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