SWAP Model Research Articles

Distributed agro-hydrological modeling with SWAP to improve water and salt management of the Voshmgir Irrigation and Drainage Network in Northern Iran

Abstract The agro-hydrological model SWAP was used in a distributed manner to quantify irrigation water management effects on the water and salt balances of the Voshmgir Network of North Iran during the agricultural year 2006–2007. Field experiments, satellite images and geographical data were processed into input data for 10 uniform simulation areas. As simulated mean annual drainage water (312 mm) of the entire area was only 14% smaller than measured (356 mm), its distribution over the drainage units was well reproduced, and simulated and measured groundwater levels agreed well. Currently, water management leads to excessive irrigation (621–1436 mm year −1 ), and leaching as well as high salinity of shallow groundwater are responsible for large amounts of drainage water (25–59%) and salts (44–752 mg cm −2 ). Focused water management can decrease mean drainage water (22–48%) and salts (30–49%), compared with current water management without adverse effects on relative transpiration and root zone salinity.

Estimating surface-soil moisture for retrieving maize leaf-area index from SAR data

The leaf-area index (LAI) is a key parameter for coupling earth-observation data with crop-growth models from the perspective of crop-yield forecasting. Remote sensing is of particular interest in estimating LAI over large areas. SAR data, thanks to their systematic acquisition, offer an ideal temporal resolution throughout the crop-growing season. Nevertheless, surface soil dielectric permittivity, which is strongly correlated with soil moisture, also affects the SAR signal. Thus, surface-soil permittivity or moisture has to be taken into account. This study tackles the issues related to soil influence on the SAR signal in monitoring maize crop growth. Different methods of assessing surface-soil moisture or permittivity are explored in order to retrieve LAI values from SAR data. The first method is based on a hydrological model — the soil, water, atmosphere, and plant (SWAP) model — with which the surface-soil moisture level can be estimated as a function of time. This method is tested with two kinds of meteorological data as inputs for the hydrological model: ground meteorological data and estimated meteorological data. The second method resorts to ground-penetrating radar, an alternative means of estimating surface-soil permittivity. This study demonstrates that both soil-moisture levels estimated by the SWAP model and soil permittivity measured by ground-penetrating radar can be successfully used for retrieving maize LAI values from SAR data using the water cloud model.

Use of Physically Based Models to Evaluate USDA Soil Moisture Classes

Soil Taxonomy differs from other international soil classification systems because it includes the estimation of the soil moisture regime (SMR) and is primarily based on the estimation of dry days in a soil moisture control section (SMCS). Alternatively, the International Committee on Soil Moisture and Temperature Regimes (ICOMMOTR) has proposed a new approach based on biweekly soil water potential values determined at a fixed depth. Currently, SMR estimation is performed through simple models (standard approaches), such as the Newhall and Billaux models, that are not physically based. The aims of this work were to test the appropriateness of both standard Soil Taxonomy and the ICOMMOTR approaches through the use of a hydrologic model based on the Richards equation (SWAP), and to evaluate possible SMR classification alternatives. The SWAP‐derived SMR classifications were compared with those derived by standard approaches, as well as the ICOMMOTR proposals, under eight pedoclimatic conditions (southern and northern Italy) where the SWAP model was calibrated and validated. The standard approaches clearly overestimated the dry conditions in the SMCS. Being mainly climate based, however, they were able to separate different pedoclimatic settings. In contrast, the physically based approach showed realistic results in terms of SMR estimation but did not differentiate pedoclimatic settings for either Soil Taxonomy or ICOMMOTR approaches. Three possible alternatives to SMR classification were investigated: (i) applying the ICOMMOTR classification approach supported by the use of hydrologic Richards‐based models; (ii) changing the rules of Soil Taxonomy for classifying SMR and SMCS to better fit “real” soil hydrologic behavior; and (iii) continuing to apply Newhall and Billaux models but clearly clarifying their climate‐driven criteria.

Development and credibility assessment of a metamodel relating water table depth to agricultural production

Abstract Phreatic groundwater pumping is affecting water availability for crops in areas with a shallow water table. This can reduce crop growth and so affect farm income. There is a need for a generic and transparent method to assess the agricultural damage caused by water table drawdown. This paper proposes such a method that consists of ‘damage tables’ relating agricultural production losses to the groundwater regime for different soil/crop combinations found in Northern Belgium. The damage tables are constructed based on numerous simulations with the agrohydrological model SWAP, in which the bottom boundary conditions are gradually changed to reflect different groundwater regimes. The credibility of the resulting metamodel is assessed in three ways: using (1) field data, (2) an existing local expert system for land suitability assessment and (3) literature applying to a wider region. Field data of actual transpiration for two grasslands do not systematically deviate from the model predictions. This provides some credibility to the claim that the model captures the processes determining evapotranspiration and agricultural production. The local expert system allows us to evaluate the range of groundwater regimes where optimal growth is expected for maize and grassland across different soil types. Diverging predictions of the optimal groundwater regime between the metamodel and the local expert system can be explained in terms of differences in assumptions underlying both models. One notable limitation of the damage tables is that only direct physiological stress is reckoned while indirect effects of wet conditions (decreased accessibility of the terrain, soil structural damage) may also limit growth on soils with a water table near the surface. Further comparison with literature data focused on two issues: the contribution of groundwater to evapotranspiration and the extinction depth, i.e., the depth at which groundwater no longer contributes to evapotranspiration. This comparison revealed that damage tables developed for our area of interest are only valid under similar climatic conditions for the following two reasons: they assume a relatively small groundwater contribution to evapotranspiration, which is typical for humid climates, and they take into account temporal variations in plant characteristics such as root depth, which is also climate dependent.

New ways of thinking about nurse scheduling

PurposeThe purpose of this paper is to present a new look at solving the national nursing shortage problem by discussing new approaches to minimize the impact of nursing miss‐utilization and or miss‐allocation.Design/methodology/approachAmong the approaches explored is the well‐known financial engineering derivative interest rate swap as well as new scheduling applications including inventory management and queuing models.FindingsThe study found that there is great interest in the field of the nurse scheduling and staffing problem. The problems are, however, studied in isolation from one another, while their respective nature is inter‐related: staffing policies impact nursing problem model constraints. This gap may be one of the contributing factors to a well‐known gap between industry application and academic literature. Industry solutions focus encouraging the growth of nurses in the field, most of which result in a financial paradox due to increasing rate capitation for health service facilities, which began in the mid‐1990s.Research limitations/implicationsPossible limitations include a resistance to application from industry, as it has not been possible to minimize the application gap.Originality/valueThis paper introduces a swap model that satisfies the key problems resulting from the shortage: increasing costs and gap needs. The additional approaches offer a new look from outside industry application that have yielded solutions needed in the nurse scheduling problem.

Estimating actual irrigation application by remotely sensed evapotranspiration observations

Water managers and policy makers need accurate estimates of real (actual) irrigation applications for effective monitoring of irrigation and efficient irrigation management. However, this information is not readily available at field level for larger irrigation areas. An innovative inverse modeling approach was tested for a field in an irrigation scheme in southern Spain where observed actual evapotranspiration by satellites was used to assess irrigation application amounts. The actual evapotranspiration was used as the basis for an optimization procedure using the physical based SWAP model and the parameter optimization tool PEST. To evaluate the proposed techniques two steps were taken. First, actual observed evapotranspiration from remote sensing was used to optimize two parameters of the SWAP model to determine irrigation applications. Second, a forward–backward approach was applied to test the minimum overpass return time of satellites and the required accuracy of remotely sensed actual evapotranspiration for accurate assessment of irrigation applications. Results indicate that irrigation application amounts can be estimated reasonably accurately, providing data are available at an interval of 15 days or shorter and the accuracy of the signal is 90% or higher.

Estimating seepage intensities from groundwater level time series by inverse modelling: A sensitivity analysis on wet meadow scenarios

Summary Mesotrophic wet meadows with an upward seepage of fresh, alkaline groundwater are famous for their high species richness. However, due to the lack of seepage data on an appropriate spatial scale, no quantitative relationships have been established as yet between seepage and the occurrence of seepage-dependent plant communities. Since there is no established method to directly measure upward seepage in the field, we investigated the possibility of inferring the seepage intensity by using measurable hydrological quantities such as ground and surface-water levels. To this end, we designed 16 representative plots of virtual hydrological situations, using known sets of geohydrological parameters. Then we applied the integrated soil–water–atmosphere–plant model SWAP to generate ‘measured’ time series of ground and surface-water levels for these plots. Finally, using the SCEM-UA optimisation algorithm, we calibrated parameters that affect seepage onto these time series. We analysed how the accuracy and uncertainty of calibrated seepage fluxes depend on the measurement interval of input data and on the accuracy and uncertainty of inferred local geohydrological parameters and boundary conditions. Our analysis shows that it is possible to make reliable estimates of seepage intensities from data provided by easy to place piezometers and water level gauges. For application on real datasets, the analysis gives insight into the limitations of both the approach and the data requirements. For example, data supplied every fortnight was found to be just as valuable for seepage estimation as modern high frequency measurements. When setting up a monitoring programme, our method can help to decide what and when to measure. Furthermore, our method can be used establish quantitative relationships between seepage and plant communities on an appropriate spatial scale.

Simulation of moisture profiles in a Latossol in Dourados region, in the State of Mato Grosso do Sul, Brazil

The understanding of unsaturated soil water flow at process-level is essential to develop proper management actions for environmental protection in agricultural systems. One important tool for simulation of soil water flow that has been used worldwide is the SWAP model. The aim of this work was to test and to calibrate the SWAP model by inverse modeling to describe moisture profiles in a Brazilian very clayey Latossol in Dourados, State of Mato Grosso do Sul, Brazil. The SWAP model was tested in an experimental field of 0.09 ha cultivated with soybean and soil profiles were sampled eight times between December 2006 and October 2007. The SWAP input values (i.e. soil water retention curves and meteorological data) were based on in-situ measurements. Simulations with uncalibrated soil water retention curves resulted in moisture profiles that were too wet for almost all sampling dates, in particular between 0-10 cm depth. After calibration of soil water retention curves, there was a good improvement in the simulated moisture profiles, which were within the range of measured values for almost all depths and sampling dates.

PRICING CMS SPREAD OPTIONS IN A LIBOR MARKET MODEL

We present two approximation methods for the pricing of CMS spread options in Libor market models. Both approaches are based on approximating the underlying swap rates with lognormal processes under suitable measures. The first method is derived straightforwardly from the Libor market model. The second one uses a convexity adjustment technique under a linear swap model assumption. A numerical study demonstrates that both methods provide satisfactory approximations of spread option prices and can be used for calibration of a Libor market model to the CMS spread option market.

Parameterization of Macropore Flow Using Dye-Tracer Infiltration Patterns in the SWAP Model

Preferential flow is known to influence infiltration, soil moisture content distribution, groundwater response, and runoff generation. Various model concepts are used to simulate preferential flow. Preferential flow parameters are often determined by indirect optimization using outflow or discharge measurements, thereby providing limited insight into model performance concerning soil moisture distribution. In this study, we used a physically based macropore concept, embedded in the SWAP model, in combination with dye infiltration patterns to parameterize macropore infiltration for three locations in a catchment: hilltop, hillslope, and valley bottom. The model with the calibrated macropore parameters was applied and validated under natural field conditions, using detailed data on soil moisture content, rainfall, and discharge. The results show that the macropore model parameters can be optimized well to reproduce the dye-tracer infiltration patterns. The simulations of the dye patterns show much better results when macropore flow is included. Using the tracer infiltration patterns, however, the optimized maximum depth of macropores depends completely on the maximum depth of the stained area, while the macropores are known to extend deeper into the soil. Therefore, for long-term simulations, the wetting of deeper layers is too slow for the simulations both with and without macropores. Runoff production was better simulated with macropores. For the simulations without macropores, a higher lumped saturated conductivity was used; despite the resulting increased infiltration into the soil matrix, runoff generation remained far too high

1P074 The structure of neuroserpin polymer is different from that proposed by the domain swap model.(Protein:Property,The 48th Annual Meeting of the Biophysical Society of Japan)

1P074 The structure of neuroserpin polymer is different from that proposed by the domain swap model.(Protein:Property,The 48th Annual Meeting of the Biophysical Society of Japan)

Patterns of dissolved organic carbon and nitrogen fluxes in deciduous and coniferous forests under historic high nitrogen deposition

Abstract. Numerous recent studies have indicated that dissolved organic carbon (DOC) and nitrogen (DON) play an important role in C and N cycling in natural ecosystems, and have shown that N deposition alters the concentrations and fluxes of dissolved organic substances and may increase leaching losses from forests. Our study was set up to accurately quantify concentrations and flux patterns of DOC, DON and dissolved inorganic nitrogen (DIN) in deciduous and coniferous forest in Flanders, Belgium, under historical high nitrogen deposition. We measured DOC, DON and DIN concentrations at two weekly intervals in a silver birch (SB) stand, a corsican pine (CP) stand and a pine stand with higher N deposition (CPN), and used the SWAP model (calibrated with PEST) for generating accurate water and matter fluxes. The input with precipitation was an important source of DON, but not for DOC. Release of DOC from the forest floor was minimally affected by forest type, but higher N deposition (CPN stand) caused an 82% increase of DOC release from the forest floor. Adsorption to mineral soil material rich in iron and/or aluminum oxyhydroxides was suggested to be the most important process removing DOC from the soil solution, responsible for substantial retention (67–84%) of DOC entering the mineral soil profile with forest floor leachate. Generally, DON was less reactive (i.e. less removal from the soil solution) than DOC, resulting in decreasing DOC/DON ratios with soil depth. We found increased DOC retention in the mineral soil as a result of higher N deposition (84 kg ha−1 yr−1 additional DOC retention in CPN compared to CP). Overall DON leaching losses were 2.2, 3.3 and 5.0 kg N yr−1 for SB, CP and CPN, respectively, contributing between 9–28% to total dissolved N (TDN) leaching. The relative contribution to TDN leaching from DON loss from SB and CP was mainly determined by (large) differences in DIN leaching. The large TDN leaching losses are alarming, especially in the CPN stand that was N saturated.

Water balance in afforestation chronosequences of common oak and Norway spruce on former arable land in Denmark and southern Sweden

Precipitation, throughfall and soil moisture were measured, and interception, transpiration and water recharge were estimated in four afforestation chronosequences on former arable land at two Danish locations (Vestskoven and Gejlvang) and at one southern Swedish location (Tönnersjöheden). Afforestation was performed using Norway spruce ( Picea abies (Karst.) L.) and common oak ( Quercus robur L.) at Vestskoven and only Norway spruce at Gejlvang and Tönnersjöheden. Four to five stands of different ages (5–92 years) were studied in each of these chronosequences. Hydrological fluxes were calculated using the soil hydrological model SWAP. Throughfall flux and soil water content were used for calibration of the model. The simulated water recharge decreased with increased stand age within 30–40 years of afforestation. This was mainly due to increased interception evaporation with age. The annual water recharge was higher below oak stands (149–192 mm yr −1) than below spruce stands (107–191 mm yr −1) of similar age. The relative water recharge was also considerably higher from the sandy glaciofluvial soils at Gejlvang and Tönnersjöheden than from the sandy loamy till soils at Vestskoven.

Probing Neuroserpin Polymerization and Interaction with Amyloid- β Peptides Using Single Molecule Fluorescence

Neuroserpin is a member of the serine proteinase inhibitor superfamily. It can undergo a conformational transition to form polymers that are associated with the dementia familial encephalopathy with neuroserpin inclusion bodies and the wild-type protein can inhibit the toxicity of amyloid- β peptides in Alzheimer's disease. We have used a single molecule fluorescence method, two color coincidence detection, to determine the rate-limiting steps of the early stages of the polymerization of fluorophore-labeled neuroserpin and have assessed how this process is altered in the presence of A β 1–40. Our data show that neuroserpin polymerization proceeds first by the unimolecular formation of an active monomer, followed by competing processes of both polymerization and formation of a latent monomer from the activated species. These data are not in keeping with the recently proposed domain swap model of polymer formation in which the latent species and activated monomer are likely to be formed by competing pathways directly from the unactivated monomeric serpin. Moreover, the A β 1–40 peptide forms a weak complex with neuroserpin (dissociation constant of 10 ± 5 nM) that increases the amount of active monomer thereby increasing the rate of polymerization. The A β 1–40 is displaced from the complex so that it acts as a catalyst and is not incorporated into neuroserpin polymers.

Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation

Evapotranspiration (ET) is one of the indicators of water use efficiency. Periodic information of ET based on remote sensing is useful for an on-demand irrigation (ODI) management. The main objective of this paper was to develop an ET data assimilation scheme to optimize the parameters of an agro-hydrology model for ODI scheduling. The soil, water, atmosphere, and plant (SWAP) simulation model has been utilized for this purpose. We computed remote sensing-based ET for a wheat field in the Sirsa Irrigation Circle, Haryana, in India using 18 cloud-free moderate resolution imaging spectroradiometer images taken between December 2001 and April 2002. The surface energy balance algorithm for land (SEBAL) was used for this purpose. Because ET estimates from SEBAL provide information on the surface soil moisture state, they were treated as observations to estimate unknown parameters of the SWAP model via a stochastic data assimilation (genetic algorithm) approach. The SWAP parameters were optimized by minimizing the residuals between SEBAL and SWAP model-based ET values. The optimized parameters were used as input to SWAP to estimate soil water balance for ODI scheduling. The results showed that the selected parameters (i.e. sowing, harvesting, and irrigation scheduling dates) were successfully estimated with the data assimilation methodology. The SWAP model produced reasonable states of water balance by assimilating ET observations. The root mean square of error was 0.755 and 2.132 cm3/cm3 for 0–15 and 15–30 cm soil depths the same layers, respectively. With optimized parameters for ODI, SWAP predicted higher yield and water use efficiency than traditional farmer’s irrigation criteria. The data assimilation methodology produced can be considered as an operational tool at the field scale to schedule irrigation or predict irrigation requirements from remote sensing-based ET.

Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models

Abstract. Actual evapotranspiration from typical Mediterranean crops has been assessed in a Sicilian study area by using surface energy balance (SEB) and soil-water balance models. Both modelling approaches use remotely sensed data to estimate evapotranspiration fluxes in a spatially distributed way. The first approach exploits visible (VIS), near-infrared (NIR) and thermal (TIR) observations to solve the surface energy balance equation whereas the soil-water balance model uses only VIS-NIR data to detect the spatial variability of crop parameters. Considering that the study area is characterized by typical spatially sparse Mediterranean vegetation, i.e. olive, citrus and vineyards, alternating bare soil and canopy, we focused the attention on the main conceptual differences between one-source and two-sources energy balance models. Two different models have been tested: the widely used one-source SEBAL model, where soil and vegetation are considered as the sole source (mostly appropriate in the case of uniform vegetation coverage) and the two-sources TSEB model, where soil and vegetation components of the surface energy balance are treated separately. Actual evapotranspiration estimates by means of the two surface energy balance models have been compared vs. the outputs of the agro-hydrological SWAP model, which was applied in a spatially distributed way to simulate one-dimensional water flow in the soil-plant-atmosphere continuum. Remote sensing data in the VIS and NIR spectral ranges have been used to infer spatially distributed vegetation parameters needed to set up the upper boundary condition of SWAP. Actual evapotranspiration values obtained from the application of the soil water balance model SWAP have been considered as the reference to be used for energy balance models accuracy assessment. Airborne hyperspectral data acquired during a NERC (Natural Environment Research Council, UK) campaign in 2005 have been used. The results of this investigation seem to prove a slightly better agreement between SWAP and TSEB for some fields of the study area. Further investigations are programmed in order to confirm these indications.

Application of swap model to predict yield and soil salinity for sustainable agriculture in an arid region

Due to lack of suitable water resources, many farmers in Iran are using saline river or groundwater for irrigation, which causes gradual accumulation of salts in the soil. For sustainable agricultural productions, appropriate irrigation management practices should be applied if the saline irrigation water is to be used for irrigation. The soil–water–atmosphere–plant model is a physical-based model that can be used to simulate crop yield and soil salinity, among others. To collect fi eld data to apply to this model as input and calibrate it, a fi eld experiment planted with wheat was conducted on a silty clay loam soil, in central part of Iran (Rudasht region near Isfahan with annual average precipitation of about 80 mm), with three irrigation water salinity levels of 2, 8 and 12 dS/m with/without leaching levels of 4, 19 and 32% with two different irrigation water managements, using factorial design with four replications. The results showed that the model is applicable in this arid region and have low sensitivity to input data of root distribution depth and irrigation water salinity and medium sensitivity to climate data, soil surface layer hydraulic characteristics, leaf area index and amount of irrigation. The model simulated wheat yield and soil salinity and the calibration coeffi cients were obtained. The results showed that the model could be used to predict yield and soil salinity for sustainable agricultural production in an arid region.

Macroscopic Root Water Uptake Distribution Using a Matric Flux Potential Approach

Hydrological models featuring root water uptake usually do not include compensation mechanisms such that reductions in uptake from dry layers are compensated by an increase in uptake from wetter layers. We developed a physically based root water uptake model with an implicit compensation mechanism. Based on an expression for the matric flux potential (M) as a function of the distance to the root, and assuming a depth‐independent value of M at the root surface, uptake per layer is shown to be a function of layer bulk M, root surface M, and a weighting factor that depends on root length density and root radius. Actual transpiration can be calculated from the sum of layer uptake rates. The proposed reduction function (PRF) was built into the SWAP model, and predictions were compared to those made with the Feddes reduction function (FRF). Simulation results were tested against data from Canada (continuous spring wheat [(Triticum aestivum L.]) and Germany (spring wheat, winter barley [Hordeum vulgare L.], sugarbeet [Beta vulgaris L.], winter wheat rotation). For the Canadian data, the root mean square error of prediction (RMSEP) for water content in the upper soil layers was very similar for FRF and PRF; for the deeper layers, RMSEP was smaller for PRF. For the German data, RMSEP was lower for PRF in the upper layers and was similar for both models in the deeper layers. In conclusion, but dependent on the properties of the data sets available for testing, the incorporation of the new reduction function into SWAP was successful, providing new capabilities for simulating compensated root water uptake without increasing the number of input parameters or degrading model performance.

A Chernozem soil water regime response to predicted climate change scenarios

Climate, hydrology and vegetation are closely linked at local, regional and global scales. The recent land use and plant production systems are adapted to the present climatic conditions. Thus, studies on the influence of possible climate change scenarios on the water and heat regimes of the soil-plant-atmosphere system are important in order to work out plant production strategies, adjusted to changed conditions. In this study the effect of two possible climate change scenarios on the soil water regime of a Chernozem soil was estimated for a Hungarian site. Soil water content dynamics simulated for different conventional and soil conserving soil tillage systems were evaluated, using the SWAP soil water balance simulation model. The combined effect of different soil tillage systems and climate scenarios was analysed. Climate scenarios were represented through the cumulative probability function of the annual precipitation sum. The SWAP model was calibrated against the measured in the representative soil profiles soil water content data. The site- and soil-specific parameters were set and kept constant during the scenario studies. According to the simulation results, increase in the average growing season temperature showed increase in climate induced soil drought sensitivity. The evaluated soil water content dynamics indicated more variable and less predictable soil water regime compared to the present climate. It was found that appropriate soil tillage systems that are combined with mulching and ensure soil loosening could reliably decrease water losses from the soil. From this aspect cultivator treatment created the most favourable for the plants soil conditions. It was concluded that soil conserving soil management systems, adapted to local conditions could contribute to soil moisture conservation and could increase the amount of plant available water under changing climatic conditions.

Condições de drenagem relacionadas ao trânsito de máquinas em solo de várzea (RS-Brasil)

A exploração dos solos de várzeas do Rio Grande do Sul, Brasil, tem sido historicamente restrita ao binômio arroz irrigado-pecuária extensiva. Dentre os diversos fatores que contribuem para este quadro, as más condições de drenagem são dos mais restritivos, haja vista que, especificamente em relação ao arroz irrigado, tal situação acarreta atraso no plantio com conseqüente queda da produtividade. Como objetivo inicial, o modelo SWAP foi validado com dados de um solo típico das várzeas do Rio Grande do Sul, para posteriormente se empregá-lo na simulação de diversos cenários sob diferentes condições de drenagem e os resultados dessas simulações foram confrontados com critérios que avaliam a possibilidade de preparo do solo, com base em sua umidade. Este confronto mostrou haver relação muito estreita entre a drenagem subsuperficial e superficial; quando esta é satisfatória, os perfis de umidade podem ser compatíveis com o preparo do solo, mesmo com drenos subsuperficiais bastante espaçados; caso contrário, o crescimento no número de dias aptos para a mecanização exige a presença de drenos subsuperficiais muito próximos.