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.

Root density, soil hydraulic functions, and hydraulic head gradients play an important role in the determination of transpirationrate-limiting soil water contents. We developed an implicit numerical root water extraction model to solve the Richards equation for the modeling of radial root water extraction. The average soil water content at the moment root water potential dropped below a defined critical value was then estimated. The dependence of average water content at the onset of plant water stress on potential transpiration and root density was compared with an analytical solution for hydraulic conditions in the root sphere. The critical value was a function of potential transpiration rate, soil hydraulic properties, and root density. Matric flux potential appears to be a convenient hydraulic property to determine the onset of limiting hydraulic conditions, as numerical simulations showed that, at onset, matric flux potential vs. distance from the root surface is independent of soil type. This was also determined analytically under the constant-rate assumption. Mean water content occurs at about 0.53 times the half-distance between roots. This allows calculation of the mean limiting soil water content and pressure head from the matric flux potential at this distance, which is a function of transpiration rate and root density only. A nomogram was developed that-given the transpiration rate, the root density, and the soil hydraulic functions-allows determination of the values of mean water content and mean pressure head that occur at the onset of transpiration reduction.

Abstract:Koeppen's climate classification, developed one century ago, is still used as a reference for large-scale climate mapping and useful for climatologic research (climate shift, climate model testing, and ecological research). This paper describes a software that was developed to allow non-assisted Koeppen climate classification based on a simple database (monthly mean temperatures and rainfall) as input. The software follows, as strictly as possible, the original concepts suggested by Koeppen, but does not include climates that cannot be defined by simple input parameters. Combined with GIS tools, the software was used to produce the first comprehensive climate map of Brazil using a large database. The map showed 21 Climate Equations in three climate zones (A = tropical moist; B = dry; C = moist with mild winter). This map was compared with an existing climate map of Brazil (FAO/SDRN). The two maps showed good agreement only in the less detailed information (climate zone and type). The complete Climate Equations were not comparable in relation to spatial distribution and class coincidence. Improvements were made for Koeppen's climate classification, avoiding computational or database errors, and it increased the details for climate classification of Brazil.

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SUMMARYPedotransfer functions (PTF) were developed to estimate the parameters (α α α α α, n, θ θ θ θ θr and θ θ θ θ θs) of the van Genuchten model (1980) to describe soil water retention curves. The data came from various sources, mainly from studies conducted by universities in Northeast Brazil, by the Brazilian Agricultural Research Corporation (Embrapa) and by a corporation for the development of the São Francisco and Parnaíba river basins (Codevasf), totaling 786 retention curves, which were divided into two data sets: 85 % for the development of PTFs, and 15 % for testing and validation, considered independent data. Aside from the development of general PTFs for all soils together, specific PTFs were developed for the soil classes Ultisols, Oxisols, Entisols, and Alfisols by multiple regression techniques, using a stepwise procedure (forward and backward) to select the best predictors. Two types of PTFs were developed: the first included all predictors (soil density, proportions of sand, silt, clay, and organic matter), and the second only the proportions of sand, silt and clay. The evaluation of adequacy of the PTFs was based on the correlation coefficient (R) and Willmott index (d). To evaluate the PTF for the moisture content at specific pressure heads, we used the root mean square error (RMSE). The PTFpredicted retention curve is relatively poor, except for the residual water content. The inclusion of organic matter as a PTF predictor improved the prediction of parameter α α α α α of van Genuchten. The performance of soil-class-specific PTFs was not better than of the general PTF. Except for the water content of saturated soil estimated by particle size distribution, the tested models for water content prediction at specific pressure heads proved satisfactory. Predictions of water content at pressure heads more negative than -0.6 m, using a PTF considering

Transpiration reduction functions are often used in hydrological modeling to estimate actual transpiration as a function of soil water status. Empirical reduction functions are most frequently used due to the higher data needs and computational requirements of mechanistic models. Empirical models, however, lack a description of physical mechanisms and their parameters require extensive calibration. We derive a process‐based reduction function predicting system potentials, resistances, and water flows. An analytical solution for a special case of Brooks and Corey soils is presented. A numerical version of the reduction function for van Genuchten soils was implemented in the Soil–Water–Atmosphere–Plant (SWAP) hydrological model, allowing predictions for layered soil profiles and root length density variations over depth. The analytical and numerical versions of the model allow an increasingly quantitative insight into the mechanism of root water uptake, such as the existence of a maximum root water uptake rate as a function of soil water status, soil hydraulic properties, root length density, and root radius, in addition to the fact that sensitivity of simulated root water uptake to the radial root conductivity and axial conductance decrease when root length density increases. The approach can be used for the estimation of threshold values for empirical reduction functions.

(3) RESUMO A capacidade de campo ou seu equivalente para culturas em vaso, a "capacidade de vaso", é um dado importante para o manejo da irrigação. O objetivo deste trabalho foi determinar, em vasos preenchidos com material de dois solos diferentes (um argiloso e um de textura média), o teor de água e a taxa de perda de água em função do tempo e, a partir dessas determinações, estabelecer valores para a capacidade de vaso a partir de diferentes critérios. Conclui-se que o teor de água final extrapolado da curva observada θ θ θ θ θ-t é um bom estimador da capacidade de vaso, especialmente para os casos em que a tolerância de perda de água é pequena. Não é recomendável estimar a capacidade de vaso com base em valores "tradicionais" de potencial matricial ou de tempo de drenagem, pois os valores são superestimados em relação aos teores obtidos com base na curva θ θ θ θ θ-t e correspondem a altas taxas de redução de teor de água.Termos de indexação: água no solo, cultivo em vasos, manejo da irrigação.

Northeastern Brazil is hydrologically characterized by recurrent droughts leading to a highly vulnerable natural water resource system. The region contains the Caatinga biome, covering an area of approximately 800 000 km 2 . To increase insight in water balance components for this sparsely studied ecosystem, hydrology simulations were performed with the SWAP (Soil Water Atmosphere Plant) model for a Caatinga basin of 12 km 2 . SWAP model was developed to simulate hydrology under short-cycle crops, and its parameterization and validation to a diverse ecosystem is a novelty. The validation of the simulations was performed using a dataset of daily soil water content measurements taken at 0.2 m depth in three sites in the basin in the period from 2004 to 2012. Average Nash-Sutcliffe efficiency coefficient for these simulations was 0.57 and root mean square error of prediction was 4.3%. The results of the simulations suggest that water components do not diverge statistically among different sites of the biome. The Caatinga biome returns 75% (±17%) of the annual precipitation to the atmosphere, whereas the partitioning of total evapotranspiration into its components (transpiration, evaporation and interception) on annual basis accounts for 41% (±7%), 40% (±6%) and 19% (±3%) respectively. Regarding water availability, the surface soil layer (0.0-0.2 m) is the most important layer in the rooted profile, supplying up to 90% of atmospheric water demand. According to our analysis performed on daily basis, evapotranspiration and air temperature are most sensitive to soil moisture during the periods