Scale issues in soil moisture modelling: problems and prospects

Soil moisture storage is an important component of the hydrological cycle and plays a key role in land-surface-atmosphere interaction. The soil-moisture storage equation in this study considers precipitation as an input and soil moisture as a residual term for runoff and evapotranspiration. A number of models have been developed to estimate soil moisture storage and the components of the soil-moisture storage equation. A detailed discussion of the impli cation of the scale of application of these models reports that it is not possible to extrapolate processes and their estimates from the small to the large scale. It is also noted that physically based models for small-scale applications are sufficiently detailed to reproduce land-surface- atmosphere interactions. On the other hand, models for large-scale applications oversimplify the processes. Recently developed physically based models for large-scale applications can only be applied to limited uses because of data restrictions and the problems associated with land surface characterization. It is reported that remote sensing can play an important role in over coming the problems related to the unavailability of data and the land surface characterization of large-scale applications of these physically based models when estimating soil moisture storage.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Application of the Hybrid-Maize model for limits to maize productivity analysis in a semiarid environment

Effects of meteorological variables on crop production can be evaluated using various models. We have evaluated the ability of the Hybrid-Maize model to simulate growth, development and grain yield of maize (Zea mays L.) cultivated on the Loess Plateau, China, and applied it to assess effects of meteorological variations on the performance of maize under rain-fed and irrigated conditions. The model was calibrated and evaluated with data obtained from field experiments performed in 2007 and 2008, then applied to yield determinants using daily weather data for 2005-2009, in simulations under both rain-fed and irrigated conditions. The model accurately simulated Leaf Area Index , biomass, and soil water data from the field experiments in both years, with normalized percentage root mean square errors < 25 %. Gr.Y and yield components were also accurately simulated, with prediction deviations ranging from -2.3 % to 22.0 % for both years. According to the simulations, the maize potential productivity averaged 9.7 t ha-1 under rain-fed conditions and 11.53 t ha-1 under irrigated conditions, and the average rain-fed yield was 1.83 t ha-1 less than the average potential yield with irrigation. Soil moisture status analysis demonstrated that substantial potential yield may have been lost due to water stress under rain-fed conditions

CSM-CERES-Rice model to determine management strategies for lowland rice production

The cropping system model, namely, the crop environment resource synthesis-rice (CSM-CERES-Rice) model, is a decision supporting tool for the design of crop management. This study aimed to determine management practices for increasing rice (Oryza sativa L.) production in Laos by using the CSM-CERES-Rice model. The model was evaluated with data sets from the TDK8 and TDK11 cultivars in farmers’ fields in the Vientiane plain in 2012. Anthesis and harvesting dates, growth and yield for various management scenario combinations (eight transplanting dates × two levels of plant densities × three rates of nitrogen (N) fertilizer application) for both cultivars were simulated by the model from 1980 to 2012. The model evaluation results showed strong agreement between simulated and observed data for days to harvest with a difference within four days. The model provided acceptable accuracy for grain yields with normalized root mean square error values ranging between 1 and 16 %. The results from the model application indicated that TDK8 and TDK11 produced similar yields. Transplanting TDK8 with two plant densities produced similar yields. The highest yield for both cultivars was achieved on the transplanting date of 15 Jan. N-fertilizer application at 60 and 120 kg N ha−1 was able to increase yield for TDK8 by 50 and 87 %, respectively, and for TDK11 by 54 and 70 %, respectively. Rice transplanted on 15 Jan with 5 seedlings hill−1 and N-fertilizer at 120 kg N ha−1 had the highest average yield for both cultivars with 6,460 and 6,351 kg ha−1 for TDK8 and TDK11, respectively. The CSM-CERES-Rice model is an alternative tool in determining crop management practices for rice production

Sugarcane root length density and distribution from root intersection counting on a trench-profile

Root length density (RLD) is a critical feature in determining crops potential to uptake water and nutrients, but it is difficult to be measured. No standard method is currently available for assessing RLD in the soil. In this study, an in situ method used for other crops for studying root length density and distribution was tested for sugarcane (Saccharum spp.). This method involved root intersection counting (RIC) on a Rhodic Eutrudox profile using grids with 0.05 x 0.05 m and modeling RLD from RIC. The results were compared to a conventional soil core-sampled method (COR) (volume 0.00043 m³). At four dates of the cropping season in three tillage treatments (plowing soil, minimum tillage and direct planting), with eight soil depths divided in 0.1 m soil layer (between 0-0.6 and 1.6-1.8 m) and three horizontal distances from the row (0-0.23, 0.23-0.46 and 0.46-0.69 m), COR and RIC methods presented similar RLD results. A positive relationship between COR and RIC was found (R² = 0.76). The RLD profiles considering the average of the three row distances per depth obtained using COR and RIC (mean of four dates and 12 replications) were close and did not differ at each depth of 0.1 m within a total depth of 0.6 m. Total RLD between 0 and 0.6 m was 7.300 and 7.100 m m-2 for COR and RIC respectively. For time consumption, the RIC method was tenfold less time-consuming than COR and RIC can be carried out in the field with no need to remove soil samples. The RLD distribution in depth and row distance (2-D variability) by RIC can be assessed in relation to the soil properties in the same soil profiles. The RIC method was suitable for studying these 2-D (depth and row distance in the soil profile) relationships between soil, tillage and root distribution in the field.A densidade de comprimento de raízes (DCR) é uma característica importante para determinar o potencial de absorção de água e nutrientes das plantas, mas é difícil de ser medida. Nenhum método padrão está atualmente disponível para avaliar a DCR no solo. Neste estudo, um método in situ usado em outras culturas para estudo da densidade de comprimento e distribuições das raízes foi testado para a cana-de-açúcar (Saccharum spp.). O método envolveu contagem de intersecções de raízes (CIR) no perfil de um Latossolo Vermelho eutroférrico, usando grade com quadrículas de 0.05 x 0.05 m, modelizando a DCR a partir da CIR. Os resultados foram comparados com o método do trado cilíndrico (TRA) (volume de 0.00043 m-3). Em quatro épocas durante o ciclo em três manejos do solo (plantio convencional, cultivo mínimo e plantio direto), em oito profundidades divididas a cada 0.1 m (entre 0 - 0.6 e 1.6 - 1.8 m) e três distâncias horizontais em relação à linha de plantio (0 - 0.23, 0.23 - 0.46 e 0.46 - 0.69 m), os métodos TRA e CIR apresentaram resultados de DCR similares. Encontrou-se positiva entre TRA e CIR (R² = 0,76). As DCRs nos perfis, considerando as médias das três distâncias da linha por profundidade, obtida utilizando-se de TRA e CIR (média de quatro datas e 12 repetições), foram próximas e não diferiram a cada 0.1 m de profundidade até 0.6 m de profundidade. A DCR total entre 0 e 0.6 m foi de 7.300 e 7.100 m m-2 para TRA e CIR, respectivamente. Para o tempo de realização, o método CIR foi 10 vezes mais rápido do que TRA e o método CIR pode ser realizado no campo, sem necessidade de remover amostras de solo. A distribuição da DCR em profundidade e distância da linha (variabilidade 2D) pelo método CIR pode ser avaliada em relação às propriedades do solo nos mesmos perfis do solo. O método CIR foi apropriado para estudos dessas relações 2D (profundidade e distância da linha no perfil do solo) entre solo, manejo e distribuição de raízes no campo