scholarly journals Risk Assessment of Soil Salinization due to Tomato Cultivation in Mediterranean Climate Conditions

2018 ◽  
Author(s):  
Angela Libutti ◽  
Anna Rita Bernadette Cammerino ◽  
Massimo Monteleone
Keyword(s):  
Irrigation Water ◽  
Soil Profile ◽  
Mediterranean Climate ◽  
Daily Basis ◽  
Soil Salinization ◽  
Weather Data ◽  
Physical Parameters ◽  
Catchment Scale ◽  
Climate Conditions ◽  
Tomato Yield

Mediterranean climate is marked by arid climate conditions in summer, therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, also triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (Southern Italy), close to the Adriatic sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was arranged to perform, on daily basis, a water and salt balance along the soil profile. Long-term weather data and soil physical parameters representative of the considered area were utilized in applying the model, also considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn-winter rainfall resulted frequently insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were prospected.

scholarly journals Risk Assessment of Soil Salinization Due to Tomato Cultivation in Mediterranean Climate Conditions

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1503 ◽  
Author(s):  
Angela Libutti ◽  
Anna Cammerino ◽  
Massimo Monteleone
Keyword(s):  
Irrigation Water ◽  
Soil Profile ◽  
Mediterranean Climate ◽  
Daily Basis ◽  
Soil Salinization ◽  
Weather Data ◽  
Catchment Scale ◽  
Climate Conditions ◽  
Tomato Yield ◽  
The Mediterranean

The Mediterranean climate is marked by arid climate conditions in summer; therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping system is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, as well as triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (southern Italy), close to the Adriatic Sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was first arranged, then validated, and finally applied to perform a water and salt balance along a representative soil profile on a daily basis. In this regard, long-term weather data and physical soil characteristics of the considered area (both taken from international databases) were utilized in applying the model, as well as considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn–winter rainfall frequently proved to be insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were investigated.

scholarly journals Alternate Partial Root-Zone Drip Nitrogen Fertigation Reduces Residual Nitrate Loss While Improving the Water Use but Not Nitrogen Use Efficiency

2021 ◽  
Vol 12 ◽  
Author(s):  
Rui Liu ◽  
Peng-Fei Zhu ◽  
Yao-Sheng Wang ◽  
Zhen Chen ◽  
Ji-Rong Zhu ◽  
...  
Keyword(s):  
Water Use ◽  
Irrigation Water ◽  
Soil Profile ◽  
Root Zone ◽  
Soil Column ◽  
Tomato Yield ◽  
N Supply ◽  
Use Efficiency ◽  
Residual Nitrate ◽  
N Use

The efficient utilization of irrigation water and nitrogen is of great importance for sustainable agricultural production. Alternate partial root-zone drip irrigation (APRD) is an innovative water-saving drip irrigation technology. However, the coupling effects of water and nitrogen (N) supply under APRD on crop growth, water and N use efficiency, as well as the utilization and fate of residual nitrates accumulated in the soil profile are not clear. A simulated soil column experiment where 30–40 cm soil layer was 15NO3-labeled as residual nitrate was conducted to investigate the coupling effects of different water [sufficient irrigation (W1), two-thirds of the W1(W2)] and N [high level (N1), 50% of N1 (N2)] supplies under different irrigation modes [conventional irrigation (C), APRD (A)] on tomato growth, irrigation water (IWUE) and N use efficiencies (NUE), and the fate of residual N. The results showed that, compared with CW1N1, AW1N1 promoted root growth and nitrogen absorption, and increased tomato yield, while the N absorption and yield did not vary significantly in AW2N1. The N absorption in AW2N2 decreased by 16.1%, while the tomato yield decreased by only 8.8% compared with CW1N1. The highest IWUE appeared in AW2N1, whereas the highest NUE was observed in AW2N2, with no significant difference in NUE between AW2N1 and CW1N1 at the same N supply level. The 15N accumulation peak layer was almost the same as the originally labeled layer under APRD, whereas it moved 10–20 cm downwards under CW1N1. The amount of 15N accumulated in the 0-40 cm layer increased with the decreasing irrigation water and nitrogen supply, with an increase of 82.9–141.1% in APRD compared with that in CW1N1. The utilization of the 15N labeled soil profile by the tomato plants increased by 9–20.5%, whereas the loss rate of 15N from the plant-soil column system decreased by 21.3–50.1% in APRD compared with the CW1N1 treatment. Thus, APRD has great potential in saving irrigation water, facilitating water use while reducing the loss of residual nitrate accumulated in the soil profile, but has no significant effect on the NUE absorbed.

IRRIGATION SCHEDULE OF LONG-FRUIT CUCUMBERS AND HYDROAMELIORATIVE REQUIREMENTS FOR DRIP IRRIGATION IN PLASTIC GREENHOUSE

2019 ◽  
pp. 307-313
Author(s):  
Rumiana Kireva ◽  
Roumen Gadjev
Keyword(s):  
Irrigation Water ◽  
Drip Irrigation ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Water Source ◽  
Climate Conditions ◽  
Irrigation Technology ◽  
Irrigation Technologies ◽  
Pressure Flows ◽  
Efficient Water Use

The deficit of the irrigation water requires irrigation technologies with more efficient water use. For cucumbers, the most suitable is the drip irrigation technology. For establishing of the appropriate irrigation schedule of cucumbers under the soil and climate conditions in the village of Chelopechene, near Sofia city, the researchеs was conducted with drip irrigation technology, adopting varying irrigation schedules and hydraulic regimes - from fully meeting the daily crops water requirements cucumbers to reduced depths with 20% and 40%. It have been established irrigation schedule with adequate pressure flows in the water source, irrigation water productivity and yields of in plastic unheated greenhouses of the Sofia plant.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

Catchment-scale evaluation of pollution potential of urban snow at two residential catchments in southern Finland

2013 ◽  
Vol 68 (10) ◽  
pp. 2164-2170 ◽  
Author(s):  
Nora Sillanpää ◽  
Harri Koivusalo
Keyword(s):  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Cold Climate ◽  
Road Maintenance ◽  
Catchment Scale ◽  
Small Surface ◽  
Climate Conditions ◽  
Snow Water ◽  
High Level ◽  
Mass Loads

Despite the crucial role of snow in the hydrological cycle in cold climate conditions, monitoring studies of urban snow quality often lack discussions about the relevance of snow in the catchment-scale runoff management. In this study, measurements of snow quality were conducted at two residential catchments in Espoo, Finland, simultaneously with continuous runoff measurements. The results of the snow quality were used to produce catchment-scale estimates of areal snow mass loads (SML). Based on the results, urbanization reduced areal snow water equivalent but increased pollutant accumulation in snow: SMLs in a medium-density residential catchment were two- to four-fold higher in comparison with a low-density residential catchment. The main sources of pollutants were related to vehicular traffic and road maintenance, but also pet excrement increased concentrations to a high level. Ploughed snow can contain 50% of the areal pollutant mass stored in snow despite its small surface area within a catchment.

scholarly journals Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

2011 ◽  
Vol 15 (7) ◽  
pp. 2259-2274 ◽  
Author(s):  
S. Ly ◽  
C. Charles ◽  
A. Degré
Keyword(s):  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Gaussian Model ◽  
Daily Rainfall ◽  
Spherical Model ◽  
Daily Basis ◽  
Catchment Scale ◽  
Multivariate Geostatistics ◽  
Variogram Models ◽  
Thiessen Polygon

Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.

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