Dielectric sensors have been widely used for nondestructive determination of volumetric soil water content (θ, m3 m−3). Since the output of such sensors is affected by soil temperature (T, °C), the calibration for the effect is indispensable for accurate determination of θ. The objectives of this paper were (i) evaluation of the temperature effects on outputs of the commercial capacitance probes called ECH2O probes for various types of soils, and (ii) to include temperature in empirical calibration equations. Laboratory experiments were performed to obtain probe outputs at various T (5– 35°C) and θ (air‐dry– near‐saturation), using four soils and four probe models with different oscillation frequencies (5 and 70 MHz). The results showed that the outputs linearly responded to T at constant θ for all tested soil–probe combinations. The slope values of the linear responses to T depended on θ. The curves of the output–θ functions at a reference temperature (25°C) varied among the soils and probe models. A calibration equation describing the probe output as a function of θ and T was derived for each soil–probe combination by combining the output–θ function at the reference temperature and the slope–θ function. The derived calibration equations substantially reduced the temperature effects on the probe outputs for all soil–probe combinations. We also briefly considered the theoretical background of temperature effects on the probe outputs based on the results from the experiments and the properties of the soils tested. To demonstrate the importance of temperature calibration, the derived calibration equations were applied to two field observations from arid reasons.

Abstract. In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type (∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

A method of determination of irrigation depth using a numerical model of crop response to irrigation and weather forecast was presented. To optimize each irrigation depth, a concept of virtual income, which is proportional to an increment in transpiration amount during an irrigation interval, is introduced. A numerical model that simulates water, solute, and heat transport and crop response is used in a numerical experiment. Results indicated that the optimized irrigation depth can be smaller than the value which attains maximum yield.

Abstract. In this paper, we present and analyze a global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and USA. In addition to its global spatial coverage, the collected infiltration curves cover a time span of research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use were gathered along with the infiltration data, which makes the database valuable for the development of pedo-transfer functions for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76 %) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on the land use is available for 76 % of experimental sites with agricultural land use as the dominant type (~40 %). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for use by public domain only and can be copied freely by referencing it. Supplementary data are available at doi:10.1594/PANGAEA.885492. Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend/update the SWIG by uploading new data to it.

No abstract

The number of sensor types available for measuring soil water content has increased but investigations to compare their performance in saline soils needs clarification. In this study the performance of commercially available, low‐cost soil moisture sensors [time domain reflectometry (TDR), PR1 and WET], all measuring changes in the dielectric constant of the soil water, was evaluated under laboratory conditions in a saline sandy soil. The three sensors were also tested in the same sandy soil growing drip irrigated sorghum (Sorghum bicolor L. cv. Moench) in a greenhouse. Plants were irrigated daily with either saline water (ECw: 9.4 dS/m) or fresh water (0.11 dS/m). The volume of irrigation was equivalent to 100% of the pan evaporation. The results showed that measurement accuracy was strongly dependent on the salinity of the soil. The PR1 sensor overestimated volumetric water content (θ) when the salinity level exceeded 4 dS/m [root mean square of the standard error (RMSE) = 0.009 cm3/cm3]. The WET sensor significantly overestimated θ irrespective of the salinity level (RMSE = 0.014 cm3/cm3). The TDR sensor estimated θ with more accuracy (RMSE = 0.007 cm3/cm3) and thus can be considered as more reliable than the other two sensors. The calibrations were strongly affected by the salinity level of the water, so we recommend that calibration equations are modified to take account of salinity.

No abstract

Abstract:The Loess Plateau in China constitutes an important source area for both water and sediments to the Yellow River. Thus, improved prediction techniques of rainfall may lead to better estimation of discharge and sediment content for the Yellow River. Consequently, the objective of this study was to establish better links between rainfall of the Loess Plateau in China and sea surface temperature (SST) in the Pacific Ocean. Results showed that there is a strong lagged correlation between and SST and rainfall. The SST for Micronesia and areas south of the Aleutian Islands showed significant correlations (s.f. < 0Ð001; 99Ð9%) with rainfall over the dryer region of the Loess Plateau for a lag of 4 to 6 months. The SST over the equator on the east Pacific Ocean also showed significant negative correlation with rainfall. Low and middle latitude areas (S10-20°and around 30°) of the south-east Pacific Ocean displayed significant positive and negative correlation with rainfall on the semiarid Loess Plateau. The differenced SST values (positive SST minus negative SST) increased these correlations with rainfall. An artificial neural network (ANN) model was used to predict summer rainfall from the differenced SST during the spring period. The correlation between predicted and observed monthly rainfall was in general larger than 0Ð7. This indicates that major annual rainfall (during summer season) can be predicted with good accuracy using the suggested approach.