Relative performances of textural models in estimating soil moisture characteristic

The soil moisture characteristic (SMC) forms an important input to mathematical models of water and solute transport in the unsaturated-soil zone. Owing to their simplicity and ease of use, texture-based regression models are commonly used to estimate the SMC from basic soil properties. In this study, the performances of six such regression models were evaluated on three soils. Moisture characteristics generated by the regression models were statistically compared with the characteristics developed independently from laboratory and in-situ retention data of the soil profiles. Results of the statistical performance evaluation, while providing useful information on the errors involved in estimating the SMC, also highlighted the importance of the nature of the data set underlying the regression models. Among the models evaluated, the one possessing an underlying data set of in-situ measurements was found to be the best estimator of the in-situ SMC for all the soils. Considerable errors arose when a textural model based on laboratory data was used to estimate the field retention characteristics of unsaturated soils.

Comparison of electrical resistivity by geophysical method and neutron probe logging for soil moisture monitoring in a forested watershed

Geophysical methods are becoming more popular nowadays in the field of hydrology due to their time and space efficiency. So an attempt has been made here to relate electrical resistivity with soil moisture content in the field. The experiments were carried out in an experimental watershed `Mulehole' in southern India, which is a forested watershed with approximately 80% red soil. Five auger holes were drilled to perform the soil moisture and electrical resistivity measurements in a toposequence having red and black soils, with sandy weathered soil at the bottom. Soil moisture was measured using neutron probe and electrical resistivity was measured using electrical logging tool. The results indicate that electrical resistivity measurements can be used to measure soil moisture content for red soils only.

Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

Estimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to be tested for a broader range of climatic conditions and crop types, to assess its potential for spatial applications. (C) 2012 Elsevier B.V. All rights reserved.

Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

The current study presents an algorithm to retrieve surface Soil Moisture (SM) from multi-temporal Synthetic Aperture Radar (SAR) data. The developed algorithm is based on the Cumulative Density Function (CDF) transformation of multi-temporal RADARSAT-2 backscatter coefficient (BC) to obtain relative SM values, and then converts relative SM values into absolute SM values using soil information. The algorithm is tested in a semi-arid tropical region in South India using 30 satellite images of RADARSAT-2, SMOS L2 SM products, and 1262 SM field measurements in 50 plots spanning over 4 years. The validation with the field data showed the ability of the developed algorithm to retrieve SM with RMSE ranging from 0.02 to 0.06 m(3)/m(3) for the majority of plots. Comparison with the SMOS SM showed a good temporal behaviour with RMSE of approximately 0.05 m(3)/m(3) and a correlation coefficient of approximately 0.9. The developed model is compared and found to be better than the change detection and delta index model. The approach does not require calibration of any parameter to obtain relative SM and hence can easily be extended to any region having time series of SAR data available.

A low-power, low-cost soil-moisture sensor using dual-probe heat-pulse technique

This paper presents the development and testing of an integrated low-power and low-cost dual-probe heat-pulse (DPHP) soil-moisture sensor in view of the electrical power consumed and affordability in developing countries. A DPHP sensor has two probes: a heater and a temperature sensor probe spaced 3 mm apart from the heater probe. Supply voltage of 3.3V is given to the heater-coil having resistance of 33 Omega power consumption of 330 mW, which is among the lowest in this category of sensors. The heater probe is 40 mm long with 2 mm diameter and hence is stiff enough to be inserted into the soil. The parametric finite element simulation study was performed to ensure that the maximum temperature rise is between 1 degrees C and 5 degrees C for wet and dry soils, respectively. The discrepancy between the simulation and experiment is less than 3.2%. The sensor was validated with white clay and tested with red soil samples to detect volumetric water-content ranging from 0% to 30%. The sensor element is integrated with low-power electronics for amplifying the output from thermocouple sensor and TelosB mote for wireless communication. A 3.7V lithium ion battery with capacity of 1150 mAh is used to power the system. The battery is charged by a 6V and 300 mA solar cell array. Readings were taken in 30 min intervals. The life-time of DPHP sensor node is around 3.6 days. The sensor, encased in 30 mm x 20 mm x 10 mm sized box, and integrated with electronics was tested independently in two separate laboratories for validating as well as investigating the dependence of the measurement of soil-moisture on the density of the soil. The difference in the readings while repeating the experiments was found out to be less than 0.01%. Furthermore, the effect of ambient temperature on the measurement of soil-moisture is studied experimentally and computationally. (C) 2015 Elsevier B.V. All rights reserved.

Estimation of soil hydraulic properties and their uncertainty: comparison between laboratory and field experiment

Often the soil hydraulic parameters are obtained by the inversion of measured data (e.g. soil moisture, pressure head, and cumulative infiltration, etc.). However, the inverse problem in unsaturated zone is ill-posed due to various reasons, and hence the parameters become non-unique. The presence of multiple soil layers brings the additional complexities in the inverse modelling. The generalized likelihood uncertainty estimate (GLUE) is a useful approach to estimate the parameters and their uncertainty when dealing with soil moisture dynamics which is a highly non-linear problem. Because the estimated parameters depend on the modelling scale, inverse modelling carried out on laboratory data and field data may provide independent estimates. The objective of this paper is to compare the parameters and their uncertainty estimated through experiments in the laboratory and in the field and to assess which of the soil hydraulic parameters are independent of the experiment. The first two layers in the field site are characterized by Loamy sand and Loamy. The mean soil moisture and pressure head at three depths are measured with an interval of half hour for a period of 1 week using the evaporation method for the laboratory experiment, whereas soil moisture at three different depths (60, 110, and 200 cm) is measured with an interval of 1 h for 2 years for the field experiment. A one-dimensional soil moisture model on the basis of the finite difference method was used. The calibration and validation are approximately for 1 year each. The model performance was found to be good with root mean square error (RMSE) varying from 2 to 4 cm(3) cm(-3). It is found from the two experiments that mean and uncertainty in the saturated soil moisture (theta(s)) and shape parameter (n) of van Genuchten equations are similar for both the soil types. Copyright (C) 2010 John Wiley & Sons, Ltd.

Influence of time step in the simulation modelling of evapotranspiration

Three simulations of evapotranspiration were done with two values of time step,viz 10 min and one day. Inputs to the model were weather data, including directly measured upward and downward radiation, and soil characteristics. Three soils were used for each simulation. Analysis of the results shows that the time step has a direct influence on the prediction of potential evapotranspiration, but a complex interaction of this effect with the soil moisture characteristic, rate of increase of ground cover and bare soil evaporation determines the actual transpiration predicted. The results indicate that as small a time step as possible should be used in the simulation.

Regional Differences in the Influence of Irrigation on Climate

A global climate model experiment is performed to evaluate the effect of irrigation on temperatures in several major irrigated regions of the world. The Community Atmosphere Model, version 3.3, was modified to represent irrigation for the fraction of each grid cell equipped for irrigation according to datasets from the Food and Agriculture Organization. Results indicate substantial regional differences in the magnitude of irrigation-induced cooling, which are attributed to three primary factors: differences in extent of the irrigated area, differences in the simulated soil moisture for the control simulation (without irrigation), and the nature of cloud response to irrigation. The last factor appeared especially important for the dry season in India, although further analysis with other models and observations are needed to verify this feedback. Comparison with observed temperatures revealed substantially lower biases in several regions for the simulation with irrigation than for the control, suggesting that the lack of irrigation may be an important component of temperature bias in this model or that irrigation compensates for other biases. The results of this study should help to translate the results from past regional efforts, which have largely focused on the United States, to regions in the developing world that in many cases continue to experience significant expansion of irrigated land.

Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence

We present here the first statistically calibrated and verified tree-ring reconstruction of climate from continental Southeast Asia.The reconstructed variable is March-May (MAM) Palmer Drought Severity Index (PDSI) based on ring widths from 22 trees (42 radial cores) of rare and long-lived conifer, Fokienia hodginsii (Po Mu as locally called) from northern Vietnam. This is the first published tree ring chronology from Vietnam as well as the first for this species. Spanning 535 years, this is the longest cross-dated tree-ring series yet produced from continental Southeast Asia. Response analysis revealed that the annual growth of Fokienia at this site was mostly governed by soil moisture in the pre-monsoon season. The reconstruction passed the calibration-verification tests commonly used in dendroclimatology, and revealed two prominent periods of drought in the mid-eighteenth and late-nineteenth enturies. The former lasted nearly 30 years and was concurrent with a similar drought over northwestern Thailand inferred from teak rings, suggesting a ``mega-drought'' extending across Indochina in the eighteenth century. Both of our reconstructed droughts are consistent with the periods of warm sea surface temperature (SST)anomalies in the tropical Pacific. Spatial correlation analyses with global SST indicated that ENSO-like anomalies might play a role in modulating droughts over the region, with El Nio (warm) phases resulting in reduced rainfall. However, significant correlation was also seen with SST over the Indian Ocean and the north Pacific,suggesting that ENSO is not the only factor affecting the climate of the area. Spectral analyses revealed significant peaks in the range of 53.9-78.8 years as well as in the ENSO-variability range of 2.0 to 3.2 years.

A linear root water uptake model

A model of root water extraction is proposed, in which a linear variation of extraction rate with depth is assumed. Five crops are chosen for simulation studies of the model, and soil moisture depletion under optimal conditions from different layers for each crop is calculated. Similar calculations are also made using the constant extraction rate model. Rooting depth is assumed to vary linearly with potential evapotranspiration for each crop during the vegetative phase. The calculated depletion patterns are compared with measured mean depletion patterns for each crop. It is shown that the constant extraction rate model results in large errors in the prediction of soil moisture depletion, while the proposed linear extraction rate model gives satisfactory results. Hypothetical depletion patterns predicted by the model in combination with a moisture tension-dependent sink term developed elsewhere are indicated.

Late Quaternary vegetational and climatic changes from tropical peats in southern India - An extended record up to 40,000 years BP

Stable carbon isotope ratios of peats dated (by C-14) back to 40 kyr BP from the montane region (> 1800 m asl) of the Nilgiris, southern India, reflect changes in the relative proportions of C3 and C4 plant types, which are influenced by soil moisture (and hence monsoonal precipitation), From prior to 40 kyr BP until 28 kyr BP, a general decline in delta(13)C values from about - 14 per mil to - 19 per mil suggests increased dominance of C3 plants concurrent with increasingly moist conditions, During 28-18 kyr BP there seems relatively little change with delta(13) C of - 19 to - 18 per mil, At about 16 kyr BP a sharp reversal in delta(13)C to a peak of - 14.7 per mil indicates a clear predominance of C4 vegetation associated with arid conditions, possibly during or just after the Last Glacial Maximum, A moist phase at about 9 kyr BP (the Holocene Optimum) with dominance of C3 vegetation type is observed, while arid conditions are re-established during 5-2 kyr BP with an overall dominance of C4 vegetation, New data do not support the occurrence of a moist phase coinciding with the Mediaeval Warm Period (at 0.6 kyr BP) as suggested earlier, Overall, the climate and vegetation in the high altitude regions of the southern Indian tropics seem to have responded to past global climatic changes, and this is consistent with other evidences from India and other tropical regions.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

Sensor network deployment for agronomical data gathering in semi-arid regions

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

Dust aerosols over India and adjacent continents retrieved using METEOSAT infrared radiance Part II: quantification of wind dependence and estimation of radiative forcing

Long-range transport of continental dust makes these particles a significant constituent even at locations far from their sources. It is important to study the temporal variations in dust loading over desert regions and the role of meteorology, in order to assess its radiative impact. In this paper, infrared radiance (10.5-12.5 mu m), acquired by the METEOSAT-5 satellite (similar to 5-km resolution) during 1999 and 2003 was used to quantify wind dependence of dust aerosols and to estimate the radiative forcing. Our analysis shows that the frequency of occurrence of dust events was higher during 2003 compared to 1999. Since the dust production function depends mainly on the surface wind speed over regions which are dry and without vegetation, the role of surface wind on IDDI was examined in detail. It was found that an increase of IDDI with wind speed was nearly linear and the rate of increase in IDDI with surface wind was higher during 2003 compared to 1999. It was also observed that over the Indian desert, when wind speed was the highest during monsoon months (June to August), the dust production rate was lower because of higher soil moisture (due to monsoon rainfall). Over the Arabian deserts, when the wind speed is the highest during June to August, the dust production rate is also highest, as soil moisture is lowest during this season. Even though nothing can be said precisely on the reason why 2003 had a greater number of dust events, examination of monthly mean soil moisture at source regions indicates that the occurrence of high winds simultaneous with high soil moisture could be the reason for the decreased dust production efficiency in 1999. It appears that the deserts of Northwest India are more efficient dust sources compared to the deserts of Saudi Arabia and Northeast Africa (excluding Sahara). The radiative impact of dust over various source regions is estimated, and the regionally and annually averaged top of the atmosphere dust radiative forcing (short wave, clear-sky and over land) over the entire study region (0-35 degrees N; 30 degrees-100 degrees E) was in the range of -0.9 to +4.5 W m(-2). The corresponding values at the surface were in the range of -10 to -25 W m(-2). Our studies demonstrate that neglecting the diurnal variation of dust can cause errors in the estimation of long wave dust forcing by as much as 50 to 100%, and nighttime retrieval of dust can significantly reduce the uncertainties. A method to retrieve dust aerosols during nighttime is proposed. The regionally and annually averaged long wave dust radiative forcing was +3.4 +/- 1.6 W m(-2).

Optimal reservoir operation for irrigation of multiple crops using genetic algorithms

This paper presents a genetic algorithm (GA) model for obtaining an optimal operating policy and optimal crop water allocations from an irrigation reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area. The model takes into account reservoir inflow, rainfall on the irrigated area, intraseasonal competition for water among multiple crops, the soil moisture dynamics in each cropped area, the heterogeneous nature of soils. and crop response to the level of irrigation applied. The model is applied to the Malaprabha single-purpose irrigation reservoir in Karnataka State, India. The optimal operating policy obtained using the GA is similar to that obtained by linear programming. This model can be used for optimal utilization of the available water resources of any reservoir system to obtain maximum benefits.