`Universal' recession curves and their geomorphological interpretation

The study of recession flows offers fundamental insights into basin hydrological processes and, in particular, into the collective behavior of the governing dominant subsurface flows and properties. We use here an existing geomorphological interpretation of recession dynamics, which links the exponent in the classic recession curve -dQ/dt - kQ(alpha) to the geometric properties of the time-varying drainage network to study the general properties of recession curves across a wide variety of river basins. In particular, we show how the parameter k depends on the initial soil moisture state of the basin and can be made to explicitly depend on an index discharge, representative of initial sub-subsurface storage. Through this framework we obtain a non-dimensional, event-independent, recession curve. We subsequently quantify the variability of k across different basins on the basis of their geometry, and, by rescaling, collapse curves from different events and basins to obtain a generalized, or `universal', recession curve. Finally, we analyze the resulting normalized recession curves and explain their universal characteristics, lending further support to the notion that the statistical properties of observed recession curves bear the signature of the geomorphological structure of the networks producing them. (C) 2014 Elsevier Ltd. All rights reserved.

Identifying regions for conservation of sloth bears through occupancy modelling in north-eastern Karnataka, India

In the absence of information on species in decline with contracting ranges, management should emphasize remaining populations and protection of their habitats. Threatened by anthropogenic pressure including habitat degradation and loss, sloth bears (Melursus ursinus) in India have become limited in range, habitat, and population size. We identified ecological and anthropogenic determinants of occurrence within an occupancy framework to evaluate habitat suitability of non-protected regions (with sloth bears) in northeastern Karnataka, India. We employed a systematic sampling methodology to yield presence absence data to examine a priori hypotheses of determinants that affected occupancy. These covariates were broadly classified as habitat or anthropogenic factors. Mean number of termite mounds and trees positively influenced sloth bear occupancy, and grazing pressure expounded by mean number of livestock dung affected it negatively. Also, mean percentage of shrub coverage had no impact on bear inhabitance. The best fitting model further predicted habitats in Bukkasagara, Agoli, and Benakal reserved forests to have 38%, 75%, and 88%, respectively, of their sampled grid cells with high occupancies (>0.70) albeit little or no legal protection. We recommend a conservation strategy that includes protection of vegetation stand-structure, maintenance of soil moisture, and enrichment of habitat for the long-term welfare of this species.

Hydrological importance of sacred forest fragments in Central Western Ghats of India

Sacred groves are patches of forests of special spiritual significance to humans, offering also a diverse range of ecological and environmental services. We have attempted here to understand the local hydrological dynamics of a sacred forest, in terms of the benefits the village community derive, in central Western Ghats region of India. A comparative assessment has been made between two small watersheds in terms of their landscape structure (woody species composition) with soil water properties and availability of water in the respective downstream villages. The result shows that, sacred site with more primeval vegetation has close association with soil moisture in comparison to non-sacred site during dry spell of the year. The higher soil moisture ensures year long availability of water in the downstream village of the sacred site which facilitates farming of commercial crops with higher economic returns to the farmers, unlike the farmers in the other village where they face water crisis during the lean season. The study emphasizes the need for conservation endeavour on sacred groves highlighting its potential for water conservation at local and regional levels.

Reservoir Operation with Fuzzy State Variables for Irrigation of Multiple Crops

This paper presents the development and application of a stochastic dynamic programming model with fuzzy state variables for irrigation of multiple crops. A fuzzy stochastic dynamic programming (FSDP) model is developed in which the reservoir storage and soil moisture of the crops are considered as fuzzy numbers, and the reservoir inflow is considered as a stochastic variable. The model is formulated with an objective of minimizing crop yield deficits, resulting in optimal water allocations to the crops by maintaining storage continuity and soil moisture balance. The standard fuzzy arithmetic method is used to solve all arithmetic equations with fuzzy numbers, and the fuzzy ranking method is used to compare two or more fuzzy numbers. The reservoir operation model is integrated with a daily-based water allocation model, which results in daily temporal variations of allocated water, soil moisture, and crop deficits. A case study of an existing Bhadra reservoir in Karnataka, India, is chosen for the model application. The FSDP is a more realistic model because it considers the uncertainty in discretization of state variables. The results obtained using the FSDP model are found to be more acceptable for the case study than those of the classical stochastic dynamic model and the standard operating model, in terms of 10-day releases from the reservoir and evapotranspiration deficit. (C) 2015 American Society of Civil Engineers.

Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change

Climate change in response to a change in external forcing can be understood in terms of fast response to the imposed forcing and slow feedback associated with surface temperature change. Previous studies have investigated the characteristics of fast response and slow feedback for different forcing agents. Here we examine to what extent that fast response and slow feedback derived from time-mean results of climate model simulations can be used to infer total climate change. To achieve this goal, we develop a multivariate regression model of climate change, in which the change in a climate variable is represented by a linear combination of its sensitivity to CO2 forcing, solar forcing, and change in global mean surface temperature. We derive the parameters of the regression model using time-mean results from a set of HadCM3L climate model step-forcing simulations, and then use the regression model to emulate HadCM3L-simulated transient climate change. Our results show that the regression model emulates well HadCM3L-simulated temporal evolution and spatial distribution of climate change, including surface temperature, precipitation, runoff, soil moisture, cloudiness, and radiative fluxes under transient CO2 and/or solar forcing scenarios. Our findings suggest that temporal and spatial patterns of total change for the climate variables considered here can be represented well by the sum of fast response and slow feedback. Furthermore, by using a simple 1-D heat-diffusion climate model, we show that the temporal and spatial characteristics of climate change under transient forcing scenarios can be emulated well using information from step-forcing simulations alone.

Fuzzy State Real-Time Reservoir Operation Model for Irrigation with Gridded Rainfall Forecasts

A short-term real-time operation model with fuzzy state variables is developed for irrigation of multiple crops based on earlier work on long-term steady-state policy. The features of the model that distinguish it from the earlier work are (1) apart from inclusion of fuzziness in reservoir storage and in soil moisture of crops, spatial variations in rainfall and soil moisture of crops are included in the real-time operation model by considering gridded command area with a grid size of 0.5 degrees latitude by 0.5 degrees longitude; (2) the water allocation model and soil moisture balance equations are integrated with the real-time operation model with consideration of ponding water depth for Paddy crop; the model solution specifies reservoir releases for irrigation in a 10-day time period and allocations among the crops on a daily basis at each grid by maintaining soil moisture balance at the end of the day; and (3) the release policy is developed using forecasted daily rainfall data of each grid and is implemented for the current time period using actual 10-day inflow and actual daily rainfall of each grid. The real-time operation model is applied to Bhadra Reservoir in Karnataka, India. The results obtained using the real-time operation model are compared with those of the standard operating policy model. Inclusion of fuzziness in reservoir storage and soil moisture of crops captures hydrologic uncertainties in real time. Considerations of irrigation decisions on a daily basis and the gridded command area result in variations in allocating water to the crops, variations in actual crop evapotranspiration, and variations in soil moisture of the crops on a daily basis for each grid of the command area. (C) 2015 American Society of Civil Engineers.

Mineralogical changes and geotechnical properties of an expansive soil interacted with caustic solution

The type and amount of clay mineral plays an important role in the behaviour of fine-grained soils. Clay minerals are the primary source and moisture is often the external agent of swelling in soils. Also soils may exhibit increased/reduced swelling due to interaction with chemicals. Alkalis used in industrial operations are one such example. Concentrations of alkali and mineral type are the key factors in such interactions. The present paper reports the changes in the properties of an expansive Black Cotton soil containing a mixed layer mineral, rectorite upon interaction with high concentration caustic solutions. X-ray diffraction studies have shown that the rectorite present in the soil undergoes changes with increase in the concentration of alkali. Saponite gets transformed to nantronite. Small amount of kaolinitic mineral present in the soil also reacts with alkali producing some changes in its mineralogy. Many hydroxides are produced. Differential thermal analysis studies have been supportive of these changes. Consequent of these changes, the soil-specific surface increases, changes its Atterberg limits and free swell volume increases. The results have been supported by the characteristics and behaviour of samples contaminated in the field with alkali from an alumina extraction plant.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Role of clay content and moisture on characteristics of cement stabilised rammed earth

Rammed earth is an energy efficient and low carbon emission alternative for load bearing walls. This paper attempts to examine the influence of clay content and moisture content on the compressive strength of cement stabilised rammed earth (CSRE) through experimental investigations. Compressive strength of CSRE prisms was monitored both in dry and wet (saturated) conditions. Major conclusions of the study are:(a) Optimum clay content for maximum compressive strength is about 16%, (b) the strength of CSRE is sensitive to the moisture content at the time of testing, (c) Strength in saturated condition is less than half of the dry strength and (d) Water absorption (saturated water content) increases as the clay content of the soil mix increases and it is in the range of 12 to 16% for the CRSE prisms with 8% cement.

Lead Retention by Soils at Field Moisture Contents

The majority of studies pertaining to lead retention by clays and soils have examined the mechanisms, kinetics, and adsorption isotherms using the batch experiment technique that employs solid: water extracts of 1:10 and 1:20. Field soil deposits generally have much lower gravimetric water content ranging between 9 and 45%. Given the wide disparity in the solids: water ratio employed in the batch experiment technique and that prevailing in field deposits, this paper examines the lead retention characteristics of soils at field moisture contents (6%, 13%, and 25%) using artificially lead-contaminated soil specimens. A residually derived (i.e., formed by in-situ weathering of parent rock) red soil was used to prepare the artificially contaminated soil specimens. The impact of variations in clay content on lead retention was examined by diluting the residual soil with various amounts (0 to 60%) of river sand. Soil specimens remolded at 6 and 13% moisture contents produced very stiff to hard soils on compaction, while specimens remolded at 25% moisture content existed in the slurry state. The soil specimens were contaminated with low (30mg/kg) to high (2500mg/kg) concentrations of lead ions by remolding them with 160ppm to 10,000ppm ionic lead solutions. Lead retention by soils at field moisture contents was determined by extracting the lead from the soil using a water leach test. Experimental results showed that the bulk (71 to 99%) of the added lead was retained by the soil in insoluble form at the field moisture content. Correlations between the amount of lead retained and soil/solution parameters indicated that the amounts of Pb retained at field moisture content is a function of the initial Pb addition, total sand content, effective clay porosity, and soil pH.

Fire and soil temperatures during controlled burns in seasonally dry tropical forests of southern India

Fire and soil temperatures were measured during controlled burns conducted by the Forest Department at two seasonally dry tropical forest sites in southern India, and their relationships with fuel load, fuel moisture and weather variables assessed using stepwise regression. Fire temperatures at the ground level varied between 79 degrees C and 760 degrees C, with higher temperatures recorded at high fuel loads and ambient temperatures, whereas lower temperatures were recorded at high relative humidity. Fire temperatures did not vary with fuel moisture or wind speed. Soil temperatures varied between <79 degrees C and 302 degrees C and were positively correlated with ground-level fire temperatures. Results from the study imply that fuel loads in forested areas have to be reduced to ensure low intensity fires in the dry season. Low fire temperatures would ensure lower mortality of above-ground saplings and minimal damage to root stocks of tree species that would maintain the regenerative capacity of a tropical dry forest subject to dry season wildfires.