In the literature, two distinct flow phenomena, namely, flow through the main body of the soil, and flow through preferential flow paths, have been identified. Models which try to incorporate the effects of these two phenomena require either an explicit or an implicit knowledge of the probability distribution functions associated with the soil properties affecting flow. In keeping with the fact that these properties are influenced by two distinct phenomena, it is postulated that they should be represented by heterogeneous distribution functions. These distribution functions are, by design, suitable for representing mixed population data. Procedures were developed for fitting heterogeneous distribution functions to data. These procedures are encoded in Microsoft QUICKBASIC with some additional FORTRAN routines. The fitting procedures do not utilize any moment above the second order, and are markedly different from the use of regression methods for fitting multiple parameter distributions. Procedures were developed for two types of mixtures. One type is suitable for instances where a measured quantity is the sum of values from two populations, while the other is applicable when a measured quantity may be from one population or from another, but not from both at the same time or location. The procedures were applied to several data sets, including flow data, infiltrability data, and pH data. In many instances, the use of heterogeneous distributions resulted in an improvement in fit quality as compared to the fit quality for homogeneous distributions. The most dramatic improvement are observed in the fit to extreme data values. Procedures were also developed to incorporate heterogeneous distribution functions into three common processes in Soil and Water Engineering, namely, Monte Carlo simulation, stochastic field generation, and interpolation. In these procedures, data which are best represented by heterogeneous distributions are transformed to Gaussian space and existing Gaussian-based procedures are applied. In several validation efforts the modified processes were found to as good as, or better than, conventional procedures. In the process of developing the modified spatial processes mentioned above, a robust trend surface procedure and a new matrix decomposition procedure were developed. These ancillary procedures were shown to be useful in other engineering applications. / Ph. D.

M.S.

Drought is one of the most severe natural disasters and detrimentally impacts water resources, agricultural production, the environment, and the economy. Climate change is expected to influence the frequency and severity of extreme droughts. This dissertation evaluates drought conditions using a variety of hydrologic modeling approaches include short-term drought forecasting, long-term drought projection, and a coupled surface-groundwater dynamic drought assessment. The economic impacts of drought are also explored through a linked economic impact model. Study results highlight the need for various drought assessment approaches and provide insights into the array of tools and techniques that can be employed to generate decision-support tools for drought mitigation plans and water resource allocation. For short-term drought forecasting, the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models are used with a meteorological forecasting dataset. Results indicate that eight weeks of lead-time drought forecasting show good drought predictability for the Contiguous United States (CONUS). For the drought projection at a finer scale, both SWAT and VIC models are applied with Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model outputs to derive multiple drought indices for the Chesapeake Bay watershed and five river basins in Virginia. The results indicate that current climate change projections will lead to increased drought in the entire Chesapeake Bay watershed and Virginia river basins because of increases in the sum of evapotranspiration, and surface and groundwater discharge. The impacts of climate change on future agricultural droughts and associated economy-wide implications are then evaluated using the VIC and IMPLAN (IMpact analysis for PLANning) model for the several congressional districts in Virginia. The result indicated that increases in agricultural drought in the future would lead to decreases in agricultural productions and job losses. Finally, a coupled framework using the VIC and MODFLOW models is implemented for the Chesapeake Bay and the Northern Atlantic Coastal Plain aquifer system, and the results of a drought index that incorporates groundwater conditions performs better for some drought periods. Hydrologic modeling framework with multiple hydrologic models and various scales can provide a better understanding of drought assessments because the comparisons and contrasts of diverse methods are available. / PHD / Drought is one of the most severe natural hazards and negatively impacts the water resources, agricultural production, the environment, and the economy. Climate change influences the frequency and severity of extreme droughts. This dissertation assesses drought conditions using various hydrologic-modeling methods, which are drought forecasting, climate change impacts on drought, economic influences of droughts, and a coupled model approach. Study results highlight the need for various drought evaluation techniques that can generate decision-support tools for drought mitigation plans and water resource management. For short-term drought forecasting, two hydrologic models are used with a meteorological forecasting dataset. Results indicate that eight weeks of lead-time drought forecasting show good drought predictability for the Contiguous United States (CONUS). For the drought projection at a finer scale, two models are also used with multiple climate models for the Chesapeake Bay (CB) watershed and five river basins in Virginia. The results indicate that current climate change projections will lead to increased drought in the entire CB watershed and Virginia river basins. The impacts of climate change on future agricultural droughts and associated economy-wide implications are then evaluated using the hydrologic and economic models for the several congressional districts in Virginia. The results indicate that increases in agricultural drought in the future would lead to decreases in agricultural productions and job losses. Finally, a coupled model is implemented for the CB and the Northern Atlantic Coastal Plain (NACP) aquifer system, and the results of a drought index that incorporate groundwater conditions performs better for some drought periods. Hydrologic modeling framework with multiple hydrologic models and various scales can provide a better understanding of drought assessments because the comparisons and contrasts of diverse methods are available.

This study proposes a stratified approach of drought severity assessment using multi-layer simulated soil moisture. SWAT (Soil and Water Assessment Tool) models are calibrated for 50 watersheds in the South-Atlantic Gulf region of the Southeastern US and a high-resolution daily soil moisture dataset is obtained at Hydrologic Unit Code (HUC-12) resolution for a period of January 1982 through December 2013. A near real-time hydrologic simulation framework by coupling the calibrated SWAT models with the National Centers for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) weather data is developed to forecast various water balance components including soil moisture (SM), actual evapotranspiration (ET), potential evapotranspiration ET (PET), and runoff (SURQ) for near-real time drought severity assessment, and drought forecasting for a lead of 9-months. A combination of the surface and total rooting depth soil moisture percentiles proves to be an effective increment over conventional drought assessment approaches in capturing both, transient and long-term drought impacts. The proposed real-time drought monitoring approach shows high accuracy in capturing drought onset and propagation and shows a high degree of similarity with the U.S. Drought Monitor (USDM), the long-term (PDSI, PHDI, SPI-9 and SPI-12), and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices. / Master of Science / Drought, a recurring and worldwide phenomenon, with spatial and temporal characteristics varying significantly from across globe, lead to long-term and cumulative environmental changes. Often referred to as creeping phenomena, droughts are difficult to predict and constant monitoring is required to capture the signs of the onset of drought. Spatial variability in drought severity requires an understanding of the hydrology of the region and a knowledge of the relationship between drought inducing climatic extremes and other regional or local characteristics which help build, sustain and propagate droughts. In the absence of long-term observed hydrologic variables like soil moisture, evapotranspiration, simulated hydrologic variables serve an important purpose in understanding the impact of drought on various components of the water budget. However, several continental scale, physics-based models, and large scale remote sensing products find themselves restricted in explaining the watershed scale and sub-watershed scale variability in relation to drought. This study provides a high-resolution simulation of hydrological variables for 50 watersheds in the South-Atlantic Gulf region of the Southeastern US. The high resolution hydrologic simulations provide bedrock for retrospective drought simulations and understanding the response of various hydrologic variables of these watersheds to drought. It also aids in understanding the spatial variability in the relationship, and understanding the impact of seasonality and hydroclimatology on drought. The understanding of the interplay of various water budget components at watershed scale is used in developing a reliable seasonal drought forecasting framework based on the forecasted hydrologic variables from SWAT-CFSv2 coupled models for application in real time with a lead time of 9 months.

Meadows are important within forest ecosystems because they provide diverse species habitats, facilitate water cycling, help with sediment capture, aid in carbon sequestration, and create natural fire breaks in forested regions. However, fire suppression, poor grazing practices, and climate change have accelerated the encroachment of conifers into historical meadow habitat. This has led to an extensive loss of meadow habitat within the Sierra Nevada and Cascade Mountain Ranges. Therefore, the purpose of this study is to quantify changes in percent soil moisture and groundwater levels following the removal of encroached lodgepole pine (Pinus contorta) in a historic meadow habitat near Lake Almanor, California. A before-after control-intervention (BACI) study design was used, with Marian Meadow (MM) as the control and Rock Creek Meadow (RCM) as the restored meadow. Soil moisture and groundwater level data was collected one year before (water year 2019), and three years after (water years 2020-2023) the removal of lodgepole pine from RCM in the fall of 2020. This data was then analyzed using multiple linear regression and estimated marginal means (EMMs) models. Percent soil moisture increased each year after restoration, with significant increases from pre-restoration values occurring in year 2 and year 3 post-restoration. The overall mean soil moisture content increased from 30.69% (pre-restoration) to 40.42% by the 3rd year post-restoration. Groundwater has had a much more mixed response to restoration, with the 1st year post-restoration seeing a significant decrease in groundwater availability. Years 2 and 3 showed gradual recovery of groundwater levels, although on average they were still less than pre-restoration groundwater levels. This can likely be attributed to moderate drought occurring in the 2020 and 2021 water years. Sources of variability include the 2021 Dixie Fire which burned through both meadows at different severity levels, gaps in the data due to issues with the data loggers, differences in snowmelt timing, and differences in soil attributes. Collectively, however, all these factors converge toward a wetter meadow habitat. Hopefully, the results of this research will help promote a better understanding of how meadow restoration can improve California forestland management.

No description available.

In the past, derivative-based optimization algorithms have not frequently been used to calibrate conceptual rainfall -riff (CRR) models, partially due to difficulties associated with obtaining the required derivatives. This research applies a recently- developed technique of analytically computing derivatives of a CRR model to a complex, widely -used CRR model. The resulting least squares response surface was found to contain numerous discontinuities in the surface and derivatives. However, the surface and its derivatives were found to be everywhere finite, permitting the use of derivative -based optimization algorithms. Finite difference numeric derivatives were computed and found to be virtually identical to analytic derivatives. A comparison was made between gradient (Newton- Raphsoz) and direct (pattern search) optimization algorithms. The pattern search algorithm was found to be more robust. The lower robustness of the Newton-Raphsoi algorithm was thought to be due to discontinuities and a rough texture of the response surface.

Introduction: The movement of moisture and the simultaneous transfer of water and solutes in unsaturated porous media are problems of practical interest in ground water hydrology and soil physics. A large fraction of the water falling as rain on the land surfaces of the earth moves through unsaturated zone of soil during the subsequent processes of infiltration, drainage, evaporation, and absorption of soil -water by plant roots. A soil profile is characteristically nonuniform in its properties, nonisothermal, and may be nonrigid. Microorganisms and the roots of higher plants are a part of the system. This region is characterized by cylic fluctuation of water content as water is removed from the soil profile by evaportranspiration and replenished by recharge, irrigation, or rainfall. In unsaturated porous media the problem of movement and retention of water may be approached from (1) the molecular, (2) the microscopic, or (3) the macroscopic standpoint. In the molecular viewpoint theories of the mechanisms of flow and retention in terms of the behavior of water molecules are devised. At microscopic level a theory of flow treating the fluid in pores as a continuum and applying the principles of continuum mechanics to understand the detailed behavior of fluid within the pores is developed. The complicated pore geometry and consequent impossibility of specifying the boundary conditions on flow, preclude any practical progress by this appraoch. Since the behavior of individual molecules and the distributions of fluid velocity and pressure cannot be observed in porous media, a macroscopic theory of flow is needed. In the macroscopic approach, all variables are treated continuous functions of time and space. Velocity, pressure, and other variables are assumed as point functions. Thus, any theory of water transport to be useful must be developed to the point of describing the transfer of water on the macroscopic level. The coefficients of transport such as permeability and diffusivity can be defined microscopically. In many investigations which involve the transport of pesticides and fertilizes along with water , the simultaneous movement of water and solutes is of primary concern. These pollutants when mixed with water move in the unsaturated soil and finally join the region of saturated soil or water table, resulting in the contamination of fresh water existing below the water table. The scope of this report is to review the available literature, that may be categorized into two parts; one, the movement of water in unsaturated soil, and the other, the simultaneous movement of water and solutes in unsaturated soil. The papers, reviewed in this report, pertain to the theoretical study, laboratory study and field study on the two problems. At the end, an appendix appears which lists the references, categorizing the kind of study by various investigators.

Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements. Estimation of soil parameters by inverse modelling 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 for heterogeneous layered soils. Although extensive soil data is becoming more and more available at various scales in the form of digital soil maps there is still a large gap between this available information and the input parameters needed for hydrological models. Inverse modeling has been extensively used but the spatial variability of the parameters and insufficient data sets restrict its applicability at the catchment scale. Use of remote sensed soil moisture data to estimate soil properties using the inverse modeling approach received attention in recent years but yielded only an estimate of the surface soil properties. However, in multilayered and heterogeneous soil systems the estimation of soil properties of different layers yielded poor results due to uncertainties in simulating root zone soil moisture from remote sensed surface soil moisture. Surface soil properties can be estimated by inverse approach using surface soil moisture data retrieved from remote sensing data. Since soil moisture retrieved from remote sensing is representative of the top 5 cm only, inversion of models using surface soil moisture cannot give good estimates of soil properties of deeper layers. Crop variables like biomass and leaf area index are sensitive to the deeper layer soil properties. The main focus of this study is to develop a methodology of estimation of root zone soil hydraulic properties in heterogeneous soils by crop model based inversion techniques. Further the usefulness of the radar soil moisture and leaf area index in retrieving soil hydraulic properties using the develop approach is be tested in different soil and crop combinations. A brief introduction about the soil hydraulic properties and their importance in agro-hydrological model is discussed in Chapter 1. Soil water retention parameters are explained in detail in this chapter. A detailed review of the literature is presented in chapter 2 to establish the state of art on the following: (i) estimation of soil hydraulic properties, (ii) role of crop models in estimating soil hydraulic properties, (iii) retrieval of surface soil moisture using water cloud model from SAR data, (iv) retrieval of leaf area index from SAR (synthetic aperture radar) data and (v) modeling of root zone soil moisture and potential recharge. The thesis proposes a methodology for estimating the root zone soil hydraulic properties viz. field capacity, wilting point and soil thickness. To test the methodology developed in this thesis for estimating the soil hydraulic properties and their uncertainty, three synthetic experiments were conducted by inversion of STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) model for maize crop using the GLUE (Generalized Likelihood Uncertainty Estimation) approach. The estimability of soil hydraulic properties in a layer-wise heterogeneous soil was examined with several sets of likelihood combinations, using leaf area index, surface soil moisture and above ground biomass. The robustness of the approach is tested with parameter estimation (model inversion) in two different meteorological conditions. The details of the numerical experiments and the several likelihood and meteorological cases examined are given in Chapter 3. The likelihood combination of leaf area index and surface soil moisture provided consistently good estimates of soil hydraulic properties for all soil types and different meteorological cases. Relatively wet year provided better estimates of soil hydraulic properties as compared with a dry year. To validate the approach of estimating root zone soil properties and to test the applicability of the approach in several crops and soil types, field measurements were carried out in the Berambadi experimental watershed located in the Kabini river basin in south India. The profile soil measurements were made for every 10 cm upto 1 m depth. Maize, Marigold, Sunflower, Sorghum and Turmeric crops were monitored during the four year period from 2010 to 2013. Crop growth parameters viz. leaf area index, above ground biomass, yield, phenological stages and crop management activities were measured/monitored at 10 day frequency for all the five crops in the study area. The details of the field experiments performed, the data collected and the results of the model inversion using the ground measured data are given in Chapter 4. The likelihood combination of leaf area index and surface soil moisture provided consistently lower root mean square error (1.45 to 2.63 g/g) and uncertainty in the estimation of soil hydraulic properties for all soil crop and meteorological cases. The uncertainty in the estimation of soil hydraulic properties was lower in the likelihood combination of leaf area index and soil moisture. Estimability of depth of root zone showed sensitivity to the rooting depth. Estimating root zone soil properties at field plot scale using SAR data (incidence angle 24o, wave length 5.3 GHz) of RADARSAT-2 is presented in the Chapter 5. In the first step, an approach of estimating leaf area index from radar vegetation index using the parametric growth curve of leaf area index and the retrieval of soil moisture using water cloud model are given in Chapter 5. The parameters of the growth curve and the leaf area index are generated using a time series of RADARSAT-2 for two years 2010-2011 and 2011-12 for the crops (maize, marigold, sunflower, sorghum and turmeric) considered in this study. The surface soil moisture is retrieved using the water cloud model, which is calibrated using the ground measured values of leaf area index and surface soil moisture for different soils and crops in the study area. The calibration and validation of LAI and water cloud models are discussed in this Chapter. Eventually, the retrieved leaf area index and surface soil moisture from RADARSAT-2 data were used to estimate the soil hydraulic properties and their uncertainty in a similar manner as discussed in Chapter 4 for various crop and soil plots and the results are presented in Chapter 5. The mean and uncertainty in the estimation of soil hydraulic properties using inversion of remote sensing data provided results similar to the estimates from inversion of ground data. The estimates of soil hydraulic properties compared well (R2 of 0.7 to 0.80 and RMSE of 2.1 to 3.16 g/g) with the physically measured vales of the parameters. In Chapter 6, root zone soil moisture and potential recharge are modelled using the STICS model and the soil hydraulic parameters estimated using the RADARSAT-2 data. The potential recharge is highly sensitive to the water holding capacity of rooting zone. Variability in the root zone soil moisture for wet and dry years for different soil types on irrigated and non-irrigated crops were investigated. Potential recharge from different crop and soil types were compared. The uncertainty in the estimation of potential recharge due to uncertainty in the estimation of field capacity is quantified. The root zone soil moisture modeled by STICS showed good agreement with the measured root zone soil moisture in all crop and soil cases. This was tested for both dry and wet year and provides similar results. The temporal variability of root zone soil moisture was also modeled well by the STICS model; the model also predicted well the intra-soil variability of soil moisture of root zone. The results of the modeling of root zone soil moisture and potential recharge are presented in Chapter 6. At the end, in Chapter 7, the major conclusions drawn from the various chapters are summarized.

The vadose zone is the unsaturated zone between the ground surface and water table. This zone is of much importance as it acts as a link between surface water and ground water. Knowledge of soil moisture in this zone is very much essential to understand the meteorologic, hydrologic and agronomic process. Flow and transport in the unsaturated zone are more complex compared to saturated medium, as the pores in unsaturated zone are partly filled by air and partly by water. Most of vadose zone studies are done on agricultural plots where anthropogenic activities govern the vadose zone flows. Vadose zone studies in natural pristine conditions such as in forested areas where no anthropogenic activities are present are very limited that too in Indian conditions are rare. The present research work deals with understanding of the flow behavior in the vadose zone in a small experimental forested watershed called Mule Hole. Mule Hole watershed is 4.5 km2 and located in Bandipur National Park in Chamrajnagar District of Karnataka state, in the southern part of India. The forest is of deciduous type with 3 to 4 months of leafless dry period. The watershed has mean annual 25 years rainfall of 1120 mm and mean yearly temperature is 27o. The rainfall pattern is bimodal i.e. it receives rainfall during South West Monsoon (June -September) and North East Monsoon (October – December) with dominant rainfall occurring during South West Monsoon. Human activity is minimal as watershed is a part of Bandipur National Park, dedicated to wildlife and biodiversity preservation. The watershed consists of around 80 % of red soils, and black soil and saprolite covering the rest. The first part of the study involves soil moisture measurements by neutron probe and electrical resistivity measurements by geophysical method and their linking, i.e. developing volumetric soil moisture vs electrical resistivity relationship. The second part of the study involves application of neutron probe soil moisture measurement in identifying relationship between soil and erosion in the watershed. The third part involves development of two dimensional (2D) vadose zone model for watershed and validating it with measured data. The last part involves development of three dimensional model of watershed and validating it with observed data. Vadose zone is briefly described in chapter 1 along with its governing equations. Different soil moisture measurement techniques including invasive and non – invasive ones are also discussed. Different vadose zone modeling software which are public domain as well as commercial ones are also discussed. The chapter ends with organization of this thesis. Chapter 2 reviews relevant literature related to this study with focus on soil moisture measurement techniques and vadose zone flow modeling. Different soil moisture measurement techniques, their applications and limitations are reviewed. In the soil moisture measurement techniques, invasive and non – invasive types are reviewed. In the modeling part, different vadose zone models for 2D and 3D flow along with its applications and limitations are reviewed. Also a brief review about application of HYDRUS 2D/3D model is done which is used for the vadose zone modeling in this thesis. Chapter 3 introduces study area Mule Hole watershed, which is a forested watershed located in Bandipur National Park, Karnataka. India. The watershed has mean annual 25 years rainfall of 1120 mm and mean yearly temperature is 27o. The watershed has average regolith thickness or vadose zone of 17 m with roots of the trees able to penetrate up to groundwater. A toposequence T1 is identified in the watershed which has red soil – black soil confluence where soil moisture measurements and electrical resistivity measurements are carried out. The toposequence consists of 8 layers with organic layer forming the top layer followed by 3 red soil layer with 2 black soil layers intruding from stream into red soil layers and sandy weathered horizon at base of red and black soil. Also a sandy horizon at the top of black soil. Soil moisture measurements with neutron probe and electrical resistivity measurements with electrical logging tool which are done on toposequence periodically for two years are explained and the data are presented in this chapter. These data are used for validation of vadose zone models. Chapter 4 discusses in detail about comparison of electrical resistivity by geophysical method and neutron probe logging for soil moisture monitoring in a forested watershed. The electrical resistivity data and soil moisture data are compared for different soils and existence of relationship between them are studied and discussed in this chapter. For the red soil, existence of relationship between volumetric soil moisture content and electrical resistivity is found. Chapter 5 discusses soil moisture measurements as a tool to study erosion processes in forested watershed. Hydrodynamic behavior of the red soil – black soil system at toposequence T1 is studied using neutron probe soil moisture measurements. Two distinctive types of erosional landforms have been identified at T1 viz, rotational slips (Type 1); seepage erosion (Type 2),which are highlighted by neutron probe soil moisture measurements. Based on the observations relative chronology of formulation of different soil horizons are studied, which guided in developing four-stage model showing the relative chronology in the recent formation of the soil cover at downslope. Chapter 6 discusses application of 2D vadose zone modeling using HYDRUS – 2D model at two experimental sites in forested watershed where soil moisture monitoring and groundwater monitoring have been conducted. At the first site, which is toposequence T1 in the forested watershed, where soil moisture measurements are done, three case studies for comparison of daily scale data with hourly scale data and effects of internal layering by clubbing red soil layers and black soil layers to equivalent red soil and black soil layers respectively are performed. The model is run for two years. In that, first year results are used for calibrating the model where measured soil moisture content data are used to get soil hydraulic parameters for all the three cases by inverse modeling using Marquardt – Levenberg algorithm which is a part of HYDRUS 2D. The parameters thus obtained fall under particular soil range and performed efficiently in predicting soil moisture content. The second year results of model run is used for validation of the model in all the three cases where simulated soil moisture content is compared with measured soil moisture content. It is found that model is performing well and match between measured and simulated soil moisture contents is good in all the three cases. It can be said that having hourly scale data with detailed layering information is always advantageous in modeling soil moisture content. But, in absence of hourly scale data or finer scale data and absence of detailed layering information, the soil moisture model can also perform well. The scale of data and detailed layering information has minimal effect on soil moisture modeling. At the second site ERT profile near the watershed outlet has five monitoring wells are available and all layering information regarding regolith and hard rock layer distribution profiles. The soil hydraulic parameters obtained at toposequence T1 for soil and sandy weathered horizon are used and tested at this site to simulate the groundwater levels. The parameter for rock layer is estimated by testing different hydraulic parameters from HYDRUS database. The results are validated using observed groundwater levels at the site. The results show significant match between observed and simulated groundwater levels. Chapter 7 discusses 3D modeling of Mule Hole forested watershed using HYDRUS – 3D model. A three layer model of Mule Hole along with its topographic details is modeled. The layering information is derived from geophysical study done at 12 Electrical Resistivity Tomography (ERT) profiles distributed in the watershed. The three layers considered are top soil layer followed by sandy weathered layer and bottom rock layer. Anisotropy in hydraulic conductivity, root water uptake and sloping water table are introduced to make the model more realistic. Soil hydraulic parameters obtained during 2D vadose zone modeling of toposequence T1 are used initially for soil and sandy weathered layers and are subsequently tuned to make model more efficient. Different scenarios are considered to test flux as well as constant head boundary conditions and effect of different porosities for rock layer. The model is run for 7 years and model simulations are validated with observed groundwater levels from monitoring wells across the watershed. The result shows good fit between simulated and observed groundwater levels especially for monitoring well which has shallow groundwater level. It is found that porosity in the rock layer is not uniform and there exist different porosities for the rock layer across the watershed. Also the distribution of sandy weathered zone requires improvement. The model is also able to predict ET closer to ET predicted by COMFORT model which was developed earlier. Also the model shows rise in groundwater fluxes as groundwater starts replenishing. Over all, the 3D model of Mule Hole watershed in HYDRUS – 3D worked well with satisfactory results and HYDRUS – 3D can be used for modeling small forested watersheds. Chapter 8 concludes the study and discusses the further scope of the work.