Richa Ojha

Assessing Severe Drought and Wet Events over India in a Future Climate Using a Nested Bias-Correction Approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. DOI: 10.1061/(ASCE)HE.1943-5584.0000585.

Current and Future Challenges in Groundwater. I: Modeling and Management of Resources

AbstractGroundwater, one of the world’s most important natural resources, is under constant threat of exploitation with increasing population and economic development. Proper understanding and modeling of subsurface water movement has been an enduring challenge for hydrologists and practitioners. Current modeling efforts are plagued by the complex heterogeneity within the subsurface, reconciliation with spatial and temporal scales, and lack of supporting data. Long-term consequences of droughts in aquifers and efficient management of the available resources in arid and semiarid regions of the world deserve special attention. Assessing the potential impacts of climate change on groundwater is yet another long-term challenge that confounds both researchers and managers. With groundwater being likened to fossil fuels in some parts of the world, conservation and management of these resources have become imperative. Developing new models that account for uncertainties and provide more realistic assessment of p...

Current and Future Challenges in Groundwater. II: Water Quality Modeling

AbstractGroundwater quality has been a long-standing concern to water engineers and managers. Numerous studies have examined the causes of contamination, and designs for remediation systems have been developed for contamination from domestic, agricultural, and industrial activities. There is renewed interest in groundwater quality of aquifers from recent activities such as induced saltwater intrusion, hydraulic fracturing, carbon dioxide (CO2) sequestration, and deep geologic storage of nuclear waste. These topics are reviewed and discussed in this paper, focusing on the new challenges resulting from these activities. In this context, modeling flow and transport mechanisms in fractured rocks is relevant, and some recent advances in the field of fracture hydrology are highlighted. More advanced techniques of analysis, namely, fractional advection dispersion equations, are discussed in light of new interest in this topic. It is clear that advances in theory, numerical modeling, and experimentation are neede...

Assessing GCM Convergence for India Using the Variable Convergence Score

General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assess- ments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India. DOI: 10.1061/(ASCE)HE.1943-5584.0000888.

Predictor selection for streamflows using a graphical modeling approach

Streamflows are influenced by various hydroclimatic variables in complex ways. Accurate prediction of monthly streamflows requires a clear understanding of the dependence patterns among these influencing variables and streamflows. A graphical modeling technique, employing conditional independence, is adopted in this study to quantify the interrelationships between streamflows and a suite of available hydroclimatic variables, and to identify a reduced set of relevant variables for parsimonious model development. The nodes in the undirected graph represent relevant variables, and the strengths of the connections among the variables are learnt from the data. The graphical modeling approach is compared to the state-of-the-art method for predictor selection based on partial mutual information. For a synthetic benchmark dataset and a watershed in southern Indiana, USA, the graphical modeling approach shows more discriminating results while being computationally efficient. Along with artificial neural networks and time series models, results of the graphical model are used for formulating a variational relevance vector machine to predict monthly streamflows and perform probabilistic classification of hydrologic droughts in the watershed being studied. The parsimonious models developed for prediction at different lead times performed as well as the non-parsimonious models during both the calibration and testing periods. Drought forecasting for the study watershed at 1-month lead time was performed using the two selected predictors—soil moisture and precipitation anomalies alone, and the model performance was evaluated. The graphical model shows promise as a tool for predictor selection, and for aiding parsimonious model development applications in statistical hydrology.

A physical scaling model for aggregation and disaggregation of field-scale surface soil moisture dynamics

Scaling relationships are needed as measurements and desired predictions are often not available at concurrent spatial support volumes or temporal discretizations. Surface soil moisture values of interest to hydrologic studies are estimated using ground based measurement techniques or utilizing remote sensing platforms. Remote sensing based techniques estimate field-scale surface soil moisture values, but are unable to provide the local-scale soil moisture information that is obtained from local measurements. Further, obtaining field-scale surface moisture values using ground-based measurements is exhaustive and time consuming. To bridge this scale mismatch, we develop analytical expressions for surface soil moisture based on sharp-front approximation of the Richards equation and assumed log-normal distribution of the spatial surface saturated hydraulic conductivity field. Analytical expressions for field-scale evolution of surface soil moisture to rainfall events are utilized to obtain aggregated and disaggregated response of surface soil moisture evolution with knowledge of the saturated hydraulic conductivity. The utility of the analytical model is demonstrated through numerical experiments involving 3-D simulations of soil moisture and Monte-Carlo simulations for 1-D renderings-with soil moisture dynamics being represented by the Richards equation in each instance. Results show that the analytical expressions developed here show promise for a principled way of scaling surface soil moisture.

Local‐ and field‐scale stochastic‐advective vertical solute transport in horizontally heterogeneous unsaturated soils

Description of field-scale solute transport in unsaturated soils is essential for assessing the degree of contamination, estimating fluxes past a control plane and for designing remedial measures. The flow field is usually described by numerical solution of the Richards equation followed by numerical solution of the advection-dispersion equation to describe contaminant movement. These numerical solutions are highly complex, and do not provide the insights that are possible from simpler analytical representations. In this study, analytical solutions at the local scale are developed to describe purely advective vertical transport of a conservative solute along the principle characteristic of the flow field. Local-scale model development is simplified by using a sharp-front approximation for water movement. These local solutions are then upscaled to field-scale solute transport by adopting a lognormally distributed horizontal hydraulic conductivity field to represent the natural heterogeneity observed in field soils. Analytical expressions are developed for the mean behavior of solute transport at the field scale. Comparisons with experimental observations find that trends of field-scale solute behavior are reasonably reproduced by the model. The accuracy of the proposed solution improves with increasing spatial variability in the hydraulic conductivity as revealed by further comparisons with numerical results of the Richards equation-based field-scale solute movement. In some cases, the sharp-front approximation may lead to anomalous field-scale behavior depending on the role of pre and postponded conditions in the field, and this limitation is discussed. The proposed method shows promise for describing field-scale solute movement in loamy sand and sandy loam soils.