Sanjeev Kumar Jha

A space and time scale‐dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local-scale estimates of precipitation and temperature from General Circulation Models.

Training Images from Process-Imitating Methods

The lack of a suitable training image is one of the main limitations of the application of multiple-point statistics (MPS) for the characterization of heterogeneity in real case studies. Process-imitating facies modeling techniques can potentially provide training images. However, the parameterization of these process-imitating techniques is not straightforward. Moreover, reproducing the resulting heterogeneous patterns with standard MPS can be challenging. Here the statistical properties of the paleoclimatic data set are used to select the best parameter sets for the process-imitating methods. The data set is composed of 278 lithological logs drilled in the lower Namoi catchment, New South Wales, Australia. A good understanding of the hydrogeological connectivity of this aquifer is needed to tackle groundwater management issues. The spatial variability of the facies within the lithological logs and calculated models is measured using fractal dimension, transition probability, and vertical facies proportion. To accommodate the vertical proportions trend of the data set, four different training images are simulated. The grain size is simulated alongside the lithological codes and used as an auxiliary variable in the direct sampling implementation of MPS. In this way, one can obtain conditional MPS simulations that preserve the quality and the realism of the training images simulated with the process-imitating method. The main outcome of this study is the possibility of obtaining MPS simulations that respect the statistical properties observed in the real data set and honor the observed conditioning data, while preserving the complex heterogeneity generated by the process-imitating method. In addition, it is demonstrated that an equilibrium of good fit among all the statistical properties of the data set should be considered when selecting a suitable set of parameters for the process-imitating simulations.

Bathymetry fusion using multiple-point geostatistics: Novelty and challenges in representing non-stationary bedforms

In large rivers, complex sediment dynamics cause rapid changes in the position and shape of bed deposits. Regular monitoring of changes in river bed geometry is essential for assessing the nature of morphological change and associated bed load during low, high, and medium flow conditions. We demonstrate the application of Direct Sampling (DS) for patching partial river morphological surveys to generate complete maps of the river morphology, by incorporating prior knowledge from bathymetry data collected in different seasons at collocated or adjacent reaches. This novel approach is based on multiple-point statistics (MPS), which uses a training image (TI) to provide prior statistical and architectural constraining data. In this study high and low resolution bathymetry data from a reach of the Mississippi river have been used. High-resolution measurements were conducted using Multi-beam-echo-sounder (MBES), which provides very detailed bed geometry at high spatial resolution. These measurements cannot be acquired at intervals frequent enough to characterize the rapid sedimentological processes. Low resolution bathymetry data can be obtained at frequent intervals but at sparse locations, by installing depth measuring sensors on boats passing the study reach several times a week. The DS method is used to simulate the high resolution bathymetry at the frequency of the low-resolution data. In the simulations, the method uses the bed geometry information contained in the MBES high-resolution surveys, the local information contained in the boat-borne low-resolution measurements, and provides an updated bathymetry map with quantified uncertainty.

Parameterization of training images for aquifer 3‐D facies modeling integrating geological interpretations and statistical inference

We develop a stochastic approach to construct channelized 3-D geological models constrained to borehole measurements as well as geological interpretation. The methodology is based on simple 2-D geologist-provided sketches of fluvial depositional elements, which are extruded in the 3rd dimension. Multiple-point geostatistics (MPS) is used to impair horizontal variability to the structures by introducing geometrical transformation parameters. The sketches provided by the geologist are used as elementary training images, whose statistical information is expanded through randomized transformations. We demonstrate the applicability of the approach by applying it to modeling a fluvial valley filling sequence in the Maules Creek catchment, Australia. The facies models are constrained to borehole logs, spatial information borrowed from an analogue and local orientations derived from the present-day stream networks. The connectivity in the 3-D facies models is evaluated using statistical measures and transport simulations. Comparison with a statistically equivalent variogram-based model shows that our approach is more suited for building 3-D facies models that contain structures specific to the channelized environment and which have a significant influence on the transport processes.

A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights

Identification of system predictors forms the first step towards formulating a predictive model. Approaches for identifying such predictors are often limited by the need to assume a relationship between the predictor and response. To address this limitation, (Sharma and Mehrotra, 2014) presented a nonparametric predictive model using Partial Informational Correlation (PIC) and Partial Weights (PW). This study describes the open source Nonparametric Prediction (NPRED) R-package. NPRED identifies system predictors using the PIC logic, and predicts the response using a k-nearest-neighbor regression formulation based on a PW based weighted Euclidean distance. The capabilities of the package are demonstrated using synthetic examples and a real application of predicting seasonal rainfall in the Warragamba dam near Sydney, Australia. The results show clear improvements in predictability as compared to the use of linear predictive alternatives, as well as nonparametric alternatives that use an un-weighted Euclidean distance. Open source R package NPRED for system identification and prediction.Estimate Partial Informational Correlation (PIC) and Partial Weight (PW).Improves predictability compared to existing alternatives.

Influence of Alluvial Morphology on Upscaled Hydraulic Conductivity.

The hydraulic conductivity of aquifers is a key parameter controlling the interactions between resource exploitation activities, such as unconventional gas production and natural groundwater systems. Furthermore, this parameter is often poorly constrained by typical data used for regional groundwater modeling and calibration studies performed as part of impact assessments. In this study, a systematic investigation is performed to understand the correspondence between the lithological descriptions of channel-type formation and the bulk effective hydraulic conductivities at a larger scale (Kxeff , Kyeff , and Kzeff in the direction of channel cross section, along the channel and in the vertical directions, respectively). This will inform decisions on what additional data gathering and modeling of the geological system can be performed to allow the critical bulk properties to be more accurately predicted. The systems studied are conceptualized as stacked meandering channels formed in an alluvial plain, and are represented as two facies. Such systems are often studied using very detailed numerical models. The main factors that may influence Kxeff , Kyeff , and Kzeff are the proportion of the facies representing connected channels, the aspect ratio of the channels, and the difference in hydraulic conductivity between facies. Our results show that in most cases, Kzeff is only weakly dependent on the orientations of channelized structures, with the main effects coming from channel aspect ratio and facies proportion.

Modeling the Diffusion and Transport of Suspended Sediment in Open Channels, Using Two-Phase Flow Theory

Sediment transport in open channels can be characterized as a two-phase flow, complicated by the interaction between the phases and turbulence. Mathematical models based on two-phase flow theory provide insight into the leading physical mechanisms which are observed in natural flows, such as the flows in rivers and estuaries. This chapter presents a general framework for modeling the transport of sediments in open channels. Within the scope of turbulence averaged equations, the modeling framework is composed of mass and momentum equations for both phases (water and sediment). We start by presenting the derivations of the governing equations of the two-fluid model. We then present and discuss two levels of model complexity based on the nature of the terms involved in modeling: the complete two-fluid model (CTFM), and a partial two-fluid model (PTFM). The resulting equations become very involved and contain several correlation terms, which require closure. We propose potential closures for the terms related to turbulence, interaction forces, inter-particle collisions, and sediment diffusivities. We finally turn to discussing the effect of dilute and non-dilute nature of the flow in determining the relative importance of these unknown and less understood correlation terms.

Drought Characterization for a Snow-Dominated Region of Afghanistan

AbstractDroughts are extreme natural phenomena that cause severe damage to economy, environment, and life. Palmer’s drought model is widely used in drought characterization. However, its use in a m...