Urban base flow with low impact development

A novel form of urbanization, low impact development (LID), aims to engineer systems that replicate natural hydrologic functioning, in part by infiltrating stormwater close to the impervious surfaces that generate it. We sought to statistically evaluate changes in a base flow regime because of urbanization with LID, specifically changes in base flow magnitude, seasonality, and rate of change. We used a case study watershed in Clarksburg, Maryland, in which streamflow was monitored during whole‐watershed urbanization from forest and agricultural to suburban residential development using LID. The 1.11‐km² watershed contains 73 infiltration‐focused stormwater facilities, including bioretention facilities, dry wells, and dry swales. We examined annual and monthly flow during and after urbanization (2004–2014) and compared alterations to nearby forested and urban control watersheds. We show that total streamflow and base flow increased in the LID watershed during urbanization as compared with control watersheds. The LID watershed had more gradual storm recessions after urbanization and attenuated seasonality in base flow. These flow regime changes may be because of a reduction in evapotranspiration because of the overall decrease in vegetative cover with urbanization and the increase in point sources of recharge. Precipitation that may once have infiltrated soil, been stored in soil moisture to be eventually transpired in a forested landscape, may now be recharged and become base flow. The transfer of evapotranspiration to base flow is an unintended consequence to the water balance of LID. © 2016 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.     

Sedimentological analysis of a Late Quaternary coastal dune system: An example from Gopnath,south‐east Saurashtra,Western India

Coastal dune systems consisting of allochemical grains are important sedimentary archives of Pleistocene age in both of the hemispheres between the latitudes of 20° to 40°. The south Saurashtra coast in western India exhibits a large section of Middle Pleistocene aeolianites in the form of coastal cliffs, which is famous as ‘Miliolite’. Miliolites of Gopnath in south‐east Saurashtra are the oldest known coastal aeolianite deposits (age >156 ka which corresponds to Marine Isotope Stage 6) in western India. Aeolian deposits of similar ages have also been reported from the Thar Desert in north‐west India and from Southern Arabia which were largely controlled by the south‐west monsoon wind system that affects the entire belt corresponding to Sahara–Sahel, the Arabian Peninsula and north‐western India. Miliolite deposits in Gopnath are characterized by grainfall, grainflow and wind ripple laminations. At least three types of aeolian bounding surfaces have been identified. Five major facies have been identified which represent the dune and interdune relationship within the coastal aeolian system. The major dune bodies are identified as transverse dune types. The Gopnath aeolianites were deposited under dominantly dry aeolian conditions. Facies association reveals two different phases of aeolian accumulation, namely initiation of aeolian sedimentation after a prolonged hiatus and the establishment of a regularized aeolian sedimentation system. While initiation of aeolian sedimentation is marked by vast stretches of sheet sand with occasional dune bodies, the overlying thick, tabular, laterally extensive cross‐stratified units manifest regular aeolian sedimentation. However, the dune building events in Gopnath were interrupted by development of laterally extensive palaeosol horizons. Eustasy and climate exerted the major allogenic controls on the aeolian sedimentation by affecting the sediment budget as well as influencing the sedimentation pattern.     

Stormwater management network effectiveness and implications for urban watershed function: A critical review

Deleterious effects of urban stormwater are widely recognized. In several countries, regulations have been put into place to improve the conditions of receiving water bodies, but planning and engineering of stormwater control is typically carried out at smaller scales. Quantifying cumulative effectiveness of many stormwater control measures on a watershed scale is critical to understanding how small‐scale practices translate to urban river health. We review 100 empirical and modelling studies of stormwater management effectiveness at the watershed scale in diverse physiographic settings. Effects of networks with stormwater control measures (SCMs) that promote infiltration and harvest have been more intensively studied than have detention‐based SCM networks. Studies of peak flows and flow volumes are common, whereas baseflow, groundwater recharge, and evapotranspiration have received comparatively little attention. Export of nutrients and suspended sediments have been the primary water quality focus in the United States, whereas metals, particularly those associated with sediments, have received greater attention in Europe and Australia. Often, quantifying cumulative effects of stormwater management is complicated by needing to separate its signal from the signal of urbanization itself, innate watershed characteristics that lead to a range of hydrologic and water quality responses, and the varying functions of multiple types of SCMs. Biases in geographic distribution of study areas, and size and impervious surface cover of watersheds studied also limit our understanding of responses. We propose hysteretic trajectories for how watershed function responds to increasing imperviousness and stormwater management. Even where impervious area is treated with SCMs, watershed function may not be restored to its predevelopment condition because of the lack of treatment of all stormwater generated from impervious surfaces; non‐additive effects of individual SCMs; and persistence of urban effects beyond impervious surfaces. In most cases, pollutant load decreases largely result from run‐off reductions rather than lowered solute or particulate concentrations. Understanding interactions between natural and built landscapes, including stormwater management strategies, is critical for successfully managing detrimental impacts of stormwater at the watershed scale.     

Can the Southern annular mode influence the Korean summer monsoon rainfall?

We demonstrate that a large-scale longitudinally symmetric global phenomenon in the Southern Hemisphere sub-polar region can transmit its influence over a remote local region of the Northern Hemisphere traveling more than 100° of latitudes (from ~70°S to ~40°N). This is illustrated by examining the relationship between the Southern Annular Mode (SAM) and the Korean Monsoon Rainfall (KMR) based on the data period 1983-2013. Results reveal that the May-June SAM (MJSAM) has a significant in-phase relationship with the subsequent KMR. A positive MJSAM is favorable for the summer monsoon rainfall over the Korean peninsula. The impact is relayed through the central Pacific Ocean. When a negative phase of MJSAM occurs, it gives rise to an anomalous meridional circulation in a longitudinally locked air-sea coupled system over the central Pacific that propagates from sub-polar to equatorial latitudes and is associated with the central Pacific warming. The ascending motion over the central Pacific descends over the Korean peninsula during peak-boreal summer resulting in weakening of monsoon rainfall. The opposite features prevail during a positive phase of SAM. Thus, the extreme modes of MJSAM could possibly serve as a predictor for ensuing Korean summer monsoon rainfall.     

An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing

Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification (SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory, which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy. The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes Effect.     

Tropical cyclone prediction over Bay of Bengal: a comparison of the performance of NCEP operational HWRF, NCAR ARW, and MM5 models

Much progress has been made in the area of tropical cyclone prediction using high-resolution mesoscale models based on community models developed at National Centers for Environmental Predication (NCEP) and National Center for Atmospheric Research (NCAR). While most of these model research and development activities are focused on predicting hurricanes in the Atlantic and Eastern Pacific domains, there has been much interest in using these models for tropical cyclone prediction in the North Indian Ocean region, particularly for Bay of Bengal storms that are known historically causing severe damage to life and property. In this study, the advanced operational hurricane modeling system developed at NCEP, known as the Hurricane Weather Research and Forecast (HWRF) model, is used to simulate two recent Bay of Bengal tropical cyclones??Nargis of November 2007 and Sidr of April 2008. The advanced NCEP operational vortex initialization procedure is adapted for simulating these Bay of Bengal tropical cyclones. Two additional regional models, the NCAR Advanced Research WRF and NCAR/Penn State University Mesoscale Model version 5 (MM5) are also used in simulating these storms. Results from these experiments highlight the superior performance of HWRF model over other models in predicting the Bay of Bengal cyclones. These results also suggest the need for a sophisticated vortex initialization procedure in conjunction with a model designed exclusively for tropical cyclone prediction for operational considerations.