Due to unavailability of sufficient discharge data for many rivers, hydrologists have used indirect methods for deriving flood discharge amount, that is, application of channel geometry and hydrological models, for the estimation of peak discharge in the selected ungauged river basin(s) in their research/project works. This paper has studied the estimation of peak flood discharge of the Kunur River Basin, a major tributary of the Ajay River in the lower Gangetic plain. To achieve this objective, field measurements, GIS technique, and several channel geometry equations are adopted. Three important geomorphic based hydrological models&#x2014;<i>manning&#x2019;s</i> equation, kinematic wave parameter (KWP), and SCS curve number (CN) method&#x2014;have been used for computing peak discharge during the flood season, based on daily rainfall data of September, 2000. Peak discharges, calculated by different given models, are 239.44&#x2009;m<sup>3</sup>/s, 204.08&#x2009;m<sup>3</sup>/s, and 146.52&#x2009;m<sup>3</sup>/s, respectively. The hydrograph has demonstrated the sudden increase with heavy rainfall from the 18th to the 22nd of September, 2000. As a result, a havoc flood condition was generated in the confluence zone of Ajay and Kunur Rivers. This hydrograph might be not only successful application for flood forecasting but also for management of  the lower Ajay River Basin as well as the downstream area of Kunur Basin.

Suvendu Roy

Biswaranjan Mistri

Estimation of Peak Flood Discharge for an Ungauged River:  A Case Study of the Kunur River, West Bengal

This project tested the accuracy and repeatability of geomorphic stream channel assessments conducted by two different middle school classes from the Walt Morey Middle School in Troutdale, OR and college students from Portland State University in Portland, OR. Each group surveyed the same three cross-sections in Fairview Creek, a tributary to the Lower Columbia River, in order to assess stream channel geometry, discharge, composition of the bed material, and water quality. The three student groups were all able to accurately document the stream channel geometry, including stream width and mean depth, indicating that these data can be successfully collected by volunteers of various ages. However, stream velocity obtained using the float method was consistently overestimated leading to a biased calculation of discharge, and the low precision of the measurements did not allow for a correction of the bias. The median particle size of the bed material determined by a pebble count was also overestimated by each group, but the low precision also negated the possibility of correcting the estimate. The stored fine sediment in the bed was underestimated by each group and again with low precision. The temperature, pH, and conductivity measured with a calibrated multimeter were accurate and precise for all groups.

Martin D. Lafrenz

An Evaluation of Data Collected by Middle School and College-Level Students in Stream Channel Geomorphic Assessment

Convergence of the Indian plate towards Eurasia is reflected in neotectonics along several zones throughout the Indian plate. Neotectonics of the upper Narmada river basin following one of the active Son-Narmada Fault (SNF central part) zones in central Peninsular India has been studied through tectonic geomorphometric parameters. The study area is 175&#x2009;km wide and 400&#x2009;km long valley and catchment area of upper Narmada river basin in Madhya Pradesh state. High resolution ASTER data indicates neotectonic features like sudden changes in drop of Narmada river floor at two locations around Jabalpur formed by conjugate normal faults. Cross profiles indicate uplift of the entire area by a few hundred meters south of the Son-Narmada south fault. Basin asymmetry parameter indicates northward shifting of the river course from middle of the basin due to uplift of the southern block.

Girish Ch. Kothyari

B. K. Rastogi

Tectonic Control on Drainage Network Evolution in the Upper Narmada Valley: Implication to Neotectonics

The method of producing food animals has changed in the United States over the past century, moving from traditional burns to enclosed structures resembling industrial buildings, where animals are raised in high stocking density (commonly known as &#x201c;Concentrated Animal Feeding Operations,&#x201d; CAFOs). The objective to maximize profit has resulted in poor farm management; raised issues of environmental pollution, public health, animal rights, and environmental justice, and had socio-economic impacts. Studies concerning the issues are limited to specific regions and types of CAFOs. In addition, studies on the spatial distribution and temporal changes of CAFO at a country scale are lacking. This study bridges some of the gaps by analyzing the spatial distribution of industrial farms in the United States in 2002 and 2007 and their relationship with vulnerable population and exploring the relationships among the concentrations of farms, socio-economic, health, and environmental characteristics of the counties. A range of spatial statistics tools were applied in this study. The study revealed variations in spatial distribution depending on the type of the CAFOs. The issue of environmental justice was found prevalent depending on the types of industrial farms. Each type of industrial farm was found to interact uniquely with the selected demographic, health, and environmental parameters.

S. M. Rafael Harun

Yelena Ogneva-Himmelberger

Distribution of Industrial Farms in the United States and Socioeconomic, Health, and Environmental Characteristics of Counties

This study selects 20 indices of economic and environmental conditions over 15 years (1996&#x2013;2010) for 14 cities in Liaoning province, China. We calculate the economic score <svg     style="vertical-align:-0.0pt;width:10.7px;" id="M1" height="11.175" version="1.1" viewBox="0 0 10.7 11.175" width="10.7"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.113)"><path id="x1D439" d="M599 650l-18 -160l-30 -2q-2 70 -11 94q-5 16 -26 24t-75 8h-94q-27 0 -33.5 -6.5t-12.5 -32.5l-43 -226h108q52 0 72.5 5t31 18t28.5 52h28l-40 -200h-28q-1 42 -6 56.5t-24.5 21t-70.5 6.5h-108l-32 -177q-13 -65 1 -80.5t88 -22.5l-8 -28h-279l6 28q61 5 77.5 20.5&#xA;t29.5 81.5l72 387l7.5 40.5t1 26.5t-4 18t-15.5 10t-24 6t-37 4l8 28h461z" /></g>

		
	
</svg> and environmental score <svg     style="vertical-align:-0.23206pt;width:12.4375px;" id="M2" height="11.75" version="1.1" viewBox="0 0 12.4375 11.75" width="12.4375"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.4)"><path id="x1D43A" d="M713 296l-5 -25q-47 -7 -59 -20t-23 -72l-15 -79q-9 -48 -3 -74q-15 -3 -55 -13t-63 -15t-59.5 -10t-70.5 -5q-149 0 -243 80t-94 220q0 169 127.5 276.5t336.5 107.5q91 0 206 -36l-10 -165l-29 -1q1 85 -47.5 126t-146.5 41q-153 0 -243.5 -97t-90.5 -242&#xA;q0 -122 68.5 -198.5t188.5 -76.5q121 0 139 75l20 86q13 58 -1.5 70.5t-99.5 20.5l5 26h267z" /></g>

		
	
</svg> of each city by processing 4200 data points through SPSS 16.0 and establish synthesis functions between the economy and the environment. For the time dimension, we study the temporal evolution of the economic and environmental coordination development degree <svg     style="vertical-align:-3.41936pt;width:56.337502px;" id="M3" height="16.450001" version="1.1" viewBox="0 0 56.337502 16.450001" width="56.337502"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,12.138)"><path id="x28" d="M300 -147l-18 -23q-106 71 -159 185.5t-53 254.5v1q0 139 53 252.5t159 186.5l18 -24q-74 -62 -115.5 -173.5t-41.5 -242.5q0 -130 41.5 -242.5t115.5 -174.5z" /></g><g transform="matrix(.017,-0,0,-.017,5.944,12.138)"><path id="x1D437" d="M780 372q0 -191 -178 -297q-128 -75 -335 -75h-250l7 27q61 7 77 22t28 76l77 398q11 62 0 76.5t-77 22.5l6 28h292q164 0 258.5 -67.5t94.5 -210.5zM678 375q0 119 -73 180.5t-201 61.5q-62 0 -82 -13q-17 -7 -25 -53l-80 -427q-8 -47 -4 -62t26 -20q24 -6 80 -6&#xA;q166 0 262.5 95t96.5 244z" /></g>

			<g transform="matrix(.012,-0,0,-.012,19.6,16.213)"><path id="x45" d="M517 162l29 -4q-18 -100 -35 -158h-482v28q68 5 83.5 20t15.5 77v398q0 66 -13 80t-73 19v28h446q0 -8 8 -144l-29 -4q-11 51 -32 85q-11 17 -35 23t-78 6h-76q-23 0 -28 -5t-5 -27v-228h94q67 0 83 12t25 63h29v-191h-29q-10 54 -26 65.5t-81 11.5h-95v-189&#xA;q0 -65 24 -81q19 -13 104 -13q57 0 83 7t43 27q28 33 50 94z" /></g><g transform="matrix(.012,-0,0,-.012,26.358,16.213)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,33.117,16.213)"><path id="x43" d="M614 175l29 -10q-33 -109 -57 -154q-121 -26 -184 -26q-90 0 -160.5 29t-112.5 77t-63.5 105.5t-21.5 119.5q0 157 108 253t277 96q36 0 71.5 -5t69 -13.5t36.5 -8.5q15 -102 20 -150l-29 -8q-20 79 -66.5 114t-128.5 35q-119 0 -187.5 -86t-68.5 -207&#xA;q0 -140 73.5 -227.5t188.5 -87.5q73 0 119.5 37.5t86.5 116.5z" /></g><g transform="matrix(.012,-0,0,-.012,41.03,16.213)"><path id="x44" d="M43 650h280q194 0 282 -85q85 -82 85 -215q0 -173 -123 -267q-107 -83 -297 -83h-236v27q65 5 79.5 19.5t14.5 77.5v402q0 45 -6 62.5t-21.5 24t-57.5 9.5v28zM213 547v-416q0 -55 24.5 -75t81.5 -20q133 0 203 86t70 214q0 80 -30 143t-88 97q-66 41 -170 41&#xA;q-62 0 -78 -13q-13 -9 -13 -57z" /></g>

			<g transform="matrix(.017,-0,0,-.017,50.388,12.138)"><path id="x29" d="M275 270q0 -296 -211 -440l-19 23q75 62 116.5 174t41.5 243t-42 243t-116 173l19 24q211 -144 211 -440z" /></g>

		
	
</svg>. Based on Exploratory Spatial Data Analysis (ESDA) techniques and using GeoDa, we calculate Moran's index of local spatial autocorrelation and explore the spatial distribution character of  <svg     style="vertical-align:-3.41936pt;width:44.575001px;" id="M4" height="15.4375" version="1.1" viewBox="0 0 44.575001 15.4375" width="44.575001"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D437"/></g>

			<g transform="matrix(.012,-0,0,-.012,13.712,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,20.471,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,27.229,15.187)"><use xlink:href="#x43"/></g><g transform="matrix(.012,-0,0,-.012,35.142,15.187)"><use xlink:href="#x44"/></g>

		
	
</svg> in Liaoning province through a LISA cluster map. As we found in the temporal dimension, the results show that <svg     style="vertical-align:-3.41936pt;width:44.575001px;" id="M5" height="15.4375" version="1.1" viewBox="0 0 44.575001 15.4375" width="44.575001"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D437"/></g>

			<g transform="matrix(.012,-0,0,-.012,13.712,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,20.471,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,27.229,15.187)"><use xlink:href="#x43"/></g><g transform="matrix(.012,-0,0,-.012,35.142,15.187)"><use xlink:href="#x44"/></g>

		
	
</svg> of the 14 cities has been rising for 15 years and that <svg     style="vertical-align:-3.41936pt;width:44.575001px;" id="M6" height="15.4375" version="1.1" viewBox="0 0 44.575001 15.4375" width="44.575001"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><use xlink:href="#x1D437"/></g>

			<g transform="matrix(.012,-0,0,-.012,13.712,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,20.471,15.187)"><use xlink:href="#x45"/></g><g transform="matrix(.012,-0,0,-.012,27.229,15.187)"><use xlink:href="#x43"/></g><g transform="matrix(.012,-0,0,-.012,35.142,15.187)"><use xlink:href="#x44"/></g>

		
	
</svg> increases year by year, which indicates that the economic and environmental coordination development condition has been improving from disorder to highly coordinated. A smaller gap between economic strength and environmental carrying capacity in Liaoning province exists, which means that economic development and environmental protection remain synchronized. In the spatial dimension, the highly coordinated cities have changed from a scattering to a concentration in the middle-south region of Liaoning province. Poorly coordinated cities are scattered in the northwestern region of Liaoning province.

Hui Wang

Fan Liu

Ying Yuan

Liang Wang

Spatial-Temporal Analysis of the Economic and Environmental Coordination Development Degree in Liaoning Province

West Lidder River, in the Northwest Greater-Himalayan mountain range, is the major source of irrigation and drinking water supplies for the Kashmir Valley with a population of seven million people. The major source of water for the whole Lidder River is snow and icemelt emanating from its two subcatchments East Lidder and West Lidder. Snowmelt significantly contributes to the evolution of drainage patterns in this area. Quantitative morphometry plays a vital role in routing the snowmelt and other hydrological processes. Morphometric analysis of the West Lidder River catchment was carried out using geospatial technique. The outcome revealed that the entire study area has uniform lithology and is structurally permeable. The high drainage density of all subwatersheds indicate more surface runoff. The morphometric analysis also indicates that the area is more prone to weathering due to very-coarse to coarse drainage texture. All the subwatersheds showed dendritic to subdendritic drainage pattern. An immense control of structure on the drainage in some subwatersheds is indicated by their high bifurcation ratios. Circulatory and elongation ratios show that the subwatersheds have elongated to circular shapes. From the integrated analysis of the morphometric parameters, important hydrologic behaviour of 17 subwatersheds could be inferred.

Farrukh Altaf

Gowhar Meraj

Shakil A. Romshoo

Morphometric Analysis to Infer Hydrological Behaviour of Lidder Watershed, Western Himalaya, India

A comprehensive diachronic analysis (1951&#x2013;2010) of precipitation and temperature regimes has been carried out at the national and regional scale in Italy to investigate the impact of climate aridity on the agricultural system. Trends in climate aridity have been also analysed using UNEP aridity index which is the ratio between rainfall and potential evapotranspiration on a yearly basis. During the examined time period, and particularly in the most recent years, a gradual reduction in rainfall and growing temperatures have been observed which have further widened the gap between precipitation amounts and water demand in agriculture.

Luca Salvati

Marco Zitti

Rosanna Di Bartolomei

Luigi Perini

Climate Aridity under Changing Conditions and Implications for the Agricultural Sector: Italy as a Case Study

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Geography Journal