45 research outputs found
スベリ オヨビ エツリュウ ニ ヨル テンネン ダム ノ ケッカイ ニ カンスル ケンキュウ
No full text京都大学0048新制・課程博士博士(工学)甲第14136号工博第2970号新制||工||1441(附属図書館)26442UT51-2008-N453京都大学大学院工学研究科社会基盤工学専攻(主査)教授 中川 一, 教授 関口 秀雄, 教授 藤田 正治学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDFA
Potential Impact of Climate Change on Irrigation Water Requirements for Some Major Crops in the Northern High Plains of Texas
No full textFuture irrigation water requirements (IWRs) for different crops will be affected by the variation of rainfall and evapotranspiration that are projected to be impacted by future climate change. Thus, there is a need to investigate the potential impact of climate change and increasing climate extremes on the sustainability of agricultural production systems. The main goal of this study is to analyze the potential impact of climate change on IWRs for four major crops (corn, cotton, sorghum and winter wheat) in the Northern High Plains of Texas (NHPT). Specific objectives are to (i) generate and analyze projected daily climate data based on different Global Climate Models (GCMs) and (ii) assess the potential impact of climate change on IWRs and other water balance components of four major crops. Daily gridded climate data from the National Center for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) for the 1981 to 2010 period were used to represent observed historical daily climate data. We applied the statistical downscaling model Long Ashton Research Station Weather Generator (LARS-WG) to generate projected daily climate data at each grid cell (approximately 38 × 38 km) within the study region. The climate data for three future periods, that is, the 2020s, 2050s, and 2090s, were generated using outputs from 15 GCMs under three emission scenarios (B1, A1B, and A2). The hydrologic parameters of the major soil types in the study region were derived from the Soil Survey Geographic Database (SSURGO). Irrigation water requirements and major water budget components for all grid cells were calculated using the Irrigation Management System (IManSys) model based on crop specific growth parameters, site-specific soil hydrological properties, irrigation system efficiency, and long-term daily climate data (current and future climate scenarios). Monthly temperature and reference evapotranspiration were projected to increase; however, annual precipitation was expected to decrease in the future projection periods. Thus, gross irrigation requirements (GIRs) of all four crops, with an irrigation system efficiency of 75%, were assumed to increase (2.08-3.77% in the 2020s, 6.23-9.25% in 2055s and 6.81-19.52% in 2090s), threatening the possibility of serious groundwater depletion and long-term sustainable agriculture in this region. Further work is needed to predict crop yield responses to potential climate change scenarios for these different future periods
Hydropower development in Nepal
No full textThis paper provides an overview of a 100 year history of hydropower in Nepal. The importance of hydropower in Nepal is highlighted and major issues that the country has to consider for the development of hydropower have been analysed in detail. It is the only resource available to generate electricity, both for large export projects and small village mini grid projects in almost any part of the country. The challenges of demand and supply fluctuations, mainly due to the seasonal fluctuation of river discharges, are also described. An analysis of river flow trends shows that the impact of river flow has to be analysed river by river, as the trends are not consistent throughout the country. The social and organisational issues and their relationship with the political stability in the country have also been discussed. Crown Copyright © 2013 Published by Elsevier Ltd. All rights reserved
Estimating reference crop evapotranspiration under limited climate data in West Texas
No full textStudy region: West Texas, USA Study focus: Estimation of crop reference evapotranspiration (ETo) is essential for many aspects of water resources planning and management such as irrigation scheduling. Available widely used methods for calculating ETo include American Society of Civil Engineers’ Standardized Reference Evapotranspiration and Food and Agriculture Organization\u27s Penman-Monteith equations (FAO-ETo). These methods use complete climate datasets to estimate daily ETo, whereas simple evapotranspiration models based on radiation and temperature use limited climate data. In this study, daily ETo estimated using the temperature based Hargreaves-Samani (HS) equation were compared and evaluated with those estimated using the standard FAO-ETo at different stations of West Texas Mesonet. New hydrological insights for the region: The results showed that the HS equation with original coefficients underestimated daily ETo values as compared to FAO-ETo data. New coefficients of the globally, monthly and regionally calibrated HS equation against FAO-ETo data were derived and proposed for more accurate daily ETo estimates in West Texas. Based on the results of global, monthly and regional calibration scenarios, ETo estimated by the calibrated and validated HS equation using fitted month-specific coefficients showed better agreement with FAO-ETo both within and outside the calibration region. No significant improvement in ETo estimation was observed for the HS equation using interpolated coefficients derived from station-specific calibrated coefficients as compared with commonly calibrated coefficients derived based on datasets of all selected meteorological stations in West Texas
Optimum turf grass irrigation requirements and corresponding water- energy-CO2 Nexus across Harris County, Texas
No full textHarris County is one of the most populated counties in the United States. About 30% of domestic water use in the U.S. is for outdoor activities, especially landscape irrigation and gardening. Optimum landscape and garden irrigation contributes to substantial water and energy savings and a substantial reduction of CO2 emissions into the atmosphere. Thus, the objectives of this work are to (i) calculate site-specific turf grass irrigation water requirements across Harris County and (ii) calculate CO2 emission reductions and water and energy savings across the county if optimum turf grass irrigation is adopted. The Irrigation Management System was used with site-specific soil hydrological data, turf crop water uptake parameters (root distribution and crop coefficient), and long-term daily rainfall and reference evapotranspiration to calculate irrigation water demand across Harris County. The Irrigation Management System outputs include irrigation requirements, runoff, and drainage below the root system. Savings in turf irrigation requirements and energy and their corresponding reduction in CO2 emission were calculated. Irrigation water requirements decreased moving across the county from its north-west to its south-east corners. However, the opposite happened for the runoff and excess drainage below the rootzone. The main reason for this variability is the combined effect of rainfall, reference evapotranspiration, and soil types. Based on the result, if the average annual irrigation water use across the county is 25 mm higher than the optimum level, this will result in 10.45 million m3 of water losses (equivalent water use for 30,561 single families), 4413 MWh excess energy use, and the emission of 2599 metric tons of CO2
Analysis of potential future climate and climate extremes in the brazos headwaters Basin, Texas
No full textTexas\u27 fast-growing economy and population, coupled with cycles of droughts due to climate change, are creating an insatiable demand for water and an increasing need to understand the potential impacts of future climates and climate extremes on the state\u27s water resources. The objective of this study was to determine potential future climates and climate extremes; and to assess spatial and temporal changes in precipitation (Prec), and minimum and maximum temperature (Tmin and Tmax, respectively), in the Brazos Headwaters Basin under three greenhouse gas emissions scenarios (A2, A1B, and B1) for three future periods: 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). Daily gridded climate data obtained from Climate Forecast System Reanalysis (CFSR) were used to downscale outputs from 15 General Circulation Models (GCMs) using the Long Ashton Research Station-Weather Generator (LARS-WG) model. Results indicate that basin average Tmin and Tmax will increase; however, annual precipitation will decrease for all periods. Annual precipitation will decrease by up to 5.2% and 6.8% in the 2055s and 2090s, respectively. However, in some locations in the basin, up to a 14% decrease in precipitation is projected in the 2090s under the A2 (high) emissions scenario. Overall, the northwestern and southern part of the Brazos Headwaters Basin will experience greater decreases in precipitation. Moreover, precipitation indices of the number of wet days (prec ≥ 5 mm) and heavy precipitation days (prec ≥ 10 mm) are projected to slightly decrease for all future periods. On the other hand, Tmin and Tmax will increase by 2 and 3 °C on average in the 2055s and 2090s, respectively. Mostly, projected increases in Tmin and Tmax will be in the upper range in the southern and southeastern part of the basin. Temperature indices of frost (Tmin \u3c 0 °C) and ice days (Tmax \u3c 0 °C) are projected to decrease, while tropical nights (Tmin \u3e 20 °C) and summer days (Tmax \u3e 25 °C) are expected to increase. However, while the frequency distribution of meteorological drought shows slight shifts towards the dry range, there was no significant difference between the baseline and projected meteorological drought frequency and severity
COVID-19 and the improvement of the global air quality: The bright side of a pandemic
No full textThe objective of this investigation is to study the impacts of the global response to COVID-19 on air pollution and air quality changes in major cities across the globe over the past few months. Air quality data (NO2, CO, PM2.5, and O3) were downloaded from the World Air Quality Index project for the January 2019–April 2020 period. Results show a significant reduction in the levels of 2020 NO2, CO, and PM2.5 compared to their levels in 2019. These reductions were as high as 63% (Wuhan, China), 61% (Lima, Peru), and 61% (Berlin, Germany), in NO2, CO, and PM2.5 levels, respectively. In contrast, 2020 O3 levels increased substantially, as high as 86% (Milan, Italy), in an apparent response to the decrease in titration by nitrogen monoxide and its derivatives. Significant differences in the weather conditions across the globe do not seem to impact this air quality improvement trend. Will this trend in the reduction in most air pollutants to unprecedented levels continue in the next few weeks or even months? The response to this and other questions will depend on the future global economic and environmental policies
Exploring the Impact of Winter Storm Uri on Power Outage, Air Quality, and Water Systems in Texas, USA
No full textTexas was hit by a record-setting cold snap from the 14–17 February 2021 after three decades that resulted in power outages, disruption of the public water systems, and other cascading effects. This study investigates the unprecedented impact of winter storm Uri on power outages, air quality, and water systems in Texas, USA. Analysis of the Parameter Regression of Independent Slopes Model (PRISM) gridded climate data showed that the average daily freezing temperature range was 0–−19 °C on 14 February 2021, with severe levels (−17–−19 °C) occurring in the Texas High Plains. Our results showed that the extreme freezing temperature persisted from 14–17 February 2021, significantly affecting power operation and reliability, and creating power outages across Texas. Uri impacted the public water systems and air quality on time scales ranging from a few minutes to several days, resulting in 322 boiling notices. The air quality index level exceeded the standard limit by 51.7%, 61.7%, 50.8%, and 60% in Dallas–Fort Worth, Houston–Galveston, Austin, and Lubbock regions. The level of the pollutants exceeded the EPA NAAQS standard allowable limits during winter storm Uri. In general, this study gives information on the government’s future preparedness, policies, communication, and response to storm impacts on vulnerable regions and communities
Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions
No full textStudies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions
