Hydro-Climatic Data Network (HCDN) Streamflow Data Set, 1874-1988

1993 ◽  
Keyword(s):  
Climatic Data ◽  
Data Set ◽  
Data Network ◽  
Streamflow Data

scholarly journals Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1564 ◽  
Author(s):  
Melanie Oertel ◽  
Francisco Meza ◽  
Jorge Gironás ◽  
Christopher A. Scott ◽  
Facundo Rojas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Monitoring ◽  
Moisture Index ◽  
Data Set ◽  
Basin Scale ◽  
Soil Moisture Index ◽  
Streamflow Data ◽  
Governmental Institutions ◽  
Negative Impacts ◽  
Combined Data

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

scholarly journals Picturing and modeling catchments by representative hillslopes

2017 ◽  
Vol 21 (2) ◽  
pp. 1225-1249 ◽  
Author(s):  
Ralf Loritz ◽  
Sibylle K. Hassler ◽  
Conrad Jackisch ◽  
Niklas Allroggen ◽  
Loes van Schaik ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Water Dynamics ◽  
Rainfall Runoff ◽  
Data Set ◽  
Physically Based Model ◽  
Optimal Representation ◽  
Physically Based ◽  
Physically Based Models ◽  
Streamflow Data ◽  
Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

Application of the Maximum Entropy Method for Determining a Sensitive Distribution in the Renewable Energy Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Gholamhossein Yari ◽  
Zahra Amini Farsani
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Maximum Entropy ◽  
Energy Systems ◽  
Rayleigh Distribution ◽  
Entropy Method ◽  
Climatic Data ◽  
Data Set ◽  
Renewable Energy Systems

In the field of the wind energy conversion, a precise determination of the probability distribution of wind speed guarantees an efficient use of the wind energy and enhances the position of wind energy against other forms of energy. The present study thus proposes utilizing an accurate numerical-probabilistic algorithm which is the combination of the Newton’s technique and the maximum entropy (ME) method to determine an important distribution in the renewable energy systems, namely the hyper Rayleigh distribution (HRD) which belongs to the family of Weibull distribution. The HRD is mainly used to model the wind speed and the variations of the solar irradiance level with a negligible error. The purpose of this research is to find the unique solution to an optimization problem which occurs when maximizing Shannon’s entropy. To confirm the accuracy and efficiency of our algorithm, we used the long-term data for the average daily wind speed in Toyokawa for 12 yr to examine the Rayleigh distribution (RD). This data set was obtained from the National Climatic Data Center (NCDC) in Japan. It seems that the RD is more closely fitted to the data. In addition, we presented different simulation studies to check the reliability of the proposed algorithm.

Selection of Climatic Design Points for Gas Turbine Power Augmentation

2011 ◽  
Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Optimal Solution ◽  
Climatic Conditions ◽  
Weather Data ◽  
Climatic Data ◽  
Data Set ◽  
Suggested Approach ◽  
Design Point ◽  
Impact Performance ◽  
Selection Of

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling or mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of ASHRAE data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as TMY, EWD, and IWS. This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance and economics of the installation. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.

scholarly journals Simulating the Impact of Climate Change on Maize Productivity in Trans-gangetic Plains using Info Crop Model

2020 ◽  
Author(s):  
Baljeet Kaur ◽  
Som Pal Singh ◽  
P.K. Kingra
Keyword(s):  
Climate Change ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Data Set ◽  
Gangetic Plains ◽  
Impact Of Climate Change ◽  
Model Average ◽  
In Trans ◽  
The Impact

Background: Climate change is a nonpareil threat to the food security of hundred millions of people who depends on agriculture for their livelihood. A change in climate affects agricultural production as climate and agriculture are intensely interrelated global processes. Global warming is one of such changes which is projected to have significant impacts on environment affecting agriculture. Agriculture is the mainstay economy in trans-gangetic plains of India and maize is the third most important crop after wheat and rice. Heat stress in maize cause several changes viz. morphological, anatomical and physiological and biochemical changes. Methods: In this study during 2014-2018, impact of climate change on maize yield in future scenarios was simulated using the InfoCrop model. Average maize yield from 2001-15 was collected for Punjab, Haryana and Delhi to calibrate and validate the model. Future climatic data set from 2020 to 2050 was used in the study to analyse the trends in climatic parameters.Result: Analysis of future data revealed increasing trends in maximum temperature and minimum temperature. Rainfall would likely follow the erratic behaviour in Punjab, Haryana and Delhi. Increase in temperature was predicted to have negative impact on maize yield under future climatic scenario.

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