Hydro geo-sphere integrated hydrologic model in modeling of wide basins

Talebmorad, Hossein; Ostad‐Ali‐Askari, Kaveh

doi:10.1007/s40899-022-00689-y

Cited by 70 publications

(6 citation statements)

References 40 publications

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“…Research on water resources focuses on water resource management (Talebmorad et al 2022;Varis and Vakkilainen 2001), water security (Ghashghaie et al 2022;Van Ginkel et al 2018), and water resource and sustainable development direction (Ostad-Ali-Askari 2022a, b, c;Nyam et al 2021). Yuan et al (2006) proposed that WRCC is the scale of water resources' support for economic and social growth and that the concept has obvious spatial and temporal connotations.…”

Section: Literature Reviewmentioning

confidence: 99%

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

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The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

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Section: Literature Reviewmentioning

confidence: 99%

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

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“…After measuring the sample, the moist soil in the oven was dried at 105° C for 24 h to obtain the weight of the dried soil. By the weight of dry and wet soil, weight moisture in the field capacity (FC) was obtained and the apparent density was calculated from the ratio of dry soil weight to the total volume of soil sample (Jafari Haghighi, 2003;Ghazan Shahi 2006;Talebmorad et al 2022;Vanani et al 2022;Eslamian et al, 2018c). Also, the characteristics of usual water, magnetic effluent and effluent used at the beginning, middle and end of the study period, physically (electrical conductivity, total suspended solids,…), chemical (acid-base degree, different actions and anions) And bioavailability (oxygen requirements of bacteria and abundance of pathogenic bacteria) were studied.…”

Section: Measurement Of Soil Physical Propertiesmentioning

confidence: 99%

Effects of magnetized municipal effluent on some physical properties of soil in furrow irrigation

Ramesh

Ostad‐Ali‐Askari

2022

Appl Water Sci

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Due to the water crisis, effluent reuse is an effective method in order to supply crop water requirements. In order to study the impact of treated and magnetic effluent on the soil properties, this research was conducted in randomized complete block design with three irrigation treatments; including "effluent", "magnetic effluent" and "normal water" in four replications. Soil samples were taken from two surface and subsurface soil layers and at the upper and the downstream of each furrow. Evaluating the apparent specific gravity of soil in different water quality treatments showed that this soil property was increased effectively at the upstream part of the furrow. Similar results were also observed in the field capacity but more fluctuations were observed. Simultaneously, effluent treatment showed decreasing effects on soil water holding capacity during the time, which can be attributed to the increasing of soil organic matters. It seems that the high fluctuations between different design blocks are related to the gravelly nature of soil. However, intensification or weakening effect of these changes was not even found by magnetic.

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“…The hydrological cycle serves as a crucial link that binds them together, with evaporation playing a pivotal role. Evaporation is an essential process within the hydrological cycle, contributing to the maintenance of energy and water balance on the Earth's surface [1][2][3]. In areas with vegetation, evaporation encompasses two processes: the escape of liquid water from the surface of the object, known as surface evaporation, and the transpiration process, where liquid water evaporates within plants and is released into the atmosphere as water vapor through leaf stomata.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China

Shen,

Wang,

Feng

et al. 2024

PLoS ONE

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In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

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Hydro geo-sphere integrated hydrologic model in modeling of wide basins

Cited by 70 publications

References 40 publications

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Effects of magnetized municipal effluent on some physical properties of soil in furrow irrigation

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China

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