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Applicability of Soft Computational Models for Integrated Water Resource Management
Published in Satya Prakash Maurya, Akhilesh Kumar Yadav, Ramesh Singh, Modeling and Simulation of Environmental Systems, 2023
Stress on water resources is increasing day by day due to natural (climate change) and anthropogenic causes (demographic, industrial uses, land use, and land cover change). The approach of Integrated Water Resources Management (IWRM) is the widely accepted way forward to manage water resources in a sustainable manner, which promotes co-ordinated development and management of water, land, and related resources. This helps to tackle water related challenges such as water scarcity, presence of extreme hydrologic events like flood and drought, water quality deterioration, and soil erosion. A wide variety of modeling approaches were adopted and applied successfully in different parts of the world to tackle these challenges. Enormous amount of data is required to represent different hydrologic processes meaningfully and their availability is rare in many river basins. Moreover, the quality of data received is poor in many cases.
Drought as an Opportunity for Legal and Institutional Change in Texas
Published in Kathleen A. Miller, Alan F. Hamlet, Douglas S. Kenney, Kelly T. Redmond, Water Policy and Planning, 2017
Drought is a recurrent condition in Texas, and it has played a key role in driving the evolution of the state’s water law, infrastructure, and management practices. Droughts are distinctive among climate conditions in that they develop slowly but can ultimately have social, economic, and environmental consequences as devastating as floods, tornadoes, and hurricanes. The consequences of drought result in crop, livestock and wildlife losses, damage to aquatic and upland ecosystems, increases in fire threats, reduced surface-water supplies, and increases in water demands. Although drought harms many water users and resources, its damages fall heavily on agriculture. The 2011–2012 drought affected over 80% of the south-central United States, resulting in estimated damages and costs over $31 billion (NOAA 2013). In Texas, the 2011 drought was one of the most intense 1-year droughts since 1895 when statewide records began (Neilson-Gammon 2011). By September of 2011, nearly all of the state was in exceptional drought (see Figure 21.1). Direct and indirect agricultural losses exceeded $8 billion (Texas Comptroller 2012). Other estimates put losses at over $13 billion (StateImpact Texas 2012). Wildfires resulting from the 2011 drought burned some 4 million acres (1.6 million ha.)* and destroyed almost 3000 homes (StateImpact Texas 2012). The Bastrop County Complex fire that started in September of 2011 was the most destructive in Texas history. It burned some 32,000 acres, destroyed 1660 homes and caused two deaths (Texas A&M Forest Service 2011).
Adaption to Droughts
Published in Saeid Eslamian, Faezeh Eslamian, Handbook of Drought and Water Scarcity, 2017
As described earlier, a drought is a prolonged period of abnormally low rainfall, leading to a shortage of water. It is a period of dryness, which, especially when prolonged, causes extensive damage to crops or prevents their successful growth. However, operational definitions of droughts attempt to identify their beginning, end, impact, and degree of severity. If one knows when a drought begins, water conservation measures can be implemented by the time drought begins. Drought has many causes. It can be caused by very less or no rain or snowfall over a period of time. The causes of droughts are easily understood, but hard to prevent [13]. Depending on the location, crop failures, famine, high food prices, and deaths can occur. One of the scariest aspects of a drought is the onset time. Unlike other forms of severe weather or natural disasters, droughts often develop slowly and thus are hard to manage promptly.
State of China’s climate in 2019
Published in Atmospheric and Oceanic Science Letters, 2020
Hongling ZENG, Chan XIAO, Xianyan CHEN, Yu CHEN, Dianxiu YE
From March to May, periodic spring droughts occurred in North China, Huanghuai, and Jianghuai. From April to June, Yunnan experienced severe spring–summer droughts. From late July to mid-November, the middle and lower reaches of the Yangtze River experienced serious summer–autumn droughts. The average precipitation in Hubei, Hunan, Jiangxi, Jiangsu, Anhui, Zhejiang, and Fujian was the lowest it had been since 1961, whereas the temperature was the highest. From September to early October, meteorological droughts developed rapidly. On 4 October, moderate to extreme meteorological droughts covered an area of 901 000 km2. Droughts affected the growth of crops in the above areas and led to a high risk of fire in forests and grasslands, which also caused the water level of rivers and lakes to decrease significantly.
Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield
Published in International Journal of Digital Earth, 2020
Jianxi Huang, Wen Zhuo, Ying Li, Ran Huang, Fernando Sedano, Wei Su, Jinwei Dong, Liyan Tian, Yanbo Huang, Dehai Zhu, Xiaodong Zhang
Furthermore, the relative dominance of irrigation vs. rain-fed cultivation appears to be a key factor that affects the accuracy of drought monitoring by remote sensing, so integration of irrigation probability data and remotely sensed drought indices is likely to improve the perfornamce of drought monitoring. The direct impact of drought in agriculture is seen in decreasing crop yields. We used a long time series of remotely sensed drought indices to reveal the winter wheat yield responses to agricultural drought during different growth stages. Crop growth models can simulate crop growth under different environmental and management conditions, thereby dynamically accounting for various limiting factors (e.g. soil, weather, water, and nitrogen) that we could not account for in the present study; these models can also output various simulated parameters including state variables and crop yield during crop growth season (Huang, Ma, et al. 2015; Huang, Tian, et al. 2015, 2016; Huth et al. 2008). Assimilating remote sensing data into crop models has increasingly been applied to improve the ability to simulate crop growth and yield, and would let us expand our analysis from the field scale to larger spatial scales. Thus, in future research, we will examine the ability of the crop model-data assimilation to simulate the response of crop yields to drought at large spatial scales.
Predicting Standardized Streamflow index for hydrological drought using machine learning models
Published in Engineering Applications of Computational Fluid Mechanics, 2020
Shahabbodin Shamshirband, Sajjad Hashemi, Hana Salimi, Saeed Samadianfard, Esmaeil Asadi, Sadra Shadkani, Katayoun Kargar, Amir Mosavi, Narjes Nabipour, Kwok-Wing Chau
Drought is a periodic atmospheric event associated with a lack of sufficient available water resources in a geographical area with a significant duration of time. This environmental event is considered as an inseparable part of climatic changes and a recurring event in different climatic regions all over the world (Wilhite, 2000). Drought can affect natural habitats and ecosystems, as well as many economic and social sectors (Heim, 2002). Recently, in diverse parts of the world, drought frequently occurred, and its effects have been more harmful owing to increased water demand and climatic changes. Therefore, the phenomenon of drought has attracted the attention of many researchers, and studying the characteristics of this phenomenon from different angles has been the subject of high priority for researchers (Mishra & Singh, 2011). Moreover, among accidental and probable aspects, precipitation plays a vital role in arid and semi-arid regions. The drought happening in most parts of the world depends mostly on precipitation. So, lack of precipitation and increased temperature profoundly affect the scarcity of surface runoff. Furthermore, the lack of precipitation leads to a shortage of surface and groundwater resources.