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The Hydration Equation: Update on Water Balance and Cognitive Performance

Shaun k riebl.

Department of Human Nutrition, Foods and Exercise (0430), 229A Wallace Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, ph. 540.231.7918

Brenda M. Davy

Laboratory for Eating Behaviors and Weight Management, Department of Human Nutrition, Foods and Exercise (0430), 221 Wallace Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, ph. 540.231.6784, fax 540.231.3916

LEARNING OBJECTIVES

• To become aware of the most practical measures of hydration status.
• To describe sources of water input and output and the basics of water balance.
• To understand how hydration status may impact daily cognitive performance.

CONDENSED VERSION AND BOTTOM LINE

Water is a crucial nutrient and euhydration is necessary for optimal daily functioning. Water balance is precisely regulated within the body and many methods exist for assessing hydration status. Cognitive performance measures an individual’s attentiveness, critical thinking skills, and memory. Traditionally a 2% or more body water deficit was thought to produce cognitive performance decrements; however, recent literature suggests that even mild dehydration – a body water loss of 1–2% – can impair cognitive performance. Counseling clients about their health and wellbeing should include conveying the importance of water for normal body functioning, as well as its effects on physical and cognitive performance.

Although it is often overlooked as an essential nutrient, water is vital for life as it serves several critical functions. Total body water comprises approximately 45–75% of a person’s body weight ( 27 ). Muscle mass is 70–75% water, while water in fat tissue can vary between 10 and 40% ( 25 ). Water acts as a transporter of nutrients, regulates body temperature, lubricates joints and internal organs, provides structure to cells and tissues, and can help preserve cardiovascular function ( 26 ). Water consumption may also facilitate weight management ( 15 , 17 ). Water deficits can impact physical performance ( 25 , 38 ), and recent research suggests that cognitive performance may also be impacted ( 4 , 13 , 20 – 22 , 35 , 36 ). This article will address water balance, hydration assessment, and the effect of water balance on cognitive performance.

Water Balance

Water balance (i.e., input vs. output) is influenced by dietary intake, physical activity level, age, and environmental conditions. Although total body water balance is tightly regulated over a 24-hour period ( 25 ), deficits and excesses can occur. Dehydration develops from inadequate fluid intake or excessive fluid losses, and overhydration can result from excessive water (or fluid) intake with or without proper electrolyte replacement ( 25 , 33 ).

Water output and its regulation

The skin, kidneys, lungs, and digestive system are all sources of water output ( Figure 1 ). Environmental factors (e.g., humidity, temperature) and intensity and duration of physical activity also impact urine output (e.g. increased urine output in colder climates, decreased urine output in hot climates and greater water loss via sweat with longer duration activities) ( 25 ). Respiratory water loss averages 250–350mL/d in sedentary adults; however, physical activity can increase losses to about 600mL/d ( 19 , 25 ). Insensible water loss, which includes sweat loss, can vary with environmental conditions (i.e. wind speed, humidity, and sun exposure), activity level, body composition, degree of physical fitness, and other variables (e.g. clothing worn, sweat rate) ( 19 , 25 , 38 ). On average insensible water losses are about 450mL/d; however, during vigorous physical activity in a hot environment, losses in excess of 3L/hr are possible ( 37 ). Urine output generally ranges 1000–2000mL/d, but can be altered by exercise and heat strain ( 25 ). Gastrointestinal and fecal water output accounts for 100–300mL/d ( 19 , 25 , 27 ). Total water output is estimated to be approximately 1500–3100mL/d for adults in temperate climates ( 19 , 25 ).

Average daily fluid balance in adults.

One cup equals 237 mL. Adapted from Jéquier and Constant, 2010.

When water loss exceeds intake, blood volume decreases and plasma osmolality increases. The reduction in blood volume decreases blood pressure, leading to increases in renin and angiotensin II concentrations. The latter, along with aldosterone, promote sodium and chloride reabsorption in the kidneys and thus water via osmosis, and decreased urine output. Increased blood osmolality and angiotensin II stimulates the hypothalamus and arginine vasopressin (AVP) is released, promoting renal water retention and reduced urinary output. Increased plasma osmolality also stimulates thirst through peripheral osmoreceptors in the mouth and gastrointestinal tract to replace the remaining water lost. Baroreceptors promote AVP release and thirst when reductions in plasma volume are significant; however, this mechanism is not as sensitive as the osmotic regulation of thirst ( 31 ).

Water input and its regulation

Water input comes from food and beverage ingestion, and normal metabolic processes ( Figure 1 ). There are regulated or physiological (e.g. osmoreceptors in the brain and mouth, baroreceptors in blood vessels and atrium) and non-regulated (e.g. social, cultural, behavioral) factors that influence water intake ( 25 , 35 , 43 ) and fluid balance. The thirst sensation is triggered with a body water loss of 1–2%; a range where physical and cognitive performance may decline ( 4 , 9 , 21 , 22 , 25 , 34 , 38 ). Typically, plasma osmolality is tightly maintained between 280–290mOsm/kg; however, an increase of approximately 1–3% creates a drive to drink ( 12 , 43 ).

Fluid water intake generally accounts for ~70–80% of total water consumed ( 25 ), and ~20–30% of total water intake comes from solid foods ( 5 , 19 , 25 ). In a typical sedentary adult, this represents ~7 cups (1575mL) from beverages, ~3 cups (675mL) from foods, and ~1 cup (300mL) from normal metabolic processes ( 27 ). Despite popular myths, coffee can be considered a source of fluid ( 7 , 25 ), and although alcohol may increase fluid losses short-term, it is not believed to result in significant water loss over a day’s time ( 25 ).

When fluid is consumed, osmoreceptors in the mouth are stimulated, which reduces AVP secretion. This allows the kidneys to release excess water, and preserve water balance. If plasma osmolality decreases and blood volume increases, the thirst sensation fades. The desire to drink may cease before achieving water balance ( 13 ), however plasma osmolality will remain elevated and thirst sensations may return until water homeostasis is achieved ( 12 , 43 ).

Hyperhydration and hyponatremi

Typically, healthy individuals can maintain water balance through urination when excess fluid is consumed; hyperhydration is not commonly encountered ( 19 , 25 ). However, during extreme and extended-duration exercise, excessive consumption of hypotonic fluids and sodium losses that exceed the rate of replacement, and sometimes even in the absence of overconsumption of fluids, can cause hyponatremia ( 25 , 33 , 38 ). Hyponatremia, which is defined as a blood sodium concentration lower than 135 mmol/L ( 25 ), can have serious health implications ( 19 , 25 ). Hyperhydration (i.e., “water intoxication”) can present with symptoms such as fatigue, lethargy, disorientation, confusion, headache, nausea, vomiting, and if not treated properly, coma and death ( 23 , 25 ). The signs and symptoms of dehydration and overhydration can be similar (i.e., light-headedness, dizziness, headaches, nausea, fatigue) ( 4 , 21 , 22 , 30 ). When working with clients, health and fitness professionals can utilize a variety of methods to assess the presence and nature of water imbalance, to insure clients receive proper treatment.

Methods to Assess Hydration Status

Hydration refers to having adequate fluid within body tissues, and it can be determined through a variety of methods. Dilution techniques, plasma osmolality, neutron activation analysis, and bioelectrical impedance spectroscopy can be used to assess hydration status in a laboratory setting, while thirst, 24-hour urine volume, change in weight (i.e. body mass), urine color and specific gravity can be used in the field ( 3 ). Others have extensively reviewed these techniques, their ease of use, and potential limitations ( 2 , 3 , 11 , 38 ); however, a brief discussion of practical measures to assess hydration status is provided.

Urine specific gravity (USG) is an accurate and rapid indicator of hydration status ( 2 ). A urine specimen is placed on the glass plate at one end of a handheld refractometer and, upon holding it up to natural light and looking through the eyepiece, a fitness professional can read the USG. Normal ranges are from 1.013–1.029; a USG of ≥1.030 suggests dehydration and 1.001–1.012 may indicate overhydration ( 2 ). USG is more indicative of recent fluid consumption versus overall chronic hydration status ( 8 ), however it can be used in conjunction with other practical measures of hydration status such as changes in body weight ( 19 , 38 ). In order to obtain accurate information, weight should be measured upon waking on three successive days, after voiding, and before consumption of any fluids ( 3 , 38 ). If fluctuations exceed ~1% from baseline, water imbalance may be present ( 3 ). While more subjective, urine color can be a marker of hydration status when used in combination with a more quantifiable method, such as USG ( 6 , 8 , 38 ). A person’s urine sample is compared to a color chart that identifies euhydration or the need to consume additional fluids ( 8 , 32 ). A lighter color indicates adequate hydration, while darker colors indicate the need for fluid consumption. However, diet, supplements, and medications can affect body weight and urine color ( 19 , 32 ), thus these factors must be considered when using this method.

Cheuvront and Sawka suggest athletes use the WUT framework, which takes into account not only body mass, but also degree of thirst and urine parameters ( 11 ) (available at: http://www.gssiweb.com/Article/sse-97-hydration-assessment-of-athletes ). Additionally, a client’s usual fluid intake can be measured using the beverage intake questionnaire (BEVQ-15) ( 24 ), which can be rapidly administered by the practitioner (~3–4 minutes) to provide a valid and reliable estimate of total beverage intake (including water, juice, and sports drinks) in terms of volume and calories ( 24 ). Although there are several measures to estimate hydration status, all have limitations ( 3 ); using multiple methods may allow the health and fitness professional to obtain the most accurate assessment of a client’s hydration status ( 5 , 6 , 8 , 38 ).

Water Intake Recommendations

Water needs can vary from person to person – and no one person will need the same amount of fluid from one day to the next - thus, developing a recommended dietary allowance (RDA) for water is challenging. The Institute of Medicine (IOM) established an Adequate Intake (AI) for water, which is a guideline to help most healthy individuals avoid dehydration ( 25 , 26 ). Table 1 outlines the AI for total water and total fluid intake for various age groups. On average, Americans typically consume about one liter (~ 4 cups) of drinking water per day ( 40 ). While the AI addresses water needs of the general public, the health and fitness professional must consider an individual’s physical activity regimen and environment when assessing hydration needs ( 25 , 38 ). The ACSM’s Exercise and Fluid Replacement guidelines can be utilized when counseling clients on appropriate hydration strategies to avoid dehydration and overhydration. Dehydration can negatively impact physical performance ( 25 , 34 , 38 ), and the magnitude of decrements in physical performance may be influenced by fitness level, environmental acclimatization, and mode of activity ( 25 , 38 ). As the level of dehydration increases, physical performance decreases – that is, performance suffers with greater degrees of dehydration ( 25 ) – and recent literature suggests the same for cognitive performance ( 9 , 36 , 41 ).

The Institute of Medicine’s Water Intake Recommendations * .

Cognitive performance and assessment

Cognition refers to the process or act of knowing - a person’s awareness and judgment. Cognitive functions can include a person’s concentration or attentiveness, concept learning, critical thinking, and memory ( 39 ). Likewise, motivation, mood, arousal, and physical health affect cognitive processing ( 39 ). Cognitive performance is a measure of cognitive functioning ( 39 ), or how someone uses their judgment, memory, reasoning, and concentration to complete one or more tasks. Many tests exist to measure cognitive performance; however, debate on which assessment method(s) is superior persists among practitioners ( 29 ). There are few standardized assessment methods, and causal mechanisms as to how dehydration may impact cognitive performance are unknown ( 30 , 35 ).

The degree of precision and/or rapidity of a response is commonly evaluated in cognitive performance assessments ( 39 ). For example, the time it takes for someone to respond to a visual stimulus would measure speed/reaction time and a word recall would measure an individual’s cognitive accuracy. Table 2 provides definitions of common terminology and examples of cognitive performance assessment methods.

Common measures and methods of cognitive performance assessment.

Two cognitive assessments that may be of practical use for the fitness professional are the ruler drop test and Trail Making Test (TMT) A and B ( 1 , 14 , 42 , 44 ). To conduct a ruler drop test the practitioner holds a ruler vertically hovering above the outstretched dominant hand’s index finger and thumb of a client. The zero centimeter line of the ruler is parallel to the client’s thumb. The client catches the ruler following the practitioner “dropping” it without notification. The distance is recorded and can converted into a reaction time or interpreted as follows: Poor: >28cm; Below average: 204-28cm; Average: 15.9–20.4cm; Above average: 7.5–15.9cm; and Excellent: <7.5cm ( 14 ). The TMT can measure vigilance and consists of form “A” on which a client is asked to connect 25 randomly placed numbers in sequence with a pen/pencil ( 42 ). The second form (i.e. “B”) is similar to the first except in addition to numbers, alphabetical letters are incorporated ( 42 ). For example, a client would have to connect the number 1 with letter A and connect A to number 2, which would then be connected to letter B, and B would be connected to 3, etc.( 42 ). The outcome measure is time-to- completion and mistakes do not stop timing. Average score for form A is 29 seconds with scores greater than 78 seconds considered below average ( 1 ). On form B, average score is 75 seconds and below average is 273 seconds ( 1 ).

Due to the complexity involved with cognitive processes, a battery of assessments should be administered to obtain the most accurate analysis ( 45 ). One such battery is called the Montreal Cognitive Assessment 7.1 (MoCA) ( 10 ). This screening tool was developed to assess mild cognitive impairment and early Alzheimer’s dementia through attention and concentration, executive functions, memory, language, visuoconstructional skills, conceptual thinking, calculations, and orientation analyses ( 10 ). The MoCA can be administered in ~10 minutes and a normal score is considered 26 out of 30; however, scores of 24 may be acceptable ( 10 ). While the tool was extensively tested in adults aged ≥ 49 years, it can also detect mild cognitive impairments in younger, active individuals ( 16 ). The health and fitness professional may find the MoCA useful due to its rapid administration and scoring; however, if clients participate in contact sports or have experienced a concussion in years past, scores may be lower than suggested norms ( 16 ). The test and administration and scoring instructions can be found at http://www.mocatest.org/default.asp .

Cognitive performance and dehydration

Cognitive performance had previously been reported to decline at or above a 2% body water loss ( 22 , 25 ). The level of reduction in cognitive performance can depend on environmental and individual factors (e.g. level of fitness, acclimatization, and dehydration tolerance) ( 41 ) and it appears that as the level of dehydration increases, efficiency of cognitive processing decreases ( 36 ). In long distance walkers and runners, increased water intake has been associated with increased visual attentiveness and short-term memory ( 9 ). In women, aspects of mood (i.e. vigor, alertness, fatigue, calmness, confusion, happiness) were negatively affected during fluid deprivation ( 36 ). Children may also have decrements in cognitive functioning as a result of inadequate water intake ( 20 ).

Recently we have learned that even mild dehydration – a body water loss of 1–2% - can impair cognitive abilities ( 4 , 21 ). This amount of dehydration equates to about 1½-3 lbs of body weight loss for a 150 lb person, which could occur through routine daily activities ( 4 ). Since many individuals experience fatigue later in the day when their workout time approaches, this could be important for fitness professionals to discuss with their clients. Problems with cognitive performance that can occur with mild dehydration include poor concentration, increased reaction time, and short-term memory problems, as well as moodiness and anxiety ( 4 , 21 ). Water consumption affects cognitive performance in adults ( 18 ), and an adequate daily water intake is important for maintaining optimal cognitive functioning.

Most studies on hydration and cognitive performance are short-term (i.e., hours, days) and it is not certain if there are longer-term cognitive decrements resulting from hypohydration; however, a recent study suggests that even after replenishing a fluid deficit, effects on mood may persist ( 36 ). Meaning, even after achieving euhydration, cognitive functioning may be compromised. This is an area in need of additional research.

Case Study 1 (Feature Box)

Marion is a 38-year-old mother of three who works a full-time job from 8am to 4pm five days a week. The BEVQ-15 revealed that she typically consumes ~2700mL of fluid from beverages daily. You often train her at your gym in the afternoons, and on Monday she came to you after successfully completing a 10K on Saturday, and spending Sunday gardening and doing yard work with her family. It is the middle of August and when she shows up for her training session she was stating how tired and lethargic she feels and that she has been making careless mistakes at work, that her head has been “pounding all day”, and that she almost canceled the training session because she felt nauseous driving over from work. Marion’s baseline body weight is 150 pounds and from Friday on her weights are as follows:

• Friday: 150lbs (0% change from baseline)
• Saturday (race day): 151lbs (1% increase from baseline)
• Sunday: 149lbs (<1% decrease from baseline)
• Monday: 145lbs (~3% decrease from baseline)

You question Marion about her fluid intake and you find that her focus has not really been on hydration since finishing the race on Saturday. After obtaining a urine sample that was dark yellow you analyze Marion’s urine SG, which was 1.033. Suspecting that her fatigue and mistakes at work may be signs of compromised cognitive functioning, you administer the Montreal Cognitive Assessment (MoCA) ( 10 ) and her score is 22. Marion’s physical and cognitive signs and symptoms suggest she is dehydrated and her recent decreased morning body weight, USG, MoCA results, and urine color all confirm this. You provide Marion with guidelines for rehydration according to the ACSM ( 38 ) and make plans to follow up with her tomorrow afternoon.

Implications and Conclusions

Clients may experience mild dehydration – a 1–2% water loss – during routine daily activities ( 4 , 21 , 36 ). This may be a common problem, considering that adults drink only one liter (~ 4 cups) of water a day on average ( 40 ) – which is less than half of what is currently recommended by the IOM ( 25 ). The signs and symptoms of dehydration and overhydration can mirror each other, sharing light-headedness/dizziness, headaches, nausea, and fatigue - all subjective parameters sometimes used in hydration and cognition research ( 4 , 21 , 22 , 30 , 36 ). When working with clients, health and fitness professionals can utilize a number of means to assess water imbalances (e.g. urine SG, body weight, and urine color) to insure that clients receive proper treatment. Additionally, fitness professionals can educate their clients on monitoring their own hydration status through morning body weight and the WUT framework ( 11 ) and when counseling patients on fluid needs before, during, and following exercise, the health and fitness professional can utilize the ACSM’s Exercise and Fluid Replacement guidelines ( 38 ). However, if a client has a chronic medical condition such as hypertension, cardiovascular disease, or diabetes, referring them to a registered dietitian for a personalized hydration plan may be necessary.

Cognitive functions, such as concentration, vigilance, memory, and critical thinking can be measured through a variety of cognitive performance assessments. While there is no consensus as to which method of assessment is superior ( 29 ), tests like the ruler drop test, Trail Making Test A & B ( 42 ), and MoCA ( 10 ) may be practical means for the health and fitness professional to rapidly assess a client’s cognitive processing. Similar to physical performance, cognitive performance has been observed to decline at levels >2% body water loss ( 22 , 25 ), but recent research shows that mild dehydration (i.e. 1–2% body water loss) may impair cognitive performance ( 4 , 21 ). Current literature provides insight into how cognitive functioning may be influenced by hydration status. However, the long-term consequences of dehydration on cognitive parameters and the mechanism by which fluid imbalances impact cognitive performance are unknown ( 35 ) - areas where future research efforts are needed.

Biographies

Shaun Riebl, MS, RD, is a doctoral student in the Water INTERface Interdisciplinary Graduate Education Program (IGEP) and Department of Human Nutrition, Food, and Exercise (HNFE) at Virginia Tech.

Brenda Davy, PhD, RD, FACSM, is the program director of the Water INTERface IGEP and an associate professor of HNFE and at Virginia Tech. Her research interests include investigating lifestyle strategies to prevent and treat obesity and related comorbidities, and beverage consumption and weight control.

CONFLICT OF INTEREST: None declared.

Contributor Information

Shaun K Riebl, Department of Human Nutrition, Foods and Exercise (0430), 229A Wallace Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, ph. 540.231.7918.

Brenda M. Davy, Laboratory for Eating Behaviors and Weight Management, Department of Human Nutrition, Foods and Exercise (0430), 221 Wallace Hall, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, ph. 540.231.6784, fax 540.231.3916.

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• Published: 03 August 2021

Future global urban water scarcity and potential solutions

• Chunyang He   ORCID: orcid.org/0000-0002-8440-5536 1 , 2 ,
• Zhifeng Liu   ORCID: orcid.org/0000-0002-4087-0743 1 , 2 ,
• Jianguo Wu   ORCID: orcid.org/0000-0002-1182-3024 1 , 2 , 3 ,
• Xinhao Pan 1 , 2 ,
• Zihang Fang 1 , 2 ,
• Jingwei Li 4 &
• Brett A. Bryan   ORCID: orcid.org/0000-0003-4834-5641 5  

Nature Communications volume  12 , Article number:  4667 ( 2021 ) Cite this article

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• Environmental sciences
• Water resources

Urbanization and climate change are together exacerbating water scarcity—where water demand exceeds availability—for the world’s cities. We quantify global urban water scarcity in 2016 and 2050 under four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693–2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153–422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193–284, including 10–20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.

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Introduction.

The world is rapidly urbanizing. From 1950 to 2020, the global population living in cities increased from 0.8 billion (29.6%) to 4.4 billion (56.2%) and is projected to reach 6.7 billion (68.4%) by 2050 1 . Water scarcity—where demand exceeds availability—is a key determinant of water security and directly affects the health and wellbeing of urban residents, urban environmental quality, and socioeconomic development 2 , 3 , 4 , 5 , 6 . At present, many of the world’s urban populations face water scarcity 3 . Population growth, urbanization, and socioeconomic development are expected to increase urban industrial and domestic water demand by 50–80% over the next three decades 4 , 7 . In parallel, climate change will affect the spatial distribution and timing of water availability 8 , 9 . As a result, urban water scarcity is likely to become much more serious in the future 10 , 11 , 12 , potentially compromising the achievement of the United Nations Sustainable Development Goals (SDGs) especially SDG11 Sustainable Cities and Communities and SDG6 Clean Water and Sanitation 13 , 14 .

Urban water scarcity has typically been addressed via engineering and infrastructure. Reservoirs are commonly used to store water during periods of excess availability and continuously supply water to cities to avoid water shortages during dry periods 15 . Desalination plants are increasingly used to solve water deficit problems for coastal cities 16 . For cities where local water resources cannot meet demand, inter-basin water transfer can also be an effective solution 17 (Supplementary Table  8 ). However, investment in water infrastructure is costly; requires substantial human, energy, and material resources; is limited by natural conditions such as geographic location and topography; and may have very significant environmental impacts 2 , 3 , 18 . Hence, a comprehensive understanding of water scarcity and the potential solutions for the world’s cities is urgently required to promote more sustainable and livable urban futures 7 , 18 , 19 .

Previous studies have evaluated urban water scarcity 2 , 3 , 7 , 19 (Supplementary Table  3 ). However, these studies have been limited in a number of ways including: assessing only a subset of the urban population (e.g., large cities only or regional in focus); considering only part of the water scarcity problem (i.e., availability but not withdrawal); or lacking a future perspective. For example, in assessing global urban water scarcity, Flörke et al. 7 considered 482 cities (accounting for just 26% of the global urban population) under a business-as-usual scenario, and while McDonald et al. 2 assessed a larger range of cities and scenarios, they considered water availability only, not withdrawals. As a result, significant uncertainty in estimates of current and future extent of urban water-scarcity remain, varying from 0.2 to 1 billion people affected in 2000 and from 0.5 to 4 billion in 2050 (Supplementary Table  4 ). A comprehensive assessment of global urban water scarcity is needed to identify cities at risk and provide better estimates of the number of people affected.

In addition, although many studies have discussed potential solutions to urban water scarcity, few have investigated the feasibility of these solutions for water-scarce cities at the global scale. Proposed solutions include groundwater exploitation, seawater desalination, increased water storage in reservoirs, inter-basin water transfer, improved water-use efficiency, and urban landscape management 2 , 3 , 14 , 19 . However, the potential effectiveness of these solutions for the world’s water-scarce cities depends on many factors including the severity of water scarcity, urban and regional geography and hydrogeology, socio-economic characteristics, and environmental carrying capacity 7 , 20 . Pairing the identification of water scarce cities with an evaluation of potential solutions is essential for guiding investment in future urban water security.

In this study, we comprehensively assessed global urban water scarcity in 2016 and 2050 and the feasibility of potential solutions for water-scarce cities. We first quantified the spatial patterns of the global urban population for 2016 at a grid-cell resolution of 1 km 2 by integrating spatial urban land-use and population data. We then identified water-scarce areas at the catchment scale by combining global water resource availability and demand data, and calculated the global urban population in water-scarce areas in 2016. We also quantified the global urban population in water-scarce areas for 2050 under four socioeconomic and climate change scenarios by combining modeled projections of global urban area, population, and water availability and demand. Finally, we evaluated the feasibility of seven major solutions for easing water scarcity for each affected city. We discuss the implications of the results for mitigating global urban water scarcity and improving the sustainability and livability of the world’s cities.

Current urban water scarcity

Globally, 933 million (32.5%) urban residents lived in water-scarce regions in 2016 (Table  1 , Fig.  1b ) with 359 million (12.5%) and 573 million (20.0%) experiencing perennial and seasonal water scarcity, respectively. India (222 million) and China (159 million) had the highest urban populations facing water scarcity (Table  1 , Fig.  1c ).

a spatial patterns of large cities in water-scarce areas (cities with population above 10 million in 2016 were labeled). b Water-scarce urban population at the global scale. c Water-scarce urban population at the national scale (10 countries with the largest values were listed). Please refer to Supplementary Data for urban water scarcity in each catchment.

Of the world’s 526 large cities (i.e., population >1 million), 193 (36.7%) were located in water-scarce regions (96 perennial, 97 seasonal) (Fig.  1a ). Of the 30 megacities (i.e., population >10 million), 9 (30.0%) were located in water-scarce regions (Table  2 ). Six of these, including Los Angeles, Moscow, Lahore, Delhi, Bangalore, and Beijing, were located in regions with perennial water scarcity and three (Mexico City, Istanbul, and Karachi) were seasonally water-scarce (Fig.  1a ).

Urban water scarcity in 2050

At the global scale, the urban population facing water scarcity was projected to increase rapidly, reaching 2.065 (1.693–2.373) billion people by 2050, a 121.3% (81.5–154.4%) increase from 2016 (Table  1 , Fig.  2a ). 840 (476–905) million people were projected to face perennial water scarcity and 1.225 (0.902–1.647) billion were projected to face seasonal water scarcity (Table  1 ). India’s urban population growth in water-scarce regions was projected to be much higher than other countries (Fig.  2b ), increasing from 222 million people to 550 (376–644) million people in 2050 and accounting for 26.7% (19.2%–31.2%) of the world’s urban population facing water scarcity (Table  1 ).

a Changes in water-scarce urban population at the global scale. Bars present the simulated results using the ensemble mean of runoff from GCMs, the total values (i.e., perennial and seasonal), and percentages are labeled. Crosses (gray/black) present the simulated results (total/perennial) using runoff from each GCM. b Changes in water-scarce urban population at the national scale (10 countries with the largest values were listed). Bars present the total values simulated using the ensemble mean of runoff from GCMs. Crosses present the total values simulated using runoff from each GCM. Please refer to Supplementary Data for urban water scarcity in each catchment.

Nearly half of the world’s large cities were projected to be located in water-scarce regions by 2050 (Fig.  3 , Supplementary Fig.  3 ). The number of large cities facing water scarcity under at least one scenario was projected to increase to 292 (55.5%) by 2050. The number of megacities facing water scarcity under at least one scenario was projected to increase to 19 (63.3%) including 10 new megacities (i.e., Cairo, Dhaka, Jakarta, Lima, Manila, Mumbai, New York, Sao Paulo, Shanghai, and Tianjin) (Table  2 ).

Only the water-scarce cities are listed. Cities with a population >10 million in 2016 are labeled.

Factors influencing urban water scarcity

Growth in urban population and water demand will be the main factor contributing to the increase in urban water scarcity (Fig.  4 ). From 2016 to 2050, population growth, urbanization, and socioeconomic development were projected to increase water demand and contribute to an additional 0.990 (0.829–1.135) billion people facing urban water scarcity, accounting for 87.5% (80.4–91.4%) of the total increase. Climate change was projected to alter water availability and increase the urban population subject to water scarcity by 52 (−72–229) million, accounting for 4.6% (−9.0–18.4%) of the total increase.

Bars present the simulated results using the ensemble mean of runoff from GCMs, crosses present the simulated results using runoff from each GCM.

Potential solutions to urban water scarcity

Water scarcity could be relieved for 276 (94.5%) large cities, including 17 (89.5%) megacities, via the measures assessed (Table  3 , Supplementary Table  5 ). Among these, 260 (89.0%) cities have the option of implementing two or more measures. For example, Los Angeles can adopt desalination, groundwater exploitation, inter-basin water transfer, and/or virtual water trade (Table  3 ). However, 16 large cities, including two megacities (i.e., Delhi and Lahore) in India and Pakistan, are restricted by geography and economic development levels, making it difficult to adopt any of the potential water scarcity solutions (Table  3 ).

Domestic virtual water trade was the most effective solution, which could alleviate water scarcity for 208 (71.2%) large cities (including 14 (73.7%) megacities). Inter-basin water transfer could be effective for 200 (68.5%) large cities (including 14 (73.7%) megacities). Groundwater exploitation could be effective for 192 (65.8%) large cities (including 11 (57.9%) megacities). International water transfer and virtual water trade showed potential for 190 (65.1%) large cities (including 10 (52.6%) megacities). Reservoir construction could relieve water scarcity for 151 (51.7%) large cities (including 10 (52.6%) megacities). Seawater desalination has the potential to relieve water scarcity for 146 (50.0%) large cities (including 12 (63.2%) megacities). In addition, water scarcity for 68 (23.3%) large cities, including five megacities (i.e., New York, Sao Paulo, Mumbai, Dhaka, and Jakarta), could be solved via the water-use efficiency improvements, slowed population growth rate, and climate change mitigation measures considered under SSP1&RCP2.6.

We have provided a comprehensive evaluation of current and future global urban water scarcity and the feasibility of potential solutions for water-scarce cities. We found that the global urban population facing water scarcity was projected to double from 933 million (33%) in 2016 to 1.693–2.373 billion (35–51%) in 2050, and the number of large cities facing water scarcity under at least one scenario was projected to increase from 193 (37%) to 292 (56%). Among these cities, 276 large cities (95%) can address water scarcity through improving water-use efficiency, limiting population growth, and mitigating climate change under SSP1&RCP2.6; or via seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, or virtual water trade. However, no solutions were available to relieve water scarcity for 16 large cities (5%), including two megacities (i.e., Delhi and Lahore) in India and Pakistan.

Previous studies have estimated the global urban population facing water scarcity to be between 150 and 810 million people in 2000, between 320 and 650 million people in 2010, and increasing to 0.479–1.445 billion people by 2050 (Supplementary Table  4 ). Our estimates of 933 million people in 2016 facing urban water scarcity, increasing to 1.693–2.373 billion people by 2050, are substantially higher than previously reported (Supplementary Fig.  5a ). This difference is attributed to the fact that we evaluated the exposure of all urban dwellers rather than just those living in large cities (Supplementary Table  3 ). According to United Nations census data, 42% of the world’s urban population lives in small cities with a total population of <300,000 (Supplementary Fig.  4 ). Therefore, it is difficult to fully understand the global urban water scarcity only by evaluating the exposure of large cities. This study makes up for this deficiency and provides a comprehensive assessment of global urban water scarcity.

In addition, we used spatially corrected urban population data, newly released water demand/availability data, simulated runoff from GCMs in the most recent CMIP6 database, catchment-based estimation approach covering the upstream impacts on downstream water availability, and the new scenario framework combining socioeconomic development and climate change. Such data and methods can reduce the uncertainty in the spatial distribution of urban population and water demand/availability in the future, providing a more reliable assessment of global urban water scarcity.

Our projections suggest that global urban water scarcity will continue to intensify from 2016 to 2050 under all scenarios. By 2050, near half of the global urban population was projected to live in water-scarce regions (Figs.  2 ,  3 ). This will directly threaten the realization of SDG11 Sustainable Cities and Communities and SDG6 Clean Water and Sanitation . Although 95% of water-scarce cities can address the water crisis via improvement of water-use efficiency, seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, or virtual water trade (Supplementary Table  5 ), these measures will not only have transformative impacts on society and the economy, but will also profoundly affect the natural environment. For example, the construction of reservoirs and inter-basin water transfer may cause irreversible damage to river ecosystems and hydrogeology and change the regional climate 4 , 15 , 17 , 21 , 22 . Desalination can have serious impacts on coastal zones and marine ecosystems 16 , 23 . Virtual water trade will affect regional economies, increase transport sector greenhouse gas emissions, and may exacerbate social inequality and affect the local environments where goods are produced 19 , 24 .

Water scarcity solutions may not be available to all cities. The improvement of water-use efficiency as well as other measures require the large-scale construction of water infrastructure, rapid development of new technologies, and large economic investment, which are difficult to achieve in low- and middle-income countries by 2050 14 . In addition, there will be 16 large cities, such as Delhi and Lahore, that cannot effectively solve the water scarcity problem via these measures (Supplementary Table  5 ). These cities also face several socioeconomic and environmental issues such as poverty, rapid population growth, and overextraction and pollution of groundwater 25 , 26 , which will further affect the achievement of SDG1 No Poverty , SDG3 Good Health and Well-being , SDG10 Reduced Inequalities , SDG14 Life below Water and SDG15 Life on Land .

To address global urban water scarcity and realize the SDGs, four directions are suggested. We need to:

Promote water conservation and reduce water demand. Our assessment provides evidence that the proposed water conservation efforts under SSP1&RCP2.6 are effective, which results in the least water-scarce urban population (34–241 million fewer compared to other SSPs&RCPs) at the global scale and can mitigate water scarcity for 68 (23.3%) large cities. The application of emerging water-saving technologies and the construction of sponge cities, smart cities, low-carbon cities, and resilient cities as well as the development of new theories and methods such as landscape sustainability science, watershed science, and geodesign will also play an important role for the further water demand reduction 5 , 6 , 27 , 28 , 29 . To implement these measures, the cooperation and efforts of scientists, policy makers and the public, as well as sufficient financial and material support are required. In addition, international cooperation must be strengthened in order to promote the development and dissemination of new technologies, assist in the construction of water infrastructure, and raise public awareness of water-savings, particularly in the Global South 30 .

Control population growth and urbanization in water-scarce regions by implementing relevant policies and regional planning. Urban population growth increases both water stress and the exposure of people, making it a key driver exacerbating global urban water scarcity 2 . Hence, the limitation of urban population growth in water-scarce areas can help to address this issue. According to our estimation, the control of urbanization under SSP3&RCP7.0, which has the lowest urbanization rate among four scenarios, can reduce the urban population subject to water scarcity by 93–207 million people compared with the business-as-usual scenario (SSP2&RCP4.5) and the rapid urbanization scenario (SSP5&RCP8.5), including 80–178 million people in India alone by 2050 (Fig.  2 ). To realize this pathway, policies that encourage family planning as well as tax incentives and regional planning for promoting population migration from water-scarce areas to other areas are needed 18 . In particular, for cities such as Delhi and Lahore that are both restricted by geography and socioeconomic disadvantage and have few options for dealing with water scarcity, there is an urgent need to control urban population growth and urbanization rates.

Mitigate climate change through energy efficiency and emissions abatement measures to avoid water resource impacts caused by the change in precipitation and the increase in evapotranspiration due to increased temperature. Our contribution analysis shows that the impacts of climate change on urban water scarcity is quite uncertain (ranging from a reduction of 72 million water-scarce urban people to an increase of 229 million) under different scenarios and GCMs (Fig.  4 ). On average, climate change under the business-as-usual scenario (SSP2&RCP4.5) will increase the global water-scarce urban population by 31 million in 2050. If the emissions reduction measures under SSP1&RCP2.6 are adopted, the increase in global water-scarce urban population due to climate change will be cut by half (16 million) in 2050. Thus, mitigating climate change is also important to reducing urban water scarcity. Considering that climate change in water-scarce areas would be affected by both internal and external impacts, mitigating climate change requires a global effort 31 .

Undertake integrated local sustainability assessment of water scarcity solutions. Our assessment reveals that 208 (71.2%) large cities may address water scarcity through seawater desalination, groundwater exploitation, reservoir construction, interbasin water transfer, and/or virtual water trade (Supplementary Table  5 ). While our results provide a guide at the global scale, city-level decisions about which measures to adopt to alleviate water scarcity involve very significant investments and should be supported by detailed local assessments of their relative effectiveness weighed against the potentially significant financial, environmental, and socio-economic costs. Integrated analyses are needed to quantify the effects of potential solutions on reducing water scarcity, their financial and resource requirements, and their potential impacts on socio-economic development for water-scarce cities and the sustainability of regional environments. To guard against the potential negative impacts of these measures, comprehensive impact assessments are required before implementing them, stringent regulatory oversight and continuous environmental monitoring are needed during and after their implementation, and policies and regulations should be established to achieve the sustainable supply and equitable distribution of water resources 24 , 32 .

Uncertainty is prevalent in our results due to limitations in the methodology and data used. First, constrained by data availability, in the evaluation of urban water scarcity in 2016 we used water demand/availability data for 2014 derived from the simulation results of the PCRGLOBWB 2 model, and only considered the inter-basin water transfers listed in City Water Map and the renewable groundwater simulated from the PCRGLOBWB 2 model instead of all available groundwater 3 , 33 . In the assessment of urban water scarcity and feasibility of potential solutions in 2050, we used water demand data derived from Hanasaki et al. 34 , in which irrigated area expansion, crop intensity change, and improvement in irrigation water efficiency were considered, but the change in irrigation to adapt to climate change as well as the impacts of energy systems (e.g., bio-energy production, mining, and fossil fuel extraction) on water demand were not fully considered 35 . Second, in order to maintain consistency and comparability of the water stress index (WSI) with the PCRGLOBWB 2 outputs 33 , environmental flow requirements were not considered. Following Mekonnen and Hoekstra 36 and Veldkamp et al. 37 (2017), we used an extreme threshold for WSI of 1.0 (where the entire water available is withdrawn for human use). If a more conservative threshold (e.g., WSI = 0.4 which is the threshold defining high water stress) was used, estimated global water scarcity and the urban population exposed to water stress would be much higher 7 .

In summary, global urban water scarcity is projected to intensify greatly from 2016 to 2050. By 2050, nearly half of the global urban population (1.693–2.373 billion) were projected to live in water-scarce regions, with about one quarter concentrated in India, and 19 (63%) global megacities are expected to face water scarcity. Increases in urban population and water demand drove this increase, while changes in water availability due to climate change compounded the problem. About 95% of all water-scarce cities could find at least one potential solution, but substantial investment is needed and solutions may have significant environmental and socioeconomic consequences. The aggravation of global urban water scarcity and the consequences of potential solutions will challenge the achievement of several SDGs. Therefore, there is an urgent need to further improve water-use efficiency, control urbanization in water-scarce areas, mitigate water availability decline due to climate change, and undertake integrated sustainability analyses of potential solutions to address urban water scarcity and promote sustainable development.

Description of scenarios used in this study

To assess future urban water scarcity, we used the scenario framework from the Scenario Model Intercomparison Project (ScenarioMIP), part of the International Coupled Model Intercomparison Project Phase 6 (CMIP6) 38 . The scenarios have been developed to better link the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to support comprehensive research in different fields to better understand global climatic and socioeconomic interactions 38 , 39 . We selected the four ScenarioMIP Tier 1 scenarios (i.e., SSP1&RCP2.6, SSP2&RCP4.5, SSP3&RCP7.0, and SSP5&RCP8.5) to evaluate future urban water scarcity. SSP1&RCP2.6 represents the sustainable development pathway of low radiative forcing level, low climate change mitigation challenges, and low social vulnerability. SSP2&RCP4.5 represents the business-as-usual pathway of moderate radiative forcing and social vulnerability. SSP3&RCP7.0 represents a higher level of radiative forcing and high social vulnerability. SSP5&RCP8.5 represents a rapid development pathway and very high radiative forcing 38 .

Estimation of urban water scarcity

To estimate urban water scarcity, we quantified the total urban population living in water-scarce areas 2 , 3 , 7 , 19 . Specifically, we first corrected the spatial distribution of the global urban population, then identified water-scarce areas around the world, and finally quantified the urban population in water-scarce areas at different scales (Supplementary Fig.  1 ).

Correcting the spatial distribution of global urban population

The existing global urban population data from the History Database of the Global Environment (HYDE) provided consistent information on historical and future population, but it has a coarse spatial resolution of 10 km (Supplementary Table  1 ) 40 , 41 . In addition, it was estimated using total population, urbanization levels, and urban population density, and does not align well with the actual distribution of urban land 42 . Hence, we allocated the HYDE global urban population data to high-resolution urban land data. We first obtained global urban land in 2016 from He et al. 42 . Since the scenarios used in existing urban land forecasts are now dated 43 , 44 , we simulated the spatial distribution of global urban land in 2050 under each SSP at a grid-cell resolution of 1km 2 using the zoned Land Use Scenario Dynamics-urban (LUSD-urban) model 45 , 46 , 47 (Supplementary Methods 1). The simulated urban expansion area in this study was significantly correlated with that in existing datasets (Supplementary Table  6 ). We then converted the global urban land raster layers for 2016 and 2050 into vector format to characterize the spatial extent of each city. The total population within each city was then summed and the remaining HYDE urban population cells located outside urban areas were allocated to the nearest city. Assuming that the population density within an urban area was homogeneous, we calculated the total population per square kilometer for all urban areas and converted this back to raster format at a spatial resolution of 1 km 2 . The new urban population data had much lower error than the original HYDE data (Supplementary Table  7 ).

Identification of global water-scarce areas

Annual and monthly WSI values were calculated at the catchment level in 2014 and 2050 as the ratio of water withdrawals (TWW) to availability (AWR) 33 . Due to limited data availability, we combined water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. WSI for catchment i for time t as:

For each catchment defined by Masutomi et al. 48 , the total water withdrawal (TWW t,i ) equalled the sum of water withdrawals (WW t , n , i ) for each sector n (irrigation, livestock, industrial, or domestic), while the water availability equalled the sum of available water resources for catchment i ( R t , i ), inflows/outflows of water resources due to interbasin water transfer ( \(\varDelta {{{{\mathrm{W{R}}}}}}_{t,i}\) ), and water resources from each upstream catchment j (WR t , i , j ):

The changes of water resources due to interbasin water transfer were calculated based on City Water Map produced by McDonald et al. 3 . The number of water resources from upstream catchment j was calculated based on its water availability (AWR t , i , j ) and water consumption for each sector n (WC t , n , i,j ) 49 :

For areas without upstream catchments, the number of available water resources was equal to the runoff. Following Mekonnen and Hoekstra 36 , and Hofste et al. 33 , we did not consider environmental flow requirements in calculating water availability.

Annual and monthly WSI for 2014 were calculated directly based on water withdrawal, water consumption, and runoff data from AQUEDUCT3.0 (Supplementary Table  1 ). The data from AQUEDUCT3.0 were selected because they are publicly available and the PCRaster Global Water Balance (PCRGLOBWB 2) model used in the AQUADUCT 3.0 can better represent groundwater flow and available water resources in comparison with other global hydrologic models (e.g., the Water Global Assessment and Prognosis (WaterGAP) model) 33 . The annual and monthly WSI for 2050 were calculated by combining the global water withdrawal data from 2000 to 2050 provided by the National Institute of Environmental Research of Japan (NIER) 34 and global runoff data from 2005 to 2050 from CMIP6 (Supplementary Table  1 ). Water withdrawal \({{{{{\mathrm{W{W}}}}}}}_{s,m,n,i}^{2050}\) in 2050 for each sector n (irrigation, industrial, or domestic), catchment i , and month m under scenario s was calculated based on water withdrawal in 2014 ( \({{{{{\mathrm{W{W}}}}}}}_{m,n,i}^{2014}\) ):

adjusted by the mean annual change in water withdrawal from 2000 to 2050 (WWR s , m , n , i ), calculated using the global water withdrawal for 2000 ( \({{{{{\mathrm{W{W}}}}}}}_{{{{{\mathrm{NIER}}}}},m,n,i}^{2000}\) ) and 2050 ( \({{{{{\mathrm{W{W}}}}}}}_{{{{{\mathrm{NIER}}}}},s,m,n,i}^{2050}\) ) provided by the NIER 34 :

Based on the assumption of a constant ratio of water consumption to water withdrawal in each catchment, water consumption in 2050 ( \({{{{{\mathrm{W{C}}}}}}}_{s,m,n,i}^{2050}\) ) was calculated as:

where \({{{{{\mathrm{W{C}}}}}}}_{m,n,i}^{2014}\) denotes water consumption in 2014. Due to a lack of data, we specified that water withdrawal for livestock remained constant between 2014 and 2050, and used water withdrawal simulation under SSP3&RCP6.0 provided by the National Institute of Environmental Research in Japan to approximate SSP3&RCP7.0.

To estimate water availability, we calculated available water resources ( \({R}_{s,m,i}^{2041-2050}\) ) for each catchment i and month m under scenario s for the period of 2041–2050 as:

based on the amount of available water resources with 10-year ordinary least square regression from 2005 to 2014 ( \({R}_{m,i}^{{{{{\mathrm{ols}}}}},\,2005-2014}\) ) from AQUEDUCT3.0 (Supplementary Table  1 ). \({\overline{R}}_{m,i}^{2005-2014}\) and \({\overline{R}}_{s,m,i}^{2041-2050}\) denote the multi-year average of runoff (i.e., surface and subsurface) from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the average values of simulation results from 10 global climate models (GCMs) (Supplementary Table  2 ).

We then identified water-scarce catchments based on the WSI. Two thresholds of 0.4 and 1.0 have been used to identify water-scarce areas from WSI (Supplementary Table  4 ). While the 0.4 threshold indicates high water stress 49 , the threshold of 1.0 has a clearer physical meaning, i.e., that water demand is equal to the available water supply and environmental flow requirements are not met 36 , 37 . We adopted the value of 1.0 as a threshold representing extreme water stress to identify water-scarce areas. The catchments with annual WSI >1.0 were identified as perennial water-scarce catchments; the catchments with annual WSI equal to or <1.0 and WSI for at least one month >1.0 were identified as seasonal water-scarce catchments.

Estimation of global urban water scarcity

Based on the corrected global urban population data and the identified water-scarce areas, we evaluated urban water scarcity at the global and national scales via a spatial overlay analysis. The urban population exposed to water scarcity in a region (e.g., the whole world or a single country) is equal to the sum of the urban population in perennial water-scarce areas and that in seasonal water-scarce areas. Limited by data availability, we used water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. Projected water-scarce areas and urban population in 2050 under four scenarios were then used to estimate future urban water scarcity. In addition, we obtained the location information of large cities (with population >1 million in 2016) from the United Nations’ World Urbanization Prospects 1 (Supplementary Table  1 ) and identified those in perennial and seasonal water-scarce areas.

Uncertainty analysis

To evaluate the uncertainty across the 10 GCMs used in this study (Supplementary Table  2 ), we identified water-scarce areas and estimated urban water scarcity using the simulated runoff from each GCM under four scenarios. To perform the uncertainty analysis, the runoff in 2050 for each GCM was calculated using the following equation:

where \({R}_{s,g,m,i}^{2050}\) denotes the runoff of catchment i in month m in 2050 for GCM g under scenario s . \({R}_{g,m,i}^{2005-2014}\) and \({R}_{s,g,m,i}^{2041-2050}\) denote the multi-year average runoff from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the simulation results from GCM g . Using the runoff for each GCM, the WSI in 2050 for each catchment was recalculated, water-scarce areas were identified, and the urban population exposed to water scarcity was estimated.

Contribution analysis

Based on the approach used by McDonald et al. 2 and Munia et al. 50 , we quantified the contribution of socioeconomic factors (i.e., water demand and urban population) and climatic factors (i.e., water availability) to the changes in global urban water scarcity from 2016 to 2050. To assess the contribution of socioeconomic factors ( \({{{{{\mathrm{Co{n}}}}}}}_{s,{{{{\mathrm{SE}}}}}}\) ), we calculated global urban water scarcity in 2050 while varying demand and population and holding catchment runoff constant ( \({{{{{\mathrm{UW{S}}}}}}}_{s,{{{{\mathrm{SE}}}}}}^{2050}\) ). Conversely, to assess the contribution of climate change ( \(Co{n}_{s,CC}\) ), we calculated scarcity while varying runoff and holding urban population and water demand constant ( \({{{{{\mathrm{UW{S}}}}}}}_{s,{{{{\mathrm{CC}}}}}}^{2050}\) ). Socioeconomic and climatic contributions were then calculated as:

Feasibility analysis of potential solutions to urban water scarcity

Potential solutions to urban water scarcity involve two aspects: increasing water availability and reducing water demand 2 . Approaches to increasing water availability include groundwater exploitation, seawater desalination, reservoir construction, and inter-basin water transfer; while approaches to reduce water demand include water-use efficiency measures (e.g., new cultivars for improving agricultural water productivity, sprinkler or drip irrigation for improving water-use efficiency, water-recycling facilities for improving domestic and industrial water-use intensity), limiting population growth, and virtual water trade 2 , 3 , 18 , 32 . To find the best ways to address urban water scarcity, we assessed the feasibility of these potential solutions for each large city (Supplementary Fig.  2 ).

First, we divided these solutions into seven groups according to scenario settings and the scale of implementation of each solution (Supplementary Fig.  2 ). Among the solutions assessed, water-use efficiency improvement, limiting population growth, and climate change mitigation were included in the simulation of water demand and water availability under the ScenarioMIP SSPs&RCPs simulations 34 . Here, we considered the measures within SSP1&RCP2.6 which included the lowest growth in population, irrigated area, crop intensity, and greenhouse gas emissions; and the largest improvements in irrigation, industrial, and municipal water-use efficiency 34 .

We then evaluated the feasibility of the seven groups of solutions according to the characteristics of water-scarce cities (Supplementary Fig.  2 ). Of the 526 large cities (with population >1 million in 2016 according to the United Nations’ World Urbanization Prospects), we identified those facing perennial or seasonal water scarcity under at least one scenario by 2050. We then selected the cities that no longer faced water scarcity under SSP1&RCP2.6 where the internal scenario assumptions around water-use efficiency, population growth, and climate change were sufficient to mitigate water scarcity. Following McDonald et al. 2 , 3 and Wada et al. 18 , we assumed that desalination can be a potential solution for coastal cities (distance from coastline <100 km) and groundwater exploitation can be feasible for cities where the groundwater table has not significantly declined. For cities in catchments facing seasonal water scarcity and with suitable topography, reservoir construction was identified as a potential solution. Inter-basin water transfer was identified as a potential solution for a city if nearby basins (i.e., in the same country, <1000 km away [the distance of the longest water transfer project in the world]) were not subject to water scarcity and had sufficient water resources to address the water scarcity for the city. Domestic virtual water trade was identified as a potential solution for a city if it was located in a country without national scale water scarcity. International water transfer or virtual water trade was identified as a feasible solution for cities in middle and high-income countries. Based on the above assumptions, we identified potential solutions to water scarcity in each city (see Supplementary Table  1 for the data used).

Data availability

All the data created in this study are openly available and the download information of supplementary data can be found in Github repositories with the identifier https://github.com/zfliu-bnu/Urban-water-scarcity . Other data are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank Prof. N. Hanasaki (National Institute for Environmental Studies, Tsukuba, Japan) and Dr. Rutger W. Hofste (World Resources Institute, Washington, DC, USA) for providing global water demand/availability data. This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (Grant No. 2019QZKK0405) and the National Natural Science Foundation of China (Grant No. 41871185 & 41971270). It was also supported by the project from the State Key Laboratory of Earth Surface Processes and Resource Ecology, China.

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Chunyang He, Zhifeng Liu, Jianguo Wu, Xinhao Pan & Zihang Fang

School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China

School of Life Sciences and School of Sustainability, Arizona State University, Tempe, AZ, USA

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C.H., Z.L., J.W., and B.B. designed the study and planned the analysis. Z.L., X.P., Z.F., and J.L. did the data analysis. C.H., Z.L., and B.B. drafted the manuscript. All authors contributed to the interpretation of findings, provided revisions to the manuscript, and approved the final manuscript.

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He, C., Liu, Z., Wu, J. et al. Future global urban water scarcity and potential solutions. Nat Commun 12 , 4667 (2021). https://doi.org/10.1038/s41467-021-25026-3

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DOI : https://doi.org/10.1038/s41467-021-25026-3

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Water supply and water scarcity.

1. Prolegomena

2. the main contribution of this special issue, 2.1. water management under water scarcity regimes, 2.2. rainwater harvesting (rwh), 2.3. quality of water resources, 2.4. climate change impacts on water resources, 3. challenges and opportunities for in improving water supply, 3.1. growing population and urbanization, 3.2. climate change (and/or variability), 3.3. improving water use efficiency, 3.4. alternative (non-conventional) water resources, 3.4.1. wastewater reuse, 3.4.2. rainwater harvesting, 3.4.3. desalination, 3.5. preserving water quality, 4. epilogue, author contributions, acknowledgments, conflicts of interest.

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Tzanakakis, V.A.; Paranychianakis, N.V.; Angelakis, A.N. Water Supply and Water Scarcity. Water 2020 , 12 , 2347. https://doi.org/10.3390/w12092347

Tzanakakis VA, Paranychianakis NV, Angelakis AN. Water Supply and Water Scarcity. Water . 2020; 12(9):2347. https://doi.org/10.3390/w12092347

Tzanakakis, Vasileios A., Nikolaos V. Paranychianakis, and Andreas N. Angelakis. 2020. "Water Supply and Water Scarcity" Water 12, no. 9: 2347. https://doi.org/10.3390/w12092347

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Enhancing Agricultural Water Productivity Using Deficit Irrigation Practices in Water-Scarce Regions

• First Online: 10 March 2023

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• Truptimayee Suna 3 ,
• Arti Kumari 4 ,
• Pradosh Kumar Paramaguru 5 &
• N. L. Kushwaha 6  

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3 Citations

As the world grapples with climate change concerns, particularly changes in temperature and precipitation, modifying these climate variables as a result of global warming leads to a water scarcity crisis in the country. Water scarcity is defined as annual water availability per capita is less than 1000 cubic metres, according to a World Bank assessment. Following the Falkenmark Index, water shortage exists in more than half of the country’s 20 river basins, with availability of less than 1000 cubic metres per capita per annum (Singh and Kaur, India’s water crisis: challenges, solutions and barriers, working paper, Rajiv Gandhi Institute for Contemporary Studies, 2019 ). Along with this India has endowed only 4% of the world’s freshwater resources despite of 17% of world population clearly highlights the need for its sagacious use. The country’s water availability has worsened as a result of the disproportionate availability of freshwater and the delayed monsoon as a consequence of climate change. The situation extensively affects the country’s agricultural productivity which is the mainstay of Indian economy and principal livelihood for over 58 percent of the rural households. However, an ever-increasing population puts a strain on food supplies. As a result, scientific water management in agricultural practice is widely recognized as critical to long-term agrarian reform in water-stressed situations, which necessitates a paradigm shift away from maximizing productivity per unit of land area and towards maximizing productivity per unit of water. Keeping these facts, potent irrigation water management in a future of water shortage must be required, with the goal of conserving water and optimizing its output. In addition, a new management paradigm based on maximizing net benefit rather than yield must be implemented. This can be accomplished by lowering irrigation water demand and diverting the saved water to irrigate greater area while maintaining a relatively high water yield. To deal with this, the most important intervention is deficit irrigation, which involves purposely under-irrigating crops by applying water below the evapotranspiration requirements (English and Nuss, J Am Soc Civil Eng 108:91–106, 1982 ). As a result, this chapter has discussed a methodical and plausible strategy for increasing water productivity through deficit irrigation.

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Suna, T., Kumari, A., Paramaguru, P.K., Kushwaha, N.L. (2023). Enhancing Agricultural Water Productivity Using Deficit Irrigation Practices in Water-Scarce Regions. In: Naorem, A., Machiwal, D. (eds) Enhancing Resilience of Dryland Agriculture Under Changing Climate. Springer, Singapore. https://doi.org/10.1007/978-981-19-9159-2_11

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Research Article

Effect of climate change-induced water-deficit stress on long-term rice yield

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Agronomy, National Taiwan University, Taipei, Taiwan

Roles Data curation, Funding acquisition, Investigation

Affiliation Taichung District Agricultural Research and Extension Station, Council of Agriculture, Changhua, Taiwan

Roles Data curation

• Hungyen Chen, 
• Yi-Chien Wu, 
• Chia-Chi Cheng, 
• Chih-Yung Teng

• Published: April 17, 2023
• https://doi.org/10.1371/journal.pone.0284290
• Reader Comments

The water requirements of crops should be investigated to improve the efficiency of water use in irrigated agriculture. The main objective of the study was to assess the effects of water deficit stress on rice yields throughout the major cropping seasons. We analyzed rice yield data from field experiments in Taiwan over the period 1925–2019 to evaluate the effects of water-deficit stress on the yield of 12 rice cultivars. Weather data, including air temperatures, humidity, wind speed, sunshine duration, and rainfall were used to compute the temporal trends of reference evapotranspiration and crop water status (CWS) during rice growth stages. A negative CWS value indicates that the crop is water deficient, and a smaller value represents a lower water level (greater water-deficit stress) in crop growth. The CWS on rice growth under the initial, crop development, reproductive, and maturity stages declined by 96.9, 58.9, 24.7, and 198.6 mm in the cool cropping season and declined by 63.7, 18.1, 8.6, and 3.8 mm in the warm cropping season during the 95 years. The decreasing trends in the CWSs were used to represent the increases in water-deficit stress. The total yield change related to water-deficit stress on the cultivars from 1925–1944, 1945–1983, and 1996–2019 under the initial, crop development, reproductive, and maturity stages are -56.1 to 37.0, -77.5 to -12.3, 11.2 to 19.8, and -146.4 to 39.1 kg ha -1 in the cool cropping season and -16.5 to 8.2, -12.9 to 8.1, -2.3 to 9.0, and -9.3 to 8.0 in the warm cropping season, respectively. Our results suggest that CWS may be a determining factor for rice to thrive during the developmental stage, but not the reproductive stage. In addition, the effect of water-deficit stress has increasingly affected the growth of rice in recent years.

Citation: Chen H, Wu Y-C, Cheng C-C, Teng C-Y (2023) Effect of climate change-induced water-deficit stress on long-term rice yield. PLoS ONE 18(4): e0284290. https://doi.org/10.1371/journal.pone.0284290

Editor: Josily Samuel, CRIDA: Central Research Institute for Dryland Agriculture, INDIA

Received: September 22, 2022; Accepted: March 28, 2023; Published: April 17, 2023

Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: This work was supported by funding from the National Science and Technology Council, Taiwan (109-2313-B-002-027-MY3) and Taichung District Agricultural Research and Extension Station, Council of Agriculture, Executive Yuan, Taiwan (111a20-1) to HC. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Global climate change, including increased temperatures and fluctuating rainfall, has become a threat with a high potential to affect the water supply and agricultural sectors [ 1 – 3 ]. The increase in temperature and decrease in rainfall negatively affects the growth of plants because plants are subjected to temperature and water stress due to an increase in evapotranspiration [ 4 , 5 ]. The impact of climate change on the hydrological cycle, water balance, and runoff characteristics has emerged as a significant stressor at local and district levels, although there are uncertainties regarding the impacts of climate variability on water resources [ 6 , 7 ]. It is suggested that water availability and crop productivity will decrease significantly, and climate change will have an impact on irrigation water requirements and crop yield [ 8 , 9 ]. Crop yield change is expected due to the shifting growth phase and photosynthetic capacity, and increasing respiration and water requirements, which result from climate change [ 10 , 11 ]. To investigate the general effects of crop yield change on climate change, it is necessary to analyze long-term temporal variations between crop yields and climate variables [ 12 – 14 ].

To improve water-use efficiency in irrigated agriculture, it is important to study and understand crop water requirements. Evapotranspiration is a vital component when describing the hydrological cycle in ecological systems, estimating water balance, and determining water availability along with precipitation [ 15 , 16 ]. Reference evapotranspiration (ET 0 ) is a parameter of climatic conditions that has been widely investigated as an indicator of climate change [ 17 , 18 ]. Crop evapotranspiration (ET C ) is a variable for the optimization of irrigation water productivity and designing the schedule of irrigation in the implementation of agricultural water management [ 19 ] (Gong et al., 2020) and is highly influenced by irrigation water supply under different irrigation levels [ 20 , 21 ]. The Penman-Monteith (PM) model based on the Food and Agriculture Organization (FAO)-56 guidelines has served as a reference method because it produces the most accurate results compared to lysimetric measurements [ 22 , 23 ]. The PM model has been widely used for the estimation of daily or monthly ET 0 in different agro-climatic zones by many researchers for decades [ 22 , 24 , 25 ].

Rice is a semi-aquatic plant that depends on the rainfall and temperature of the cultivation area and hence, is heavily affected by climate change [ 5 , 26 ]. Severe effects of drought and high temperature on the growth and yield of rice due to insufficient water supply and improper scheduling of irrigation have been reported [ 27 , 28 ]. Some reports have revealed that rice yield may be affected by temperature and precipitation because of some physiological mechanisms [ 29 , 30 ]. Although many studies have revealed the impacts of climate change on crop production utilizing climate model projections of temperatures and rainfall [ 31 , 32 ], the number of studies that analyze the effect of water-deficit stress on rice yield using long-term field experimental data is limited.

Water deficit stress occurs when the amount of water required is greater than the amount of water available during a certain time. Our goal was to assess the effects of water deficit stress on rice yields throughout the major cropping seasons. In this study, we analyzed the yield data of 12 rice cultivars in cool and warm cropping seasons, separately, from field experiments conducted under irrigated conditions with optimal management at a research station in Taichung, Taiwan over the period 1925–2019. First, weather data, including average, maximum, and minimum temperatures, humidity, wind speed, and sunshine duration, collected at the research farm were used to compute the long-term temporal trends of reference evapotranspiration during the initial, crop development, reproductive, and maturity stages of rice growth during the 95 years. Second, the crop evapotranspiration of rice under the growth stages was calculated using the estimated reference evapotranspiration and crop coefficient of rice. Third, the crop water status during the growth stages was calculated using the estimated crop evapotranspiration and collected rainfall data. Fourth, long-term temporal trends in crop water status during the growth stages were deduced to reveal the temporal trend in water-deficit stress. Fifth, a multiple linear regression model was applied to evaluate the relationships between rice grain yield and water-deficit stress during the four growth stages. Finally, total yield changes computed from the regression coefficients for each growth stage over the periods 1925–1944, 1945–1983, and 1996–2019 were used separately to reveal the effects of water-deficit stress on rice yield and the temporal variations during the experimental period.

Materials and methods

Field experiment.

A field experiment on rice growth in two cropping seasons was conducted from 1925 to 2019 at the Taichung District Agricultural Research and Extension Station, Council of Agriculture, Executive Yuan, Taiwan (1925–1983: 24º09′ N 120º41′ E, altitude 77 m above mean sea level; 1996–2019: 24º00′ N 120º32′ E, altitude 19 m above mean sea level). The rice seeds were sown in the cool cropping season in mid-January, and the seeded area was dibbled either in February or March every year over the period 1925–2019 except for 1948–1951, 1985–1995, and 2014–2016. Rice from the cool cropping season was harvested either in June or July. The rice seeds were sown in the warm cropping season in June, and the area was dibbled either in July or August every year over the period 1925–2019, except for 1945, 1947–1951, 1985–1995, and 2013–2015. Rice in the warm cropping season was harvested either in October or November. The seedlings were transplanted into the fields by hand. The area of the plot for each cultivar was 27 m 2 . Continuous flooding irrigation to 5 cm above the soil surface was carried out in the field during the period between transplanting and drying. Re-irrigation was applied when the field water subsided to the soil surface. The grain yield was obtained by harvesting from all the hills in the plots (at a grain maturity rate of 98%), except for the side rows, and then measuring the grain weight. No field permits were required for this work at the research station which the authors are affiliated with.

Rice yield data

Twelve rice cultivars were used throughout the experimental period in cool and warm cropping seasons, separately. In cool cropping season, Nakamura (NM; 1925–1931), Taichung S2 (TCS2; 1925–1932), Baiker (BK; 1925–1944), Taichung S6 (TCS6; 1933–1944), Wugen (WG; 1925–1947, 1952–1976), Baimifun (BMF; 1945–1947, 1952–1976), Taichung 65 (TC65; 1930–1947, 1952–1983), Taichung 150 (TC150; 1945–1947, 1952–1983), Taiagro 67 (TA67; 1996–2013, 2017–2019), Taichung 189 (TC189; 1996–2013, 2017–2019), Taichung Indica 10 (TCI10; 1996–2013, 2017–2019), and Tai Japonica 9 (TJ9; 2000–2013, 2017–2019) were used. In warm cropping season, Nakamura (NM; 1925–1931), Taichung S2 (TCS2; 1925–1944), Jingou (JG; 1925–1944), Nyaoyao (NY; 1925–1944), Swanjian (SJ; 1946, 1952–1976), Sianlou (SL; 1946, 1952–1976), Taichung 65 (TC65; 1930–1944, 1946, 1952–1983), Taichung 150 (TC150; 1946, 1952–1983), Taiagro 67 (TA67; 1996–2012, 2016–2019), Taichung 189 (TC189; 1996–2012, 2016–2019), Taichung Indica 10 (TCI10; 1996–2012, 2016–2019), and Tai Japonica 9 (TJ9, 2000–2012, 2016–2019) were used.

For each cropping season, three groups of cultivars with overlapping cultivation periods were clustered together to calculate the group average value, which represents the effect of water stress on rice yield in each period. Based on the cultivation period among cultivars, four cultivars were included in the 1925–1944, 1945–1983, and 1996–2019 periods, separately.

Four distinct stages of rice growth were used for the analyses. For the cool cropping season, the initial stage was from March 1–31, the crop development stage was from April 1–30, the reproductive (mid-season) was from May 1–31, and the maturity (late season) stage was from June 1–30. For the warm cropping season, the initial stage was from August 1–31, the crop development stage was from September 1–30, the reproductive (mid-season) stage was from October 1–31, and the maturity (late season) stage was from November 1–30.

Weather data

A weather station was set up on the research station farm. The site is surrounded by field crops and the topography is flat. Daily weather data recording began on January 1, 1925. Meteorological instruments at the station included a solarimeter, glass thermometers for minimum and maximum temperatures, a psychrometer, and a thermo-hygrograph. The air temperature, humidity, wind speed, rainfall, and sunshine duration during the cropping seasons throughout the experimental period were used for the analyses. The average, minimum, and maximum temperatures, average relative humidity, and average wind speed (2 m above the soil surface) under the four growth stages for each year were calculated as the average of the daily values in the cool and warm cropping seasons, respectively. Rainfall and sunshine durations under the four growth stages for each year were calculated as the sum of the daily values in the two cropping seasons.

Statistical models

The Kc values for rice during the initial, crop development, reproductive (mid-season), and maturity (late season) stages were 1.15, 1.23, 1.14, and 1.02, respectively, as estimated by Tyagi et al. [ 35 ].

The value of the CWS represents the water status of crop growth under weather conditions. A negative CWS value indicates that the crop is water deficient, and a smaller value represents a lower water level (greater water-deficit stress) in crop growth. In cases where all the water needed for optimal crop growth is provided by rainfall, irrigation is not required, and the CWS equals zero. The CWS determined in this study was inspired by the formula for irrigation water need (IN) [ 36 ], IN = ET C + PERC + WL—PE. The value of the CWS equals the negative number of the value of IN.

The total rice yield change (kg ha -1 ) related to water stress under each growth stage was computed using the regression coefficients for water stress ( β CWS ) and the estimated change in crop water status (ΔCWS) throughout each cultivation period.

Long-term temporal variations in rice yield

In the cool cropping season, the yield of the four rice cultivars for the period 1925–1944 ranged between 1,530 and 8,055 kg ha -1 , with an average ± standard deviation (SD) of 4,236 ± 1,114 kg ha -1 . The yield of the four rice cultivars for the period 1945–1983 ranged between 2,550 and 8,215 kg ha -1 , with an average ± SD of 4,708 ± 774 kg ha -1 . The yield of the four rice cultivars during 1996–2019 ranged between 4,181 and 9,268 kg ha -1 , with an average ± SD of 6,523 ± 1,080 kg ha -1 ( Fig 1a–1d ). In the warm cropping season, the yield of the four rice cultivars for the period 1925–1944 ranged between 2,181 and 6,493 kg ha -1 , with an average ± standard deviation (SD) of 3,880 ± 873 kg ha -1 , the yield of the four rice cultivars for the period 1945–1983 ranged between 2,180 and 6,384 kg ha -1 , with an average ± SD of 4,149 ± 796 kg ha -1 , and the yield of the four rice cultivars for the period 1996–2019 ranged between 2,861 and 7,269 kg ha -1 , with an average ± SD of 4,704 ± 971 kg ha -1 ( Fig 1e–1h ).

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https://doi.org/10.1371/journal.pone.0284290.g001

Long-term temporal variations in reference evapotranspiration

In the cool cropping season, the ET 0 ranged between 2.3 to 4.7, 2.6 to 5.0, 3.4 to 5.7, and 3.4 to 5.8 mm day -1 under the initial, crop development, reproductive, and maturity stages, respectively ( Fig 2a–2d ). The average values ± SDs of ET 0 were 3.3 ± 0.5, 3.8 ± 0.4, 4.3 ± 0.5, and 4.4 ± 0.5 mm day -1 under the four growth stages, respectively ( Fig 2a–2d ). In the warm cropping season, the ET 0 ranged between 3.4–5.7, 3.6–5.9, 3.2–5.1, and 2.2–4.2 mm day -1 under the initial, crop development, reproductive, and maturity stages, respectively ( Fig 2e–2h ). The average values ± SDs of ET 0 were 4.6 ± 0.4, 4.3 ± 0.4, 3.8 ± 0.3, and 2.8 ± 0.3 mm day -1 under the four growth stages, respectively ( Fig 2e–2h ).

https://doi.org/10.1371/journal.pone.0284290.g002

Long-term temporal variations in crop water status

In the cool cropping season, the CWS on rice growth under initial, crop development, reproductive, and maturity stages ranged between -422.4 to -145.0, -428.9 to -42.0, -438.8 to 105.0, and -407.0 to 617.3 mm, respectively ( Fig 3a–3d ). The average values ± SDs of CWS were -327.2 ± 58.6, -323.1 ± 75.4, -263.9 ± 122.7, and -177.9 ± 195.1 mm under the four growth stages, respectively ( Fig 3a–3d ). In the warm cropping season, the CWS on rice growth under initial, crop development, reproductive, and maturity stages ranged between -455.7 to 333.8, -466.2 to 136.4, -435.2 to -260.1, and -416.3 to -244.8 mm, respectively ( Fig 3e–3h ). The average values ± SDs of WDS were -208.3 ± 173.4, -322.9 ± 123.5, -380.2 ± 24.9, and -366.2 ± 24.3 mm under the four growth stages, respectively ( Fig 3e–3h ). The CWS on rice growth under the initial, crop development, reproductive, and maturity stages declined by 96.9, 58.9, 24.7, and 198.6 mm in the cool cropping season, respectively ( Fig 3a–3d ), and declined by 63.7, 18.1, 8.6, and 3.8 mm in the warm cropping season, respectively ( Fig 3e–3h ) from 1925 to 2019.

Grey line represents the linear regression line. * represents p-value < 0.05.

https://doi.org/10.1371/journal.pone.0284290.g003

Effects of crop water status on rice yield

The long-term temporal variation in the estimated effects of the CWS on the grain yield of 12 rice cultivars in the cool and warm cropping seasons from 1925 to 2019 are shown in Fig 4 . A positive value of the regression coefficient reflects a coincident pattern between grain yield and CWS, and a negative value indicates an inverse response of grain yield to CWS. In the cool cropping season, the average regression coefficients under the initial and maturity stages revealed positive values in 1925–1944 and 1996–2019, but a negative average value over the period 1945–1983 ( Fig 4a and 4d ); the average regression coefficients under the crop development stage revealed all positive values and decreased throughout the experimental period ( Fig 4b ); and the average regression coefficients under the reproductive stage revealed all negative values and increased throughout the experimental period ( Fig 4c ). In the warm cropping season, the average regression coefficients under the initial stage revealed positive values in the periods 1925–1944 and 1945–1983, but a negative average value from 1996–2019 ( Fig 4e ). The average regression coefficients under the crop development and maturity stages revealed positive values in the periods 1925–1944 and 1996–2019, but a negative average value from 1945–1983 ( Fig 4f and 4h ). The average regression coefficients in the reproductive stage revealed a negative value from 1925–1944 and 1945–1983, but a positive value from 1996–2019, and increased throughout the experimental period ( Fig 4g ).

Filled circles represent the value of a cultivar. Open circles represent the average value of a group of cultivars having overlapped cultivation period. The length of the black line on the open circle represents the value of standard deviation. Horizontal dash lines separate the groups of cultivars having overlapped cultivation periods. In each panel, the upper, mid, and lower zone represent the periods of 1925–1944, 1945–1983, and 1996–2019, respectively.

https://doi.org/10.1371/journal.pone.0284290.g004

Yield changes responding to crop water status

The mean total yield change relating to the CWS on the cultivars in cool cropping seasons over the periods 1925–1944, 1945–1983, and 1996–2019 are -43.2, 37.0, and -56.1 kg ha -1 in the initial stage, respectively, -77.5, -39.0, and -12.3 kg ha -1 in the crop development stage 19.3, 19.8, and 11.2 kg ha -1 in the reproductive stage, and -146.4, 39.1, and -12.4 kg ha -1 in the maturity stage, respectively ( Table 1 ). The mean total yield change related to the CWS on the cultivars in the warm cropping seasons over the periods 1925–1944, 1945–1983, and 1996–2019 are -1.3, -16.5, and 8.2 kg ha -1 in the initial stage, -12.9, 8.1, and -0.4 kg ha -1 in the crop development stage, 9.0, 4.1, and -2.3 kg ha -1 in the reproductive stage, and -2.8, 8.0, and -9.3 kg ha -1 in the maturity stage, respectively ( Table 1 ).

https://doi.org/10.1371/journal.pone.0284290.t001

In most cultivated areas of Taiwan, two cropping seasons are maintained throughout the year. The cool cropping season starts in late February or early March (initial stage) and ends in late June (maturity stage), and the warm cropping season starts in late July or early August (initial stage) and ends in late November (maturity stage). The patterns of temperature variation are opposite in the two cropping seasons. The average temperature increases throughout the cool cropping season while the average temperature decreases throughout the warm cropping season. The patterns of variation in reference evapotranspiration under the four growth stages of rice were the opposite between the cool and warm cropping seasons ( Fig 2 ). Thus, to reveal the climatic effect, the rice yield response to climate variables needs to be analyzed separately in the cool and warm cropping seasons. The value for Qiu et al. [ 37 ] differentiates the changes in the seasonal crop evapotranspiration of rice in terms of growth duration under varying types of warming patterns using an evapotranspiration estimation model. The reference evapotranspiration increased throughout the cool cropping season, whereas it decreased throughout the warm cropping season ( Fig 2 ). The different patterns of temporal and spatial variation in the reference evapotranspiration and sensitivity coefficient responses to precipitation and temperature were investigated [ 38 , 39 ].

Long-term decreasing trends and negative values in crop water status were observed at all four growth stages in the two cropping seasons ( Fig 3 ). This result showed that increasing crop water deficiency led to greater water-deficit stress on rice growth. The decreased crop water status was due to the increased air temperature and decreased rainfall [ 1 ]. The water deficit is limiting the growth and productivity of crops and has been a major problem for crop production worldwide, especially in rain-fed agricultural areas [ 40 – 42 ]. In the cool cropping season, the decreasing trend of crop water status was severe during the initial and maturity stages and mild during the reproductive stage ( Fig 3 ). Compared with the cool cropping season, the decreasing trend in crop water status in the warm cropping season was relatively small under the four growth stages ( Fig 3 ). This result may be due to the greater temperature increase and rainfall decrease in the cool season as opposed to the warm season [ 43 ].

The rice yield changes related to the crop water status were negative during the rice development stage (except for the warm cropping season from 1945–1983). This result suggests that crop water may be a determining factor for rice growth during the development stage [ 22 , 44 ]. The rice yield changes related to the crop water status were positive during the rice reproductive stage (except for the warm cropping season over the period 1996–2019). This result suggests that crop water may not be a determining factor for rice growth during the reproductive stage [ 22 , 45 ]. In recent years, from 1996 to 2019, negative yield changes were observed under all four growth stages in the cool cropping season and under crop development, reproductive, and maturity stages in the warm cropping season. This result may suggest that water-deficit stress has had a greater effect on rice growth in recent years [ 46 , 47 ].

The values of crop water status under the four growth stages had little correlation with each other in both the cool (| r | ≤ 0.24) and warm (| r | ≤ 0.16) cropping seasons ( Table 2 ). The annual variations in the yields of the cultivars with overlapping cultivation periods were correlated with each other in the same groups ( Fig 3 ). The correlation coefficients of the yearly yields among the rice cultivar pairs with overlapping cultivation periods averaged 0.838, 0.605, and 0.665 in the cool cropping season during 1925–1944, 1945–1983, 1996–2019, respectively, and 0.716, 0.566, and 0.735 in the warm cropping season during 1925–1944, 1945–1983, 1996–2019, respectively. The correlations between the changes in crop water status under the four growth stages may make it difficult to separate the effects of different growth stages due to the co-linearity [ 48 ]. Although these problems have been discussed, the observations at our station showed low to little correlation among the values at different growth stages during the cropping seasons.

https://doi.org/10.1371/journal.pone.0284290.t002

The cultivars of japonica type rice in Taiwan are the only japonica type rice that can grow under relatively high temperatures and produce good-quality rice with a high yield [ 49 , 50 ]. Crops in tropical regions have been reported to be more sensitive to warming because their temperature is already close to their optimum temperature during the growing period [ 3 ]. In many regions, a slight increase in temperature with sufficient rainfall may have a positive effect on crops [ 51 ]. The lowland rice varieties were reported to be highly sensitive to soil drying, and their yields decline when the soil dries below saturation [ 52 ].

Data were collected from the same research station during the long-term experimental period. To obtain general results, the analysis of the crop yield response to global or national water-deficit stress should be extended. Data collected in different areas or on different temporal and spatial scales may result in different conclusions [ 53 , 54 ]. For example, up to 45% yield reductions of rice are expected by the end of this century due to climate change, including water deficit, in the countries in eastern Africa [ 55 ]. In Iran, it was reported that water deficit during vegetative, flowering and grain filling stages reduced mean grain yield by 21, 50 and 21% on average in comparison to control, respectively [ 56 ]. In this study, long-term temporal variation in the rice yield response to water-deficit stress was revealed, even though the rice cultivars varied throughout the study period. During an experimental period of over 90 years since 1925, it is impossible to maintain the crop yield experiments using the same cultivar and maintaining the same environmental and cultivational conditions consistently. It is also difficult to consider the factors that may affect the growth and production of crops, such as insects, diseases, and soil fertility [ 57 – 60 ], as well as human-induced effects, such as modern management, improving technology, and cultivator practices [ 26 , 58 ] for long-term observations. Crop evapotranspiration could be influenced by other factors, such as soil condition, canopy cover, and the fraction of leaf senescence; thus, the information of these coefficients may be considered for the calculation of crop evapotranspiration, if possible [ 19 , 22 ]. In addition, extreme climatic events, such as floods and heatwaves, may pose additional risks to crop production [ 61 ].

This study revealed the effect of water-deficit stress on rice yield in both cool and warm cropping seasons. The results provide long-term evidence of declining crop water status during the rice-growing seasons. The average values of ET 0 were estimated as 3.3–4.4 mm day -1 , and 2.8–4.6 mm day -1 in cool and warm cropping seasons, respectively, under the rice growth stages. The crop water status has decreased by 24.7–198.6 mm in the cool cropping season and 3.8–63.7 mm in the warm cropping season under the rice growth stages since 1925 and during the 95 years. Compared with the cool cropping season, the decreasing trend in crop water status in the warm cropping season was relatively slight under the four growth stages. The total water-deficit stress related yield change in the cultivars in the cool cropping season during 1925–1944, 1945–1983, and 1996–2019 were -56.1 to 37.0, -77.5 to -12.3, 11.2 to 19.8, and -146.4 to 39.1 kg ha -1 under the initial, crop development, reproductive, and maturity stages, respectively. The total yield change related to the CWS on the cultivars in the warm cropping season during 1925–1944, 1945–1983, and 1996–2019 are -16.5 to 8.2, -12.9 to 8.1, -2.3 to 9.0, and -9.3 to 8.0 kg ha -1 under the initial, crop development, reproductive, and maturity stages, respectively. Our results suggest that crop water may be a determining factor for rice growth during the developmental stage, but not during the reproductive stage. In addition, water-deficit stress has been increasingly affecting rice growth in recent years. To maintain high productivity and quality, our results on the effect of water-deficit stress on rice grain yield should be considered along with other adaptation strategies targeting agronomic efforts and breeding technologies.

Supporting information

S1 file. field experimental data..

https://doi.org/10.1371/journal.pone.0284290.s001

Acknowledgments

The authors wish to thank Dr. Jia-Ling Yang and other researchers in Taichung District Agricultural Research and Extension Station, Council of Agriculture, Taiwan who assisted in the field investigation and data collection.

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