Access to water-related services strongly modulates human development

1. Introduction The human development discussion explores the conditions of living that promote human life to flourish (Ranis et al., 2005). An influential definition of human development is “a process of enlarging people's choices” (UNDP,1990,p.10). It implies that the end of development is human well-being rather than economic growth. Water is essential for human development because it contributes to human well-being, expanding human capabilities and choice by contributing to human health and enabling the ability to undertake productive activities (Chenoweth,2008; Mehta,2014). Without access to safe drinking water and sanitation services there can be limits on health, food, dignity, and well-being. In short, the full enjoyment of life is restricted under water shortage (UN General Assembly,2010; WHO,2003). Water supply and sanitation infrastructure have been widely recognized as a pre-condition for development due to their connection to quality of life, health, and wealth of human communities (Arimah,2017). Personal access to water supply and sanitation is essential for guaranteeing basic human needs (e.g., direct consumption, food preparation, sanitation, and hygiene) (Chenoweth,2008). Still, it is estimated that around 0.84 and 2.3 billion people lack basic drinking water and sanitation services, respectively (WHO & UNICEF,2017). To address this, Abstract Water enables health, education, and economic well-being opportunities for humanity. Access to basic water and sanitation services, freshwater variability, and water storage are some of the dimensions that may impact on human development worldwide. Yet few studies quantitatively explore the relationship between water and human development. This study uses a statistical approach to quantify the Water-Human Development relation in a global sample, both in terms of correlation and causality between variables. Correlation is established using a multiple linear regression approach, while causality is explored by implementing the multi-spatial convergent cross mapping technique. Our study finds strong interdependence between water-related variables and human development globally. Access to water services positively influences the Human Development Index (HDI), seasonal variability of freshwater resources restricts it, and large water storage is not significant. The analysis is robust between 2000 and 2017, and implies that a 1% increment in a country's HDI is associated with a 1.3%–3.2% increment in water and sanitation access. Causal analyses show strong coupling, suggesting positive feedback between access to water services and HDI that could be exploited. Reaching Sustainable Development Goal 6 requires closing the water and sanitation access gaps while addressing freshwater variability challenges. This will result in global human development benefits. Plain Language Summary This research explores the relation between water dimensions and human development across the world. To do so we used publicly available global datasets of international organizations in the period between 2000 and 2017. Statistical tools were used to determine the strength and causality in the relation between water and development. We found that access to drinking water and sanitation services are deeply interrelated with human development. However, heavily marked dry and wet seasons represent a significant threat for human development. Interestingly, we also encounter that neither the density of large water reservoirs in a country nor the amount of stored water per person in a country affect human development in a significant way. Our results evidence that raising a country's water and sanitation coverage by around 2% is tied to a 1% increase of human development in the long run. More efforts are needed to prevent the negative effects of floods and droughts in countries with high water variability. Evidence shows that to have access to water rather than just store it represents the most benefit for people across the globe. Therefore, to prioritize access to basic water services will bring long term benefits for human development worldwide. AMOROCHO-DAZA ETAL. © 2023. The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Access to Water-Related Services Strongly Modulates Human Development H. Amorocho-Daza1 , P. van der Zaag1,2 , and J. Sušnik1 1Land and Water Management Department, IHE Delft Institute for Water Education, Delft, The Netherlands, 2Water Management Department, Delft University of Technology, Delft, The Netherlands Key Points: • Human development is correlated to access to water services and freshwater variability, yet not statistically linked to large water storage • Water variables are long-term predictors of human development (2000–2017) • Causality analyses indicate that water and human development are mutually interdependent Supporting Information: Supporting Information may be found in the online version of this article. Correspondence to: H. Amorocho-Daza, [email protected] Citation: Amorocho-Daza, H., van der Zaag, P., & Sušnik, J. (2023). Access to water-related services strongly modulates human development. Earth's Future, 11, e2022EF003364. https://doi. org/10.1029/2022EF003364 Received 17 NOV 2022 Accepted 18 MAR 2023 Author Contributions: Conceptualization: P. van der Zaag, J. Sušnik Data curation: H. Amorocho-Daza Formal analysis: H. Amorocho-Daza, J. Sušnik Investigation: H. Amorocho-Daza Methodology: H. Amorocho-Daza, J. Sušnik Project Administration: J. Sušnik Resources: J. Sušnik Software: H. Amorocho-Daza Supervision: P. van der Zaag, J. Sušnik Visualization: H. Amorocho-Daza Writing – original draft: H. AmorochoDaza, J. Sušnik 10.1029/2022EF003364 RESEARCH ARTICLE 1 of 19 Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 2 of 19 the UN2030 Agenda includes Sustainable Development Goal (SDG) 6: “Water and Sanitation for all” that aims to reach, among others, universal access to basic water services (UN General Assembly,2015). However, the goal is currently off-track and several societal efforts and transformations are needed to reach it (Sadoff etal.,2020). The lack of access to water, and sanitation services worldwide is a societal burden (Hutton & Chase,2016). Poor water and sanitation services are estimated to account for 3.3% of the deaths and 4.6% of the health-related lost years (i.e., disability-adjusted life years -DALYs) worldwide (WHO,2019). These figures are even more worrying for children under 5years, accounting for 13% and 12% of the global deaths and DALYs of this group (WHO,2019). It is estimated that nearly 1.6 million deaths and 105 million DALYs are preventable annually with better water, sanitation and hygiene (WASH) services (Prüss-Ustün etal.,2019). The health related impacts of poor water and sanitation services extend to other human wellbeing dimensions such as education and income. Children are a priority group in this dimension. Child undernutrition is a condition strongly related to diarrheal disease and evidenced in features such as stunting (Checkley etal.,2008; Victora etal.,2008). There is strong evidence which indicates that child undernutrition has long lasting effects. Negative impacts are evident not only in shorter adult height, but also impact human capital negatively (i.e., in terms of lower school attendance, reduced adult income). Intergenerational effects are particularly worrying (i.e., decreased offspring birthweight) (Victora etal.,2008). Evidence suggests that child undernutrition follows a causal pathway that is driven not only by inadequate diets, but also by fecal contamination of domestic environments (Humphrey,2009). Fecal contamination is the root cause of diarrhea and gut health disorders (i.e., environmental enteropathy) during childhood, and both issues are evidently linked to poor access to sanitation and handwashing (Humphrey,2009; Ngure etal.,2014). Therefore, the health benefits derived from improved access to basic water and sanitation services may have long-lasting positive impacts in terms of educational and economic benefits, especially for children. The economic costs of the lack of access to basic water and sanitation services are extensive. The economic burden associated to poor water and sanitation is estimated in 1.5% of the global GDP (WHO,2012). Yet, some reports suggest that impacts can reach 7% of the GDP in countries that face serious water and sanitation related issues (The World Bank,2008). Estimates of the capital costs required to reach universal access to basic services are 28.4 billion per year (ranging between US$13.8 to US$46.7 billion), this is equivalent to merely 0.1% (range 0.05%–0.16%) of the global GDP during the 2015–2030 period (Hutton & Varughese,2016). As such, water investments are expected to be highly cost-effective. Time savings and health benefits are expected to be 11 times greater than the monetary resources to fund water infrastructural improvements (Banerjee & Morella,2011). These benefits are likely to relate to overall human development indicators. Research suggests a strong relation between access to drinking water and the Human Development Index (HDI) (Sušnik & van der Zaag,2017) and GDP per capita (Fukuda etal.,2019). Severe hydroclimatological conditions (e.g., droughts and floods) have been highlighted as restricting factors for human wellbeing. High seasonal rainfall variability is related to poverty, and anomalously dry/wet conditions restrict economic growth (Brown & Lall,2006; Brown etal.,2013). It has been argued that dams play an important role in dealing with hydroclimatogical variability, and therefore are development mediators (Grey & Sadoff,2007; Tortajada,2014). Thus, it has been proposed that water storage per capita (e.g., m 3 capita −1) can act as a proxy for resilience to droughts and floods, as well as a determinant factor for economic prosperity and development (Grey & Sadoff,2007; Hall etal.,2014). However, this approach has received criticism for being reductionist and for misinforming policy recommendations (Zeitoun etal.,2016). Little research has quantitatively explored the relation between water and development (Brown & Lall,2006; Hall etal.,2014; Sušnik & van der Zaag,2017). Previous efforts have limitations both in terms of number of water related predictors, and because they have mostly relied on narrow indicators that account for “development” via income (i.e., GDP per capita) without considering other human-wellbeing dimensions. UNDP's HDI represents a more comprehensive development indicator, as it considers the dimensions of health and education, in addition to sufficient income. Yet, the explicit relationships between the HDI and water-related variables needs further exploration. For instance, often water supply is the focus, while access to sanitation facilities is glaringly overlooked in analyses (e.g., Fukuda etal.(2019)). As a further limitation, the joint effect of water supply, sanitation, hydroclimatology, and water storage, remains unexplored, representing a significant opportunity of knowledge advance. In light of these previous shortcomings, this study aims to fill these gaps, and therefore represents a novel analysis which hopes to lead to new insight on the role of water supply, sanitation, hydroclimatology, and water storage as likely contributing factors to human development. These limitations are addressed in this study by identifying the joint impact of various water-related factors on human development using a cross country analysis at global Writing – review & editing: H. Amorocho-Daza, P. van der Zaag, J. Sušnik 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 3 of 19 scale covering the period 2000–2017. A robust statistical approach, also often overlooked in previous work, including a multiple linear regression model as well as causality analysis evaluation are employed, demonstrating the combined effect of water infrastructure (supply, sanitation and storage) and hydroclimatological conditions (seasonal and interannual water availability) on human development globally. The paper aims to demonstrate the value and impact of investing in water and sanitation expansion to promote human development. 2. Materials and Methods This section provides a description of the data sources and the proposed statistical analysis. 2.1. Data The Human Development Index (HDI) is the output variable of the present study, it is used as a human development proxy (UNDP,1990). Established by the United Nations Development Programme (UNDP) in 1990, the HDI is constituted by three equally weighted dimensions measured at country level: Longevity (i.e., life expectancy at birth), knowledge (i.e., mean years of schooling, expected years of schooling) and living standards (Gross National Income (GNI) per capita) (UNDP,1990). A high HDI score aims to account for a long and healthy life, with access to education, and a decent standard of living, thus being subjectively “better” than lower HDI scores. UNDP reports the index on a yearly basis as a long-term estimate of global human development progress (UNDP,2019). Harmonized HDI time series are available since 1990 and can be found in UNDP(2023). A detailed account of the index estimation can be found in the UNDP(2019) accompanying Technical Note 1. For this paper, six water related response variables were classified into three dimensions: Access, Storage and Hydroclimatology. The Access dimension includes access to basic drinking water and sanitation services. In essence, a basic drinking water service come from sources that have the potential to deliver safe water, while basic sanitation facilities hygienically separate excreta from human contact (WHO & UNICEF,2017). Access is measured as “people using at least basic drinking water/sanitation services (% of population),” as defined by the WHO/ UNICEF Joint Monitoring Programme (JMP) (2017). It is worth noting that this indicator refers to the coverage in terms of basic, rather than safely managed water and sanitation services, the later being a more comprehensive indicator taking into account dimensions of accessibility, availability and quality (WHO & UNICEF,2017). In this paper, data on basic services (supply and sanitation) was used. This was done so as to be consistent through the period 2000–2017 which includes both the Millennium Development Goals (MDGs) and the updated SDGs. The Storage dimension covers water storage per capita and reservoir density in a country. Water storage per capita is the amount of water that is stored in a country's reservoirs divided by its population (i.e., m 3/capita), as proposed by Grey and Sadoff(2007), and reservoir density is the number of reservoirs that exist per area unit within a country (i.e., #Reservoirs/km 2) as used by Tian etal.(2020). Data on dam capacity per capita, number of reservoirs and country area was obtained from the Food and Agricultural Organisation's (FAO) AQUASTAT database (FAO,2021). The Hydroclimatology dimension considers intraand inter-annual blue water variability, that is water available in streams, lakes and aquifers (Lundqvist & Steen,1999), and can be estimated using the coefficient of variation. Intra-annual variability accounts for the typical water variability within a year, for example, in terms of dry and wet seasons; while interannual variability accounts for water variability across years, for example, due to global phenomena such as El Niño Southern Oscillation (ENSO). Intra-annual (or seasonal) variability is defined as “the standard deviation of monthly total blue water divided by the mean of total blue water calculated using the monthly mean” (UNESCO IHP,2013b); similarly, inter-annual variability is “the standard deviation of annual total blue water divided by the mean of total blue water from 1950 to 2010” (UNESCO IHP,2013a). A summary of the data used is shown in Table1. 2.2. Statistical Analysis The statistical approach analyses both the correlation and the causality among water variables and human development. Table2 summerizes the implemented methods as well as their corresponding time frames, variables and number of observations. Both aspects are described below. 2.2.1. Correlation Assessment Correlation among variables was tested to identify significant HDI predictors. Statistical models are estimated based on a cross-country analysis for different years as detailed in Table2. A correlogram for the studied variables 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 4 of 19 was estimated. Multiple linear regression models (ordinary least squares; OLS) were also examined, including all water-related variables and subsequently re-run to report only statistically significant water variables on human development. These models are a globally representative estimation valid at country scale for a particular year. Additionally, segmented multiple linear regression models were carried out according to the UNDP's human development categories (Table3). This analysis was based on the approach proposed by Schell etal.(2007), who assessed the significance of health related determinants across different development categories (e.g., low, middle and high income countries). The correlation analysis was done initially on 2017 data. The 2017 multiple linear regression model results were further analyzed in two ways. First, by estimating a regression model comparing the water-variable forecasted HDI with the actual HDI values. The model's prediction interval can be visualized along with a scatterplot of both variables to explore their proximity. And second, by estimating models to assess the water-human development for the period 2000–2016 and comparing these to the 2017 model to assess the long-term robustness of this approach. Rather than aiming to validate the 2017 model backwards, this analysis intends to explore the long-term water variables' significance in relation to human development, as well as the evolution of their coefficients over a long time span. Visualization tools were used to improve analysis and understanding regarding the relationships between key water variables and human development. Dynamic and interactive 2D and 3D plots including color, size and time dimensions were developed to visualize variables such as seasonal variability as well as time-varying relationships between the variables (see the links in Figures2–4 and Supporting InformationS1). Several R packages such as ggplot2, gganimate, plot3D, and plotly were used for this purpose. Table 1 Median (Range) of the Studied Variables for all Countries and Stratified by HDI Rank for the Year 2017 (UNDP,2019) Dimension Variables All countries HDI rank countries' classification Missing data Low Medium High Very high Country or region with missing data Human Development Human Development Index (HDI) (−) 72.7 (37.3.7–95.3) 48.7 61.2 74.4 87.1 None Number of countries 188 36 37 53 62 Access Drinking water coverage (% population) 95.6 (38.7–100.0) 64.3 82.5 95.6 99.9 Caribbean (2), Sub-Saharan Africa (2), Argentina Number of countries 183 34 37 51 61 Sanitation coverage (% population) 89.7 (7.3–100.0) 29.6 62.1 90.9 99.1 Caribbean (2), Sub-Saharan Africa (2), Argentina, Brunei Darussalam Number of countries 182 34 37 51 60 Hydroclimatology Interannual variability (−) 1.5 (0.6–4.9) 1.5 1.5 1.8 1.2 East Asia and Pacific (12), Caribbean (10), Sub-Saharan Africa (7), Cyprus, Moldova, Malta, Maldives Number of countries 155 33 29 39 54 Seasonal variability (−) 2.3 (0.3–4.6) 3.3 3.1 2.3 1.2 East Asia and Pacific (12), Caribbean (10), Sub-Saharan Africa (7), Cyprus, Moldova, Malta, Maldives Number of countries 155 33 29 39 54 Storage Dam capacity per capita (m 3/capita) 395.8 (1.7–33499.0) 128.8 555.3 361 552 East Asia and Pacific (20), Europe and Central Asia (15), Sub-Saharan Africa (15), Latin America and Caribbean (12), Middle East and North Africa (8), South Asia (4) Number of countries 114 22 20 30 42 Reservoir density (10^4×Reservoirs/km 2) 1.1 (0.1–409.1) 0.8 0.6 1.7 1.7 Sub-Saharan Africa (12), East Asia and Pacific (10), Middle East and North Africa (6), Caribbean (5), Europe and Central Asia (4), Maldives Number of countries 150 28 27 46 49 Note. The full list of countries (n=188) is available in Supporting InformationS1 (Table S1). 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 5 of 19 2.2.2. Causality Analysis To identify potential causality in the study of complex systems is both conceptually and technically challenging. To do this, Sugihara etal.(2012) developed convergent cross mapping (CCM) as a methodology to determine causality beyond correlation. CCM studies time series of paired variables which belong to a common dynamical Table 2 Summary of the Statistical Analysis Statistical analysis Method Year Variables Number of observations Correlation Correlation coefficient 2017 Human Development Index (HDI) 108 (countries that measure all the variables simultaneously) Drinking water coverage Sanitation coverage Interannual variability Seasonal variability Dam capacity per capita Reservoir density Multiple linear regression 2017 Human Development Index (HDI) 151 (countries that measure the significant variables simultaneously) Drinking water coverage Sanitation coverage Interannual variability Seasonal variability Dam capacity per capita Reservoir density 2000–2016 Human Development Index (HDI) 138-155 (countries that measure all the variables simultaneously) Drinking water coverage Sanitation coverage Seasonal variability Causality Multi-spatial convergent cross mapping (mCCM) 2000–2017 Human Development Index (HDI) 3,245 (a time series of the variables from 2000 to 2017) Drinking water coverage 2000–2017 Human Development Index (HDI) 3,237 (a time series of the variables from 2000 to 2017) Sanitation coverage Table 3 Results of Statistical Models Linking Human Development to Water-Related Variables Main categories Predictor variables All countries Country HDI rank classification Low Medium High Very high Access Drinking water coverage 0.3500*** 0.1573*** 0.1151*** - 0.9952*** Sanitation coverage 0.1963*** - 0.0925*** 0.0952** 0.4957*** Hydroclimatology Interannual variability - - - - −1.8383** Seasonal variability −4.4917*** −2.532*** - - - Storage Dam capacity per capita - - - - - Reservoir density - - - - - Overall Number of observations 151 31 37 51 52 Significance <0.0001 0.0013 <0.0001 0.0129 <0.0001 R 20.831 0.3787 0.5631 0.1197 0.3501 Note. Shaded color intensities indicate the significance of regression coefficients. Red colors are negative correlations, blue is positive. Green indicates the number of observations and the strength of R 2. **p=0.05 ***p=0.001. 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 6 of 19 system and analyses causation by measuring the extent to which values of “Y” can reliably estimate states of “X”, something that happens only if X is causally influencing Y (Sugihara etal.,2012). Predictability using CCM increases with the time series length (L). Clark etal.(2015) extended the CCM approach to multispatial CCM (mCCM) for contexts where the length of the time series is limited but spatial information is abundant. An important assumption of this approach is that separate observational plots being considered share similar dynamics and are not heavily influenced by stochastic noise. In this paper, the idea of the replicate observational plots in Clark etal.(2015) are represented by the reporting countries for which observed data are available (i.e., replicate plots here equals reporting countries). In brief, mCCM tests to determine the dominant causal direction (if any) in a system of two variables “X” and “Y.” It tests to see whether X can be said to cause changes in Y or vice-versa, or if such a distinction can even be made due to very close coupling between the two causal directions (i.e., X causing Y, or Y causing X). Results are given over a “library length”, L, and are reported using the Pearson correlation coefficient, ρ. Such causal forcing is determined on two conditions: (a) “when ρ is significantly greater than zero for large library length L”; and (b) ρ increases significantly with increasing L. These criteria are determined graphically resulting from the output of mCCM analysis (carried out in this paper using the “multispatial CCM” package in R (Clark,2022)). An example is used here to guide interpretation of the graphs shown later in this paper. In Figure1, two contrasting examples from Sušnik(2018) are shown. The graphs in the current paper can be interpreted in the same way as those in Figure1. Each graph in Figure1 shows two lines (black and red), with one line being the results for the “X causes Y” direction, and the other line showing results for the “Y causes X” direction. The x-axis is the series length (called the library length, L), and the y-axis is the ρ value. Figure1a shows a system comprising of gross domestic product (GDP) and electricity consumption. This figure suggests that there is a strong bi-directional Figure 1. Examples of the application of the output from mCCM analysis. Panel (a) showing a system of GDP and electricity consumption; (b) a system of food production and electricity consumption. Panels (a and b) show contrasting causal dynamics within these two systems. See text for explanation and interpretation. Adapted from Sušnik(2018). 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 7 of 19 causal relation between GDP and electricity consumption. Both criteria are fulfilled: ρ is >>0 and; the rise of ρ away from 0 is rapid with increasing L. Both directions (GDP causing electricity consumption—black line; electricity consumption causing GDP—red line; Figure1a) rise rapidly to very similar “rho” values (∼0.95). This indicates strong causal dynamics in this system, and makes statements regarding a dominant causal direction challenging. In contrast, Figure1b shows a system comprising of food production and electricity consumption. Neither line (electricity consumption causing food production—black line; food production causing electricity consumption—red line; Figure1b) rises very high above 0 (∼0.45 maximum) indicating a weak causal relationship in this system. In addition, the black line rises (relatively) much higher then the red, suggesting that this relationship is the stronger in this system, driving the dynamics in this system. A more detailed technical description of the mCCM method can be sound Text S3 in Supporting InformationS1, and a complete descriptive, mathematical, and algorithmic description of the CCM and mCCM methods are found in Sugihara etal.(2012) and Clark etal.(2015). In this paper, variables are the waterand human-development variables. The causality analysis is performed at a global scale and aims to characterize the long-term causal relation, if any, between water and human development variables. Observational plots are reporting countries, and it is assumed that the dynamics within the water-development systems are broadly similar globally. Due to the large number of countries (observational plots), and the length of data available for each (2000–2017), the size of L in the causal analysis is >3,200 (Table2), which will lead to good causal descriptive power, as descriptive power using mCCM increases with L (Clark etal.,2015). Sušnik and van der Zaag(2017) implemented the mCCM method to assess causality between human development and personal wealth and use of resources, where plots represent countries, as is the case in this paper. The present research uses this approach and implements the algorithm proposed by Clark etal.(2015) to assess causal relationships, if any, between access to basic water services and the Human Development Index (HDI). Hydroclimatological variables cannot be used for the causality analysis as are indices capturing the longterm water variability (i.e., they do not change on a yearly basis). The code used to implement the mCCM approach described in Clark etal.(2015) is linked to the article and freely available as open-source R code. In addition, the datasets used in this paper are all in the public domain, and can be found in Supporting InformationS1 to this paper. 3. Results This section presents the main insights from the correlation and causality analyses. 3.1. Correlation Between Water Variables and Human Development Access to drinking water and sanitation, and hydroclimatology, are the most influential factors on human development (Figure2). Results show strong positive correlation between drinking water access and HDI (ρ=0.84), consistent with previous research (Fukuda etal.,2019; Sušnik & van der Zaag,2017). There is an even stronger positive correlation between HDI and access to sanitation services (ρ=0.87). Freshwater seasonal variability is negatively and strongly correlated with HDI (ρ=−0.7). This is compatible with and extends previous research, which focused solely on economic development variables (Brown & Lall,2006; Brown etal.,2013). Other water-related variables do not to show statistically significant relationships with human development. Freshwater interannual variability is not correlated with HDI globally, supporting and extending previous analyses (Brown & Lall,2006). Water storage variables (i.e., dam capacity per capita and reservoir density) do not show a significant relationship with HDI nor seasonal variability. 3.2. Water Variables Are a Strong Predictor of HDI A statistical model assessing the combined effect of water variables on development is proposed (see Table3 and Equation1). There is a significant and positive effect of drinking water and sanitation access on human development which is counterbalanced through the negative effects of freshwater seasonal variability at a global scale. Water storage variables do not have a significant effect on HDI. The three predictors have a strong effect on HDI at a global scale (Figures3 and4). There is a clear trend showing that countries with high access to water services and low freshwater seasonal variability have higher HDI scores than countries with lower access and higher seasonal variability. 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 8 of 19 Results show that the importance of water variables is not consistent across UNDP development categories (Nielsen,2013) (Table3). This means that the influence on HDI of water-related variables can differ across HDI categories. The dominant variables come from the “access” and “hydroclimatology” dimensions. Access to water and sanitation are significant at every development category and have a very strong combined effect in the global model. Regarding hydroclimatology, the most important variable related to HDI is seasonal variability when all countries are considered, and particularly for low development countries. Interannual variability is significant only for countries of very high human development. None of the water storage variables are significant (Figure2, Table3), neither when considering all countries, nor segmented by development category. These results strongly suggest that access to basic water and sanitation services, as well the modulating influence of intra-annual variability on freshwater resources, are influential determinants of HDI globally. In 2017, the HDI score for a country, 𝐴𝐴𝐴𝐴 , can be estimated using a multiple linear regression model that includes three water related variables as shown in Equation1: HDI 𝑖 =0.3500 × % Access to drinking water 𝑖 +0.1963 × % Access to sanitation𝑖 −4.4917 × Seasonal variability𝑖+ 36.1806 (1) Figure 2. Correlogram of human development and water related variables. Significant relationships are colored (p=0.05). Positive correlations are shown in blue, negative in red. The estimations were done using the Pearson coefficient for complete pairs of countries' observations (n=108). A correlogram including scatterplots is also available in Supporting InformationS1 (Figure S1). 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 9 of 19 This model accounts for approximately 83% of the observed HDI variance worldwide. When the predicted HDI is compared with the reported HDI in 2017, a linear, unbiased, and strong relation is found (Figure5 and Table4). Results indicate that for the vast majority of countries the relation between the predicted and observed HDI is Figure 3. Water variables in space and HDI as a color variable. Countries with the lowest HDI scores generally have very low access to sanitation, relatively high seasonal variability and a wide spread of values for drinking water coverage. The cluster of the most developed countries (red dots) have virtually universal access to water and sanitation and generally low seasonal variability. An interactive version of this model is available online. Table 4 Linear Regression Model's Main Results With HDI Forecast as HDI Predictor Main results of the model Number of observations 151 P-Value <0.0001 R-squared 0.831 Root MSE 6.449 Coefficient HDI forecast (−) 0.9999 P-Value <0.0001 Standard error 0.0369 95% Confidence Interval (Lowest, Upper) (0.9269, 1.0729) Note. The model is strongly significant, unbiased (as the coefficient is virtually 1 and the confidence interval includes 1), and can predict 83% of the observed HDI variance. The expected error of the model is ±6.4 percent points of the reported HDI score. 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 16 of 19 Current hydroclimatology related findings also have important implications in the context of climate change. A global temperature increase is intensifying the earth's hydrologic cycle, evidenced in the form of increased water variability and more recurrent extreme events (i.e., floods and droughts) (Donat etal.,2016). Despite limited spatiotemporal scale, the present study's variability indicator (i.e., coefficient of variation) is able to capture the effect of water variability on human development. The present article shows evidence of a long-term increasingly negative effect of seasonal variability on human development (Table5), a process likely linked to climate change effects. This observation is in line with recent literature studying the socio-economic impacts of hydroclimatology (Damania,2020), for instance: rainfall variability negatively impacts national economic growth (Brown etal.,2013); more severe droughts and floods are expected to have broad worldwide implications for human health (Watts etal.,2021); and droughts seriously detriment human capital with intergenerational effects (Hyland & Russ,2019). Yet, the present article is, to the best of our knowledge, the first assessing a long-term systematic effect of seasonal variability on an overall human development indicator. In short, our results suggest that seasonal variability is posing a ever increasing challenge for human development worldwide, presumably as a consequence of climate change. 4.2. The Role of Water Storage It has been claimed that large water storage infrastructure in “difficult” hydrologies is a precondition to deal with water seasonal variability (Grey & Sadoff,2007; Hall etal.,2014) and promote development. The empirical analysis in this study does not support these claims. This study directly contradicts the thesis of Grey and Sadoff(2007) asserting that water storage helps to manage water variability and thus can help promote development. It neither supports research suggesting relationships between economic development (i.e., GDP per capita) and a combination of water storage and institutional aspects (Hall etal.,2014). In contrast, these results suggest that while seasonal variability of water resources is a burden for socio-economic development, conventional large water storage is not likely the solution to promote human development. This research offers global empirical evidence that water storage is not a dominant factor in promoting socio-economic development, contradicting Grey and Sadoff's(2007) thesis. More recently Hall etal.(2014) report on a combined indicator for storage capacity per capita and institutional capacity that was related to rainfall seasonal variability and economic development. However, that work did not determine the individual significance of water storage on development. The results presented here contradict Hall etal.(2014) by suggesting that water storage per capita is neither related to seasonal variability nor to human development. The impact of small water storage solutions on human development needs further quantification. Studies highlight the need for small, nature-based, water storage for crop irrigation and local access to water supply, particularly in rural Sub-Saharan Africa (Bossio, Jewitt, & van der Zaag,2011; IWMI,2009; R. Lasage etal.,2008; Mccully & Pottinger,2009). A wide spectrum of storage alternatives (i.e., natural wetlands, soil moisture, aquifers, ponds, tanks and small reservoirs) are potentially scalable and may have broad benefits for farmers in arid and semi-arid regions (Duker etal.,2020; R Lasage etal.,2013; Tuinhof, etal.,2012). However, the results of this, and other research (Grey & Sadoff,2007; Hall etal.,2014), do not consider such alternative water storage strategies and are therefore generally “blind” to identify their likely development benefits. This represents a major future direction of research in the water storage—human development discussion. Evidence hints at the negative effect of high seasonal variability on the lowest development countries, but our results contest discourse promoting large water storage infrastructure (i.e., large dams) as an unequivocal development driver. Water infrastructure does have an important role for socio-economic development, yet it comes in form of access to basic water and sanitation and not in form of large-scale water storage infrastructure. This is an important policy and investment message. 4.3. Policy Implications for the Water and Sanitation Sector The article's findings have important implications for policy in the water and development sectors. They suggest that closing the water and sanitation access gap to citizens will have long-term tangible returns in terms of human development (i.e., HDI - health, education and income) and vice-versa, offering promising directions toward reaching SDG 6 targets, among others. More effort, including political backing and targeted investments are needed to expand access to basic water and sanitation services (Alaerts,2019; Banerjee & Morella,2011), especially in less-developed countries, and in places experiencing rapid population and urban development. Several 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 17 of 19 constraints and challenges need to be addressed to close the water and sanitation coverage gaps faster than the population growth (Hunter etal.,2010; Sadoff etal.,2020). Our results support evidence pointing out to global health indicators improvement in the previous decades. Diarrheal disease is still the most burdensome water and sanitation related disease worldwide (Prüss-Ustün etal.,2019). Yet, an analysis of the recent global evidence has shown that the DALYs associated to diarrheal disease has decreased from 1990 to 2017 (Karambizi etal.,2021). This is congruent with these results showing a significant trend of increasing water and sanitation access as closely related with higher human development metrics across the 21st century. Despite recent improvement, the situation remains critical, especially for children under 5years old (Cheng etal.,2012). More targeted investments and interventions are urgent to provide better water and sanitation services to reduce the child mortality and the disease burden worldwide, specially in world regions characterized by low and medium HDI scores. Despite the urgency of this global issue, it is important to recognize that water issues related to health are complex and go beyond simply diarrheal prevention (Humphrey,2009; Hunter etal.,2010; Mara etal.,2010; WHO,2019). Recent research has explored the causal pathways that explain how water services are expected to affect human wellbeing, extending from health to education and economic dimensions (Humphrey,2009; Victora etal.,2008). The indicator that was proposed to assess human development in this study (i.e., HDI) aims to capture these benefits. Therefore, these results are congruent with this hypothesis as it was found that the effect of water related variables on human development is significant and long-lasting at a global scale. By examining the effect of water related variables on human development, the statistical models presented here were able to explain between 83% and 88% of the HDI global variance from 2000 to 2017. These findings add up to the extensive evidence that calls for urgent action to close water and sanitation access gaps worldwide (Hutton & Chase,2016; UNDP,2006). Results provide robust evidence for policy makers to prioritize extensive investment in the water and sanitation services provision sector to reap long term global societal benefits in terms health, education and income. 5. Conclusions This paper explored the relationship between water-related variables and human development indicators globally during most of the 21st Century (2000–2017). It was shown that access to water supply, and especially to sanitation infrastructure, are significant drivers for improvements in human development progress, particularly for the least developed countries. Seasonal variability in freshwater resources is inversely related to human development progress. A statistical model was developed for the year 2017, showing that a 1% increment in a country's HDI is associated with a 1.3%–3.2% joint country-level increment in water and sanitation access. The 2017 analysis was repeated for the years 2000–2016, and results demonstrate that the relationships are nearly identical globally over this period. Complementary causal analyses show the tight two-way relationships between water variables and human development, strongly suggesting that investment in the improvement in one aspect will lead to concomitant improvements in the other, and vice-versa, potentially kick-starting a beneficial positive feedback loop where mutual benefits “feed” off each other to improve living standards more generally. The main implication of our findings is that investment in water and sanitation infrastructure will very likely have long-run societal benefits, and will pay back in terms of healthier citizens living better lives, thus able to contribute more fully to personal and national growth and development. Another crucial finding is that contrary to other studies, these results show no robust statistical link between large water storage infrastructure (i.e., dams and reservoirs) and improvements in human development figures. Thus, it is suggested that investing in large water storage is not the best way to further nation-level human development. However, the role of small, local, nature-based water storage is generally overlooked, and represents an avenue for future research in the context of its benefits for human development. This article provides strong evidence to strengthen investment for improving national access to water and sanitation worldwide. This will be significantly beneficial in terms of human development and evidenced in the improvement of national health, education and income indicators. This is an important long-term, large-scale financing finding for advancing human development. 23284277, 2023, 4, Downloaded from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF003364 by Spanish Cochrane National Provision (Ministerio de Sanidad), Wiley Online Library on [16/12/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Earth’s Future AMOROCHO-DAZA ETAL. 10.1029/2022EF003364 18 of 19 Data Availability Statement The supporting data used for the correlation and causality analyses in the study are available at 4TU.ResearchData via DOI: https://doi.org/10.4121/20110397 with CC BY 4.0 license. References Alaerts, G. (2019). 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