Optimized water resource allocation is critical for promoting sustainable regional development. However, the intensification of climate change impacts has increased the variability of water availability, thereby compounding uncertainty and complexity in water allocation decision-making. Meanwhile, mismatches in the spatial distribution of water supply and demand have also become an essential constraint to effectiveness and fairness of water allocation. Prior studies show weak correspondence between modeling frameworks to real world conditions and insufficiently describe regional balance, as well as the dynamic interaction between water inflow and demand under varying hydrological regimes. To address these challenges, this study develops a multi-objective water resource allocation model guided by spatial equilibrium principles, ensuring not only fair inter-regional allocation but also balanced co-development among water, socio-economic, and ecological subsystems. Regional balance is quantified with the Gini coefficient. Subsystem co-development is enforced via the coupling coordination degree. To address uncertainty, a nested multi-scenario robust optimization framework is proposed. It applies copula function to generate realistic encounter scenarios and applies a multi-objective probabilistic robustness evaluation to test solution stability and adaptability across scenarios. Applied to the upper and middle reaches of the Huaihe River Basin (HRB), the approach reduces the water-deficit rate by 45.42 % under extreme dry scenarios, lifts the coordination index to > 0.75, and increases reliability by 45.3 % compared with a traditional robust optimization baseline, demonstrating effectively optimize complex water resource systems while keeping both fairness and stability. This research offers a theoretical and practical framework for optimizing water resource allocation in complex and uncertain environments, contributing to the advancement of resilient and equitable water governance.

Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

Food security is important for human well-being worldwide. However, changing climate, population growth and shrinking land resources are threatening food security in many regions of the world. Jiangsu Province, China, is one such region. It is a major food-producing region of the country but is witnessing rapid population growth and urbanization that is putting pressure on agricultural water and land resources and threatening food security of the region. This paper interprets the nexus between regional water availability and food security in Jiangsu Province under different climate change and socio-economic scenarios of population growth and land resource availability. Climate change scenarios are generated based on historical data and Global Climate Model (GCM) products. Socio-economic scenarios are generated based on population growth and crop planted area projections. The uptake of water and nutrients are considered as two dominant biophysical processes of crop growth and food production. Complementing it is human agency, including human labor, irrigation and land-preparation machinery, which are the factors behind water and nutrient use efficiencies of crops grown. Two dominant crops are considered, rice and wheat, that contribute to 61.4% of total crops produced in the province. Results show that adaptation by human agency is necessary to ensure that food supply meets at least the demand of the province under all climate change and socio-economic scenarios. Under relatively favorable scenarios, labor could replace land-preparing machinery since the level of food production can be easily maintained with abundant water and land availability. Mechanization in agricultural production significantly increases food production under unfavorable conditions, since it improves water and nutrient use efficiencies and leads to higher crop yields. This demonstrates that human agency plays an important role in securing food under stressful scenarios of drier climate, population growth, and contraction of agricultural lands.

Efficient use of water and nutrients in crop production are critical for sustainable water and crop production systems. Understanding the role of humans in ensuring water and nutrient use efficiency is therefore an important ingredient of sustainable development. Crop production functions are often defined either as functions of water and nutrient deficiency or are based on economic production theory that conceptualizes production as a result of economic activities that take in inputs such as water, capital and labor and produce crop biomass as output. This paper fills a gap by consistently treating water and nutrient use and human agency in crop production, thus providing a better understanding of the role humans play in crop production. Uptake of water and nutrients are two dominant biophysical processes of crop growth while human agency, including irrigation machine power, land-preparing machine power and human labor force, determine limits of water and nutrient resources that are accessible to crops. Two crops, i.e., winter wheat and rice, which account for the majority of food crop production are considered in a rapidly developing region of the world, Jiangsu Province, China, that is witnessing the phenomenon of rural to urban migration. Its production is modeled in two steps. First water and nutrient efficiencies, defined as the ratios of observed uptake to quantities applied, are modeled as functions of labor and machine power (representing human agency). In the second step, crop yields are modeled as functions of water and nutrient efficiencies multiplied by amounts of water and fertilizers applied. As a result, crop production is predicted by first simulating water and nutrient uptake efficiencies and then determining yield as a function of water and nutrients that are actually taken up by crops. Results show that modeled relationship between water use efficiency and human agency explains 68% of observed variance for wheat and 49% for rice. The modeled relationship between nutrient use efficiency and human agency explains 49% of the variance for wheat and 56% for rice. The modeled relationships between yields and actual uptakes in the second step explain even higher percentages of observed the variance: 73% for wheat and 84% for rice. Leave-one-out cross validation of yield predictions shows that relative errors are on average within 5% of the observed yields, reinforcing the robustness of the estimated relationship and of conceptualizing crop production as a composite function of bio-physical mechanism and human agency. Interpretations based on the model reveal that after 2005, mechanization gradually led to less labor being used relative to machinery to achieve same levels of water use efficiency. Labor and irrigation equipment, on the other hand, were found to be complimentary inputs to water use efficiency. While the results suggest interventions targeting machinery are most instrumental in increasing wheat productivity, they may exasperate rural – urban migration. Policy strategies for alleviating rural-urban migration while ensuring regional food security can nonetheless be devised where appropriate data are available.

Migration is often seen as an adaptive human response to adverse socio-environmental conditions, such as water scarcity. A rigorous assessment of the causes of migration, however, requires reliable information on the migration in question and related variables, such as, unemployment, which is often missing. This study explores the causes of one such type of migration, from rural to urban areas, in the Jiangsu province of China. A migration model is developed to fill a gap in the understanding of how rural to urban migration responds to variations in inputs to agricultural production including water availability and labor and how rural population forms expectations of better livelihood in urban areas. Rural to urban migration is estimated at provincial scale for period 1985–2013 and is found to be significantly linked with rural unemployment. Further, migration reacts to a change in rural unemployment after 2–4 years with 1% increase in rural unemployment, on average, leading to migration of 16,000 additional people. This implies that rural population takes a couple of years to internalize a shock in employment opportunities before migrating to cities. The analysis finds neither any evidence of migrants being pulled by better income prospects to urban areas nor being pushed out of rural areas by water scarcity. Corroborated by rural–urban migration in China migration survey data for 2008 and 2009, this means that local governments have 2–4 years of lead time after an unemployment shock, not necessarily linked to water scarcity, in rural areas to prepare for the migration wave in urban areas. This original analysis of migration over a 30-year period and finding its clear link with unemployment, and not with better income in urban areas or poor rainfall, thus provides conclusive evidence in support of policy interventions that focus on generating employment opportunities in rural areas to reduce migration flow to urban areas.

As one of the fundamental behaviors of human, migration can be understood as an active adjustment of the spatial distribution of population, often in response to water stress. Water resources dynamics, a basic element for civilization development, slowly but firmly imposes its potential force on every aspect of human activities. We study the phenomenon of increasing migration from rural areas to cities and towns, specific to Jiangsu province in China, in response to rural and urban unemployment rates over 15 years from 1999 to 2013. Located in the south-east region of China, Jiangsu province features its high density of population and high frequency of migration from its rural to urban areas. First, rural-urban migration rate for the aforementioned period is calculated based on census data on population, fertility and mortality rates of rural and urban areas of the province. The fluctuating net migration rate from its rural areas to cities shows a general positive trend - 2 peaks in years 2000 and 2010 respectively. In order to interpret this trend, we model labor demand in the agricultural sector that dominates rural areas based on a Cobb-Douglas production function, and confront it with labor supply data to generate the unemployment rate in the rural areas of Jiangsu (Ua). With the observed dataset of urban unemployment rate in Jiangsu (Uc), the difference between unemployment rate in cities and rural areas (Ua- Uc) is obtained and compared with the migrating population from rural areas to cities. The result shows that from 1999 to 2004, migration towards cities increases when Ua- Uc decreased, which means the employment rate in cities was higher than rural areas. The migration also decreases when the difference of unemployment rate shrank. This may support a hypothesis that migration responds to gradient in employment opportunities. However, from 2005, onward migration to urban areas continued to rise in spite of higher unemployment in urban area. Thus, it appears the phenomenon of increased rural to urban migration cannot be confidently interpreted as a consequence of the unemployment gradient based on the current availed datasets and model used. It is therefore necessary to explore the unknown potential feedbacks which would impel the unusual movement of people. Some preliminary results of an improved socio-hydrological model in this direction will be presented that figures out ‘unknown’ feedbacks between water resources, agricultural and industrial production, and human migration trend in Jiangsu.