Many regions has been facing formidable freshwater management and planning challenges. Concerns about limited water allocations, conservation of environmental and water qualities and policies for sustainable water use have been increased because rising water demand would cause water shortage in the near future. Therefore, it is necessary to look into possible alternative water resources management plans to mitigate the potential water shortage. However, it is not straightforward to predict and analyze the various situations likely to be occurred in the future. Also, finding an optimal solution among many alternatives to mitigate the water shortage is a complex task. In this study, a methodology of predicting and analyzing the water resources situations in the future using the K-WEAP (Korea Water Evaluation and Planning system) is presented and an optimal alternative is determined using the MCDA (Multi-Criteria Decision Analysis) that takes into account the economic, environmental, and social sectors. The proposed methodology is applied to the Nakdong River basin in South Korea to calculate water budget and possible water shortage. An optimal water shortage mitigation policy for the study basin is also suggested to help decision maker develop long-term water resources management strategies.

Streamflow is documented as a key element of the hydrological cycle, and alterations in streamflow may result in hydrological disaster. The accurate quantification of streamflow under various climatological and anthropogenic factors, as well as decomposing their respective effects, remains challenging. In this study, we quantified and split the effects of climate variation and human action on streamflow changes in lower watersheds of the Han River. We utilized distinct methodologies and compared their outcomes. The methodologies include Budyko-based curve decomposition, the Budyko-based hydrological elasticity method, the conceptual eco-hydrological method, and the hydrological sensitivity analysis method. Subsequently, the hydrological change point was inspected in 1997. During the baseline period (1966–1997), variations in climate led to changes in the streamflow. In contrast, in the post-baseline period (1998–2013), the variation in the streamflow was attributed to the combined effects of climate variation and human action. Climate variation was observed to be a prominent accountable feature in streamflow alterations. The results obtained from different methodologies appeared to align with each other, showing that 70% to 91% (average 80%) of the alteration in the streamflow occurred in response to variations in climate-related parameters. The contribution from human action was less, accounting for 9% to 30% (average 20%) of the change. Moreover, the relative proportion of effects was perceived to be sensitive to the methods applied and the type of Budyko-based function. Further, the relative impacts of both factors can enhance the uncertainty in the management of water resources. Thus, this information is essential for the execution of water management on the spatial and temporal scales to reduce the risk of hydrological disasters in the watershed.

Climate change is a global stressor that can undermine water management policies developed with the assumption of stationary climate. While the response-surface-based assessments provided a new paradigm for formulating actionable adaptive solutions, the uncertainty associated with the stress tests poses challenges. To address the risks of unsatisfactory performances in a climate domain, this study proposed the incorporation of the logistic regression into a decision-centric framework. The proposed approach replaces the “response surfaces” of the performance metrics typically used for the decision-scaling framework with the “logistic surfaces” that describes the risk of system failures against predefined performance thresholds. As a case study, water supply and environmental reliabilities were assessed within the eco-engineering decision-scaling framework for a complex river basin in South Korea. Results showed that human-demand-only operations in the river basin could result in the water deficiency at a location requiring environmental flows. To reduce the environmental risks, the stakeholders could accept increasing risks of unsatisfactory water supply performance at the sub-basins with small water demands. This study suggests that the logistic surfaces could provide a computational efficiency to measure system robustness to climatic changes from multiple perspectives together with the risk information for decision-making processes.

The main reasons behind the occurrence of floods are climate change and rapid urbanization. Floods bring heavy casualties and enormous property damage. Therefore, a lot of support and flood-defence policies should be implemented and indexes should be developed in an effort to figure out the flood vulnerability. This study will investigate and analyse these indexes and consider Korean regional features to develop an index suitable for Korea and estimate its flood vulnerability. For this purpose, 115 subbasins in Korea were selected, and data related to regional flood losses from 1990 to 2007 were investigated to establish a database of the flood-related proxy variables for the period. In order to confirm the validity of the evaluation index, the results of Gangwon province from the calculated flood vulnerability assessment index (FVAI) are compared with actual flood damage records in 2008. Encouraged by this, FVAI was utilized as the means to estimate and present the regional flood vulnerability of Korea in the future. This study is expected to provide not only one of the various policy decision backgrounds, but also diversified information to better understand the current situation in Korea.

Drought has significant impacts on both society and the environment, but it is a gradual and comprehensive process that affects a region over time. Therefore, non-structural measures are necessary to prepare and respond to the damage caused by drought in a flexible manner according to the stage of drought. In this study, an AI-based water demand prediction model was developed using deep neural network (DNN) and long short-term memory (LSTM) models. The model was trained from 2004 to 2015 and verified from 2016 to 2021. Model accuracy was evaluated using data, with the LSTM model achieving a correlation coefficient (CC) of 0.95 and normalized root mean square error (NRMSE) of 8.38, indicating excellent performance. The probability of the random variable X falling within the interval [a,b], as described by the probability density function f(x), was calculated using the water demand data. The cumulative distribution function was used to calculate the probability of the random variable being less than or equal to a specific value. These calculations were used to establish the criteria for each stage of the crisis alert system. Decision tree (DT) and random forest (RF) models, based on AI-based classification, were used to predict water demand at the Gurye intake station. The models took into account the impact of water demand from the previous day, as well as the effects of rainfall, maximum temperature, and average temperature. Daily water demand data from the Gurye intake station and the previous day’s rainfall, maximum temperature, and average temperature data from a nearby observatory were collected from 2004 to 2021. The models were trained on data from 2004 to 2015 and validated on data from 2016 to 2021. Model accuracy was evaluated using the F1-score, with the random forest model achieving a score of 0.88, indicating excellent performance.

The global demand for food is growing with the population and urbanization, which puts pressure on water resources, which need assessing and quantifying water requirements. Adopting efficient irrigation methods to optimize water use is essential in this situation. In this study, crop water productivity (CWP) of major crops in the Rohri canal command area was estimated by the ratio of yield and actual evapotranspiration (ET_a). Analyzing the CWP of major crops, water scarcity challenges can be tackled by selecting the most feasible irrigation methods. However, ET_a was calculated and aggregated for all four stages of the crop growth period: initial, crop development, flowering stage, and maturity seasons. The crop yield data were obtained from the districts’ agricultural statistics. For this purpose, evapotranspiration products of Landsat 5 and 8 were downloaded from Earth Engine Evapotranspiration Flux (EEFlux). Landsat images were processed in a GIS environment to calculate ET_a. The approach suggests developing a CWP database for major crops like wheat, cotton, and rice to improve irrigation water management. The objectives of this study are to estimate and analyze the difference in the CWP and evapotranspiration of major crops for the Rabi and Kharif seasons with high and moderate flows during 1998–2019. It comprises nine districts of Sindh that come under the Rohri Canal command area. To analyze the difference in CWP between the Rabi and Kharif seasons for all study crops of the seasons of Rabi (2014–2015 and 2016–2017) and Kharif (1998 and 2017). The growing periods for wheat, cotton, and rice in the Rohri Canal command area are 160, 195, and 180 days, respectively. The estimated ET_a of the Rohri canal command area and CWP were in good agreement with the literature-reported values. Hence, enhanced agricultural productivity can be achieved by making considerable investments to improve agricultural research and extension systems.