Assessment of Future Climate Change Impact on an Agricultural Reservoir in South Korea

Drought has been frequently occurring in South Korea due to climate change. Analyzing the water supply capacity of the water resource system provides essential information for water resource management. This study evaluates the future water supply capacity of the Gwanghye (GH) agricultural reservoir based on the representative concentration pathways 4.5 and 8.5 climate change scenarios. We performed a reservoir simulation by reflecting the full water level of the reservoir before and after reservoir heightening. Climate change is expected to decrease the GH reservoir’s future available water resources due to the overall reduction in the reservoir’s runoff. After the reservoir-heightening project, an overall improvement was observed in the stability of the future irrigation water supply. Moreover, the remaining water after the supply of the irrigation water could supply 0.6–7.2 × 103 m3 of daily instream water. Thus, flexible reservoir operations are necessary according to climate change scenarios and the reservoir operation period. The use of climate change information should be expanded to establish reasonable water management policies for future climate change scenarios.

Anomaly Detection in Reservoir Water Level Data Using the LSTM Model Based on Deep Learning

Weather and hydrological phenomena have been changing due to climate change as evidenced by localized torrential rainfall and precipitation falling by more than 30% compared to the annual average. From 2013 to 2017 the ninety-nine reservoirs reached a water storage rate of 0%, making a secure stable water supply for agriculture uncertain. There is an increased need for information regarding agricultural water management to respond to the changes in the agricultural environment and climate. Therefore, automatic water level measurement facilities have been introduced to determine the real-time reservoir storage capacity and agricultural water supply. According to the Ministry of Agriculture, Food and Rural Affairs' guidelines for the installation and operation of water level measurement instruments, automatic water level facilities are currently installed at 1,734 reservoirs and 1,880 irrigation canals, with water level data generated at 10-minute intervals. The official recognition of hydrological water level data for agricultural reservoirs increased from six in 2016 to forty-nine in 2019. Anomaly detection algorithm methods for data regarding the agricultural reservoir level as well as quality control measures based on agricultural reservoir characteristics are required to minimize data quality degradation and generate reliable hydrological data over time. Though it was practically impossible to analyze the correlation between the water level or run-off and influential factors such as weather and terrain, recently a non-linear hydrological analysis has been possible using models such as Artificial Neural Networks (ANNs). This study aims to present an anomaly detection algorithm for reservoir level data using deep learning based LSTM (Long Short-Term Memory) models, in combination with other neural networks for managing quantitative information of agricultural water supply.

Assessment of Vulnerability to Drought Disaster in Agricultural Reservoirs in South Korea

Drought is a natural disaster affecting agriculture worldwide. Drought mitigation and proactive response require a comprehensive vulnerability mapping approach considering various factors. This study investigates the vulnerability to agricultural drought in South Korea based on exposure, sensitivity, and adaptability. The evaluation of agricultural drought factors yielded 14 items, which are categorized into meteorological, agricultural reservoir, social, and adaptability factors. Each item is assigned a weight using the analytical hierarchy process (AHP). We analyzed vulnerability to drought disaster in agricultural reservoirs, and generated vulnerability maps by applying the vulnerability framework for climate change. The generated map was divided into four categories based on drought vulnerability: A (Very high), B (High), C (Moderate), and D (Low). The weights for the meteorological (0.498), agricultural reservoir (0.286), social (0.166), and adaptability (0.05) factors were obtained using AHP. The rating frequencies were 41.91%, 19.76%, 9.58%, and 5.39% for A, B, C, and D, respectively. The western region is extremely vulnerable to meteorological and agricultural reservoir factors, whereas the eastern region is more vulnerable to adaptability. The results of this study visually represent agricultural drought and can be used for evaluating regional drought vulnerability for assisting preemptive drought responses to identify and support drought-prone areas.

Macroinvertebrate colonisation associated with native and invasive leaf litter decomposition

Lake and reservoir ecosystems are regarded as heterotrophic detritus-based habitats which are dependent on both autochthonous and allochthonous organic matter for the majority of energy inputs. In particular, allochthonous detritus is in particular important for the trophic dynamics of microbial organisms, macroinvertebrates and benthic plants in freshwaters. Here, we assess macroinvertebrate colonisation, and quantify decomposition rates, of leaf litter from species of native and invasive plants in a small agricultural reservoir. Native fig Ficus sycomorus and silver cluster–leaf Terminalia sericea were compared to invasive tickberry Lantana camara and guava Psidium guajava, whereby macroinvertebrate colonisation was assessed over time. Leaf treatments had a significant, group-specific effect on abundances and composition among focal macroinvertebrates. Invasive leaves reduced Physidae and Oligochaeta abundances, yet Ostracoda were significantly more abundant in the presence of invasive P. guajava. Chironomidae relative abundances increased under invasive L. camara treatments, whilst differences among leaf treatment effects on Coenogrionidae abundances were not statistically clear. In turn, macroinvertebrate diversity did not differ significantly among plant treatment groups. The decomposition rate of the leaf litter demonstrated differences among the species, following a decreasing order of L. camara > F. sycomorus > T. sericea > P. guajava. The study results highlight that leaf litter species identity among invasive and native plants plays an important role in the colonisation of macroinvertebrates in small reservoirs, thereby differentially supporting aquatic environments and food webs. However, differences were not uniform across invader-native groupings. Nonetheless, certain invasive leaf litter decomposes faster than native litter, with possible implications for broader nutrient dynamics and subsequent community composition.