Connection between Anthropogenic Water Diversion and Hydrodynamic Condition in Plain River Network

The increase in the rate of water renewal driven by hydrodynamics contributes to improving the water quality of the plain river network. Taking the lakeside river network in Wuxi as an example, through numerical simulation, polynomial fitting, correlation analysis, and principal component analysis, the hydrodynamic responses of urban lake-connected river networks to water diversion and hydrodynamic grouping were researched. Based on numerical model and influence weight analysis, we explored the improvement of hydrodynamic conditions of plain river network with strong human intervention and high algal water diversion. The results showed that: (1) The relationship between water diversion impact on river network flow velocity and water diversion flux was not as simple a linear relationship. It could be reflected by polynomial. The water transfer interval in dry season with high hydrodynamic efficiency (HE) was lower than 10 m3/s and higher than 30 m3/s, and the HE increased significantly when the water transfer flow was higher than 20 m3/s in the wet season. (2) According to the main hydrodynamic driving factors, the channels in the river network could be divided into three types: water conservancy projects, river and lake water level difference, and river channel characteristic. The correlations of rivers’ flow velocity in each group were very high. (3) The influence weights of water conservancy projects, river and lake water level difference, and river channel characteristic on the whole river network dynamics were 65, 21, and 12.4%, respectively, and the other factors contributed 1.6% of the weight.

Changing Causes of Drought in the Urmia Lake Basin—Increasing Influence of Evaporation and Disappearing Snow Cover

The water level of the Urmia Lake Basin (ULB), located in the northwest of Iran, started to decline dramatically about two decades ago. As a result, the area has become the focus of increasing scientific research. In order to improve understanding of the connections between declining lake level and changing local drought conditions, three common drought indices are employed to analyze the period 1981–2018: The Standard Precipitation Index (SPI), the Standard Precipitation-Evaporation Index (SPEI), and the Standardized Snow Melt and Rain Index (SMRI). Although rainfall is a significant indicator of water availability, temperature is also a key factor since it determines rates of evapotranspiration and snowmelt. These different processes are captured by the three drought indices mentioned above to describe drought in the catchment. Therefore, the main objective of this paper is to provide a comparative analysis of drought over the ULB by incorporating different drought indices. Since there is not enough long-term observational data of sufficiently high density for the ULB region, ECMWF Reanalysis data version 5(ERA5) has been used to estimate SPI, SPEI, and SMRI drought indicators. These are shown to work well, with AUC-ROC > 0.9, in capturing different classes of basin drought characteristics. The results show a downward trend for SPEI and SMRI (but not for SPI), suggesting that both evaporation and lack of snowmelt exacerbate droughts. Owing to the increasing temperatures in the basin and the decrease in snowfall, drought events have become particularly pronounced in the SPEI and SMRI time series since 2010. No significant SMRI drought was detected prior to 1995, thus indicating that sufficient snowfall was available at the beginning of the study period. The study results also reveal that the decrease in lake water level from 2010 to 2018 was not only caused by changes in the water balance components, but also by unsustainable water management.

Advanced Hybrid Machine Learning Algorithms for Multistep Lake Water Level Forecasting

Random Tree (RT) and Iterative Classifier Optimizer (ICO) based on Alternating Model Tree (AMT) regressor machine learning (ML) algorithms coupled with Bagging (BA) or Additive Regression (AR) hybrid algorithms were applied to forecasting multistep ahead (up to three months) Lake Superior and Lake Michigan water level (WL). Partial autocorrelation (PACF) of each lake’s WL time series estimated the most important lag times — up to five months in both lakes — as potential inputs. The WL time series data was partitioned into training (from 1918 to 1988) and testing (from 1989 to 2018) for model building and evaluation, respectively. Developed algorithms were validated through statistically and visually based metric using testing data. Although both hybrid ensemble algorithms improved individual ML algorithms’ performance, the BA algorithm outperformed the AR algorithm. As a novel model in forecasting problems, the ICO algorithm was shown to have great potential in generating robust multistep lake WL forecasts.

Estimation of the Average Retention Time of Precipitation at the Surface of a Catchment Area for Lake Biwa

In a lake catchment system, we analyzed the lake water-level responses to precipitation. Moreover, we identified the average precipitation retention time—due to subsurface flows—from the delay time calculated using the response function with data of water level and catchment precipitation (both rainfall and snowfall) collected over 30 years of continuous observations of Lake Biwa, Japan. We focused on the snow reserves and the water-level response delay due to the snowmelt of Lake Biwa catchment. We concluded that the average precipitation retention time of the catchment subsurface flow (i.e., above the impermeable layer) in Lake Biwa was approximately 45 days. Additionally, the precipitation retention time during snowmelt was shorter than that during the dry season. Overall, the shape of the response function reflects the lake system. This knowledge improves the understanding of lake systems and can be helpful for lake resource managers. Furthermore, finding the delay time from the response function may be useful for determining the contribution of rainfall to increasing the water levels of other lakes. Therefore, our results can contribute to the development of management strategies to address inland aquatic ecosystems and conservation.