Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098-0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Release process identification of non-instantaneous point source pollution in rivers via reverse flow and pollution routing.

Source identification is fundamental for managing sudden river water pollution; however, it is a challenging task. Although numerous studies have investigated this issue, most involve optimization or statistical models for instantaneous pollution and do not consider the reverse propagation and release processes. Herein, we propose an approach for identifying the release process of non-instantaneous point source pollution in rivers, based on reverse flow and pollution routing. The identification approach can trace the historical trajectory of pollutants and their release processes, providing the necessary information for treating accidental pollution. The effectiveness and efficiency of the proposed approach were tested and demonstrated using hypothetical and real-world river cases. The results indicated that the approach identified the release process with high accuracy, and second-round identification using the ensemble Kalman filter could generally improve the identification results from the reverse routing model. This approach was feasible in different cases of observation error, although the error considerably reduced its accuracy. The identification results were also found to be substantially influenced by release duration, with a shorter release time corresponding to an inferior identification result. Nevertheless, the approach worked well in real-world river cases and was generally not affected by the release location, pollutant diffusion, or river geomorphology. In addition, the new approach has advantages in computational efficiency and applicability over traditional methods.

Regime shifts in the thermal dynamics of offshore China due to accelerated global warming.

Thermal dynamics play a pivotal role in offshore ecosystems, influencing a multitude of ecological and biogeochemical processes. Assessing how water temperature (WT) responds to climate change is vital for the sustainable development of marine ecosystems. Despite the scarcity of long-term sea surface temperature (SST) data, this study reconstructs SSTs from 1973 to 2020 in China's coastal zones using the data-driven Air2water model. A probabilistic approach was applied to investigate the joint dependency structures between air temperature (AT) and WT at offshore oceanic stations in China, focusing on variations during periods of decelerated and accelerated warming. The results indicate that the Air2water model performs well in reconstructing SSTs of the coastal zone of China. Furthermore, the joint probability of AT-WT events, characterized by bimodal distributions, tends to increase during accelerated warming. This suggests intensified extreme SST events in the coastal zone of China due to global warming, with the significant warming primarily related with regional oscillations, atmospheric dynamics, and the complex temperature trends in the regional marine environment. These findings highlight the escalating impact of global warming on marine ecosystems in China's coastal regions, underscoring the urgency of developing adaptive strategies to mitigate these effects.

Spatiotemporal links between meteorological and agricultural droughts impacted by tropical cyclones in China.

Both droughts and tropical cyclones (TCs) are among the world's most widespread natural disasters. This paper is concentrated on the effects of TCs on the links between meteorological droughts (MDs) and agricultural droughts (ADs). Specifically, changes in characteristics of drought events and variations in propagation features of matched MD and AD event pairs are quantified by using the renowned three-dimensional connected components algorithm; both alleviation and exacerbation effects of TCs are evaluated; and the Spearman's correlation is employed to identify potential contributors to exacerbated droughts after TCs. The results show that TCs exhibit more pronounced and widespread alleviation effects on MD events compared to AD events. >98 % of small-scale drought events are terminated by TCs, leading to 65 % reduction in the total area of MD events smaller than 50,000 km2 and 32 % reduction in AD events of the same scale. In the meantime, TCs can reshape the spatiotemporal links between MDs and ADs by reducing the overall propagation rate from 77 % to 40 % and ameliorating the characteristics of drought event pairs with higher propagation efficiency, by >40 %. After TCs, over 55 % of drought exacerbations in TC-affected regions occur first in the vicinity of the residual large-scale AD events. This occurrence is partially associated with the reduction in moisture exports from these residual droughts downwind to the interior of TC-affected regions, a process potentially facilitated by the TC-induced temperature cooling. The in-depth evaluation of this paper presents useful information for better drought preparation and mitigation under TCs.

Unravelling the potential of global streamflow reanalysis in characterizing local flow regime.

While global streamflow reanalysis provides valuable information for environmental modelling and management, it is not yet known how effective they are in characterizing the local flow regime. This paper presents a novel evaluation of the potential of streamflow reanalysis in the flow regime analysis by accounting for the effects of reservoir operation. Specifically, the indicators of hydrologic alteration (IHA) are used to characterize the five components of flow regime for both reservoir inflow and outflow; the performance of raw reanalysis is evaluated and the raw reanalysis is furthermore corrected by using the quantile mapping for improved flow regime analysis. The results of 35 major reservoirs in California show that raw reanalysis tends to be effective in characterizing the regime of reservoir inflow and that it is generally less effective in capturing outflow. For both inflow and outflow, the performance of raw reanalysis is beset by the existence of systematic errors. The quantile mapping is effective in error correction and therefore considerably improves the performances of reanalysis in characterizing the regime of not only reservoir inflow but also outflow. Nevertheless, for both reservoir inflow and outflow, the low flow part tends to be more difficult to handle than the high flow part. The evaluation conducted in this paper can serve as a roadmap for further exploitations of the potential of global streamflow reanalysis for flow regime analysis at regional and even continental scales.

A review on algal-bacterial symbiosis system for aquaculture tail water treatment.

Aquaculture is one of the fastest growing fields of global food production industry in recent years. To maintain the ecological health of aquaculture water body and the sustainable development of aquaculture industry, the treatment of aquaculture tail water (ATW) is becoming an indispensable task. This paper discussed the demand of environmentally friendly and cost-effective technologies for ATW treatment and the potential of algal-bacterial symbiosis system (ABSS) in ATW treatment. The characteristics of ABSS based technology for ATW treatment were analyzed, such as energy consumption, greenhouse gas emission, environmental adaptability and the possibility of removal or recovery of carbon, nitrogen and phosphorus as resource simultaneously. Based on the principle of ABSS, this paper introduced the key environmental factors that should be paid attention to in the establishment of ABSS, and then summarized the species of algae, bacteria and the proportion of algae and bacteria commonly used in the establishment of ABSS. Finally, the reactor technologies and the relevant research gaps in the establishment of ABSS were reviewed and discussed.

Annual shifts of flow regime alteration: new insights from the Chaishitan Reservoir in China.

Reservoir regulation is variable for flow regime alterations and mainly depends on operational objectives and hydro-meteorological conditions. In this study, the flow regime metrics (i.e., magnitude, variability and frequency, duration, timing and rate of change) are adopted to describe variations in both long-term inflow and outflow series of the Chaishitan Reservoir in China. Deviations between the inflow and outflow metrics are calculated to assess the flow regime alterations at annual scale. Further, dimensions of both time and flow regimes are reduced by multivariate statistical analysis, and the regulation patterns and their annual shifts are identified. Results show that: four regulation patterns are identified from 2004 to 2015. The regulation is gradually enhanced over time with typical features of different hydrological years. In dry years, the pattern is slightly regulated flow regimes with slightly discharging stored water and flattening outflow, moderate stability and intermittency. In normal years, the pattern is slightly regulated flow regimes with extremely increasing flow magnitude in the pre-nonflood season, high stability and slight intermittency. In wet years, the pattern is moderately regulated flow regimes with moderately decreasing flow magnitude in the flood season but extremely increasing flow magnitude in the nonflood season, slight stability and high intermittency.

Deriving Flood-Mediated Connectivity between River Channels and Floodplains: Data-Driven Approaches.

The flood-mediated connectivity between river channels and floodplains plays a fundamental role in flood hazard mapping and exerts profound ecological effects. The classic nearest neighbor search (NNS) fails to derive this connectivity because of spatial heterogeneity and continuity. We develop two novel data-driven connectivity-deriving approaches, namely, progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS). These approaches are illustrated through a case study in Northern Australia. First, PNNS and PiNNS are employed to identify flood pathways on floodplains through forward tracking. That is, progressive search is performed to associate newly inundated cells in each time step to previously inundated cells. In particular, iterations in PiNNS ensure that the connectivity is continuous - the connection between any two cells along the pathway is built through intermediate inundated cells. Second, inundated floodplain cells are collectively connected to river channel cells through backward tracing. Certain river channel sections are identified to connect to a large number of inundated floodplain cells. That is, the floodwater from these sections causes widespread floodplain inundation. Our proposed approaches take advantage of spatial-temporal data. They can be applied to achieve connectivity from hydro-dynamic and remote sensing data and assist in river basin planning and management.