Impact of climate change on the water resources of the Atbara River using novel hydrological models.

Rivers respond directly to climate change, as well as incorporating the effects of climate-driven changes occurring within their watersheds. In this research, climate change's impact on the Atbara River, one of the main tributaries of the Nile River, was studied. Various statistical methods of analysis were applied to study the basic characteristics of the climatic parameters that affect the discharge of the Atbara River. The three hydrological gauging stations on the Atbara River, namely, the Upper Atbara and Setit reservoirs, Khashm el-Girba reservoir, and Atbara Kilo 3 station, were included in the study. The correlation between the meteorological parameters and the hydrology of the Atbara River and the prediction of the future hydrology of the Atbara River Basin was determined. Many hydrological models were developed and tested to predict the hydrology of the river. Finally, forecasting for river hydrology was built. No significant trend was found in the precipitation in the study area. The developed model simulates the observed data with a high coefficient of determination ranging from 0.7 to 0.91 for the three hydrological gauging stations studied. Results predicted a slight decrease in river discharge in future years.

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach.

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed Zhanjiang Bay, China.

Typhoons can induce variations in hydrodynamic conditions and biogeochemical processes, potentially escalating the risk of algal bloom occurrences impacting coastal ecosystems. However, the impacts of typhoons on instantaneous changes and the mechanisms behind typhoon-induced algal blooms remain poorly understood. This study utilized high-frequency in situ observation and machine learning model to track the dynamic variations in meteorological, hydrological, physicochemical, and Chlorophyll-a (Chl-a) levels through the complete Typhoon Talim landing in Zhanjiang Bay (ZJB) in July 2023. The results showed that a delayed onset of algal bloom occurring 10 days after typhoon's arrival. Subsequently, as temperatures reached a suitable range, with an ample supply of nutrients and water stability, Chl-a peaked at 121.49 µg L-1 in algal bloom period. Additionally, water temperature and air temperature decreased by 1.61 °C and 2.8 °C during the typhoon, respectively. In addition, wind speed and flow speed increased by 1.34 and 0.015 m s-1 h-1 to peak values, respectively. Moreover, the slow decline of 8.2 % in salinity suggested a substantial freshwater input, leading to an increase in nutrients. For instance, the mean DIN and DIP were 2.2 and 8.5 times higher than those of the pre-typhoon period, resulting in a decrease in DIN/DIP (closer to16) and the alleviation of P limitation. Furthermore, pH and dissolved oxygen (DO) were both low during the typhoon period and then peaked at 8.93 and 19.05 mg L-1 during the algal bloom period, respectively, but subsequently decreased, remaining lower than those of the pre-typhoon period. A preliminary learning machine model was established to predict Chl-a and exhibited good accuracy, with R2 of 0.73. This study revealed the mechanisms of eutrophication status formation and algal blooms occurrence in the coastal waters, providing insights into the effects of typhoon events on tropical coastal biogeochemistry and ecology.

Global prediction of extreme floods in ungauged watersheds.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

A tolerant hydrologic technique for real-time selection of optimum QPFs from NWPMs for flood warning applications.

The most important information required to successfully issue a flood warning is the quantitative precipitation forecasts (QPFs). This is important to run subsequent rainfall-runoff simulations. A rainfall-runoff simulation derives its accuracy mainly from the accuracy of the input QPFs. The dynamically based global numerical weather prediction models (NWPMs) are strong candidate sources of QPFs. A main problem is the real-time selection of which NWPM should be used to provide the QPFs for flood warning simulations. This paper develops an automated technique to solve this problem. The technique performs real-time comparisons with measured rainfall fields using a novel 'tolerant' hydrologic approach. The 'tolerant' approach performs the comparison on the basin scale and allows for timing shifts in the forecasts. This is because QPFs can be good but only a few hours early or late. Two events are used for illustration, and the proposed real-time application in flood warning is presented. The developed technique, employing the tolerant approach, could eliminate the effects of the timing shifts and, accordingly, succeeded to select the QPFs to be used. A Python package was developed for automation. The developed technique is expected to also be useful for offline assessments of historical performances of NWPMs.

Coupling machine learning and physical modelling for predicting runoff at catchment scale.

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.

Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Subsurface hydrological connectivity controls nitrate export flux in a hilly catchment.

Subsurface runoff represents the main pathway of nitrate transport in hilly catchments. The magnitude of nitrate export from a source area is closely related to subsurface hydrological connectivity, which refers to the linkage of separate regions of a catchment via subsurface runoff. However, understanding of how subsurface hydrological connectivity regulates catchment nitrate export remains insufficient. This study conducted high-frequency monitoring of shallow groundwater in a hilly catchment over 17 months. Subsurface hydrological connectivity of the catchment over 38 rainfall events was analyzed by combining topography-based upscaling of shallow groundwater and graph theory. Moreover, cross-correlation analysis was used to evaluate the time-series similarity between subsurface hydrological connectivity and nitrate flux during rainfall events. The results showed that the maximum subsurface hydrological connectivity during 32 out of 38 rainfall events was below 0.5. Although subsurface flow paths (i.e., the pathways of lateral subsurface runoff) exhibited clear dynamic extension and contraction during rainfall events, most areas in the catchment did not establish subsurface hydrological connectivity with the stream. The primary pattern of nitrate export was flushing (44.7%), followed by dilution (34.2%), and chemostatic behavior (21.1%). A threshold relationship between subsurface hydrological connectivity and nitrate flux was identified, with nitrate flux rapidly increasing after the subsurface connectivity strength exceeded 0.121. Moreover, the median value of cross-correlation coefficients reached 0.67, which indicated subsurface hydrological connectivity exerts a strong control on nitrate flux. However, this control effect is not constant and it increases with rainfall amount and intensity as a power function. The results of this study provide comprehensive insights into the subsurface hydrological control of catchment nitrate export.

The stable isotope hydrology of Sable Island, Nova Scotia, Canada with implications for evaluating the water budget of wild horses.

We investigated the stable isotope hydrology of Sable Island, Nova Scotia, Canada over a five year period from September, 2017 to August, 2022. The δ2H and δ18O values of integrated monthly precipitation were weakly seasonal and ranged from -66 to -15 ‰ and from -9.7 to -1.9 ‰, respectively. Fitting these monthly precipitation data resulted in a local meteoric water line (LMWL) defined by: δ2H = 7.22 ± 0.21 · δ18O + 7.50 ± 1.22 ‰. Amount-weighted annual precipitation had δ2H and δ18O values of -36 ± 11 ‰ and -6.1 ± 1.4 ‰, respectively. Deep groundwater had more negative δ2H and δ18O values than mean annual precipitation, suggesting recharge occurs mainly in the winter, while shallow groundwater had δ2H and δ18O values more consistent with mean annual precipitation or mixing of freshwater with local seawater. Surface waters had more positive values and showed evidence of isolation from the groundwater system. The stable isotopic compositions of plant (leaf) water, on the other hand, indicate plants use groundwater as their source. Fog had δ2H and δ18O values that were significantly more positive than those of local precipitation, yet had similar 17O-excess values. δ2H values of horsehair from 4 individuals lacked seasonality, but had variations typical to those of precipitation on the island. Differences in mean δ2H values of horsehair were statistically significant and suggest variations in water use may exist between spatially disparate horse communities. Our results establish an important initial framework for ongoing isotope studies of feral horses and other wildlife on Sable Island.

Ecological responses to hydrological connectivity in grassland riparian zones: Insights from vegetation and ground-dwelling arthropods.

Riparian wetlands have suffered from degradation due to global climate change and human activities, which can alter flora and fauna community patterns and disrupt material cycles in the riparian zones. Hydrological connectivity identified by functional and structural connectivity is an important driving force of riparian ecosystems. However, the role of hydrological connectivity in linking riparian hydrology and ecology remains unclear, especially in dryland rivers. By taking the riparian zone of the Xilin River in Eurasian steppe as an example, the functional connectivity was represented by the groundwater depth in the riparian zones. The structural connectivity was quantified by integrating the soil, and vegetation properties of the riparian zone. The structural connectivity decreased from upstream to downstream. Laterally, the highest structural connectivity was found in the riparian zone 25 m away from the river channel. The abundance of three groups of ground-dwelling arthropods (except Araneae) showed a threshold behavior in response to the functional connectivity, with the highest abundance occurring in the medium level of functional connectivity. Both vegetation biomass and ground-dwelling arthropod abundance were significantly and positively correlated to the structural connectivity strength. The results of structural equation models (SEMs) also indicated that structural connectivity was a key factor affecting vegetation and ground-dwelling arthropod abundance. The results underscore the essential function of hydrological connectivity in maintaining the biodiversity in the riparian zones. The study provides a scientific reference of riparian-zone restoration based on hydrological connectivity.

Availability of the current and future water resources in Equatorial Central Africa: case of the Nyong forest catchment in Cameroon.

To anticipate disasters (drought, floods, etc.) caused by environmental forcing and reduce their impacts on its fragile economy, sub-Saharan Africa needs a good knowledge of the availability of current water resources and reliable hydroclimatic forecasts. This study has an objective to quantify the availability of water resources in the Nyong basin and predict its future evolution (2024-2050). For this, the SWAT (Soil and Water Assessment Tool) model was used. The performance of this model is satisfactory in calibration (2001-2005) and validation (2006-2010), with R2, NSE, and KGE greater than 0.64. Biases of - 11.8% and - 13.9% in calibration and validation also attest to this good performance. In the investigated basin, infiltration (GW_RCH), evapotranspiration (ETP), surface runoff (SURQ), and water yield (WYLD) are greater in the East, probably due to more abundant rainfall in this part. The flows and sediment load (SED) are greater in the middle zone and in the Southwest of the basin, certainly because of the flat topography of this part, which corresponds to the valley floor. Two climate models (CCCma and REMO) predict a decline in water resources in this basin, and two others (HIRHAM5 and RCA4) are the opposite. However, based on a statistical study carried out over the historical period (2001-2005), the CCCma model seems the most reliable. It forecasts a drop in precipitation and runoff, which do not exceed - 19% and - 18%, respectively, whatever the emission scenario (RCP4.5 or RCP8.5). Climate variability (CV) is the only forcing whose impact is visible in the dynamics of current and future flows, due to the modest current (increase of + 102 km2 in builds and roads) and future (increase of + 114 km2 in builds and roads) changes observed in the evolution of land use and land cover (LULC). The results of this study could contribute to improving water resource management in the basin studied and the region.

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China.

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Impact of hydrological drought occurrence, duration, and severity on Murray-Darling basin water quality.

The severity and frequency of droughts are projected to increase globally due to climate change, but the effects of this on water quality are uncertain. The Murray-Darling Basin (MDB) is the largest river system in Australia and has been impacted by droughts of varying severity within recent decades. In this study, we assessed the influence of hydrological droughts and their characteristics (severity and duration) on water quality, utilising a long-term (1980-2017) dataset from two monitoring sites. The main drought periods, and their duration and severity, were identified using the calculated Standardised Drought Index values (SDI) from averaged monthly streamflow data. While several hydrological drought periods were identified, the longest duration and greatest severity were during the Millennium Drought (1998-2010). Nutrient loads and concentrations of Total Nitrogen and Total Phosphorus of drought and post-drought periods were significantly different. The drought period showed the lowest median and interquartile range of nutrient (total nitrogen, TN; oxidised nitrogen, NOX; total phosphorus, TP; and soluble reactive phosphorus, SRP) concentrations and loads for both sites, whereas the highest nutrient loads and concentrations were reported during the post-drought period (approx. 1 × 103 to 1 × 105 kg day-1 increase in nutrient loads). Our analysis found significant relationships between nutrient loads and SDI during droughts. The load of N and P in the initial flush post-drought increased with drought at both sites. This suggests that nutrients were retained in the landscape during the drought and released in higher loads post-drought when the catchment became wetter, the hydrology was activated, and nutrients were mobilised. Hydrology is a key driver controlling the water quality within the inter-drought period and the peak nutrient loads post-drought. The duration and the severity of droughts had a significant (p = 0.01) influence on peak TN and TP monthly loads but not cumulative loads over a 12-month period. Hydrological droughts are important factors in controlling the water quality of the MDB. Therefore, management efforts should be focused on reducing the occurrence and duration of these events, along with the implementation of catchment nutrient control measures.

Performance evaluation of various techniques in estimating precipitation record of a sparsely gauged mountainous watershed.

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

Developmental framework for a desktop hydrogeomorphic wetland functional assessment derived from field-based data.

With loss of wetlands and their associated ecosystem services within landscapes, it is imperative to be able to understand the change in ecological functions underlying these services. Field-based functional assessments can produce a range of specific scores among a robust set of functions but are time and cost prohibitive as the number of wetlands assessed increases. Remote-based functional assessments are an alternative for broad scale assessments, but trade-off cost for limitations in scoring and functional assemblage. To address these concerns, we created a framework for the development of the Hydrogeomorphic Remote Assessment of Wetland Function (HGM-RAWF). Rooted in the hydrogeomorphic approach of an existing field-based functional assessment and its underlying models, this remote functional assessment substitutes field-based assessment methods with remotely assessed proxies. As potential remote proxies were determined through literature review and statistically screened for use in the remote assessment, a field-based reference wetland database of 222 freshwater wetlands in the Mid-Atlantic Region provided a baseline by which remote data could be compared and calibrated. The resulting HGM-RAWF protocol remotely assesses seven hydrology and biogeochemistry functions in the Mid-Atlantic with assessment scores similar to its field-based counterparts. With noted limitations, the HGM-RAWF framework provides the means to create desktop functional assessments across broad geographic scales with the diversity and specificity of field-based assessments at the reduced costs associated with remote assessments. Its basis in the HGM approach and use of public spatial datasets allows the framework to be adopted regionally and can be used as a model for national wetland functional assessment.

Evolution of a tidal channel network in the Yellow River Delta, China, and simulation of optimization scenarios.

Tidal channel networks, which characterize all river deltas, control the exchange of water and nutrients (hydrological connectivity) between the ocean and the delta area. Therefore, a tidal channel network in optimal conditions ensures the maintenance of the diversity and stability of the deltaic ecosystem. However, the developmental status of channel networks in the Yellow River Delta, China, has not been clearly determined. Here, we selected a typical tidal channel network in this delta that showed different spatial patterns (e.g., connectivity attributes) in the past three decades and explored its evolution using entropy as an index of connectivity. Seven scenarios were set up to determine the optimal status of the tidal channel network by optimizing its structure. The optimization effect was evaluated by comparing the connectivity attributes of the channel network before and after optimization. The results showed that the network experienced two obviously different developmental phases: an evolution before 2005 and a regression after 2005. Mann-Kendall analysis indicated that the channel network achieved dynamic stability before 2014 and became unstable thereafter. The simulations conducted to optimize the system showed that adding outlets changed the current patterns of the network' structural and functional connectivity. As the optimization proceeded, structural connectivity increased while functional connectivity decreased, and the tidal channel network tended to be dynamically stable. Our study elucidated the quantitative relationship between outlet number and stability within tidal channel networks, providing reference information that could be incorporated into future projects for the restoration and management of river deltas.

An electricity market-based approach to finance environmental flow restoration.

As environmental flow demands become better characterized, improved water allocation and reservoir operating solutions can be devised to meet them. However, significant economic trade-offs are still expected, especially in hydropower-dominated basins. This study explores the use of the electricity market as both an institutional arrangement and an alternative financing source to handle the costs of implementing environmental flows in river systems managed for hydropower benefits. A framework is proposed to identify hydropower plants with sustainable operation within the portfolio of power sources, including a cost-sharing mechanism based on the electricity market trading to manage a time-step compensation fund. The objective is to address a common limitation in the implementation of environmental flows by reducing the dependence on government funding and the necessity for new arrangements. Compensation amounts can vary depending on ecosystem restoration goals (level of flow regime restoration), hydrological conditions, and hydropower sites characteristics. The application in the Paraná River Basin, Brazil, shows basin-wide compensation requirements ranging from zero in favorable hydrological years to thousands of dollars per gigawatt-hour generated in others. Each electricity consumer's contribution to the compensation fund is determined by their share of energy consumption, resulting in values ranging from cents for residential users to thousands of dollars for industrial facilities. Finally, the compensation fund signals the economic value of externalities in energy production. For residential users, achieving varying levels of ecosystem restoration led to an electricity bill increase of less than 1 %. For larger companies, the increase ranged from less than 1 %-12 %.

What drives the changing characteristics of phytoplankton in urban lakes: Climate, hydrology, or human disturbance?

Phytoplankton in shallow urban lakes are influenced by various environmental factors. However, the long-term coupling effects and impact pathways of these environmental variables on phytoplankton remain unclear. This is an emerging issue due to high urbanization and the resultant complex climate, lake hydrology and morphology, human interference, and water quality parameter changes. This study used Tangxun Lake, the largest urban lake in the Yangtze River Economic Belt, as an example to assess for the first time the individual contributions and coupled effects of four environmental variables and fourteen indicators on chlorophyll-a (Chla) concentrations under two scenarios from 2000 to 2019. Additionally, the influence pathways between the environmental variables and Chla concentration were quantified. The results indicated that the Chla concentration was most affected by lake hydrology and morphology, as were the total nitrogen, total phosphorus, and transparency. Especially after urbanization (2015-2019), the coupling effect of human interference, lake hydrology and morphology, and water quality parameters was strongest (18%). This is mainly due to fluctuations in the lake water level and an increase in the shape index of lake morphology, large amounts of nutrients were input, which reduced lake transparency and indirectly changed the Chla content. In addition, due to the rapid development of Wuhan city, the expansion of construction land has led to an increase in impervious surface area and a decrease in lake area. During periods of intense summer rainfall, a substantial amount of pollutants entered the lakes through surface runoff, resulting in decreased lake transparency, and elevated concentrations of nitrogen and phosphorus, indirectly increasing the Chla content. This study provides a scientific basis for aquatic ecological assessment and pollution control in urban shallow lakes.