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Green Infrastructure (GI) is considered to be an innovative stormwater management approach that offers numerous other environmental benefits including reduction in air pollution and climate change mitigation compared with conventional gray infrastructures, and it has been gradually accepted and used worldwide. This review mainly focuses on the hydrological aspect of GI and provides a summary of the knowledge about GI as a stormwater management alternative. In this review, we discuss the operating mechanisms of a few widely-used GIs in the aspect of stormwater management. We critically examine policies for promoting GI implementation in some countries, including the sponge city in China and water sensitive urban design (WSUD) in Australia, and review the effectiveness of GI on treating stormwater quantity in real world applications. In addition, we also used Bibliometrics to analyze the GI research trends and found GI has been popular in an increasing number of countries and regions, which China has the greatest future growth potential in GIs research. Moreover, many barriers impeding the further development of GI and strategies to overcome these barriers are also summarized. This review gathers knowledge from many sources to provide an overview of GI to better understand its mechanisms and applications, and to highlight the areas that require future study.

The regulating function is the least understood but probably most valuable service provided by an ecosystem. To estimate the impact of externalities on the regulating function of a wetland, we modelled the interdependence between activities that affect wetland characteristics and land utilization patterns. Nature-based solutions (NBS) can provide benefits for society, the economy and nature. The NBS solution for wetland bioremediation is proposed through a combination of ecosystem value provided by nature-based or artificial arable land. We constructed a model to simulate the dynamic spatial and temporal changes in the regulating function of a wetland to study the impact of agricultural and fishing activities on the functions and services of a wetland ecosystem. The model was used to determine the relationship between land use change, wetland range, water quality, and fish stocks. Huangqihai Lake (HQHL) is a relatively isolated lake in an area interlaced with agriculture and pasture zones. From 1973 to 2014, the “shrinkage” of the water area of HQHL approached 60%. Under the influence of humans and natural disturbances, the HQHL wetland faced enormous ecological risks, water pollution, area reduction, sharp biodiversity reduction, and fish extinction. The phosphorus (P) concentration seems to be a key factor affecting both the reed growth in the nearshore areas of the Huangqihai Lake wetland and the breeding of phytoplankton in the core lake water area. Dividing this value by the 25,821 ha of the core wetland area affected by the reclamation, the cost per unit of lost fish production is USD$166/ha/yr. After deducting the externalities of the fishery, the net sustainable income from the conversion is USD$1717/ha. The spatial distribution of nutrient load externalities varies with the use of wetlands. In terms of fishery value, the additional nutrient load associated with the conversion of wetland uses would result in a reduction of at least 8% of the maximum sustainable yield. The conversion of wetland uses resulted in the loss of nutrient buffering functions. To compensate for the farmland's nutrient load buffer service, the government should provide arable land farmers USD$8.1 M/yr. It is theoretically feasible to implement an ecosystem service payment system that can effectively compensate for the loss of nutrient buffer function caused by the conversion of wetlands. The approach based on BNS we described may also have value in other ecosystems where regulatory functions include similar mechanisms.

Problems with water resources restrict the sustainable development of a city with water shortages. Based on system dynamics (SD) theory, a model of sustainable utilization of water resources using the STELLA software has been established. This model consists of four subsystems: population system, economic system, water supply system and water demand system. The boundaries of the four subsystems are vague, but they are closely related and interdependent. The model is applied to Zhengzhou City, China, which has a serious water shortage. The difference between the water supply and demand is very prominent in Zhengzhou City. The model was verified with data from 2009 to 2013. The results show that water demand of Zhengzhou City will reach 2.57 billion m 3 in 2020. A water resources optimization model is developed based on interval-parameter two-stage stochastic programming. The objective of the model is to allocate water resources to each water sector and make the lowest cost under the minimum water demand. Using the simulation results, decision makers can easily weigh the costs of the system, the water allocation objectives, and the system risk. The hybrid system dynamics method and optimization model is a rational try to support water resources management in many cities, particularly for cities with potential water shortage and it is solidly supported with previous studies and collected data.

Semi-distributed model based on spatial information is effective for simulating hydrologic cycle. However, it cannot completely simulate all relevant natural processes. Uncertainty analysis is necessary for achieving high accuracy of hydrological modelling. In this study, optimization algorithms and weighted network analysis techniques were adopted to explore hydrological parameter combination characteristics as well as hydrological simulating uncertainties in Yalong River Basin (YLRB) of southwestern China based on Sequential Uncertainty Fitting version 2 (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE) and Particle Swarm Optimization (PSO). The results indicated that: a) groundwater recession factor, effective hydraulic conductivity in channel and saturated hydraulic conductivity could significantly affect streamflow in the studying basin, b) complex parameter combination responded diversely under aforementioned three optimization algorithms. Comparatively, GLUE brought out higher autocorrelation parameter network and c) SUFI-2 and PSO performed better in terms of effective uncertainty analysis and fitting values than those of GLUE. This work could provide references and insights for sensitive parameter modification and prediction uncertainty reduction of streamflow simulation, furthermore contributing to an optimal water resource management.

Shallow lakes are characterized by strong hydrological fluctuations, affecting their biochemistry and carbon (C) balance. This study explored carbon dioxide (CO2) and methane (CH4) flux under strong water-level fluctuations in a shallow lake while also addressing its implications regarding wetland restoration. The DeNitrification-DeComposition (DNDC) model, a process-based biogeochemical model, was used to simulate CO2 and CH4 flux under intensive water-level fluctuations in Lake Baiyangdian (BYD), Northern China. Results showed that: (i) the DNDC model was able to reasonably capture CO2 and CH4 dynamics, which were determined to be extremely sensitive to water-depth fluctuations at a −10 cm to 10 cm range; (ii) the lowest CO2 flux was observed at a 40-day duration, while the trend in annual CH4 flux was to increase as inundation time increased, changing from −2.27 kg C/ha/y to 1.57 kg C/ha/y; and (iii) CO2 and CH4 flux increased under higher fluctuation frequencies, wherein CO2 flux was lowest in January and February and CH4 flux increased from December to March under certain frequencies. Our results indicate that water-level fluctuations (e.g., water depth, duration, frequency, and timing) affected both CO2 and CH4 flux. As it pertains to water diversion projects used for shallow lake restoration initiatives, the optimum approach, pertinent to water yields, diversion time, and duration, should first be considered carefully. Results from this study provide useful information for assisting policymakers with respect to reducing gaseous C emissions during ecological water transfer project regulations in shallow lakes.

Hydrological regimes play a vital role in regulating wetland structure and function, regarded as fundamental for wetland protection and restoration. This study explored vegetation response to water-level dynamics, simulated vegetation patterns, and the estimated evapotranspiration (ET) pertaining to different hydrological statuses. We used the vegetation dynamics simulation model and topography- and vegetation-based surface energy partitioning algorithms (TVET model) designed to estimate potential evaporation (PE) and potential transpiration (PT). Results indicated that: (i) Vegetation cover and water levels of different hydrological statuses can be used to deduce suitable water levels during flood, normal, and drought statuses (which were 1.23, 0.99, and 0.81 m, respectively, in the Ertou wetland). (ii) Dynamic vegetation simulations combined with optimal ecological water levels for Phragmites australis and Bolboschoenus planiculmis sustenance can be used to simulate vegetation pattern dynamics. Both P. australis and B. planiculmis expanded during droughts along with a reduction in open water, providing a potential increase in food availability for Siberian crane (Grus leucogeranus). (iii) Changes in vegetation patterns and characteristics (e.g., plant height and leaf area index (LAI)) that varied with water-level fluctuations inevitably altered potential evapotranspiration (PET) partitioning into PE and PT. These results indicate that subtle water-level fluctuations can dramatically alter vegetation patterns, characteristics, and ecological processes, subsequently affecting shallow wetland habitats. Further investigations are necessary to clarify wetland coupling mechanisms between hydrology, vegetation, and habitat. This will help address extreme drought and flooding events while also helping to assess wetland protection and restoration.

With climate change and the intensification of human activities, landscape patterns have undergone important changes. These changes can have multiple impact on hydrology, nutrient transport between soil and vegetation and ecological function and health in vegetative ecosystems. Dynamic changes in landscape patterns may affect the ecological water requirements (EWRs) of vegetation. Based on meteorological data, soil data and 6 years of land use data (from 1990 to 2015), the EWRs were obtained through the Penman–Monteith formula. eight suitable landscape indices (Patch Density (PD), Connectivity Index (COHESION), Largest Patch Index (LPI), Aggregation Index (AI), Shannon Diversity Index (SHDI), Contagion Index (CONTAG), Separation Index (SPLIT), and Landscape Shape Index (LSI)) were used to explore the spatial and temporal characteristics of landscape patterns. Furthermore, the influences of landscape pattern changes on the EWRs of the middle main reaches of the Yellow River, China, were investigated. From 1990 to 2015, the total number of patches in the landscape pattern of the watershed increased, and the patch shape became more complex; the EWRs decreased from 392.7 mm to 276.5 mm, and the annual volume of the EWRs in the study area decreased from 124.8 × 108 m3 to 89.6 × 108 m3. The EWRs had a positive correlation with the LPI, indicating that the changes in the largest patch had a great impact on the EWRs. The EWRs also had a negative correlation with the SPLIT, showing that the more fragmented the landscape patches, the smaller the EWRs. With an increase in SHDI, the EWRs showed a downward trend. Furthermore, the EWRs showed a downward trend with decreasing CONTAG and COHESION. These results provide useful information for ecological restoration and provide an important reference for ecological environmental protection and the optimal allocation of water resources in arid/semiarid regions around the world. In the future, the EWRs can be regulated, and the utilization rate of water resources can be improved, by combining the current ecological environmental protection policies of the Yellow River Basin and managing future trends in landscape patterns.