Exploring the effects of green infrastructure placement on neighborhood-level flooding via spatially explicit simulations

State and local governments are increasingly considering the adoption of legislation to promote green infrastructure (e.g., bioswales, green roofs) for stormwater management. This interest emerges from higher frequencies of combined sewer outflows, floods and exposure of residents and habitat to polluted water resulting from growing urbanization and related pressure on stormwater management facilities. While this approach is promising, there are many unknowns about the effects of specific implementation aspects (e.g., scale, layout), particularly as urban settlements and climate conditions change over time. If green infrastructure is to be required by law, these aspects need to be better understood. We developed a spatially-explicit process-based model (the Landscape Green Infrastructure Design model, L-GriD) developed to understand how the design of green infrastructure may affect performance at a neighborhood scale, taking into consideration the magnitude of stormevents, and the spatial layout of different kinds of land cover. We inform the mechanisms in our model with established hydrological models. In contrast with watershed data-intensive models in one extreme and site level cost-savings calculators in the other, our model allows us to generalize principles for green infrastructure design and implementation at a neighborhood scale, to inform policy-making. Simulation results show that with as little as 10% surface coverage, green infrastructure can greatly contribute to runoff capture in small storms, but that the amount would need to be doubled or tripled to deal with larger storms in a similar way. When placement options are limited, layouts in which green infrastructure is dispersed across the landscape—particularly vegetated curb cuts—are more effective in reducing flooding in all storm types than clustered arrangements. As opportunities for green infrastructure placement increase and as precipitation increases, however, patterns that follow the flow-path and accumulation of water become more effective, which can be built on an underlying curb-cut layout. If space constraints prevented any of these layouts, random placement would still provide benefits over clustered layouts.     

A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.     

Implementation and application of a distributed hydrological model using a component-based approach

The application of a model to different study areas often requires that the model be modified to conform to specific characteristics, but this can be challenging due to the poor readability and reusability of the legacy codes. Component-based programming supported by a modelling framework provides a generic means to develop and modify models. This paper describes the development of a distributed hydrological model using a component-based modelling framework, which is implemented as a set of functional components that are integrated at runtime. The model was applied to runoff simulation in a large scale and data scarce alpine basin, and was further improved by incorporating a simple empirical soil freezing-thawing component. The results show that the componentised model reproduced the daily and monthly flow hydrograph with ‘good’ accuracy. The framework is shown to be flexible enough for model development and model modification.     

松华坝水源区不同LUCC情境下地表径流深变化初步研究

通过解译昆明市松华坝水源区两期Landsat TM（1992年、2001年）影像获得该区土地利用土地覆被变化（LUCC）数据,结合流域DEM、水文、气象、土壤等数据运行SWAT水文模型,模拟了松华坝水源区1992年~2001年期间逐年、逐月的地表径流。并用实测值对该模型进行参数率定。基于率定好的模型,预测了在假定的4种LUCC情境下地表径流的变化情况,其结论如下:① 水源区内无植被,径流深增加了53.87 mm ;② 水源区内耕地和园地全部转变为林地,径流深增加了3.62 mm ;③ 水源区内居民点及工矿用地、耕地和园地全部转换为林地,径流深增加了13.73 mm ;④ 水源区内全部为林地,径流深增加了6.99 mm。其中,情境①伴随严重水土流失。在不同程度封山育林情境中,情境③更能增大地表径流深。     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题 ,许多地方实施了集水工程以收集雨水。为分析集水潜力 ,以黄土高原半干旱地区的 6个自然集水区为研究对象 ,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨 -径流关系的几个主要因素定量表示出来 ,可以快速、准确地计算一个集水区的径流量 ,克服采用径流等值线计算径流带来的误差     

Use of high‐resolution satellite data for the structural and agricultural inventory of tank irrigation systems

Tanks are small storage reservoirs impounding the runoff from monsoon rains to regulate the supply of water mainly for irrigated command areas that are typically less than 200 ha. They account for one‐third of the irrigated areas in Tamil Nadu, Karnataka and Andhra Pradesh. Years of neglect and indifference in tank maintenance and management have eroded their functional efficiency and jeopardized their multifarious benefits. In Tamil Nadu, this has resulted in a decline in their contribution to irrigation from 40% in 1995 to 25% in 2000. The modernization of these tanks requires prioritization and investment. Remote sensing technology, with its unique advantages and the latest high‐resolution sensors, can provide the information on the agricultural, hydrological and structural conditions of the tank irrigation systems necessary for prioritization. The National Remote Sensing Centre (NRSC) has carried out a study of the Nanjur tank cascade in Tamil Nadu using high‐resolution data from the IKONOS satellite during the crop season of 2003–2004. This study demonstrated the use of high‐resolution satellite images to obtain an inventory of the different components of a tank irrigation system such as tank bunds, surplus weirs, supply channels and distribution networks. It was also found useful in updating the road–rail network at village level. The 1‐m merged satellite data were useful in mapping open wells and minor roads in a tank cascade. The cropping pattern in a tank system can be mapped at cadastral level using these images, which will be useful for micro‐level water and agricultural management. The 4‐m multispectral image was found to be sufficient for mapping different crops at field level. The high‐resolution image also provided information on intrafield variability in crop condition. The reliability and cost‐effectiveness of high‐resolution images from Indian satellites provide scope for the generation of information for tank system studies as well as for micro‐level natural resource management.     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题,许多地方实施了集水工程以收集雨水。为分析集水潜力,以黄土高原半干旱地区的6个自然集水区为研究对象,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨-径流关系的几个主要因素定量表示出来,可以快速、准确地计算一个集水区的径流量,克服采用径流等值线计算径流带来的误差。     

Coupling the short-term global forecast system weather data with a variable source area hydrologic model

Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution.     

Modeling stormwater management at the city district level in response to changes in land use and low impact development

Mitigating the impact of increasing impervious surfaces on stormwater runoff by low impact development (LID) is currently being widely promoted at site and local scales. In turn, the series of distributed LID implementations may produce cumulative effects and benefit stormwater management at larger, regional scales. However, the potential of multiple LID implementations to mitigate the broad-scale impacts of urban stormwater is not yet fully understood, particularly among different design strategies to reduce directly connected impervious areas (DCIA). In this study, the hydrological responses of stormwater runoff characteristics to four different land use conversion scenarios at the city scale were explored using GIS-based Stormwater Management Model (SWMM). Model simulation results confirmed the effectiveness of LID controls; however, they also indicated that even with the most beneficial scenarios hydrological performance of developed areas was still not yet up to the pre-development level, especially where there were pronounced changes from pervious to impervious land.     

Sensitivity and uncertainty propagation in coupled models for assessing smallholder farmer food security in the Olifants River Basin,South Africa

Using family balance (i.e., combined net farm and non-farm incomes less family expenses), an output from an integrated model, which couples water resource, agronomic and socio-economic models, its sensitivity and uncertainty are evaluated for five smallholder farming groups (A–E) in the Olifants Basin. The crop management practiced included conventional rainfed, untied ridges, planting basins and supplemental irrigation. Scatter plots inferred the most sensitive variables affecting family balance, while the Monte Carlo method, using random sampling, was used to propagate the uncertainty in the model inputs to produce family balance probability distributions. A non-linear correlation between in-season rainfall and family balance arises from several factors that affect crop yield, indicating the complexity of farm family finance resource-base in relation to climate, crop management practices and environmental resources of soil and water. Stronger relationships between family balance and evapotranspiration than with in-season rainfall were obtained. Sensitivity analysis results suggest more targeted investment effort in data monitoring of yield, in-season rainfall, supplemental irrigation and maize price to reduce family balance uncertainty that varied from 42% to 54% at 90% confidence level. While supplemental irrigation offers the most marginal increase in yields, its wide adoption is limited by availability of water and infrastructure cost.     

Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture

In semi-arid and arid areas with intensive agriculture, surface water-groundwater (SW-GW) interaction and agricultural water use are two critical and closely interrelated hydrological processes. However, the impact of agricultural water use on the hydrologic cycle has been rarely explored by integrated SW-GW modeling, especially in large basins. This study coupled the Storm Water Management Model (SWMM), which is able to simulate highly engineered flow systems, with the Coupled Ground-Water and Surface-Water Flow Model (GSFLOW). The new model was applied to study the hydrologic cycle of the Zhangye Basin, northwest China, a typical arid to semi-arid area with significant irrigation. After the successful calibration, the model produced a holistic view of the hydrological cycle impact by the agricultural water use, and generated insights into the spatial and temporal patterns of the SW-GW interaction in the study area. Different water resources management scenarios were also evaluated via the modeling. The results showed that if the irrigation demand continuous to increase, the current management strategy would lead to acceleration of the groundwater depletion, and therefore introduce ecological problems to this basin. Overall, this study demonstrated the applicability of the new model and its value to the water resources management in arid and semi-arid areas.     

A new framework for modeling decentralized low impact developments using Soil and Water Assessment Tool

Assessing the performance of LID practices at a catchment scale is important in managing urban watersheds. Few modeling tools exist that are capable of explicitly representing the hydrological mechanisms of LIDs while considering the diverse land uses of urban watersheds. In this paper, we propose computational modules that simulate the hydrological processes of LIDs including green roof, rain garden, cistern, and porous pavement. The applicability of the modules was evaluated using plot scale experimental monitoring data. The effectiveness of LIDs was investigated in a highly urbanized watershed located in Austin, TX. Results indicate that the performance of LIDs is sensitive to LID configurations, application areas, and storm event characteristics, suggesting the need for studies on spatial optimization of LIDs and critical storm events to maximize the utility of LIDs. The LID modules offer a comprehensive modeling framework that explicitly simulates the water quantity processes of the LIDs considering landscape heterogeneity.     

Terrace erosion and sediment transport model: a new tool for soil conservation planning in bench-terraced steeplands

Despite widespread bench-terracing soil erosion remains a major problem in Java’s uplands. To elucidate the causes for this lack of impact, runoff and erosion processes were studied at a variety of spatial scales within a volcanic catchment in West Java. Research indicated that soil loss occurs via rain splash and wash of rainfall-detached sediment by shallow overland flow from the terrace riser and bed, and via runoff entrainment of sediment deposited in the central terrace toe drain. The terrace erosion and sediment transport mode (TEST) was developed to physically describe these processes, as a function of vegetation and soil surface cover where appropriate, yet use as few parameters as possible. Runoff generation was described by the spatially variable infiltration model (SVIM) and a two parameter rainfall depth-intensity distribution was assumed to derive a simple analytical expression for storm runoff depth. In a similar manner expressions were derived for effective rainfall kinetic energy to predict rainfall-driven transport using a newly developed model, and for effective runoff rate to predict flow-driven transport using GUEST model theory. The model and its components were tested using measured runoff and soil loss from 31 sections of terrace beds or risers and from six terrace units during two seasons. The model generally performed satisfactorily and provides a useful new tool for assessing the impacts of soil conservation measures on bench terrace runoff and soil loss.     

SRM模型在玛纳斯河流域春季洪水预警中的应用研究

在玛纳斯河流域应用SRM模型进行日径流量的预报,进一步完成对该流域春季融雪性洪水的监测和预警,为防洪、抗旱、提高水资源利用提供技术支撑。引入中国气象局T213数值产品来进行流域分带温度和降水的预报,为融雪径流预报开辟了新的数据方法。通过利用自主开发的融雪径流模拟预报软件1.0版对玛纳斯河流域肯斯瓦特水文站2004年春季融雪期进行径流量预报,从SRM模型的两个精度评价指标看来,预报结果比较满意。
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Analysing complex behaviour of hydrological systems through a system dynamics approach

The interaction among various water cycle components consists of complex, non-linear, and bidirectional (interdependent) biophysical processes which can be interpreted using feedback loops in a system dynamics (SD) environment. This paper demonstrates application of an SD approach with two case studies using a specialised software tool, Vensim. The first case study simulates water balance in a rice field system on a daily basis under aerobic conditions with provision of supplemental irrigation on demand. A physically based conceptual water balance model was developed and then implemented using Vensim to simulate the processes that occur in the field water balance system including percolation, surface runoff, actual evapotranspiration, and capillary rise. The second case study simulates surface–groundwater dynamic interactions in an irrigation area where river water and groundwater are two key sources of irrigation. The modelled system encompasses dynamically linked processes including seepage from the river, evaporation from a shallow watertable, groundwater storage, and lateral flow from upland to lowland areas. The model can be applied to simulate responses of different irrigation management scenarios, to develop strategies to improve water use efficiency and control watertable, to prevent salinization in upland, and to reduce the cost of groundwater abstraction in lowland areas. The discussed applications of the SD approach conclude that it helps to conceptualize and simulate complex and dynamic water system processes deterministically which are otherwise partly simulated by conventional hydrologic and stochastic modelling approaches. It is recognised that conceptualization and implementation phases of this approach are challenging, however, the latter is greatly assisted by modern computer softwares.     

Modelling uncertainty in social–natural interactions

Socio-ecological systems can be represented as a complex network of causal interactions. Modelling such systems requires methodologies that are able to take uncertainty into account. Due to their probabilistic nature, Bayesian networks are a powerful tool for representing complex systems where interactions between variables are subject to uncertainty. In this paper, we study the interactions between social and natural subsystems (land use and water flow components) using hybrid Bayesian networks based on the Mixture of Truncated Exponentials model. This study aims to provide a new methodology to model systemic change in a socio-ecological context. Two endogenous changes – agricultural intensification and the maintenance of traditional cropland – are proposed. Intensification of the agricultural practices leads to a rise in the rate of immigration to the area, as well as to greater water losses through evaporation. By contrast, maintenance of traditional cropland hardly changes the social structure, while increasing evapotranspiration rates and improving the control over runoff water. These results indicate that hybrid Bayesian networks are an excellent tool for modelling social–natural interactions.     

An R package for assessment of statistical downscaling methods for hydrological climate change impact studies

Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing – statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package ”musica” which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.     

Improved storm water management through irrigation rescheduling for city parks

In this work, a modeling and scheduling approach for an integrated storm water management and irrigation problem is presented. The primary objective is to simultaneously ensure that the green space is irrigated appropriately and the level of the storm water pond is maintained adequately. It is proposed to use closed-loop irrigation scheduling to achieve the objective. A steady-state model is developed to calculate the soil water storage for different irrigation amounts. To handle the uncertainty, real-time feedback from the pond is used to re-evaluate the scheduling optimization problem every week. Simulation results show that the proposed closed-loop scheduling gives much improved control performance.     

Retrieval of daily evolution of soil moisture from satellite-derived land surface temperature and net surface shortwave radiation

Soil moisture is a key parameter in water balance, and it serves as the core and link in atmosphere–vegetation–soil–groundwater systems. Soil moisture directly affects the accuracy of the simulation and prediction conducted by hydrological and atmospheric models. This article aims to develop a new model to retrieve the daily evolution of soil moisture with time series of land surface temperature (LST) and net surface shortwave radiation (NSSR). First, for the time series of soil moisture, LST and NSSR daytime data were simulated by the common land model (CoLM) with different soil types in bare soil areas. Based on these data, the variations between soil moisture and LST-NSSR during the daytime with different soil types were analysed, and a plane function was used to fit the daily evolution of soil moisture and the time series of LST and NSSR data. Further study proved that the coefficients of the soil moisture retrieval model are not sensitive to soil type. Then, a relationship model between the daily evolution of soil moisture and the time series of LST-NSSR was developed and validated using the data simulated by CoLM with different soil types and different atmospheric conditions. To demonstrate the feasibility of the soil moisture retrieval method proposed in this study, it was applied to the African continent with data from the METEOSAT Second Generation Spinning Enhanced Visible and Infrared Imager (MSG–SEVIRI) geostationary satellite. The results show that the variation of soil moisture content can be quantitatively estimated directly by the method at the regional scale with some reasonable assumptions. This study can provide a new method for monitoring the variation of soil moisture, and it also indicates a new direction for deriving the daily variation of soil moisture using the information from the time series of the land surface variables.