ArcDrain: A GIS Add-In for Automated Determination of Surface Runoff in Urban Catchments

ABSTRACT: Surface runoff determination in urban areas is crucial to facilitate ex ante water planning, especially in the context of climate and land cover changes, which are increasing the frequency of floods, due to a combination of violent storms and increased imperviousness. To this end, the spatial identification of urban areas prone to runoff accumulation is essential, to guarantee effective water management in the future. Under these premises, this work sought to produce a tool for automated determination of urban surface runoff using a geographic information systems (GIS). This tool, which was designed as an ArcGIS add-in called ArcDrain, consists of the discretization of urban areas into subcatchments and the subsequent application of the rational method for runoff depth estimation. The formulation of this method directly depends on land cover type and soil permeability, thereby enabling the identification of areas with a low infiltration capacity. ArcDrain was tested using the city of Santander (northern Spain) as a case study. The results achieved demonstrated the accuracy of the tool for detecting high runoff rates and how the inclusion of mitigation measures in the form of sustainable drainage systems (SuDS) and green infrastructure (GI) can help reduce flood hazards in critical zonesThis research was funded by the Spanish Ministry of Science, Innovation, and Universities, with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF), grant number RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE)

Enhancing Operational Flood Detection Solutions through an Integrated Use of Satellite Earth Observations and Numerical Models

Among natural disasters floods are the most common and widespread hazards worldwide (CRED and UNISDR, 2018). Thus, making communities more resilient to flood is a priority, particularly in large flood-prone areas located in emerging countries, because the effects of extreme events severely setback the development process (Wright, 2013). In this context, operational flood preparedness requires novel modeling approaches for a fast delineation of flooding in riverine environments. Starting from a review of advances in the flood modeling domain and a selection of the more suitable open toolsets available in the literature, a new method for the Rapid Estimation of FLood EXtent (REFLEX) at multiple scales (Arcorace et al., 2019) is proposed. The simplified hydraulic modeling adopted in this method consists of a hydro-geomorphological approach based on the Height Above the Nearest Drainage (HAND) model (Nobre et al., 2015). The hydraulic component of this method employs a simplified version of fluid mechanic equations for natural river channels. The input runoff volume is distributed from channel to hillslope cells of the DEM by using an iterative flood volume optimization based on Manning\u2019s equation. The model also includes a GIS-based method to expand HAND contours across neighbor watersheds in flat areas, particularly useful in flood modeling expansion over coastal zones. REFLEX\u2019s flood modeling has been applied in multiple case studies in both surveyed and ungauged river basins. The development and the implementation of the whole modeling chain have enabled a rapid estimation of flood extent over multiple basins at different scales. When possible, flood modeling results are compared with reference flood hazard maps or with detailed flood simulations. Despite the limitations of the method due to the employed simplified hydraulic modeling approach, obtained results are promising in terms of flood extent and water depth. Given the geomorphological nature of the method, it does not require initial and boundary conditions as it is in traditional 1D/2D hydraulic modeling. Therefore, its usage fits better in data-poor environments or large-scale flood modeling. An extensive employment of this slim method has been adopted by CIMA Research Foundation researchers for flood hazard mapping purposes over multiple African countries. As collateral research, multiple types of Earth observation (EO) data have been employed in the REFLEX modeling chain. Remotely sensed data from the satellites, in fact, are not only a source to obtain input digital terrain models but also to map flooded areas. Thus, in this work, different EO data exploitation methods are used for estimating water extent and surface height. Preliminary results by using Copernicus\u2019s Sentinel-1 SAR and Sentinel-3 radar altimetry data highlighted their potential mainly for model calibration and validation. In conclusion, REFLEX combines the advantages of geomorphological models with the ones of traditional hydraulic modeling to ensure a simplified steady flow computation of flooding in open channels. This work highlights the pros and cons of the method and indicates the way forward for future research in the hydro-geomorphological domain

Integration of a physically-based hydrological model with spatial soil data and GIS: an application to the Hafren Catchment, Wales

The present research aims to illustrate and evaluate the effect of spatially variable soil data on the modelling of catchment rainfall-runoff transformations, using the hydrological model Topmodel. The soil-topographic wetness index used in Topmodel has always allowed for a spatially variable To - lateral saturated transmissivity - yet very little published research has focussed on the use of spatial soil datasets to derive To. In recent years the availability of soil hydrologic parameters, either from soil classifications and/or from new measurement techniques has increased significantly and, especially with regards to remote sensing, there is still great potential for further advances. It is therefore important that models like Topmodel should be able to incorporate such distributed soil data and assess if its' inclusion may allow a better representation of rainfall-runoff transformation processes. In particular, one of the key issues is the need to use distributed data to predict internal catchment conditions — such as runoff source areas — and not only global volumetric outflows. This aspect is of importance both at the catchment scale, for improved integrated catchment management (i.e. in the presence of land-use changes), and at the GCM modelling scale for the simulation of regional land-atmosphere interactions.With regard to the soil data, particular importance is associated to soil hydraulic parameters such as porosity and saturated conductivities. Traditionally, such data have only been available from measurements on single soil samples. But in recent years, various analytical methods and hydromorphic classification schemes have been developed which allow us to estimate the above parameters or, alternatively, provide qualitative indeces of the soils behaviour in terms of runoff generation. The present research has therefore evaluated the effect of different soil classification schemes with respect to their ability to improve the prediction of soil moisture deficit using TOPMODEL.Given the strengths of GIS in storing and analysing spatial data, the research has also evaluated if and how GIS can be used to better understand the effect of spatial classification schemes applied to the soil input data. Though GIS cannot substitute the theoretical knowledge of the processes occurring, it can certainly provide the spatial functionalities often lacking in hydrological models. It is this spatial perspective that can allow us to visualise synoptically the phenomena being studied, while at the same time exploring, highlighting, and verifying the prominent spatial variables that control the rainfallrunoff transformation processes.The integration of the three different modelling perspectives was pursued to allow the user to carry out a more thorough validation of both data and modelling methods used. Ultimately, it is hoped that this multidisciplinary approach will help to better assess the validity of the adopted methodology within the context of integrated catchment management

Sensitivity analysis of time-step in modeling river and aquifer interaction

Center for Water and the Environmen

The Spatial Distribution of Imperviousness in Watershed Hydrology

Urbanization affects the hydrology of watersheds often leading to increases in runoff volumes and peak flows. These impacts are mainly attributed to the presence of imperviousness on the landscape which inhibits the soil infiltration process. Normally, these impacts are studied at the hillslope scale and under lumped watershed conditions. The impacts at the watershed scale under more spatially distributed conditions have been studied less. Advancements in spatial observations and techniques, distributed hydrologic modeling, and greater understanding of the importance of scale in hydrology have increased the feasibility and need for including spatial data sets and methods into hydrologic investigations. This dissertation focuses on understanding the role and importance of the spatial distribution of imperviousness in watershed hydrology. The spatial distribution of imperviousness is investigated by incorporating various spatial datasets, techniques, and modeling approaches that are used routinely for the hydrology of natural watersheds but less frequently for urbanized conditions. The distribution of imperviousness is investigated based on three approaches. The first approach uses optimization concepts to study where imperviousness can be placed in the watershed to reduce negative impacts on flooding. The second approach develops, implements, and tests a hydrologic event-based model to study the influence of the spatial distribution of imperviousness on the hydrologic response. The last approach relates analytically the space-time variability of rainfall, runoff, and the routing process to the imperviousness pattern, and synthesizes the complex space-time variations into a simpler framework. From the first approach distinct patterns of imperviousness were obtained that embodied water resources objectives. For example, the clustering of imperviousness along the main channel was found to globally reduce peak flows along the stream network. The second approach indicated that the overall imperviousness pattern can have a considerable impact on the hydrologic response. The last approach showed that the spatial patterns of rainfall and imperviousness can interact to increase or decrease the average amount of rainfall excess. The main contribution from this research is a larger understanding of the role of the spatial distribution of imperviousness in watershed hydrology. It also demonstrates the usefulness of applying hydrologic knowledge of natural watersheds to anthropogenically-altered watersheds

Modelling water budget at a basin scale using JGrass-NewAge system

Water resources availability and its variability is one of the most pressing global problems. Hydrological models are useful to understand the water balance of a basin, providing information for water resource forecast, assessment, and management. The effectiveness of the models in estimating the freshwater space-time availability and variability, however, depends on concurrent and explicitly modelling of all water budget components instead of a single component estimation and optimization. The whole water budget modelling at basin scale requires a combined solution from hydrological and spatial information tools, in-situ and remote sensing data. The present dissertation describes an effort to improve estimation of each water budget component, and water budget closure at various spatial and temporal scales, by combining JGrass-NewAge model system, GIS spatial toolbox, in-situ and remote sensing data. JGrass-NewAge is a system which deploys modern informatics to facilitate models maintainability and reproducible research. It integrates advanced GIS features and the Object Modelling System version 3 infrastructures, which allow for a component-based modelling experience. This means that JGrass-NewAGE is not actually a model, but a set of elements (the components) that can be combined just before runtime to produce various modelling solutions. Topics like calibration of processes, the interpolation forcing and the assessment of forecasting errors can therefore be faced with consistent and solid approaches. In this context also the use of some remote sensing resources can be inserted appropriately and with new techniques. For all the analysis, two significantly different basins, in terms of size and hydrological processes, are considered as case studies. These are Posina river basin in northeast Italy (small size basin) and Upper Blue Nile basin(large size basin) are used as case study. The uDig Spatial Toolbox (uST) GIS infrastructure that is used for generating the hydromorphological parameters is described in the second chapter. A large number of tools are included in uST for terrain analysis, river network delineation, and basin topology characterisation. In addition, the geomorphological settings necessary to run JGrass-NewAGE are shown. The third chapter studies the effect of spatial discretisation and the hillslope size on basin responses. The possible epistemic uncertainty exerted by the use of sub basin spatial discretisation of topographic information in the semi-distributed hydrological modelling has been studied. The use of different spatial representation in hydrological modelling context has been also studied by comparing JGrass-NewAGE with a model configuration called PeakFlow. The latter is an implementation of the geomorphological unit hydrograph based on the width function. The experiment indicates that the Peak-Flow model, with a more accurate spatial representation, reproduce the storm events slightly better than the JGrass-NewAGE model. In the fourth chapter, the thesis set-up JGrass-Newage modelling solution for the estimation of hydrological modelling inputs (rainfall, snow, temperature data) and estimates them, as well as with their errors. Regards to the meteorological forcings (mainly temperature and precipitation), in Posina river basin where there are relatively dense meteorological stations, the effects of different interpolation schemes were evaluated. Since the hydrological processes from rainfall is different from snowfall, a new method of separating rainfall and snowfall was introduced using MODIS imagery data. In the fifth chapter, JGrass-NewAGE was used to estimate the whole set of water balance components. For evapotranspiration (ET) estimation, the Priestley-Taylor component of JGrass-NewAGE is used. In order to calibrate its parameter a new method based on the water budget was implemented. This method uses two different hypothesis on available data (budget stationarity "Budyko hypothesis", and local proportionality of actual evapotranspiration to soil moisture availability). Finally the spatial and temporal dynamics of water budget closure of Posina river basin is presented. The sixth chapter concerns about the inputs data, particularly precipitation, for water balance modelling in a region where ground-based gauge data are scarce. Five high-resolution satellite rainfall estimation (SRE) products were compared and analysed using the available rain gauge. The basin rainfall is investigated systematically, and it was found that, at some locations, the difference in mean annual rainfall estimates between these SREs very high. In addition to the identification of the best performing products, the chapter shows that a simple empirical cumulative distribution (ecdf) mapping bias correction method can provide a means to improve the rainfall estimation of all SREs, and the highest improvement is obtained for CMORPH. In the seventh chapter, using the capability of JGrass-NewAGE components and different remote sensing data, the spatio-temporal water budget of Upper Blue Nile basin is simulated. The water budget components (rainfall, discharge evapotranspiration, and storage) were analysed for about 16 years at daily time step using the modelling solution and remote sensing data set. For the verification of the approaches followed, wide ranges of remote sensing data (MODIS ET product MOD16, GRACE, and EUMETSAT CM SAF cloud fractional cover) are used

Neuroemulation: definition and key benefits for water resources research

Neuroemulation is the art and science of using a neural network model to replicate the external behaviour of some other model and it is an activity that is distinct from neural-network-based simulation. Whilst is has become a recognised and established sub-discipline in many fields of study, it remains poorly defined in the field of water resources and its many potential benefits have not been adequately recognised to date. One reason for the lack of recognition of the field is the difficulty in identifying, collating and synthesising published neuro-emulation studies because simple database searching fails to identifying papers concerned with a field of study for which an agreed conceptual and terminological framework does not yet exist. Therefore, in this paper we provide a first attempt at defining this framework for use in water resources. We identify eight key benefits offered by neuro-emulation and exemplify these with relevant examples from the literature. The concluding section highlights a number of strategic research directions, related to the identified potential of neuroemulators in water resources modelling

Coupled hydrological and biogeochemical modelling of nitrogen transport in the karst critical zone

Transport of nitrogen (N) in karst areas is more complex than in non-karst areas due to marked heterogeneity of hydrodynamic behaviour in the karst critical zone. Here, we present a novel, distributed, coupled hydrological-biogeochemical model that can simulate water and nitrogen transport in the critical zone of karst catchments. This new model was calibrated using integrated hydrometric, water stable isotope, and nitrogen-N concentration data at the outflow of Houzhai catchment in Guizhou province of Southwest China. Hydrological dynamics appears to control N load from the study catchment. Combining flow discharge and water stable isotopes significantly constrained model parameterisation and mitigate the equifinality effects of parameters on the simulated results. Karst geomorphology and land use have functional effects on spatiotemporal variations of hydrological processes and nitrogen transport. In the study catchment, agricultural fertilizer was the largest input source of N, accounting for 86% of the total. Plant uptake consumed about 45% of inputs, primarily in the low-lying valley bottom areas and the plain covered by relatively thick soils. Thus, a large amount of N released from soil reservoirs to the epikarst (via fractures or sinkholes) is then exported to the underground channel in the limestone area to the south. This N draining into groundwater could lead to extensive, potentially long-term contamination of the karst system. Therefore, improving the efficiency of fertilization and agricultural management in valleys/depressions is an urgent need to reduce N losses and contamination risk