Atal Community Innovation Centre

Many watersheds experience scarcity of water for agricultural and domestic use for most part of the year. Ur river watershed in Tikamgarh district of Madhya Pradesh, India, falls under a drought prone region of India. Water allocation and management are essential for sustainable agriculture for this region. Water Evaluation and Planning system (WEAP)-based decision support system can prove to be an effective tool for water allocation, supply and demand analysis. In the present study, spatially distributed model by using WEAP-MABIA method has been developed for analysis and simulation of agricultural water demands in the Ur river watershed. WEAP-MABIA method uses dual crop coefficient approach which helps in computing the separate soil evaporation and transpiration under various water availability situations. Year 2012–2013 is used as base year for customizing WEAP model for 8 subwatersheds. The model was calibrated using PEST tool, available in WEAP. The calibrated model was used for estimating future water demands and unmet demands by using future climate series, from 2015 to 2030, of IPCC scenario- RCP 4.5 of GDFL-ESM2M model. As per GFDLESM2M model (RCP4.5) predictions, rainfall is going to greatly vary in the coming years. The years 2020–2021 and 2028–2029 may experience very dry climatic conditions with 500 mm or less annual rainfall, while 2017–2018, 2023–2024 and 2025–2026 may experience heavy showers (1200 mm). Straight effects of this rainfall pattern could be seen in future water availability for agriculture and resultant crop yield. A high unmet demand exists in the case of agriculture since the first priority for water supply is meant to be for domestic purpose. Area under agriculture in Ur river watershed is large, while respective water supply is low. This gap puts an extra pressure on water resources leading to over extraction of groundwater and related problems. Looking at this scenario, water allocation requires great attention to narrow down the gap between existing demands and water supply. Since area under agriculture is large, water-efficient crops should be more emphasized. Also, efficient agricultural practices and rain water harvesting should be promoted in the study area. Since the watershed falls in semiarid condition and river flows are seasonal, different stress/deficit irrigation scenarios can be built using customized WEAP model to get higher yield.

Many watersheds experience scarcity of water for agricultural and domestic use for most part of the year. Ur river watershed in Tikamgarh district of Madhya Pradesh, India, falls under a drought prone region of India. Water allocation and management are essential for sustainable agriculture for this region. Water Evaluation and Planning system (WEAP)-based decision support system can prove to be an effective tool for water allocation, supply and demand analysis. In the present study, spatially distributed model by using WEAP-MABIA method has been developed for analysis and simulation of agricultural water demands in the Ur river watershed. WEAP-MABIA method uses dual crop coefficient approach which helps in computing the separate soil evaporation and transpiration under various water availability situations. Year 2012-2013 is used as base year for customizing WEAP model for 8 subwatersheds. The model was calibrated using PEST tool, available in WEAP. The calibrated model was used for estimating future water demands and unmet demands by using future climate series, from 2015 to 2030, of IPCC scenario-RCP 4.5 of GDFL-ESM2M model. As per GFDLESM2M model (RCP4.5) predictions, rainfall is going to greatly vary in the coming years. The years 2020-2021 and 2028-2029 may experience very dry climatic conditions with 500 mm or less annual rainfall, while 2017-2018, 2023-2024 and 2025-2026 may experience heavy showers (1200 mm). Straight effects of this rainfall pattern could be seen in future water availability for agriculture and resultant crop yield. A high unmet demand exists in the case of agriculture since the first priority for water supply is meant to be for domestic purpose. Area under agriculture in Ur river watershed is large, while respective water supply is low. This gap puts an extra pressure on water resources leading to over extraction of groundwater and related problems. Looking at this scenario, water allocation requires great attention to narrow down the gap between existing demands and water supply. Since area under agriculture is large, water-efficient crops should be more emphasized. Also, efficient agricultural practices and rain water harvesting should be promoted in the study area. Since the watershed falls in semiarid condition and river flows are seasonal, different stress/deficit irrigation scenarios can be built using customized WEAP model to get higher yield.

Flood impact is one of the most significant disasters in the world. More than half of global flood damages occur in Asia. Causes of floods are due to natural factors such as heavy rainfall, high floods and high tides, etc., and human factors such as blocking of channels or aggravation of drainage channels, improper land use, deforestation in headwater regions, etc. Floods result in losses of life and damage properties. Population increase results in more urbanization, more impervious area and less infiltration and greater flood peak and runoff. Problems become more critical due to more severe and frequent flooding likely caused by climate change, socio-economic damage, population affected, public outcry and limited funds. Flood loss prevention and mitigation includes structural flood control measures such as construction of dams or river dikes and non-structural measures such as flood forecasting and warning, flood hazard and risk management, public participation and institutional arrangement, etc. This paper describes concepts, policy, plan and operation on integrated urban flood disaster and risk management. In most developing countries, flood disaster management activities are handled by government. Participation of nongovernmental agencies and private sectors are very limited. Activities are exercised rather independently without proper coordination or integration. Flood disaster management in developing countries is mostly reactive responding to prevailing disaster situations (emergency response and recovery). Reactive response should be changed to proactive response to increase effectiveness of management and reduce losses of life and properties. Proactive disaster management requires more participation from various governments, non-governmental and private agencies and public participation. It involves more effort and time, more budget, equipments, facilities and human resources which leads to integration of flood disaster management for both long term and short term activities. Strategic framework on integrated flood disaster management includes four cyclic steps namely: 1) preparedness before flood impact such as flood forecasting and warning; 2) readiness upon flood arrival; 3) emergency responses during flood impact and; 4) recovery and rehabilitation after flood impact. Examples on urban flood disaster and risk management in Thailand are illustrated and discussed. Conclusions and recommendations for further improvement are provided.

More than half of the world's natural disasters are floods, affecting millions of people every year. In fact, more than 3 billion people have been affected by floods over the last century, i.e. half of the current world population! Urban floods have long been considered as unpredictable and unavoidable natural disasters. Continuously increasing urbanization has led to an increasing flood risk whereas technological growth has raised various new strategies to predict and face this risk. Most of the urban drainage networks are aging and have been designed to manage a maximum rainfall, or so-called design rainfall, which refers to a design return period (usually 10 to 100 years). Thus, this implies an "accepted" flood risk for a rainfall greater than the design rainfall. But this flood risk can be underestimated considering factors like city growth, flash floods and climate change. Thus, being able to forecast the flood is one of the main issues of integrated flood risk management. Various flood forecasting systems have been developed and used, a lot using a large range of advanced tools and some as part of integrated decision support systems. Runoff and hydraulic models are usually essential elements for such systems, as pre-studying tools or as components of the system. This paper intends to make a review of current urban flood forecasting systems as well as the available modelling technologies, focusing on urban drainage modelling. Different types of urban real time systems are assessed and evaluated to sort out the current state-of-the-art and to give recommendations for future urban flood forecasting systems.

Flood risks are the most frequent natural risks in the world. Their consequences are particularly serious when they affect technical systems in urban areas. Experience feedbacks on guided transport systems show that they are urban technical systems that are particularly vulnerable to this type of natural risk. The resilience concept is used in a systemic approach for making an accurate analysis of this type of natural risk. The purpose of this article is to provide an analysis of guided transport systems' resilience in the face of flood risks via a study of the failure mechanisms to which component elements of these systems are subjected. By using methods resulting from operational safety concepts and designing a computer tool, all the failure scenarios for components can be produced, together with their domino effects. In this way, our work provides a methodology for characterizing guided transport systems' vulnerability in the face of natural risks and for comparing this vulnerability depending on whether the system is in an underground, ground-level or overground configuration.

Flooding is one of the most destructive natural hazards, accounting for over a third of all disaster damage worldwide. In particular in less developed countries (LDCs) this is typically attributed to poor planning, lack of warning systems and limited awareness of the hazard. A number of flood risk models have been developed, but have as yet contributed little to mapping and quantifying the risk in LDCs, for several reasons. In addition to limited human and technical capacity, these models require considerable amounts of current spatial information that is widely lacking, such as landcover, elevation and elements at risk basedata. Collecting those with ground-based methods is difficult, but remote sensing technologies have the potential to acquire them economically. To account for the variety of required information, data from different sensors are needed, some of which may not be available or affordable. Therefore, data interchangeability needs to be considered.

The effects of climate change on low-lying communities discouraged cities to reclaim seas for urban expansion. The storm surge that has occurred during the onslaught of Typhoon Haiyan in some parts of the Visayas in the Philippines discouraged the City of Catbalogan from pursuing expansion through reclamation. Instead, the local government proposed a Sky City project which intends to develop the hills surrounding the city as the new growth area. However, the development will cause various problems, one of which is the increased runoff due to change in ground cover. The paper aimed to calculate the increased runoff at various stages of development which will lead to flooding in the old city. Data from 36 scenarios (9 rainfall intensity and 4 stages of the project) using Hydrologic Modeling System (HEC-HMS 4.1) of the US Corps of Engineers, reveals that areas along the river will swell at rains smaller than 10mm in 1 hour. While potential risk on other parts of the city remains almost the same. There is a need to implement mitigating measures against the expected increase in runoff volume before the Sky City project development starts.

Over the last decade, neural network-based flood forecasts systems have been increasingly used in hydrological research. Usually, input data of the network are composed by past measurements of flows and rainfalls, without providing a description of the saturation state of the basin, which, in contrast, plays a key role in the rainfall-runoff process. This paper couples neural networks and fuzzy logic in order to enrich the description of the basin saturation state for flood forecasting purposes. The basin state is assessed analyzing the total rainfall occurred on a certain time window before the flood event. The proposed framework first classifies the basin saturation state providing a set of fuzzy memberships, and then issues the forecast exploiting a set of neural predictors, each specialized on certain basin saturation condition by means of a weighted least-square training algorithm. The outputs of the specialized neural predictors are linearly weighted, according to the basin state at forecast time: The more the training conditions of a predictor matches the current basin saturation state, the higher its weight on the final forecast. The framework has been tested on an Italian catchment and may overperform classical neural networks approaches.

The infrastructure design is primarily based on rainfall intensity-duration-frequency (IDF) curves, and the existing IDF curves are based on the concept of stationary extreme value theory (EVT) (i.e. the occurrence probability of extreme precipitation is not expected to change significantly over time). But, the extreme precipitation events are increasing due to global climate change and questioning the reliability of our current infrastructure design. Based on recent developments in the EVT, recent studies proposed a method for developing non-stationary rainfall IDF curve by incorporating linear trend in the location parameter of the generalized extreme value (GEV) distribution. Upon detecting a significant trend in the extreme rainfall series, directly applying the linear trend to develop non-stationary IDF curves may increase the bias of the non-stationary model. Hence, it is important to develop non-stationary GEV model which has less bias than the stationary model by modelling nonlinear trend in the series.

Low impact development (LID) is a land development approach that seeks to mimic a site's predevelopment hydrology. This study is a case study that assessed flood reduction capabilities of largescale adoption of LID practices in an urban watershed in central Illinois using the Personal Computer Storm Water Management Model (PCSWMM). Two flood metrics based on runoff discharge were developed to determine action flood (43 m 3 /s) and major flood (95 m 3 /s). Four land use scenarios for urban growth were evaluated to determine the impacts of urbanization on runoff and flooding. Flood attenuation effects of porous pavement, rain barrel, and rain garden at various application levels were also evaluated as retrofitting technologies in the study watershed over a period of 30 years. Simulation results indicated that increase in urban land use from 50 to 94% between 1992 and 2030 increased average annual runoff and flood events by more than 30%, suggesting that urbanization without sound management would increase flood risks. The various implementation levels of the three LID practices resulted in 3e47% runoff reduction in the study watershed. Flood flow events that include action floods and major floods were also reduced by 0e40%, indicating that LID practices can be used to mitigate flood risk in urban watersheds. The study provides an insight into flood management with LID practices in existing urban areas.

In order to mitigate urban flooding, an integrated assessment has been proposed to identify the optimum drainage solution which implements the potential green infrastructure with the existing conventional gray infrastructure. The integrated evaluation framework which consists the data of remote sensing and geographic information system, and the assessments of 2D hydrologic simulation and life cycle cost has been applied into a developed area in Shanghai under nine design storms with different return periods and durations. The results showed that increasing the pipe diameter at this area could alleviate the node flooding and pipe hydraulic load under a short return period and rainfall duration. However, according to the integrated evaluation, the potential flooding risk under a longer rainfall duration could be well controlled by implementing a combination of low impact development practices of Rain Barrel (RB) þ Pervious Concrete (PC) þ Green Roof (GR).

Climate change Detection and attribution Fingerprint method Extreme precipitation over India s u m m a r y Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant.

The availability of high-resolution topographic data enables the modeling of urban floods with a high level of accuracy. However, such a modelling has a poor computational efficiency. Subgrid models enable to decrease the computational time by using coarse cells while preserving information from the detailed topographic data to some degree. In particular, shallow-water models with porosity constitute a subgrid model well-adapted for urban flood modeling. In this article, a new set of fully dynamic shallow-water equations with depth-dependent porosities is presented. Then, the implementation of the model is detailed and preliminary results obtained for a theoretical two-dimensional urban area are analyzed. Unlike recent works, the new model solves the fully dynamic shallow-water equations with depth-dependent and anisotropic porosities, a divergent formulation of the bed slope term, a non-staggered grid with quadrilateral cells and an efficient use of look-up tables to store the porosity relations.

Risk assessments of flooding in urban areas during extreme precipitation for use in, for example, decision-making regarding climate adaptation, are surrounded by great uncertainties stemming from climate model projections, methods of downscaling and the assumptions of socioeconomic impact models. The multidisciplinary character of such risk assessments also requires that research groups and experts from different scientific disciplines combine knowledge and share model outputs. This paper describes an integrated framework and tool, the Danish Integrated Assessment System (DIAS), which has been designed to address the complex linkages between the different kinds of data required in assessing climate adaptation. It emphasizes that the availability of spatially explicit data can reduce the overall uncertainty of the risk assessment and assist in identifying key vulnerable assets. The usefulness of such a framework is demonstrated by means of a risk assessment of flooding from extreme precipitation for the city of Odense, Denmark. A sensitivity analysis shows how the presence of particularly important assets, such as cultural and historical heritage, may be addressed in assessing such risks. The output of the risk assessment for Odense indicates that highly detailed geographical data reduce the overall uncertainty and assist climate adaptation decision-makers in focusing on protecting those assets that are considered to be relevant in the given context. Also, using an integrated framework such as DIAS enables the relative importance of the different factors (i.e. degree of climate change, assets value, discount rate etc.) to be determined, thus influencing the overall output of the assessment. Ó 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

In the present paper, a multi objective optimization approach based in Genetic Algorithm, Design Of Experiments and Response Surface Method strategies, is applied in order to predict the optimal CFD model parameters, allowing to model with high accuracy the 3D free surface flow of urban flood propagation retaining the total computation time (CPU-time) as short as possible. An experimental data set was used in this study as a validation means for numerical optimized models. We can say that the constraints in terms of computation time and accuracy, related to the application of 3D CFD modeling for flood propagation problems can be overcome by making an optimal choice of the advanced parameters of model.