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Flood-attributed damages to infrastructure and public safety are expected to escalate in the future due to climate change, land use change, and associated hydrologic changes. In recent years, the reliability of flood forecasts has increased due to the availability of meteorological and hydrological data and advancements in flood prediction science. However, there is limited effort to apply emerging advanced hydrological models for flood prediction in poorly gauged watersheds. The overall objective of this study is to demonstrate applicability of climate model products to generate reliable flood predictions for data-limited and flood-prone areas. In this study, the most recent high-resolution climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were evaluated to assess the impacts of projected climate change on the flood-prone areas of the Lake Tana basin, Ethiopia. The ensemble means of the top five CMIP6 climate model forcing data were used to calibrate and validate a free open-source, spatially distributed hydrological model known as Wflow_sbm. Model-independent multi-algorithm optimization and parameter estimation tool is implemented for calibration and validation of Wflow. In terms of simulating runoff and flood events, application of Wflow_sbm to the Lake Tana basin provided promising results. This study serves as a major step towards the development and implementation of climate model product-driven hydrological model to assess flooding damages of future climate projections within the poorly gauged Lake Tana basin.

Climate change is affecting the frequency and intensity of rainfall extreme events worldwide. Despite the growing global awareness, assessing and enhancing adaptive capacity has proven to be a major challenge. To assess the lack of coping capacity measures that a country cannot cope with water-related disasters through the government's effort and existing infrastructure with the impact of the hazard, exposure, and vulnerability for Kenya and Zambia. We combined two global sources, namely Index for Risk Management (INFORM) and the Emergency Events Database (EM-DAT), to assess the existing infrastructure, and governance with climate risk indicators. The study analyzes the linkages of governance indicators to evaluate the performance of resilience using these datasets for the period 2014-2022 for climate adaptation governance. A global cluster analysis using historical governance, hazard, and resilience information was performed to obtain three clusters. Countries such as Zambia and Kenya with similar emergent characteristics are grouped under single cluster. Further, countries such as Guatemala, Morocco, South Africa, Senegal are under developing economy cluster and Germany, Japan and United Kingdom are under the developed economy cluster. With the governance and natural disaster information as the driving variable and resilience as the dependent variable, five regression models- Bayesian, ridge regression, decision tree, k-nearest neighbors, and support vector machine are built. The best ML model - Bayesian ridge regression is used to model the resilience indicators- Communication, Access to health care and Physical infrastructure with the governance and natural disaster information For Zambia, climate resilience prediction up to 2035, under the Business-as-usual scenario, with governance worsening by 20%, it is observed that the communication and physical infrastructure are least affected, with the access to health care worsening by 10%. On the other hand, for Kenya, governance improved by 10% and all resilience related indicators have remained unchanged. For the emerging economies, the governance is significantly related to the health care indicator compared to the physical infrastructure and communication indicators. However, for the developing and developed economy, the governance is related to other resilience factors. Also, we should emphasize that these are preliminary findings, and the cause-and-effect relationships are yet to be further examined by detailed studies. In conclusion, we identified the lack of coping capacity and vulnerability are two important aspects relates to the ability of a country to cope with current and future disasters that the country’s government, as well as the building of resilient infrastructure, capacity and awareness raising among policymakers contributes to the reduction of disaster risk.

Climate-related hazards can lead to agricultural losses and affect local and wider food supply via food trade. This study estimates the potential for adverse effects of climate hazards on food supply across Indian States and Union Territories (hereafter ‘states’) by quantifying climate hazard risks. Risks were estimated using the most recent data available on hazard presence, vulnerability, and volume of per capita food supply that is exposed to hazards. Historical (2000–2020) climatological and geological data sourced from meteorological stations and satellite imagery were used to estimate the state-level presence of eight climate-related hazards (droughts, forest fires, floods, extreme rainfall, landslides, cyclones, extreme temperatures, sea level rise). For each state and hazard type, we distinguished between risk to food supply produced in the state and the risk to food supply imported from other states. The source of food supply was estimated from a supply and demand balance model for 30 major food items that uses government data from 2011–12. We found that climate hazard risks to food supply vary across states and by hazard type. The largest climate hazard risks to state food supply are in Bihar, Madhya Pradesh, and Assam, where the majority of risk is to locally produced supply. Food supply in each state is at risk to all eight climate hazards via food imports from other Indian states. For 14 states, the climate hazard risk is greater for imported food supply than for locally produced supply. Just five states contribute to more than half of the climate hazard risk in interstate food trade. The findings indicate that climate-related hazards in Indian states could have potentially adverse effects on national food supply, affecting both local production and interstate trade. For policy-makers, these climate hazard risks identify potential priorities for enhancing food system resilience to mitigate impacts on local and national food security.

Landslides are among the most dangerous natural hazards, especially in developing countries. In these areas, where rain gauge networks are scarce, satellite rainfall products can be a viable alternative for landslide prediction. To date, only a few studies have investigated the capability and effectiveness of these products in regional-scale landslide prediction. We performed a comparative study on the reliability of ground-based rainfall products and satellite rainfall products for landslide prediction in India. We used a catalog of 197 rainfall-induced landslides over the 13-year period between April 2007 and October 2019. We calculated frequentist rainfall thresholds using GPM, SM2RAIN-ASCAT satellite products, and their merging, at daily and hourly temporal resolution, and ground-based data from the rainfall network of the Indian Meteorological Department (IMD) at daily resolution. The results indicate that satellite rainfall products outperform ground-based observations in the prediction of landslides due to their improved spatial (0.1° vs. 0.25°/pixel) and temporal (hourly vs. daily) resolutions. The best performance is achieved through the merging of GPM and SM2RAIN-ASCAT. These results open up the possibility for using satellite rainfall products in landslide early warning systems, particularly in poorly gauged areas.

The frequency, intensity, and variability of natural hazards are increasing with climate change. Detailed sub-national information on disaster risks associated with individual and multi-hazards enables better spatial targeting of adaptation and mitigation measures. This paper reviews the global best practices of disaster risk mapping (DRM) to assess the nature and magnitude of disasters, and the vulnerability and risks at the sub-national level in South Asian countries. While some global DRMs focus on vulnerability, others assess risks. Most DRMs focus on national-level vulnerability and risks. Those which focus on the sub-national risks have a limited scope and different methodologies for evaluating risks, mainly in relation to the population. Climate change exposes not only people but also many infrastructures, assets and their impacts to disaster risk. For DRMs to be useful tools for sub-national planning, they require a coherent methodology and a high-resolution spatial focus. The vulnerability and risk assessments should focus on different aspects, including population, infrastructure, and assets in various economic sectors of agriculture, industry, and services.

The poor across the world is very vulnerable to floods and drought disasters and have a detrimental effect on the lives and livelihoods of the poor. Weather based index insurance is one of the ways...

The severity of the climate challenge requires a change in the climate response, from an incremental to a more far-reaching and radical transformative one. There is also a need to avoid maladaptation whereby responses to climate risk inadvertently reinforce vulnerability, exposure and risk for some sections of society. Innovative technological interventions are critical but enabling social, institutional and governance factors are the actual drivers of the transformative process. Bringing about this transformation requires inter- and transdisciplinary approaches, and the embracing of social equity. In this Perspective, we unpack what this means for agricultural research and, based on our collective experience, we map out a research agenda that weaves different research components into a holistic and transformative one. We do not offer best practice, but rather reflections on how agricultural research can more readily contribute to transformative adaptation, along with the personal and practical challenges of designing and implementing such an agenda.

Floods cause catastrophic destruction to life and livelihood in South Asia than any other parts of the world. This research assessed long term (2001 to 2015) flood risk at South Asia level using eight-day Moderate Resolution Imaging Spectroradiometer data and subsequently expanded this methodology to identify potential zones for piloting flood index insurance scheme in Bihar, India. Bihar was further assessed for sub-regional segmentation of its 37 districts into four flood risk zones based on k-means clustering and Getis-Ord Gi* analysis of multi-modal dataset consisting of demographics, meteorological, agricultural, flood characteristics and economic loss from floods. Satellite based risk assessment identified parts of Indus basin in Pakistan, Ganges basin in North India and majority of Bangladesh as flood hotspots. Site prioritization for flood index insurance in Bihar identified Madhubani, Darbhanga, Muzaffarpur and Sitamarhi districts as very high flood risk districts by both methods signifying large impact of any potential interventions implemented in these districts.

Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

This research report presents the first comprehensive framework of business models in terms of developing, marketing and scaling Index-based flood insurance (IBFI). The report evaluated ten case studies on agricultural insurance schemes (macro, meso and micro levels), globally, to develop public-private partnership business models for creating value (product development) and capturing value (product marketing). This report highlights four broad groups of interrelated factors that influence the uptake and scaling of agricultural insurance: (i) behavioral factors that influence farmers’ enthusiasm to invest in insurance; (ii) financial factors that stipulate governments’ willingness to provide financial support; (iii) legal and regulatory factors, which set ground rules for fair business and govern their adherence by stakeholders; and (iv) facilitating factors, including product design and development, business models, research and development, data availability, and awareness creation, which help ensure an efficient supply of insurance services. In summary, the report highlights the need for designing innovative IBFI and its potential benefits for uptake, and efforts for implementing IBFI as a potential risk transfer tool for comprehensive climate risk management among small-scale and marginal farmers.

Drought is the most complex climate-related disaster issue in South Asia and has affected 1.46 billion people with an economic loss of over 7 billion USD in the last 56 years. South Asia is challenged with water, food, and energy security due to growing populations, incomes, resource degradation, and vulnerability to climate change. Monitoring of drought and associated agricultural production deficits using meteorological and agricultural indices is an essential component for drought preparedness. Remote sensing offers near real-time monitoring of drought conditions and IWMI’s has implemented South Asia Drought Monitoring System (SADMS) in 2014 as an online platform for drought early warning and support in drought declaration. This chapter explores the use of composite drought indices implemented in Google Earth Engine (GEE) and evaluates the crop yield variability during drought years. The study provides a rapid overview of drought-prone conditions that could enhance the present capabilities of early warning systems and enable science based policies for addressing water security in the agriculture sector and develop a drought response plan between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts of drought events.

In the Gash Delta of Eastern Sudan, spate irrigation (flood-recession farming) contributes substantially to rural livelihoods by providing better yields than rainfed dryland farming. However, spate irrigation farmers are challenged by the unpredictability of flooding. In recent decades, the number of farmers practicing spate irrigation has decreased, due to varying rainfall intensity and frequency, insufficient infrastructure and farmers’ limited capacity to manage such variations. One solution that may help farmers face such challenges is for them to access real-time water-related information by using smart Information and Communication a Technology (ICT). This paper shows how integrating remote sensing, Geographical Information Systems (GIS), flood-forecasting models and communication platforms can, in near real time, alert smallholder farmers and relevant government departments about incoming floods, using the Gash basin of Sudan as an example. The Ministry of Water Resources of Sudan used the findings of this study to transform farmers’ responses to flood arrival from being ‘reactive’, to planning for the flood event. Intensive on-site and institutional efforts to build the capacity of farmers, farmer organizations, development departments and officers of the Ministry helped to develop the initiative from simply sending ‘emergency alerts’ to enabling stakeholders to visually see the flood event unfolding in the region and to plan accordingly for storing water, operating spate-irrigation systems and undertaking cropping activities. The research, initially conducted on a 60 × 60 km site, was later extended to the entire Gash basin. The paper outlines how to develop tools that can monitor plot-specific information from satellite measurements, and supply detailed and specific information on crops, rather than providing very general statements on crop growth. Farmers are able to use such tools to optimize their farm profits by providing water to their crops in the right place, at the right time and in the right quantity. Finally, the work demonstrates the high potential of combining technology, namely remote sensing data and simple a agro-meteorological model with limited parameters, for large-scale monitoring of spate irrigation systems and information sharing to advise farmers as to how to apply this information to their managerial decisions. Keywords: Spate irrigation, Remote sensing, Geographic information system, ICT, Crop biomass, Sudan

Agriculture and agri-food systems of the highly vulnerable Middle East and North Africa (MENA) region needs a radical transformation under a changing climate. Based on a two-year effort, initially we developed a mega hypothesis on how to achieve climate-smart agri-food transformation in the region. In the study, we hypothesized that “Climate-Smart Lifts” implemented in the enabling environments can rapidly facilitate agri-food transformation in the region. In order to gather the stakeholders’ perception about this, we organized a collective conversation among ~400 stakeholders that represent various scales and sectors within the agriculture sector in MENA. These “listening cum learning consultations” were conducted through a survey followed by a series of webinars. The webinar discussions were strategically guided based on our hypothesis, the responses from the surveys and the regional needs. These discussions provided a forum to bring-out the stakeholders’ perspective on what new knowledge, partnerships, instruments and projects were needed in the MENA. The deliberation focused on the opportunities of public–private partnerships focusing in all the four major agroecosystems in MENA (irrigated, rainfed, rangelands, and deserts). In result, we developed an effective framework for strategic resource mobilization in the region, keeping in view the strong regional needs for climate actions and the requisite long-term commitments for the SDGs implementation.

Capturing inundation extent by floods is indispensable for decision making for mitigating hazard. Satellite images have commonly been used for flood mapping, but there are limitations such as unavailability due to satellite’s orbital period or cloud cover. Additionally, it would also be beneficial for policy makers to figure out the impact of water management measures such as water storage options on flood mitigation and irrigation water strengthening. Utilization of flood inundation models would support providing information for these demands. In this study, the rainfall–runoff inundation (RRI) model was applied in a flood-prone basin in eastern Sri Lanka, and its applicability was discussed. The RRI model was capable of simulating discharge and inundation extent during flood events, although it should be noted that the model had been calibrated targeting only the flooding period. Satellite-observed rainfall data corrected with a scale factor were able to be used as the model input to simulate long-term trends in runoff just as well as when gauged rainfall data were applied. The calibrated model was also capable of evaluating flood mitigation effects of existing and proposed water storage options by simulating discharge with and without flood capture operations. By reproducing long-term inflow to the storage facilities using satellite rainfall data, it was possible to determine that water would reach the maximum level of the proposed storage facilities even during low-rainfall years.

The satellite radar altimetry datasets are now extensively used for continental water monitoring although it was primarily designed for oceanic surface and ice cap studies. Water level estimated from satellite altimetry can help to assess many hydrological parameters like river discharge and reservoir volume. These parameters can be employed for calibration and validation purposes of hydrological and hydrodynamic models, rating curve (stage-discharge relationship) generation, near real-time flood forecasting, reservoir operations and transboundary water related issues. Satellite with Argos and AltiKa (SARAL/AltiKa), a joint venture of Indian Space Research Organisation and Centre National d’Etudes Spatiales, is one of the pioneer missions in the history of satellite radar altimetry. It is first high-frequency (Ka-band, 35.75 GHz) mission with the highest sampling rate (40 Hz). The applications of radar altimetry to inland hydrology have been significantly increased in recent years in India. Major studies have been carried out in Ganga, Brahmaputra, Tapi and Godavari river basins with AltiKa data. AltiKa datasets have been successfully used for retrieving water level in reservoir and river, estimating river discharge and calculating reservoir sedimentation. Considering the stress on India’s fresh water resources and the importance of SARAL/AltiKa mission, this work was carried out. The present review paper may be helpful to understand the working principle of altimetry, altimetry waveform, waveform retracking methods, water stage, river discharge and changes in reservoir’s water storage calculation, and the status of altimetry applications to inland hydrology, specifically solicitation of SARAL/AltiKa in the Indian context.

Northern Bihar is one of the major flood prone region in India affecting thousands of human lives and livelihoods during the recurrent floods occurring due to the monsoonal rains. While it is impossible to prevent the occurrence of extreme flood events, disaster planning can help in mitigating its detrimental effects. Monitoring flood extent using satellite observations just after the flood disasters is a core component of rapid emergency response process, which enables the emergency rescue teams to prioritize their efforts in critical areas to save lives and protect health, in addition to providing near real-time flooding information to the decision makers and planners. The main objective of this study is to demonstrate the utility of less data intensive, but equally robust hydrodynamic models to develop flood extent maps in conjunction with freely available remote sensing imageries at different scales. MODIS TERRA satellite data was used to map flood extent from 2001 to 2016 for entire Bihar. Two hydraulic models namely FLDPLN and RRI applied for the Bagmathi basin to evaluate our objectives. Both these models are of varying complexity but generate flood extent patterns with minimum amount of input data. The proposed approach is suited for mapping flood extents to provide an input information in near real time (h) when there is no availability to detailed hydraulic models and satellite datasets. Flood inundation extents from FLDPLN and RRI models were validated with Landsat-7 and MODIS TERRA derived flood extents for model performance. The results show acceptable spatial agreement between model predicted and Landsat-7 observed flood extents, denoting the utility of these tools for flood mapping application in data scarce environments.

Rice is the most important food crop. With the largest rain-fed lowland area in the world, flooding is considered as the most important abiotic stress to rice production in India. With climate change, it is expected that the frequency and severity of the floods will increase over the years. These changes will have a severe impact on the rain-fed agriculture production and livelihoods of millions of farmers in the flood affected region. There are numerous flood risk adaptation and mitigation options available for rain-fed agriculture in India. Procuring, maintaining and distributing the newly developed submergence-tolerant rice variety called Swarna-Sub1 could play an important role in minimizing the effect of flood on rice production. This paper assesses the quantity and cost of a flood-tolerant rice seed variety- Swarna-Sub1, that would be required during the main cropping season of rice i.e., kharif at a district level for 17 major Indian states. The need for SS1 seeds for rice production was assessed by developing a geospatial framework using remote sensing to map the suitability of SS1, to help stakeholders prepare better in managing the flood risks. Results indicate that districts of Bihar, West Bengal and Uttar Pradesh will require the highest amount of SS1 seeds for flood adaptation strategies. The total estimated seed requirement for these 17 states would cost around 370 crores INR, less than 0.01 percent of Indian central government’s budget allocation for agriculture sector.

A merging procedure is applied to five daily rainfall estimates achieved via SM2RAIN applied to the soil moisture products obtained by the Advanced SCATterometer, the Advanced Microwave Scanning Radiometer 2, the Soil Moisture Active and Passive mission, the Soil Moisture and Ocean Salinity mission and backscattering observations of RapidScat. The precipitation estimates are evaluated against dense ground networks in the period ranging from April to December, 2015, in India and in Italy, at 0.25°/daily spatial/temporal resolution. The merged product derived by combining the different SM2RAIN rainfall products shows better results in term of statistical and categorical metrics with respect to the use of the single products. A good agreement with reference to ground observations is obtained, with median correlations equal to 0.65 and 0.77 in India and in Italy, respectively. The merged dataset is found to slightly outperform those of the IMERG product of the Global Precipitation Measurement mission underlying the large potential of the proposed approach.

Flooding is one of the most devastating natural hazards in the world and its forecast is essential in flood risk reduction and disaster response decision. The lack of adequate monitoring networks, especially in developing countries prevents near real-time flood prediction that could help to reduce the loss of lives and economic damages. In the last few years, increasing availability of multi-satellite sensors induced to develop new techniques for retrieving river discharge and especially in supporting discharge nowcasting and forecasting activities. Recently, the potential of radar altimetry to estimate water levels and discharge in ungauged river sites with good accuracy has been demonstrated. However, the considerable benefit derived from this technique is attenuated by the low revisit time of the satellite (10 or 35 days, depending on the satellite mission) causing delays on the predicting operations. For this reason, sensors with a higher temporal resolution such as the MODerate resolution Imaging Spectroradiometer (MODIS), working in visible/Infra-Red bands, can support flood forecasting. In this study, we performed the forecast of river discharge by using MODIS and we compared it with the radar altimetry and in-situ data along the Niger-Benue River in Nigeria to develop an operational flood forecasting scheme that could help in rapid emergency response and decision making processes. In the first step, four MODIS products (daily and, 8-day from the TERRA and AQUA satellites) at two gauged sites were used for discharge estimation. Secondly, the capability of remote sensing sensors to forecast discharge a few days (~ 4 days) in advance at a downstream section using MODIS is analyzed and also compared with the one obtained by the use of radar altimetry by ENVISAT and Jason-2. The results confirmed the capability of the MODIS data to estimate river discharge with performance indices > 0.97 and 0.95 in terms of coefficient of correlation and Nash Sutcliffe efficiency. In particular, RMSE does not exceed 1300 m 3 /s and the fractional RMSE ranges between 0.15 and 0.23. For the forecasting exercise, both altimetry and MODIS provide satisfactory results with positive coefficient of persistence considering 4 days of lead time (> 0.34). Although altimetry was found to be more accurate in the forecasting of river discharge ( RMSE ~ 350 m 3 /s), the much higher temporal resolution of MODIS guarantees a continuity that is more suitable to address operational activities.

Moderate Resolution Imaging Spectro-radiometer (MODIS) time-series Normalized Differential Vegetation Index (NDVI) products are regularly used for vegetation monitoring missions and climate change ...