The Open Geospatial Consortium’s Disaster Pilot 2021 focused on turning earth observation and reporting data into decision ready indicators (DRI) for disaster response and management.  HSR.health as a Pilot participant  developed the recipe for, and produced a Medical Supply Needs Index that indicates what medical supplies, such as Personal Protective Equipment, are needed to respond to COVID-19 cases throughout a population. Medical Supply Needs Indices were calculated for areas within the Pilot focus regions and shared via a dashboard-style application. HSR.health and collaborators then set up an integrated demonstration showing the Medical Supply Needs Index updating in real-time as a result of data on the occurrence and impacts of multiple coincident natural disasters such as flooding, landslides, and pandemic spread. HSR.health also carried out work within the Pilot to apply and evaluate the draft Health Spatial Data Infrastructure (HSDI) model developed in the pre-Pilot OGC Health Spatial Data Infrastructure Concept Development Study. This included research into the availability of pandemic-related health related data in the US and in Peru, as well as investigation of the spatiotemporal granularity or resolution of observation data best suited to support indicators for community-level public health interventions.

How to cite: Churchyard, P., Gupta, A., and Lieberman, J.: Pandemic Medical Supply Needs with a Coincident Natural Disaster and an Analysis of COVID-19 Data Availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10221, https://doi.org/10.5194/egusphere-egu22-10221, 2022.

Italy is the Europe’s leading rice producer, with over half of total European production. The main rice area is in the north-western part of the country (Lombardy and Piedmont regions). In this area, irrigation of rice has been traditionally carried out by flooding; the introduction of alternative water-saving irrigation strategies could reduce water consumption, but their overall environmental and economic sustainability, as well as their social acceptability, should be investigated.

An experimental platform was set up in the core of the Italian rice district (Lomellina, PV) to compare different rice irrigation management options: wet seeding and traditional flooding (WFL), dry seeding and delayed flooding (DFL), wet seeding and alternated wetting and drying (AWD). Six plots of about 20 m x 80 m each were set-up, with two replicates for each irrigation option. One out of two replicates for each option was instrumented with: water inflow and outflow meters, set of piezometers, set of tensiometers and water tubes for the irrigation management in the AWD plots. Proper agronomic practices were adopted for the three management options. Periodic measurements of crop biometric parameters (LAI, crop height, crop rooting depth) were performed and rice grain yields and quality (As and Cd in the grain) were determined. Data measured in the field, together with those provided by the farmer, concerning the agronomic inputs and the economic costs incurred for the three irrigation options, were used to assess their economic and environmental sustainability through a set of quantitative indicators. Finally, through interviews with rice growers of the area, barriers to the adoption of the AWD technique were assessed and ways of overcoming them identified. In order to support water management decisions and policies, data collected at the farm level are extrapolated to the irrigation district level through a semi-distributed agro-hydrological model, used to compare the overall irrigation efficiency achieved implementing AWD when compared to WFL.

How to cite: Gharsallah, O., Mayer, A., Romani, M., Ricciardelli, A., Caleca, S., Rienzner, M., Corsi, S., Ottaiano, G., Gilardi, G., and Facchi, A.: Environmental, economic and social sustainability of Alternate Wetting and Drying rice irrigation in Northern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7460, https://doi.org/10.5194/egusphere-egu22-7460, 2022.

The extreme events increasingly present in the Pacific (El Nino / La Nina phenomena) have significant consequences on island territories. The effect of climate change and drought episodes is therefore a central concern in many Pacific islands like Vanuatu, Wallis-and-Futuna, French Polynesia, etc. The intense drought events have undeniable impacts on biodiversity, agricultural crops and water resource, as was the case in 2019 for New Caledonia. In particular, projections in New Caledonia count on a possible increase in temperatures of 3°C and a water deficit of 20% in 2100 with longer and more intense drought episodes and an even greater west coast/east coast disparity (Dutheil, 2018). To date, the monitoring and anticipation of these drought episodes is done via meteorological measurements providing information on the rainfall deficit and not on the water stress of plants. In addition, the data are only available on a few measurement points and are not continuous over the territories.

In order to meet this need, a tool for monitoring environmental and agricultural drought using satellite images and meteorological data is being developed and validated in New Caledonia: Earth Observations for Drought Monitoring (EO4DM) project. This project is carried out in collaboration with Météo-France NC as a technical partner and the local Rural Agency as end user, and aims to provide a tool to help decision-making to institutions and management assistance for farmers. This solution will provide data constituting a singularly important source of information whose valuations and contributions can be multiple: agriculture, resource management (water), security (monitoring of risks linked to floods, fires), environment, etc.

To do so, various surface indices reflecting the state of the vegetation and certain soil properties such as humidity and temperature were estimated from different satellite sensors (MODIS, Sentinel-2, Landsat-8, ASCAT) in order to address different space scales from the field to regional scale. These indices were normalized over a relatively long period, allowing access to drought indicators: VHI (Vegetation Health Index; Kogan et al., 1997), VAI (Vegetation Anomaly Index; Amri et al., 2011), MAI (Moisture Anomaly Index; Amri et al., 2012) or TAI (Temperature Anomaly Index; Le Page and Zribi, 2019). Combined with in-situ meteorological products like SPI (Standardized Precipitation Index; McKee et al., 1993) and SPEI (Standardized Precipitation Evapotranspiration Index; Vicente-Serrano et al., 2010), these indicators assess the intensity of drought episodes and estimate their severity over the entire territory.

How to cite: Neuhauser, M., Tilak, T., Point-Dumont, C., and Peltier, A.: Monitoring of agricultural drought from remote sensing products and in-situ meteorological data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3301, https://doi.org/10.5194/egusphere-egu22-3301, 2022.

Plain areas of Northern Italy are characterized by a strong agricultural and zootechnical vocation. In the Lombardy region, the total utilized agricultural area (UAA) is about 700,000 ha, 72% of which is irrigated. Globally, about one half of the UAA is cropped with maize, but in some provinces this crop reaches almost the totality of the UAA. Maize is typically irrigated by border irrigation; however, in the context of the climate change and of the increased competition for the use of water in the plain, it is crucial to optimize the use of this resource.

This study is aimed at demonstrating the applicability of Precision Irrigation approaches in a large farm located in the core of the maize basin of the Lombardy plain (La Canova farm, BS, Italy; http://lacanovasrl.it/). In the farm, irrigation is provided by center pivots and linear irrigation systems. Although sprinkler irrigation can reduce the applied irrigation volumes compared to border irrigation, at present, a uniform irrigation rate is provided at fixed time intervals without accounting for spatial heterogeneity of soil or crop development.

During the agricultural season 2021, in a 15 hectares surface cropped with maize under a center pivot the irrigation was applied following a variable-rate approach. The soil variability was investigated using an Electromagnetic induction (EMI) sensor; through the application of cluster analysis techniques to the EMI survey, four types of soils were detected and characterized through a traditional soil sampling. According to soil variability and pivot geometry, four management zones (MZ) were identified: two MZs were characterized prevalently by coarse soils while the other two by medium-fine soils. In one ‘coarse’ MZ and one ‘fine’ MZ the irrigation was managed with the support of soil probes installed at two depth, and by a physically based agro-hydrological model (SWAP, https://www.swap.alterra.nl/) fed with weather forecasts at 7 days (https://www.abacofarmer.com/). A MATLAB code was developed to run the whole modelling system. Irrigation in the other two MZs was applied by the farmer according to the farm’s typical management (about 25-30 mm every four days). In the MZs managed with Variable Rate irrigation, the model was used to identify the optimal water depth to be applied at each irrigation event, depending on the soil water balance computed for the following 5 days; in doing this, a 4-day turn and a minimum irrigation depth of 18-25 mm (as a function of the time of the season) were respected, since they were constraints imposed by the farmer. Despite the constraints, compared to the reference MZs, the approach adopted led to a water saving of about 20 and 25% for the ‘coarse’ and ‘fine’ MZs, respectively, without a loss of yield. In the next step, the approach adopted will be used to estimate the water and energy saving achievable at the farm scale.

How to cite: Facchi, A., Mayer, A., Ortuani, B., and Crema, A.: Can an agro-hydrological model improve the irrigation management of maize under a center pivot?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8093, https://doi.org/10.5194/egusphere-egu22-8093, 2022.

The world needs 70% more food by 2050, increasing the pressure on the available water resources. With the climate change threat approaching, the water stress will further be exacerbated that would adversely affect food security. In countries like India, with extensive cultivation of staple crops like paddy, there has been a rapid increase in the total water consumption. At the same time, cultivation of crops such as pulses and millets has not been sufficient to satisfy the nutritional requirements of India’s population. With the increased likelihood of droughts and floods due to the advent of climate change, it becomes imperative to achieve water, food, and nutritional security into the future. This study attempts to optimise cropping patterns to minimise future water requirement, while satisfying the nutritional and caloric requirements of future generations. We perform the analysis for the southern Indian state of Andhra Pradesh, where agriculture depends predominantly on irrigation. To achieve this objective of optimization, we collected bias-corrected climate datasets from three General Circulation Models (BCC-CSM2-MR, INM-CM5-0, MPI-ESM1-2 HR) that include future rainfall and temperature information from 2021 to 2050. Further, we collected crop-wise farm-level data of five major crops in the state - paddy, sugarcane, groundnut, sorghum, and red gram. The irrigation water requirement (IWR) of the selected crops was estimated using FAO’s CROPWAT model under two different scenarios - SSP 245, SSP 585. Further, we developed an optimization model to obtain the optimal cropping pattern that minimises water consumption. Future food requirements in terms of protein and calorie demands and arable land available for cultivation were used as constraints to perform this optimization. Preliminary results indicate that shifting from water-intensive crops like sugarcane to relatively more nutritious crops like red gram and sorghum has the potential to significantly reduce water consumption, while also enhancing the nutritional security of the region. Interestingly, the optimization results indicated that the southern part of the study region required more interventions in terms of crop diversification as compared to the northern part. Such insights could help decision makers to devise holistic policies, enhancing the water-food security under different climate change scenarios. Further, this research could be extended to domains such as economics, ecology, and energy to achieve overall sustainability in the agricultural sector.

How to cite: Pasula, S. R., Gudem, S. S., Gaddam, S. J., and Sampath, P. V.: Optimizing cropping patterns under the influence of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11145, https://doi.org/10.5194/egusphere-egu22-11145, 2022.

Agriculture plays a pivotal role in supporting the socioeconomic situation of millions of farmers in India, which is increasingly coming under threat due to climate change. In particular, the future changes in rainfall patterns has the potential to directly affect the irrigation water demands, thereby impacting water consumption, agricultural productivity, and influencing food security. For instance, the optimal sowing dates for crops may change according to the altered rainfall patterns. With this motivation, we studied the impacts of shifts in sowing periods in order to identify the optimal sowing dates for a particular crop. First, we collected daily temperature and rainfall data for India at a resolution of 0.25^o from different GCM models (EC-Earth 3 and EC-Earth 3 veg) under different SSP scenarios (SSP 126, SSP 245, SSP370, SSP585). Also, region-wise agricultural data such as crop acreage and sowing dates were collected for seven major crops - paddy, wheat, maize, groundnut, sugarcane, red gram, black gram, and soybean. Subsequently, we estimated the reference evapotranspiration using the modified Penman-Monteith method. The estimated reference evapotranspiration and rainfall data were incorporated into FAO’s CROPWAT model to calculate the irrigation water requirements (IWR) of the selected crops. The optimal IWR for each crop was selected by varying the sowing dates at fifteen-day intervals across the year (twenty-four dates for the year). Preliminary results indicate that there is considerable scope for water savings by shifting the sowing dates of staple crops to account for climate change impacts. These strategies may become vital for policymakers in the coming decades to reduce the stresses on water without endangering food security. Indeed, such strategies require the cooperation of various stakeholders for better implementation at multiple scales.

How to cite: Sharma, A. N., Gaddam, S. J., and Sampath, P. V.: Optimal sowing dates for major crops in India under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11068, https://doi.org/10.5194/egusphere-egu22-11068, 2022.

Due to the combined effect of human-driven depletion and anthropogenic climate change, groundwater storage is decreasing across the globe. This trend will potentially have an adverse impact on future human socio-economic development, by increasing the frequency and duration of both hydrological and socio-economic droughts as well as generating inter-sectoral competition for limited water resources.

Large-scale modelling studies on changes in groundwater availability can be separated into two big families. First, hydrological impact models actively consider water usage across sectors but ignore land-atmosphere interactions by design. Second, Earth System Models consider two-way interactions between climate and groundwater resources, but almost never consider the anthropogenic water resource depletion, except in some cases for irrigation.

The goal of this study is to connect the expertise of these two families by implementing domestic and industrial water usage in the Community Earth System Model version 2. Using land-atmosphere coupled simulations, we will revisit previously computed trends in future groundwater availability by simultaneously accounting for climate change and anthropogenic water resource usage.

How to cite: Taranu, I. S. and Thiery, W.: Implementing sectoral water usage in the Community Earth System Model for projecting future water resource availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-596, https://doi.org/10.5194/egusphere-egu22-596, 2022.

Agriculture in West Africa is highly dependent on rainfall during the rainy seasons. Therefore, modifications in rainy season characteristics due to recent and future climate change have a direct impact on crop yields and production in the region. Consequently, stakeholders and decision-makers need reliable regional climate change information on rainy seasons in order to develop appropriate adaptation measures.

Regional Climate Models (RCMs) can provide information on climate change at high temporal and spatial resolution through dynamic downscaling of climate projections generated by Earth System Models (ESMs). In order to assess the performance of RCMs in simulating rainy seasons and their characteristics such as onset and cessation, length and total sum of rainfall, a thorough evaluation of RCMs is required.

The current study evaluates the performance of three different RCMs (REMO2015, RegCM4-7 and CCLM5-0-15) in simulating rainy seasons in West Africa using gridded observational data sets. For the assessment, we will use the ERA-INTERIM driven simulations of the RCMs from the Coordinated Output for Regional Evaluations (CORE) embedded in the WCRP Coordinated Regional Climate Downscaling Experiment (CORDEX) for Africa with a spatial resolution of about 25 km.

How to cite: Weber, T., Ajayi, V. O., Gbode, I. E., Abel, D., Ziegler, K., Paeth, H., and Traore, S. B.: Performance of regional climate models in simulating rainy seasons in West Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5834, https://doi.org/10.5194/egusphere-egu22-5834, 2022.

Quantification of the Water Losses (WL) components in Water Distribution Networks (WDNs) is a vital task towards their reduction. However, current WL estimation methods rely on semi-empirical approaches with high uncertainty levels, which usually lead to inaccurate estimates of the lost volume. Here, we compare the probabilistic Minimum Night Flow (MNF) estimation method introduced by Serafeim et al. (2021) to the Water Balance components analysis, introduced by the International Water Association (IWA). The strong point of the Serafeim et al. (2021) approach is that it uses statistical metrics to filter out noise effects in the flow timeseries used for MNF estimation, leading to more accurate estimation of the low flows during night hours. The effectiveness of the applied methods is tested via a large-scale, real world application to the 4 largest Pressure Management Areas (PMAs) of the WDN of the city of Patras, the third largest city in Greece (see Serafeim at al., 2022). Although methodologically different, the two approaches lead to very similar results, substantiating the robustness of the Serafeim at al. (2021) approach which allows for reliable confidence interval estimation of the observed Minimum Night Flows, making it particularly suited for engineering applications.

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1162).

References

Serafeim, A.V., Kokosalakis, G., Deidda, R., Karathanasi I. and Langousis A (2021) Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece, Stoch. Environ. Res. Risk. Assess., https://doi.org/10.1007/s00477-021-02042-9

Serafeim, A.V.; Kokosalakis, G.; Deidda, R.; Karathanasi, I.; Langousis, A. (2022) Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece, Water, 14, 98, https://doi.org/10.3390/w14010098

How to cite: Langousis, A., Serafeim, A., Kokosalakis, G., Deidda, R., and Karathanasi, I.: Probabilistic water losses estimation in water distribution networks and comparison with the top down - water balance approach: A large-scale application to the city center of Patras in western Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1974, https://doi.org/10.5194/egusphere-egu22-1974, 2022.

Abstract

Quantification of the leakage volume in pressure management areas (PMAs) is a vital task for water agencies’ financial viability. However, currently, there is no rigorous approach for their parametric modeling on the basis of networks’ specific characteristics and inlet/operating pressures. To bridge this gap, the current work focuses on the development of a probabilistic framework for minimum night flow (MNF) estimation in water distribution networks that: 1) parametrizes the MNF as a function of the network’s specific characteristics, and 2) parametrically describes water losses in individual PMAs as a function of the inlet/operating pressures. MNF estimates are obtained using the robust, non-parametric, probabilistic minimum night flow (MNF) estimation methodology developed and validated by Serafeim et al. (2021 and 2022), which allows for confidence interval estimation of the observed MNFs. The effectiveness of the developed model is tested in a large-scale real world application to the water distribution network of the city of Patras in western Greece, which serves approximately 200,000 consumers with more than 700 km of pipeline. The developed framework is validated through flow-pressure tests conducted by the Municipal Enterprise of Water Supply and Sewerage of the City of Patras to 78 PMAs of the network, indicating that the developed framework can be effectively used to improve water loss estimation and flow-pressure management in a morphologically and operationally diverse set of PMAs.

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1162).

References

Serafeim, A.V., Kokosalakis, G., Deidda, R., Karathanasi I. and Langousis A (2021) Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece, Stoch. Environ. Res. Risk. Assess., https://doi.org/10.1007/s00477-021-02042-9

Serafeim, A.V.; Kokosalakis, G.; Deidda, R.; Karathanasi, I.; Langousis, A. (2022) Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece, Water, 14, 98, https://doi.org/10.3390/w14010098

How to cite: Serafeim, A. V., Kokosalakis, G., Deidda, R., Karathanasi, I., and Langousis, A.: Parametric model for probabilistic estimation of water losses in water distribution networks: A large scale real world application to the city of Patras in western Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2855, https://doi.org/10.5194/egusphere-egu22-2855, 2022.

In the Anthropocene, human action and globalization are closely linked to the deterioration of natural habitats and water resources. Invasive aquatic weeds have been recognized as a major problem in watersheds worldwide due to their environmental impacts. This study focuses on the management of the Las Curias Reservoir in Cupey Puerto Rico in the Río Piedras watershed since the arrival of Salvinia molesta after Hurricane María in 2017.
Aquatic weed control consists of three methods: biological, mechanical, and chemical. Since December 2019, with the help of federal and local agencies, the University of Puerto Rico in Rio Piedras and a community-driven initiative led to the introduction of the Cyrtobagous salviniae in Las Curias Reservoir.  This insect is considered an effective biological control agent for S.  molesta.  Simultaneously, community members initiated a mechanical removal campaign using an aquatic harvester. Monthly sampling was conducted to measure physicochemical, biochemical, and biophysical variables in the reservoir in response to the reduction of S. molesta cover. In addition, monthly drone flights were conducted to create orthomosaic maps of the plant coverage over the water surface, as part of the monitoring of the ecosystem health and characterization. Probably the propagation of S. molesta occurred due to eutrophication after an increase in nutrient-rich sewage discharges from septic tanks and faulty sewage pump stations affected by power outages after Hurricane Maria. By 2019, the reservoir was completely covered with S. molesta. It is not until August 2020 that we noticed considerable changes in the reduction of plant density. Upon the reduction of S. molesta coverage, we found increases in the mean of water temperature (+3 Cِ°), dissolved oxygen (+1.4 mg/L), pH (+0.5) specific conductance (+118.3 µS/cm) and in light penetration (+255.6 μmo/m^²/s).  The water stored in Las Curias could become an invaluable source of raw water for public supply during future droughts, especially in the densely populated San Juan Metropolitan Area, where Las Curias is located. Therefore, its restoration is socially relevant and justifiable. 

How to cite: García López, X., Ortiz Zayas, J., Díaz, R., Castro Jiménez, A., and Abdelrahman López, M.: Limnological responses to active management of the invasive aquatic fern Salvinia molesta in Las Curias Reservoir, San Juan, Puerto Rico., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8392, https://doi.org/10.5194/egusphere-egu22-8392, 2022.