Next Article in Journal
Incorporating Weather Attribution to Future Water Budget Projections
Previous Article in Journal
Assessment of Potential Potable Water Reserves in Islamabad, Pakistan Using Vertical Electrical Sounding Technique
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Hydrology
Volume 10
Issue 12
10.3390/hydrology10120218
hydrology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment

by
Vassilis Litskas
1,*,
Paraskevi Vourlioti
2,
Theano Mamouka
2,
Stylianos Kotsopoulos
2 and
Charalampos Paraskevas
2
1
VL Sustainability Metrics LTD, Valtetsiou 4, 2416 Lefkosia, Cyprus
2
Neuralio A.I. P.C., 12th km Thessalonikis—N. Moudanion, 57001 Thermi, Greece
*
Author to whom correspondence should be addressed.
Hydrology 2023, 10(12), 218; https://doi.org/10.3390/hydrology10120218
Submission received: 31 October 2023 / Revised: 14 November 2023 / Accepted: 18 November 2023 / Published: 21 November 2023
(This article belongs to the Special Issue Hydrology and Water Management in Agricultural Landscapes)
Browse Figures
Versions Notes

Abstract

:
Potato cultivation is a significant agricultural activity worldwide. As a staple food in many countries, potatoes provide essential nutrients and are a significant source of income for farmers. This paper investigates current and future net irrigation requirements for potatoes in combination with LCA (life cycle assessment) to assess the GHG emissions due to irrigation. Potato cultivation in Cyprus is used as a model for insular environments, which are often neglected from such studies. The models suggest that an increase in net irrigation requirements is expected but there is a large variability among locations and between years. The increase in rainfall that some of the models predict does not mean that this water will be effectively stored in the soil (and reduce irrigation requirements). The GHG emissions due to potato irrigation in Cyprus are estimated to be 1369.41 tons CO2eq and expected to decrease after 2030 by 35%, mainly due to changes in the electricity mix (from heavy fuel to renewable energy). Further research including other important (irrigated) crops in the island will support the development of strategies towards sustainable resources management under climate change.

1. Introduction

Potatoes are a crucial staple food for millions of people worldwide. They are a rich source of carbohydrates, vitamins, and minerals [1]. Potato cultivation helps ensure food security and provides a reliable food source, especially in regions where other staple crops may not thrive as well. Potatoes can be grown in a wide range of climates and altitudes, from temperate to subtropical regions. This adaptability makes them an essential crop for diverse geographical areas and communities with varying agroclimatic conditions. As part of crop rotation systems, potatoes can help improve soil health and prevent the build-up of pests and diseases. Their inclusion in agricultural systems contributes to biodiversity and sustainable farming practices [2,3]. Potatoes have various industrial applications, such as in starch production, animal feed, and the manufacturing of alcoholic beverages [4,5]. Potatoes are a widely traded commodity in the global market. Countries with surplus potato production can export to regions with higher demand, boosting international trade and economic relations. Potato production can vary from year to year due to factors such as weather conditions, market demand, and changes in agricultural practices. Important countries for potato production are China, India, Russia, Ukraine, the US, and Germany [6]. Other countries such as Bangladesh, Canada, Poland, and the UK make a significant contribution to potato production. Potato cultivation serves as a focus for agricultural research and innovation, leading to improved crop varieties [7], better farming practices [2], and enhanced pest and disease management techniques [8] that benefit global agriculture.
With increasing concerns about climate change and its impact on water security and agriculture [9,10], potatoes’ resilience to extreme weather conditions becomes more relevant. They can serve as a valuable crop option in regions facing changing weather patterns and uncertain growing conditions [11,12]. Rising temperatures can affect the potato’s growth and development. Prolonged heat stress can reduce tuber formation and alter the crop’s phenology (timing of growth stages) [13]. On the other hand, more frequent and severe frost events can damage potato plants, especially during early growth stages [14]. Climate change may influence the distribution and prevalence of pests and diseases that affect potato crops [15,16]. Warmer temperatures can lead to the proliferation of certain pests, while altered weather patterns may create conditions conducive to the spread of diseases. Climate change can cause shifts in growing seasons, affecting planting, and harvesting times as well as productivity and carbon storage [17]. This may disrupt established agricultural practices and require adjustments to planting schedules [18]. Climate change can lead to more frequent and intense extreme weather events, such as storms, floods, and heatwaves affecting numerous important crops [19,20]. These events can cause physical damage to crops, disrupt harvesting, and lead to yield losses [18,21]. Climate change can impact the biodiversity of wild potato species, which serve as valuable genetic resources for breeding programs. Loss of genetic diversity could reduce the potato’s ability to adapt to changing environmental conditions and threaten overall crop resilience [22,23]. Changes in temperature and precipitation patterns can affect soil health and structure, potentially impacting nutrient availability and water retention, which are crucial for potato growth [24]. As certain regions become less suitable for potato cultivation due to changing climatic conditions, farmers may need to shift cultivation to more suitable areas. This can have economic and social implications for communities relying on potato farming. Changes in precipitation patterns can lead to water shortages or irregular water availability or excess water, affecting potato cultivation. Drought conditions can result in reduced yields and smaller tubers [25]. Climate change may affect the quality and nutritional content of potatoes, potentially altering starch content, mineral levels, and vitamin content [25]. Climate change impacts on potatoes can necessitate adaptation strategies such as developing climate-resilient potato varieties, adopting precision agriculture techniques, and improving water management practices [26,27]. Additionally, mitigating climate change through sustainable agricultural practices can help reduce its negative effects on potato crops. It is crucial for agricultural stakeholders, policymakers, and researchers to address these challenges and implement measures to ensure the resilience and sustainability of potato farming in the face of climate change.
Potato irrigation under climate change becomes a critical aspect of ensuring the crop’s productivity and sustainability [12]. As climate change brings about shifts in precipitation patterns and increases the frequency of extreme weather events, farmers need to adapt their irrigation practices to meet the changing water requirements of the crop. Some of the proposed practices are (1) water management and irrigation scheduling (based on irrigation water demands), (2) drought-resistant varieties, (3) mulching and organic matter addition to the soil, and (4) water conservation (e.g., crop rotation, cover cropping, reduced tillage).
Potatoes are one of the major agricultural crops in Cyprus [28]. Potato cultivation generates income for farmers and supports employment throughout the value chain, including harvesting, processing, and distribution. Cyprus exports a considerable portion of its potato production, contributing to the country’s trade balance. High-quality Cypriot potatoes are sought after in international markets, particularly in the UK and in neighboring countries and regions.
Given the importance of potato crops, the aim of this research work is to estimate irrigation water demands and the environmental impacts of irrigation, under the current and future climate. The objectives were (1) to select important areas for potato cultivation in the island, (2) to obtain current and future climatic data for these areas, (3) to estimate current and future irrigation water needs, and (4) to perform a life cycle assessment (LCA) to estimate the environmental impact of irrigation.

2. Materials and Methods

2.1. Study Area and Workflow

Potato field distribution is presented in Figure 1. In Cyprus, the main areas for potato cultivation are (1) Pafos, (2) Lefkosia, (3) Ammochostos, and (4) Limassol. Soil texture in the potato fields is clay loam [29]. Annual rainfall (1981–2010) in these areas ranges from 400–700 mm [11]. The climate is semi-arid Mediterranean with mild winters and long dry summers.
In Figure 2, the workflow is presented and each of the process steps is described in Section 2.2,Section 2.3 and Section 2.4.

2.2. Climatic Data

In this study, two primary sources of climate data are utilized: reanalysis data from ERA5-Land and climate projection data from the Copernicus Data Store (CDS). These data sources are integral to the investigation of historical climate trends and potential future climate scenarios.
The reanalysis data, represented by ERA5-Land, constitute the fifth iteration of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate and weather reanalysis. It offers a robust representation of past climate conditions over the last eight decades, with data availability dating back to 1940. ERA5-Land’s reliability is attributed to its data assimilation methodology, which combines model data and global observations using physical principles. This process optimally merges past forecasts with fresh observations every 12 h, resulting in improved estimates of atmospheric conditions. Unlike real-time weather forecasts, reanalysis operates at a lower resolution, allowing for the collection of extensive observations and the integration of enhanced versions of original observations over time, thereby enhancing data quality.
On the other hand, climate projection data are derived from the Coordinated Regional Climate Downscaling Experiment (CORDEX) via the Copernicus Data Store. CORDEX experiments involve simulations conducted by Regional Climate Models (RCMs), encompassing various future socioeconomic scenarios (forcings), different combinations of Global Climate Models (GCMs) and RCMs, and multiple ensemble members for the same GCM-RCM combinations. This ensemble-based approach enables researchers to explore uncertainties inherent in future climate change, including those arising from socioeconomic development scenarios, model limitations, and the natural variability of the climate system.
For this study, the focus lies on the ensemble of CORDEX climate projection experiments based on the Representative Concentration Pathways (RCPs) forcing scenarios, including RCPs 2.6, 4.5, and 8.5. These scenarios represent different trajectories of future climate forcing, with boundary conditions determined by the GCMs. RCP 2.6 assumes very low future emissions, RCP 4.5 assumes moderate emissions, and RCP 8.5 represents a high-emission future. The use of these scenarios allows for an in-depth examination of future climate change uncertainties, encompassing the nature of future climate forcing, the climate system’s response to these changes, and the inherent variability within the climate system. A detailed description of the climatic data used in this work is presented in Appendix A.

2.3. Potato Water Needs

This research employs AquaCrop, a widely recognized and scientifically validated tool for computing net irrigation requirements. The net irrigation requirement is the net amount of water that must be applied by irrigation to supplement stored soil water and precipitation and supply the water required for the full yield of an irrigated crop. AquaCrop provides a robust framework for estimating the water needs of potatoes under varying climatic conditions. AquaCrop [30], created by the Food and Agriculture Organization (FAO) of the United Nations, stands as a well-recognized crop modeling software, with its primary emphasis placed on assessing water productivity across various environmental settings. This model operates by striking a balance between the availability of water and its demand and is particularly designed to excel in regions where water resources are constrained. The simulations were conducted on a daily timestep, commencing on the first day of the year (January 1st) and spanning 140 days. This timeframe aligns with the typical potato cultivation period in the Cyprus region. The input data utilized encompassed climatic data, daily evapotranspiration, rainfall, root depth (1 m), and the hydraulic properties specific to each location. The hydraulic properties for each location were determined using pedotransfer functions [31], employing soil texture data sourced from the SoilGrids database [29,32]. The initial soil water conditions for all simulations were set to field capacity. The results, encompassing 848 cultivation scenarios spanning from 2018 to 2045 across four distinct locations, using a combination of three meteorological models and three diverse scenarios, are depicted in the figures in the Results section.

2.4. GHG Emissions Due to Irrigation

Life cycle assessment was performed to estimate the greenhouse gas emissions due to potato irrigation. For this purpose, the Open LCA software was employed [(v. 1.10.3; GreenDelta; Berlin, Germany)] to create the models for irrigation. The models cover drip and sprinkler irrigation and were based on existing models (created for Spain), in which modifications were made to capture the electricity production system in Cyprus. Briefly, we consider that all the equipment used (e.g., PVC and PE tubes, sprinklers) is similar, and what is changing is the method of electricity production in the two EU countries. The Agribalyse database [33] was used to build models for drip and sprinkler electricity-driven irrigation. The EF database (JRC) [34] was used to obtain the emission factors for electricity production in Spain and Cyprus. The system boundary for the irrigation related LCA was cradle to grave, for the delivery of 1 m3 of water to the crop (Figure S1, Supplementary Material). The ReCiPe 2016 Midpoint (H) method, Hierarchist version was employed [35]. The environmental impact of global warming was selected for this research. Accordingly, the net irrigation water demands (mm) calculated as presented in paragraph 2.3 were used as well as the area of the crop, obtained per location using data from CAPO. In this case, we were able to estimate the total irrigation water use in the four areas, in m3 per year. Then, we used the emissions (kg CO2 per m3) to calculate the total emissions per location, due to irrigation. For future emissions due to irrigation, we assume that the same potato area will be used per area of interest (Figure 1). We used the future irrigation water needs (RCPs 2.6, 4.5, and 8.5) for the period 2030–2034 and a lower emission factor for electricity production in Cyprus after the new National Plan for Energy and Climate (Dept of Environment; personal communication). Currently, HFO (heavy fuel oil) is used for electricity production in Cyprus. The period 2030–2034 was selected as there will be a lower emission factor for electricity generation in Cyprus (mix of natural gas and renewable energy) and we assumed that material used for irrigation (e.g., sprinklers, tubes) will be the same as they are for the baseline period (2018–2022).

3. Results

3.1. Potato Net Irrigation Water Requirements

In Figure 3 and Figure 4, the net irrigation water needs for potato, per location, are presented, for current and future climate (RCP 2.6 and 4.5). The results for the RCPs 8.5 scenario (worst case scenario) are presented in Figure S2 (Supplementary Material). In LOC1 (Pafos), the average (2018–2022) net irrigation water needs were 104.2 mm (m3/1000 m2) and the average projected values (three models; 2023–2045) were 152.9, 105.0, and 122.4 mm for RCP 2.6, 4.5, and 8.5, respectively (Figure 3). The respective numbers for LOC2 (Lefkosia) were 83.5 mm (2018–2022) and the average projected were 147.6, 150.8, and 157.2 mm for RCP 2.6, 4.5, and 8.5, respectively (Figure 3). In LOC3 (Ammochostos), the average (2018–2022) net irrigation water needs were 120.3 mm (m3/1000 m2) and the average projected values (three models; 2023–2045) were 110.7, 104.5, and 114.2 mm for RCP 2.6, 4.5, and 8.5, respectively (Figure 4). Finally, the results for LOC4 (Lemesos) were 133.0 mm (2018–2022) and the projected net irrigation water requirements for RCP 2.6, 4.5, and 8.5 were 115.2, 110.0, and 120.1 mm, respectively.

3.2. GHG Emissions Due to Irrigation

In Figure 5, the emission factors for sprinkler and drip irrigation under the current climate are presented. It was calculated that 56% (0.183 kg CO2eq/m3) of the emissions per m3 irrigation water are scope 2 emissions (electricity from the network) and the remaining 44% (0.145 kg CO2eq/m3) are scope 3 emissions (materials production and waste treatment). Electricity consumption per m3 was 0.210 kWh and the emission factor for electricity (public network) was 0.873 kg CO2eq/kWh [34]. In Figure 6, the hotspots related to emissions due to irrigation are presented, for drip (Figure 6a) and sprinkler (Figure 6b) electricity-driven irrigation. Per m3 of water applied, 0.328 and 0.331 kg CO2eq are emitted for drip and sprinkler irrigation, respectively. In Figure 7a,d, the total GHG emissions (kg CO2eq) due to irrigation water application are presented for the four areas under study, under the current (2018–2022) and future climate (2030–2034). Due to the lower emission factor for electricity production in Cyprus, the emissions for the application of 1 m3 of water in the field were estimated to be 0.205 and 0.208 kg CO2eq for drip and sprinkler irrigation, respectively. This reduction leads to a reduction in the emissions related to potato irrigation (Figure 5a,d). Total GHG emissions due to potato irrigation (four areas; 2018–2022) were estimated to be 1369.4 (S.D. 640.2) tons CO2eq.

4. Discussion

This research work is focusing on net irrigation water requirements, under the current and future climate, using Cyprus as a model area for a semiarid, insular environment. The most important areas for potato cultivation on the island were selected (Figure 1), with Ammochostos (LOC3) and Lefkosia (LOC2) being the most important in terms of potato cultivation area.
Data under the current (2018–2022) and future climate (2023–2045) were employed for modeling net irrigation water requirements for the crop, meaning that the water balance (e.g., soil water, evapotranspiration) was considered for the estimations. In addition, LCA was used to estimate GHG emissions due to potato irrigation under current (2018–2022) and future (2030–2034) climate conditions, assuming that the same land will be used for potato cultivation in the near future (2030–2034) and the materials used for drip and sprinkler irrigation will be more or less the same.
Regarding future net irrigation requirements for potato, there is a large variation among years and the models (M1–3; Figure 3 and Figure 4). Overall, the net irrigation requirements are expected to be increased in LOC1 and 2 but remain the same, or even decrease, for some future years in LOC3 and 4. These results are mainly due to projected increases in rainfall in some of the models used for future climate data generation. However, it should be noted that increased frequency in high-intensity rainfall does not lead to water storage in the soil. Nevertheless, this is something that needs to be further investigated.
Zittis et al. [11] also suggest that changes in extreme rainfall (e.g., duration and magnitude) could be expected in Cyprus. The researchers used a different timescale for their analysis than ours (three periods up to 2100) and they suggest that increased temperatures and a drier climate are expected for the island. However, they report (similar to our findings) that the absolute daily rainfall maxima exhibit strong local variability, indicating the need for high-resolution simulations to understand the potential impacts on future flooding and drought.
Our work contributes to the discussion about future water management in Cyprus. Another important aspect that we did not study is the quality and sources of irrigation water, especially in the case that increased irrigation needs are projected to take place. Additionally, we did not take into account the adaptation of the crop in future climate, where a strong temperature increase is expected (up to 4.1 °C) [11].
Irrigation is a practice that contributes to GHG emissions (Figure 5) and contributes significantly to the carbon footprint of irrigated potato crops [36,37]. Sprinkler and drip irrigation have comparable GHG emissions, in the case that they are electricity-driven. In this work, we did not study diesel use for irrigation, which is frequent in some cases of potato irrigation in Cyprus.
Changing the electricity mix production of the country, to avoid heavy fuel use, will lead to a 3-fold reduction in the emission factor, which is related to electricity production. This will have a significant impact on GHG emissions due to irrigation: a 40% reduction, as we estimate. Accordingly, this is evident from Figure 5a,d in the reduction in GHG emissions due to irrigation in the four areas studied.
Further research is required to quantify the CF (carbon footprint) of potato production in Cyprus, which will highlight the share of irrigation to the GHG emissions and the environmental impact of potato cultivation. It was estimated (Litskas et al., unpublished data) that the CF of potato cultivation in Cyprus (data from LOC3) is 0.55 tons CO2eq/ton of potato at the farm gate, and approximately 30% of the emissions are due to energy use for irrigation. Therefore, improving the emissions factor for electricity generation will have a significant impact on mitigating the emissions from this crop. Further improvement should be expected in RE used for irrigation (e.g., solar panels). According to our calculations, the 2030–2034 GHG emissions due to potato irrigation will be 35% lower, mainly due to the reduction in the emission factor for electricity production. However, a deeper level of analysis is required to include other irrigated crops and consider the life cycle (cradle to grave) of the agricultural products.

5. Conclusions

This research work is focusing on net irrigation requirements under the current and future climate for potato, using Cyprus as a model area for semiarid, insular environment. Data under the current (2018–2022) and future climate (2023–2045) were employed for modeling. In addition, LCA was used to estimate GHG emissions due to potato irrigation under current (2018–2022) and future (2030–2034) climate conditions. The results suggest an increase in net irrigation requirements, but there is high variability in model predictions (as well as baseline data; 2018–2022). On the other hand, the GHG emissions due to potato irrigation in Cyprus are expected to decrease by 35% (2030), mainly due to renewable energy used for electricity production. This approach could be improved if other crops are incorporated into the analysis for estimated irrigation requirements for key crops, under current and future climate. The LCA could be more efficient if there is analysis for potato production from “cradle to grave”, not only due to irrigation, and if it includes emissions due to soil cultivation and transportation to the market.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/hydrology10120218/s1, Figure S1: Model graph for drip irrigation (electricity powered). Figure S2: Net irrigation water needs for the four locations (LOC1—Pafos; LOC2—Lefkosia, LOC3—Ammochostos; LOC4—Limassol), under the RCP 8.5 scenario.

Author Contributions

Conceptualization, V.L. and C.P.; methodology, data curation related to climate data; irrigation calculations and Life Cycle Assessment all co-authors; writing—original draft preparation, all co-authors; review and editing, V.L. and C.P. All authors have read and agreed to the published version of the manuscript. Authorship is limited to those who have contributed substantially to the work reported.

Funding

This research received no external funding.

Data Availability Statement

Research data could be shared upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The climate data utilized for this study are reanalysis data (ERA5-Land [38]) and climate projection data [39] from the Copernicus Data Store (CDS). The reanalysis data provide a reliable representation of the past climate in the examined regions, while the climate projections look into the future by providing scenarios of climate under different emissions. More specifically, ERA5 represents the fifth iteration of ECMWF’s global climate and weather reanalysis, covering the past eight decades with data available since 1940, superseding the previous ERA-Interim reanalysis. This reanalysis employs a method known as data assimilation, which merges model data and worldwide observations into a comprehensive, consistent dataset via physics principles. This process resembles the one used by numerical weather prediction centers: every 12 h, a past forecast is optimally merged with fresh observations to generate a revised, superior estimate of atmospheric conditions—termed analysis—from which an improved forecast is created. However, reanalysis operates at a lower resolution to facilitate multidecade datasets. Without the urgency of timely forecasts, reanalysis has the luxury of collecting more observations and incorporate enhanced versions of original observations over time, improving the reanalysis product’s quality. ERA5-Land utilized in this study is a reanalysis dataset that delivers a unified interpretation of terrestrial variables’ progression over multiple decades, exhibiting superior resolution compared to ERA5. This dataset is realized through the retrospective analysis of the land constituent within the ECMWF’s ERA5 climatic reanalysis process.
The climate projection scenarios a”e pr’vided by the Coordinated Regional Climate Downscaling Experiment (CORDEX) available through CDS. The CORDEX experiments entail regional climate model (RCM) simulations that depict various future socio-economic scenarios (forcings), distinct combinations of global climate models (GCMs) and RCMs, and different ensemble members of the same GCM-RCM combinations. The design of these experiments, through the ensemble members, enables investigations that address the major uncertainties associated with future climate change. These uncertainties originate from the disparities in the future socioeconomic development scenarios, the inadequacies of the regional and global models employed, and the inherent variability of the climate system. Through this experimental setup, crucial questions about future climate change uncertainties can be explored: the nature of future climate forcing, the climate system’s response to changes in this forcing, and the uncertainty inherent to the natural variability of the climate system. For this study, the ensemble of CORDEX climate projection experiments using the representative concentration pathway (RCP) forcing scenarios are used. These scenarios are RCPs 2.6, 4.5, and 8.5, providing different pathways of the future climate forcing with boundary conditions provided by the GCMs. Scenario 2.6 assumes very low future emissions, scenario 4.5 assumes moderate future emissions, and finally, scenario 8.5 assumes very high future emissions. A description of the utilized datasets can be found in Table A1. For each scenario, three pairs of GCM-RCMs are selected to provide an ensemble of three different representations.
Table A1. Dataset description.
Table A1. Dataset description.
Data TypeNameResolutionTime Range
ReanalysisERA5-Land 0.1° × 0.1°2018–2022
Climate-Projection
RCPs: 2.6, 4.5, 8.5 		GCMRCM
cnrm_cerfacs_cm5cnrm_aladin630.11° × 0.11°2021–2045
mpi_m_mpi_esm_lrmpi_csc_remo2009
ichec_ec_earthdmi_hirham5
In Table A2, the variables downloaded from CDS per dataset type are presented. To download the data, the CDS Application Program Interface (API) service was utilized. This allowed for the creation of Python scripts to easily download the needed data. To serve the needs of the crop model used in this study, the data were further processed to aggregate the data in daily values and to calculate the daily potential evapotranspiration. The final datasets imported to the crop model are presented in Table A3. The potential evapotranspiration was calculated according to the Penman–Monteith method [40] for the reanalysis datasets, while for the climate projection scenarios, the potential evapotranspiration was calculated according to the Hargreaves method [41]. This was due to the lack of complete datasets for the climate projection scenarios to calculate the data according to the Penman–Monteith method, which incorporates more variables than the Hargreaves method. The data processing and extraction of time series at the locations of interest was achieved with the climate data operators (CDO) [42] and NetCDF operators (NCO) [43] that for allow fast computations on GRIB and NetCDF files.
Table A2. Variables per dataset retrieved from CDS.
Table A2. Variables per dataset retrieved from CDS.
Data TypeVariables Units Temporal Resolution
Reanalysis Two-meter Temperature KelvinHourly
Accumulated precipitation mHourly
Two-meter dew point KelvinHourly
Surface Pressure Pa Hourly
Ten-meter u-wind m/sHourly
Ten-meter v-wind m/sHourly
Radiation Fluxes J/m2 Hourly
Climate Projection ScenariosMinimum Temperature Kelvin Daily
Maximum Temperature KelvinDaily
Mean precipitation fluxkg m−2 s−1Daily
Table A3. Final climate datasets prepared for the crop model.
Table A3. Final climate datasets prepared for the crop model.
Data TypeVariablesUnits Temporal Resolution
Reanalysis Two-meter Maximum Temperature CelsiusDaily
Two-meter Minimum TemperatureCelsius Daily
Potential Evapotranspiration (Penman–Monteith)mm Daily
Accumulated PrecipitationmmDaily
Climate Projection ScenariosTwo-meter Maximum Temperature CelsiusDaily
Two-meter Maximum Temperature Celsius Daily
Potential Evapotranspiration (Hargreaves)mmDaily
Accumulated Precipitation mm Daily

References

  1. Beals, K.A. Potatoes, Nutrition and Health. Am. J. Potato Res. 2019, 96, 102–110. [Google Scholar] [CrossRef]
  2. Vaitkevičienė, N.; Kulaitienė, J.; Jarienė, E.; Levickienė, D.; Danillčenko, H.; Średnicka-Tober, D.; Rembiałkowska, E.; Hallmann, E. Characterization of Bioactive Compounds in Colored Potato (Solanum tuberosum L.) Cultivars Grown with Conventional, Organic, and Biodynamic Methods. Sustainability 2020, 12, 2701. [Google Scholar] [CrossRef]
  3. Devaux, A.; Goffart, J.-P.; Kromann, P.; Andrade-Piedra, J.; Polar, V.; Hareau, G. The Potato of the Future: Opportunities and Challenges in Sustainable Agri-Food Systems. Potato Res. 2021, 64, 681–720. [Google Scholar] [CrossRef] [PubMed]
  4. Awogbemi, O.; Kallon, D.V.V.; Owoputi, A.O. Biofuel Generation from Potato Peel Waste: Current State and Prospects. Recycling 2022, 7, 23. [Google Scholar] [CrossRef]
  5. Khorramifar, A.; Rasekh, M.; Karami, H.; Covington, J.A.; Derakhshani, S.M.; Ramos, J.; Gancarz, M. Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes. Molecules 2022, 27, 3508. [Google Scholar] [CrossRef]
  6. Jannat, A.; Ishikawa-Ishiwata, Y.; Furuya, J. Assessing the Impacts of Climate Variations on the Potato Production in Bangladesh: A Supply and Demand Model Approach. Sustainability 2021, 13, 5011. [Google Scholar] [CrossRef]
  7. de Vries, M.E.; Adams, J.R.; Eggers, E.; Ying, S.; Stockem, J.E.; Kacheyo, O.C.; van Dijk, L.C.M.; Khera, P.; Bachem, C.W.; Lindhout, P.; et al. Converting Hybrid Potato Breeding Science into Practice. Plants 2023, 12, 230. [Google Scholar] [CrossRef]
  8. Alyokhin, A.; Chen, Y.H.; Udalov, M.; Benkovskaya, G.; Lindström, L. Evolutionary Considerations in Potato Pest Management. In Insect Pests of Potato; Elsevier: Amsterdam, The Netherlands, 2022; pp. 429–450. ISBN 978-0-12-821237-0. [Google Scholar]
  9. Allan, C.; Xia, J.; Pahl-Wostl, C. Climate Change and Water Security: Challenges for Adaptive Water Management. Curr. Opin. Environ. Sustain. 2013, 5, 625–632. [Google Scholar] [CrossRef]
  10. Kaya, Y.; Sanli, F.B.; Abdikan, S. Determination of Long-Term Volume Change in Lakes by Integration of UAV and Satellite Data: The Case of Lake Burdur in Türkiye. Environ. Sci. Pollut. Res. 2023. [Google Scholar] [CrossRef]
  11. Zittis, G.; Bruggeman, A.; Camera, C. 21st Century Projections of Extreme Precipitation Indicators for Cyprus. Atmosphere 2020, 11, 343. [Google Scholar] [CrossRef]
  12. Zagaria, C.; Schulp, C.J.E.; Malek, Ž.; Verburg, P.H. Potential for Land and Water Management Adaptations in Mediterranean Croplands under Climate Change. Agric. Syst. 2023, 205, 103586. [Google Scholar] [CrossRef]
  13. Naz, S.; Ahmad, S.; Abbas, G.; Fatima, Z.; Hussain, S.; Ahmed, M.; Khan, M.A.; Khan, A.; Fahad, S.; Nasim, W.; et al. Modeling the Impact of Climate Warming on Potato Phenology. Eur. J. Agron. 2022, 132, 126404. [Google Scholar] [CrossRef]
  14. Wang, C.; Shi, X.; Liu, J.; Zhao, J.; Bo, X.; Chen, F.; Chu, Q. Interdecadal Variation of Potato Climate Suitability in China. Agric. Ecosyst. Environ. 2021, 310, 107293. [Google Scholar] [CrossRef]
  15. Skelsey, P.; Kettle, H.; MacKenzie, K.; Blok, V. Potential Impacts of Climate Change on the Threat of Potato Cyst Nematode Species in Great Britain. Plant Pathol. 2018, 67, 909–919. [Google Scholar] [CrossRef]
  16. Dahal, K.; Li, X.-Q.; Tai, H.; Creelman, A.; Bizimungu, B. Improving Potato Stress Tolerance and Tuber Yield under a Climate Change Scenario—A Current Overview. Front. Plant Sci. 2019, 10, 563. [Google Scholar] [CrossRef]
  17. Zeng, Z.; Wu, W.; Li, Y.; Huang, C.; Zhang, X.; Peñuelas, J.; Zhang, Y.; Gentine, P.; Li, Z.; Wang, X.; et al. Increasing Meteorological Drought under Climate Change Reduces Terrestrial Ecosystem Productivity and Carbon Storage. One Earth 2023, 6, 1326–1339. [Google Scholar] [CrossRef]
  18. Raymundo, R.; Asseng, S.; Robertson, R.; Petsakos, A.; Hoogenboom, G.; Quiroz, R.; Hareau, G.; Wolf, J. Climate Change Impact on Global Potato Production. Eur. J. Agron. 2018, 100, 87–98. [Google Scholar] [CrossRef]
  19. Bracken, P.; Burgess, P.J.; Girkin, N.T. Opportunities for Enhancing the Climate Resilience of Coffee Production through Improved Crop, Soil and Water Management. Agroecol. Sustain. Food Syst. 2023, 47, 1125–1157. [Google Scholar] [CrossRef]
  20. Liu, K.; Harrison, M.T.; Yan, H.; Liu, D.L.; Meinke, H.; Hoogenboom, G.; Wang, B.; Peng, B.; Guan, K.; Jaegermeyr, J.; et al. Silver Lining to a Climate Crisis in Multiple Prospects for Alleviating Crop Waterlogging under Future Climates. Nat. Commun. 2023, 14, 765. [Google Scholar] [CrossRef]
  21. Li, L.; Wang, B.; Feng, P.; Jägermeyr, J.; Asseng, S.; Müller, C.; Macadam, I.; Liu, D.L.; Waters, C.; Zhang, Y.; et al. The Optimization of Model Ensemble Composition and Size Can Enhance the Robustness of Crop Yield Projections. Commun. Earth Environ. 2023, 4, 362. [Google Scholar] [CrossRef]
  22. Reddy, B.J.; Mandal, R.; Chakroborty, M.; Hijam, L.; Dutta, P. A Review on Potato (Solanum tuberosum L.) and Its Genetic Diversity. Int. J. Genet. 2018, 10, 360. [Google Scholar] [CrossRef]
  23. Stokstad, E. The New Potato. Science 2019, 363, 574–577. [Google Scholar] [CrossRef]
  24. Xing, Y.; Wang, N.; Niu, X.; Jiang, W.; Wang, X. Assessment of Potato Farmland Soil Nutrient Based on MDS-SQI Model in the Loess Plateau. Sustainability 2021, 13, 3957. [Google Scholar] [CrossRef]
  25. Nasir, M.W.; Toth, Z. Effect of Drought Stress on Potato Production: A Review. Agronomy 2022, 12, 635. [Google Scholar] [CrossRef]
  26. Markou, M.; Moraiti, C.A.; Stylianou, A.; Papadavid, G. Addressing Climate Change Impacts on Agriculture: Adaptation Measures For Six Crops in Cyprus. Atmosphere 2020, 11, 483. [Google Scholar] [CrossRef]
  27. Asif, Z.; Chen, Z.; Sadiq, R.; Zhu, Y. Climate Change Impacts on Water Resources and Sustainable Water Management Strategies in North America. Water Resour. Manag. 2023, 37, 2771–2786. [Google Scholar] [CrossRef]
  28. Adamides, G.; Kalatzis, N.; Stylianou, A.; Marianos, N.; Chatzipapadopoulos, F.; Giannakopoulou, M.; Papadavid, G.; Vassiliou, V.; Neocleous, D. Smart Farming Techniques for Climate Change Adaptation in Cyprus. Atmosphere 2020, 11, 557. [Google Scholar] [CrossRef]
  29. De Sousa, L.M.; Poggio, L.; Batjes, N.H.; Heuvelink, G.B.M.; Kempen, B.; Riberio, E.; Rossiter, D. SoilGrids 2.0: Producing Quality-Assessed Soil Information for the globe. Soils Nat. Environ. 2020. [Google Scholar] [CrossRef]
  30. UN FAO AquaCrop Stand-Alone Programme|AquaCrop|Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/aquacrop/software/aquacropplug-inprogramme/en/ (accessed on 24 October 2023).
  31. Schaap, M.G.; Leij, F.J.; Van Genuchten, M.T. Rosetta: A Computer Program for Estimating Soil Hydraulic Parameters with Hierarchical Pedotransfer Functions. J. Hydrol. 2001, 251, 163–176. [Google Scholar] [CrossRef]
  32. Poggio, L.; De Sousa, L.M.; Batjes, N.H.; Heuvelink, G.B.M.; Kempen, B.; Ribeiro, E.; Rossiter, D. SoilGrids 2.0: Producing Soil Information for the Globe with Quantified Spatial Uncertainty. Soil 2021, 7, 217–240. [Google Scholar] [CrossRef]
  33. OpenLCA. org. Agribalyse, v. 3.0.1; INRAE: Paris, France, 2021. [Google Scholar]
  34. JRC Environmental Footprint Reference Packages. Available online: https://eplca.jrc.ec.europa.eu/LCDN/developerEF.xhtml (accessed on 30 October 2023).
  35. Huijbregts, M.A.J.; Steinmann, Z.J.N.; Elshout, P.M.F.; Stam, G.; Verones, F.; Vieira, M.; Zijp, M.; Hollander, A.; van Zelm, R. ReCiPe2016: A Harmonised Life Cycle Impact Assessment Method at Midpoint and Endpoint Level. Int. J. Life Cycle Assess. 2017, 22, 138–147. [Google Scholar] [CrossRef]
  36. Röös, E.; Sundberg, C.; Hansson, P.-A. Uncertainties in the Carbon Footprint of Food Products: A Case Study on Table Potatoes. Int. J. Life Cycle Assess. 2010, 15, 478–488. [Google Scholar] [CrossRef]
  37. Williams, A.G.; Audsley, E.; Sandars, D.L. Environmental Burdens of Producing Bread Wheat, Oilseed Rape and Potatoes in England and Wales Using Simulation and System Modelling. Int. J. Life Cycle Assess. 2010, 15, 855–868. [Google Scholar] [CrossRef]
  38. Muñoz Sabater, J. ERA5-Land Hourly Data from 1950 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2019. Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview (accessed on 24 October 2023).
  39. Copernicus Climate Change Service, Climate Data Store. CORDEX Regional Climate Model Data on Single Levels. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 2019. Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels (accessed on 24 October 2023).
  40. Howell, T.A.; Evett, S.R. The Penman-Monteith Method; USDA-Agricultural Research Service, Conservation & Production Research Laboratory: Washington, DC, USA, 2004; p. 14. [Google Scholar]
  41. Hargreaves, G.H.; Allen, R.G. History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng. 2003, 129, 53–63. [Google Scholar] [CrossRef]
  42. Uwe Schulzweida. CDO User Guide, Version 2.1.0; Zenodo: Geneva, Switzerland, 2022. [Google Scholar] [CrossRef]
  43. Zender, C.S. Analysis of Self-describing Gridded Geoscience. Data with netCDF Operators (NCO). Environ. Modell. Softw. 2008, 23, 1338–1342. [Google Scholar] [CrossRef]
Hydrology 10 00218 g001
Figure 1. Cyprus satellite image with the potato fields in the island (black dots) (Cyprus Agricultural Payments Organization; CAPO 2020). Locations: (1) Pafos, (2) Lefkosia, (3) Ammochostos, (4) Limassol, the major potato cultivation areas.
Figure 1. Cyprus satellite image with the potato fields in the island (black dots) (Cyprus Agricultural Payments Organization; CAPO 2020). Locations: (1) Pafos, (2) Lefkosia, (3) Ammochostos, (4) Limassol, the major potato cultivation areas.
Hydrology 10 00218 g001
Hydrology 10 00218 g002
Figure 2. Workflow for the research.
Figure 2. Workflow for the research.
Hydrology 10 00218 g002
Hydrology 10 00218 g003
Figure 3. Net irrigation water needs for the four locations (LOC1—Pafos; LOC2—Lefkosia; LOC3—Ammochostos; LOC4—Limassol) under the RCP 2.6 and 4.5 scenarios.
Figure 3. Net irrigation water needs for the four locations (LOC1—Pafos; LOC2—Lefkosia; LOC3—Ammochostos; LOC4—Limassol) under the RCP 2.6 and 4.5 scenarios.
Hydrology 10 00218 g003
Hydrology 10 00218 g004aHydrology 10 00218 g004b
Figure 4. Net irrigation water needs for the four locations (LOC3—Ammochostos; LOC4—Limassol) under the RCP 2.6 and 4.5 scenarios.
Figure 4. Net irrigation water needs for the four locations (LOC3—Ammochostos; LOC4—Limassol) under the RCP 2.6 and 4.5 scenarios.
Hydrology 10 00218 g004aHydrology 10 00218 g004b
Hydrology 10 00218 g005
Figure 5. GHG emissions per m3 irrigation water applied to potato crop, after LCA.
Figure 5. GHG emissions per m3 irrigation water applied to potato crop, after LCA.
Hydrology 10 00218 g005
Hydrology 10 00218 g006
Figure 6. GHG emissions hotspots for drip (a) and sprinkler (b) electricity-driven irrigation.
Figure 6. GHG emissions hotspots for drip (a) and sprinkler (b) electricity-driven irrigation.
Hydrology 10 00218 g006
Hydrology 10 00218 g007
Figure 7. Total GHG emissions for potato irrigation in the four locations, under current and future climate. (a) LOC1, Pafos; (b) LOC2, Lefkosia; (c) LOC3, Ammochostos; (d) LOC4, Limassol.
Figure 7. Total GHG emissions for potato irrigation in the four locations, under current and future climate. (a) LOC1, Pafos; (b) LOC2, Lefkosia; (c) LOC3, Ammochostos; (d) LOC4, Limassol.
Hydrology 10 00218 g007
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Litskas, V.; Vourlioti, P.; Mamouka, T.; Kotsopoulos, S.; Paraskevas, C. Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment. Hydrology 2023, 10, 218. https://doi.org/10.3390/hydrology10120218

AMA Style

Litskas V, Vourlioti P, Mamouka T, Kotsopoulos S, Paraskevas C. Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment. Hydrology. 2023; 10(12):218. https://doi.org/10.3390/hydrology10120218

Chicago/Turabian Style

Litskas, Vassilis, Paraskevi Vourlioti, Theano Mamouka, Stylianos Kotsopoulos, and Charalampos Paraskevas. 2023. "Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment" Hydrology 10, no. 12: 218. https://doi.org/10.3390/hydrology10120218

APA Style

Litskas, V., Vourlioti, P., Mamouka, T., Kotsopoulos, S., & Paraskevas, C. (2023). Sustainable Water Resources Management under Climate Change: A Case Study with Potato Irrigation in an Insular Mediterranean Environment. Hydrology, 10(12), 218. https://doi.org/10.3390/hydrology10120218

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop