3.530
4.8
Article
Integrated Modeling of Agronomic
and Water Resources Management
Scenarios in a Degraded Coastal
Watershed (Almyros Basin,
Magnesia, Greece)
Aikaterini Lyra, Athanasios Loukas, Pantelis Sidiropoulos, Konstantinos Voudouris and
Nikitas Mylopoulos
Special Issue
Groundwater Resources Management: Reconciling Demand, High Quality Resources and
Sustainability
Edited by
Dr. Maurizio Polemio and Prof. Dr. Konstantinos Voudouris
https://doi.org/10.3390/w14071086
water
Article
Integrated Modeling of Agronomic and Water Resources
Management Scenarios in a Degraded Coastal Watershed
(Almyros Basin, Magnesia, Greece)
Aikaterini Lyra 1, * , Athanasios Loukas 2 , Pantelis Sidiropoulos 1,2 , Konstantinos Voudouris 3
and Nikitas Mylopoulos 1
1
2
3
*
Citation: Lyra, A.; Loukas, A.;
Sidiropoulos, P.; Voudouris, K.;
Mylopoulos, N. Integrated Modeling
of Agronomic and Water Resources
Management Scenarios in a
Degraded Coastal Watershed
(Almyros Basin, Magnesia, Greece).
Water 2022, 14, 1086. https://
doi.org/10.3390/w14071086
Academic Editor: Carmen Teodosiu
Received: 24 February 2022
Laboratory of Hydrology and Aquatic Systems Analysis, Department of Civil Engineering, School of
Engineering, University of Thessaly, 38334 Volos, Greece; psidirop@civ.uth.gr (P.S.); nikitas@civ.uth.gr (N.M.)
Laboratory of Hydraulic Works and Environmental Management, School of Rural and Surveying Engineering,
Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; agloukas@topo.auth.gr
Laboratory of Engineering Geology and Hydrogeology, Department of Geology, Aristotle University of
Thessaloniki, 54124 Thessaloniki, Greece; kvoudour@geo.auth.gr
Correspondence: klyra@uth.gr; Tel.: +30-242-1074-153
Abstract: The scope of this study is to assess the effects of agronomic and water resources management scenarios on groundwater balance, seawater intrusion, and nitrate pollution and the comparison
of the developed scenarios relative to the current crop production and water management regime
in the coastal agricultural Almyros basin in the Thessaly region, Greece. Agronomic and water
resources scenarios have been simulated and analyzed for a period of 28 years, from 1991 to 2018.
The analysis has been conducted with the use of an Integrated Modeling System for agricultural
coastal watersheds, which consists of coupled and interlinked simulation models of surface water
hydrology (UTHBAL), reservoir operation (UTHRL), agronomic/nitrate leaching model (REPIC),
and groundwater models for the simulation of groundwater flow (MODFLOW) and contaminant
transport of nitrates (MT3DMS) and chlorides (SEAWAT). The pressure on water resources has been
estimated with the Water Exploitation Index (WEI+) and the reservoirs’ operation with the Reliability
index to cover the water demands. The indices of Crop Water Productivity, Nitrogen Use Efficiency,
and Economic Water Productivity have been used to quantify the benefits and the feasibility of the
alternative scenarios. The best results for the sustainability of water resources are achieved under
the deficit irrigation and rain-fed scenario, while the best results for water resources and the local
economy are achieved under deficit irrigation and reduced fertilization scenario.
Keywords: integrated water resources management; coastal agricultural watershed; groundwater
nitrate pollution; seawater intrusion; agronomic efficiency; resources efficiency
Accepted: 27 March 2022
Published: 29 March 2022
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1. Introduction
Water scarcity and degradation are becoming an urgent problem around the world due
to the rising water demand and emerging conflicts among water uses (urban, agricultural,
industrial, and other water uses) [1]. The quantity and quality of water resources in many
coastal agricultural watersheds are at a critical stage. The non-existence and/or the limited
use of surface water reservoirs, the over-exploitation of groundwater resources, and overuse and application of fertilizers to maximize crop production result in lowering groundwater table and increasing nitrate concentrations and seawater intrusion [2–4]. Water resource
management strategies in Europe increasingly focus on long-term sustainability [5] while
also being in alignment with the Sustainable Development Goals (SDGs) of the United
Nations for water, land use, efficient and resilient agriculture, decrease in poverty, provision of quality food, and protection of the environment and human health [6,7], the Water
Framework Directive [8], and the Nitrates Directive [9]. In the context of an integrated
Water 2022, 14, 1086. https://doi.org/10.3390/w14071086
https://www.mdpi.com/journal/water
Water 2022, 14, 1086
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modeling approach, simulation of the multi-scale dynamics of surface and groundwater
systems can improve the understanding and modeling of complex interconnections of
water systems. A holistic approach to the environmental and socioeconomic variables that
may lead to quantitative and qualitative challenges with water sustainability can help to
enhance their management, the development of improvement initiatives, and the setting of
realistic timelines to achieve environmental targets [2,10–12]. Water scarcity has a direct
effect on crop productivity in irrigated agriculture and the development and operation
of surface water storages and reservoirs are directly associated with the alleviation of
pressure ongroundwater resources [13,14]. In integrated water resource management, the
conjunctive use of surface water and groundwater is a standard policy to meet the water
needs and cope with the environmental impacts of anthropogenic activities [15]. A large
number of studies have focused on the development of reservoir projects to tackle water
scarcity and groundwater degradation [14–29]. Simulation is commonly used to find the
best and/or optimized reservoir operation and groundwater use [20,30–34].
According to the Nitrates Directive, the River Basin Management Plans of Greece,
and the European Commission’s for Best Management Practices in Agriculture, every
Member State has to identify and protect Nitrate Vulnerable Zone (NVZs) and design one
or more Best Management Practices (BMPs) to be voluntarily adopted by local agriculture
professionals [9,35,36] because the effects of nitrogen fertilizer changes have a time-lag
environmental response to achieve water quality improvements [12]. Every agronomic
and water resources management scenario must also comply with legislative constraints to
the benefit of human health and crop yield. The World Health Organization (WHO) has
specified a short-term limit of nitrates concentration at 50 mg/L for potable water and the
European Union has endorsed this restriction [9,37]. Greek and international legislation
has set the upper threshold of 50 mg/L. However, the legislation recognizes that long-term
consumption of water with nitrate concentration larger than 25 mg/L may cause health
disorders in humans, including methemoglobinemia in neonates (i.e., blue baby syndrome),
cancer, and thyroid problems [9,37–40]. The maximum permitted concentration of chlorides
in urban water is established at 250 Cl mg/L, although lower concentrations are detectable
in taste and may have been associated with health issues [2,41].
Particular attention and proper planning should also be placed upon seawater intrusion when irrigation occurs from coastal aquifers that have been salinized. Although
chloride is classified as a nutrient in plants in approximate amounts of 100 mg kg–1 in
soil, larger concentrations become toxic to crops and reduce the nitrogen uptake of the
plants [42–45]. Chlorides take the place of nitrates during the nutrient uptake processes of
crops and reduce crop yield, by causing crop nitrogen deficiency and/or salt accumulation
in the leaf area and reducing its size (depending on the crop’s tolerance to salinity) [43,46].
The maximum allowable soil salinity for crops is 1–2 EC (dS/m) for alfalfa, maize, olives,
trees, vegetables, and vineyards, and 6–8 EC (dS/m) for cereals, cotton, and wheat [47]. Cereals, wheat, and cotton are classified as moderately tolerant to tolerant crops to chlorides,
alfalfa as moderately sensitive, most vegetables are moderately sensitive to sensitive, and
maize, vineyards, and trees are most sensitive to moderately sensitive [42,47].
Metrics of resources efficiency, productivity, and economic and nutrient efficiency have
been used for various water projects and agronomy practices in many scientific studies to
determine whether a management policy is sustainable [48–54]. Water Exploitation Index
(WEI) is an indicator to quantify water scarcity, Water Use Efficiency is an indicator to evaluate the impact of different irrigation scenarios on crop productivity, and Economic Water
Productivity indicates the financial benefits of agronomical and water-saving practices to
local stakeholders [48,51,55]. Nitrogen Use Efficiency indicators are used to estimate the
productivity of various crops for different nitrogen applications [56]. The aforementioned
metrics are very useful to integrate detailed results and communicate effectively to water resources managers and stakeholders the benefits and the economic sustainability of
various alternative scenarios.
Water 2022, 14, 1086
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The problem of water scarcity and its consequences may be alleviated, or even reversed, by improving the efficiency of the agronomic and water management practices and
the storage water works, increasing the socioeconomic and resources’ profits from their
implementation [3,51,52,57]. The main objective of the present study is to assess the effects
of agronomic and water resources management scenarios coupled with the development
and operation of surface water reservoirs on groundwater budget, seawater intrusion,
and nitrate pollution and the advantages of the scenarios’ schemes relative to the current
crop production and water management regime in the coastal agricultural Almyros basin
in Thessaly, Greece. The analysis has been conducted with a calibrated, validated, and
high efficiency Integrated Modeling System for agricultural coastal watersheds, developed
in an earlier study by Lyra et al. (2021), which consists of coupled and interlinked models of surface water hydrology, an agronomic/nitrate leaching model, and groundwater
models for the simulation of groundwater flow and contaminant species of nitrates and
chlorides [2]. A reservoir operation simulation model has been added to the previously
developed modeling system, the optimization of which is presented for the first time in
this study, with the aim to configure a tool that guarantees reliable irrigation projects
and water management schemes depending on the irrigation demands of arable land
and the changes of the irrigated crop pattern. Agronomic and water resources scenarios
have been developed and simulated for a period of 28 years, from 1991 to 2018, while the
groundwater quantity and quality are evaluated at the end of the simulation period (2018).
The pressure on water resources has been estimated with the Water Exploitation Index
(WEI+), and the reservoirs’ operation with a performance index of reliability to cover the
water demands and the indicators of Crop Water Productivity, Nitrogen Use Efficiency,
and Economic Water Productivity have been used to quantify the benefits and feasibility of
each alternative scenario. The methodology presented in this paper provides an integrated
and holistic approach for the simulation and evaluation of agricultural practices, water
resources development scenarios, and climate change impacts on the quantity and quality
of surface water and groundwater resources of coastal agricultural watersheds.
2. Materials and Methods
2.1. Study Area
The Almyros basin is located in the Thessaly region, central-eastern Greece (Figure 1).
The total basin area is nearly 856 km2 with an elevation range of 0–1700 m and an average
of 370 m. The basin has an intensively cultivated agricultural area of about 205 km2 , which
is mainly located in the area of the Almyros aquifer (Figure 1). The demands of irrigation
and other water uses are totally covered by groundwater abstractions.
The basin has a semi-arid Mediterranean climate with hot, dry summers but chilly
and rainy winters. The mean annual rainfall is about 570 mm with a range of 509–778 mm
with elevation, and the mean annual temperature is 15.0 ◦ C with a range of 9.8–16.9 ◦ C
with elevation. The estimated mean annual surface water runoff is about 113 mm, and
the mean annual aquifer recharge is about 54 mm [2]. The hydrographic network of the
basin is characterized by torrential streams with temporary flow. The basin is subdivided
into six sub-watersheds, namely, Kazani, Lahanorema, Holorema, Xiria, Platanorema, and
Xirorema (Figure 1). The basin is coastal, while the Almyros aquifer occupies the lowland
area of the basin and is in contact with the sea.
The lowland near the shoreline is largely made up of sandy porous solids with clay
layers. Clay lenses and clay, sand, and gravel intercalations with volcanics and conglomerates provide low permeability complexes at the western and the upper height zones.
Limited limestone outcrops form karst aquifer systems within the southernmost part of
the region, which provide physical communication to the sea (open karst) but are not in
hydraulic conduct with the alluvium aquifer. The quantity and quality status of the aquifer
is degraded and the problems of water table drawdown, nitrate pollution, and seawater
intrusion are present in recent years (Figure 2).
Water 2022, 14, 1086
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Figure 1. Map of Almyros basin, aquifer, sub-watersheds, irrigated agricultural areas, and location
of the Xirias reservoir (under construction), the Klinovos reservoir (planned), and the Mavromati
reservoir (constructed—not operated).
Figure 2. Results of previous groundwater simulation for existing agricultural and water management conditions and water supplied by groundwater pumping: (a) hydraulic heads, (b) nitrate
leaching, (c) chloride concentrations, and (d) nitrate concentration in September 2018 (as simulated
by Lyra et al. 2021 [57]).
All the water demands (i.e., agricultural, urban, and industrial) in the Almyros basin
are satisfied by unsustainable groundwater pumping. The local authorities have already
Water 2022, 14, 1086
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proceeded with the construction and operation of the Mavromati reservoir to alleviate
and mitigate the water quantity and quality problems. The Mavromati reservoir was constructed to cover the urban water supply needs of villages located in and in the vicinity of
the southernmost sub-watershed of Almyros basin (i.e., Xirorema sub-watershed) (Figure 3).
At present, there are not any other water storages in the basin and all water needs are
currently being covered by groundwater abstractions.
Figure 3. Reservoirs and the respective urban and agricultural water demand areas for (a) area A0
and (b) area A1, of the Almyros basin.
A reservoir is under construction, the Xirias reservoir, which is designed to cover crop
irrigation needs in the central part of the area and improve the quality of groundwater by
preventing seawater intrusion [36,58]. Furthermore, this study proposes the development
of a larger reservoir, the Klinovos reservoir, to cover the irrigation needs of agricultural
areas in the Xirorema sub-watershed. Klinovos reservoir is located downstream from the
Mavromati reservoir (Figure 1).
The Xirias reservoir has been designed to cover the irrigation needs of 7 km2 of
arable land. The construction of the storage work includes a water uptake dam from
the Xirias stream, water pipelines from the dam to the reservoir, and also an adjacent
sedimentation tank. The artificial reservoir will be 600 × 600 m in dimensions, with a
storage capacity of approximately 4 hm3 and zero dead storage [58,59]. The Mavromati
reservoir is located in the highlands of the Xirorema sub-watershed and, as a first stage, is
intended to cover the drinking water needs of 9 villages. The reservoir has a storage capacity
of approximately 1.2 hm3 and dead storage of 0.0003 hm3 [58,60]. The Klinovos reservoir
is proposed in an earlier study [61] to cover the irrigation needs of the southern region,
and to recharge the Almyros aquifer. The proposed reservoir will have an active storage
capacity of approximately 14 hm3 and dead storage of 0.002 hm3 [61]. The characteristics
of reservoir sub-watersheds are presented in Table 1. This information is used in the
hydrologic simulation of the sub-watersheds.
Table 1. Characteristics of sub-watershed areas contributing to the inflows of the reservoirs.
Sub–Basin
Xirias
Upstream
Xirias
Downstream
Mavromati
Upstream
Mavromati
Downstream
and Klinovos
Upstream
Klinovos
Downstream
Area (km2 )
Mean Elevation (m)
Curve Number (CN)
81.32
930.8
54.93
136.14
297.5
59.95
3.65
1054.5
60.08
40.48
641.9
54.8
129.04
174.4
50.65
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2.1.1. Crop Water Demands
Based on factual datasets supplied by the Greek Payment Authority of the Common
Agricultural Policy, irrigation water demands for every main crop type have been estimated
in a recent analysis [2]. Alfalfa, cereals, cotton, maize, olive groves, trees, vegetables,
vineyards, and wheat are the main crops cultivated in the Almyros basin. The spatial
annual average of the Near Irrigation Requirements (NIR) of the crops cultivated in the
wider Almyros watershed is presented in Figure 1. Various scenarios of water supply,
irrigation methods, and fertilization have been developed and applied. An analytical
description of these scenarios is presented in Section 2.4.
2.1.2. Urban Water Demands
The demographic data of Almyros’ Municipalities and satellite areas were obtained
by the Hellenic Statistical Authority using censuses’ estimates of the years 1991, 2001, and
2011 for the permanent population. The monthly percentage variation of annual drinking
water needs is estimated as 8% for October, 5% for November, 5% for December, 5% for
January, 5% for February, 6% for March, 8% for April, 10% for May, 12% for June, 13% for
July, 13% for August, and 10% for September [62]. Between 1991 and 2001 the total annual
water demand for the urban areas supplied by the Almyros aquifer system was 0.53 hm3 ,
from 2001 to 2011 was 0.46 hm3 , and from 2011 to 2018 was 0.45 hm3 . The Mavromati
reservoir is intended to supply nine (9) residential areas with drinking water, inside and
outside the Almyros basin (Table 2). The locations of the demand sites supplied with water
from the Mavromati reservoir are illustrated in Figure 3.
Table 2. Urban Demand Sites linked to Mavromati Reservoir.
Location
Elevation (m)
Water Demand 1991
(hm3 )
Water Demand 2001
(hm3 )
Water Demand 2011
(hm3 )
1. Achileio
2. Agia Triada
3. Agios Ioannis
4. Agioi Theodoroi
5. Drumonas
6. Gavriani
7. Pteleos
8. Sourpi
9. Vrunaina
Industry and Tourism 1
14
66
500
180
120
270
108
29
450
–
0.06
0.03
0.01
0.03
0.02
0.02
0.16
0.15
0.05
0.03
0.05
0.03
0.005
0.03
0.03
0.02
0.11
0.16
0.04
0.03
0.06
0.02
0.03
0.03
0.02
0.02
0.12
0.13
0.03
0.03
–
0.56
0.49
0.48
Sum
1
Estimated.
2.2. Integrated Modeling System
An Integrated Modeling System developed in an earlier study has been used for
the simulation of surface water and groundwater resources of the Almyros basin. The
System consists of coupled models for the simulation of surface hydrology (UTHBAL) [16],
reservoir operation (UTHRL) [16], groundwater flow (MODFLOW) [63], agronomic crop
schedules and nitrates leaching (REPIC) [2], an R-ArcGIS [64] based EPIC model [65]),
groundwater nitrate transport (MT3DMS) [66], and groundwater seawater intrusion (SEAWAT) [67]. The modeling system has been expanded to include a reservoir/lake operation
model (UTHRL) to simulate the operation of surface water storage works [16]. The monthly
areal precipitation, temperature, and potential evapotranspiration are inserted in the surface hydrology model (UTHBAL) which estimates the monthly watershed runoff and the
natural groundwater recharge. The weighted average irrigation return flow per sub-basin
and main crops are then added to the recharge. The watershed runoff is the input of the
reservoir operation model (UTHRL) along with other possible inflows, the reservoir’s
storage capacity, the desirable withdrawals from the reservoir, the direct precipitation and
Water 2022, 14, 1086
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evaporation from the reservoir’s surface, and the losses toward groundwater. The total of
natural recharge, irrigation return flow, reservoir’s groundwater losses, the sea level, and
the shoreline conductance are the input to the groundwater hydrology model (MODFLOW),
whereas the outflows are designed as wells that are assigned with monthly flow units according to the agricultural and urban and other water demands. The agronomic practices
are simulated with the crop growth/nitrates leaching model (REPIC) by modifying for each
management application the irrigated water and the fertilization amounts for each crop
type. The input data include climatic, land use, and other variables, and crop cultivation
schedules and other parameters, while the outputs of the model are the crop yield and
the nitrate leaching to groundwater. The nitrate leaching calculated by the agronomic
model (REPIC) is used as input to the groundwater pollution model (MT3DMS), and the
concentrations of chlorides are the input to the seawater intrusion of variable density model
(SEAWAT). The flow chart of the Integrated Modeling System is presented in Figure 4.
Figure 4. Flowchart of the Modeling System and the Reservoir Simulation-Optimization Module.
The Integrated Modeling System is applied and used in the Almyros basin on a semidistributed mode (i.e., sub-watershed, aquifer scale) to estimate the water balance of the
basin and the aquifer, as well as the concentrations of nitrates and chlorides in groundwater
for the developed and applied scenarios. Figure 2 presents the results of the baseline
scenario for September 2018.
The simulation of water resources requires databases of a wide range of measured
variables that span over many years of monitoring for calibration and validation purposes
of the models. Meteorological variables (e.g., precipitation and temperature), water table
measurements of wells, and groundwater nitrates and chloride concentrations were also
required and used in the analysis. Land use and crop type data were used to estimate the
crop irrigation demands, number, and groundwater well abstractions. Meteorological data
were collected by various governmental organizations and agencies, pre-processed, and
validated; land use cultivated fields and crop data yielded across the aquifer were provided
Water 2022, 14, 1086
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by the Greek Payment Authority of Common Agricultural Policy (C.A.P.) Aid Schemes
(OPEKEPE). Soil characteristics of unsaturated zones were obtained from the European Soil
Data Centre (ESDAC) [68], (OPEKEPE), and National Agricultural Research Foundation
(NAGREF); hydrogeological borehole data from the Greek Ministry of Agriculture; and
observation data of water table, nitrate concentrations, and chloride concentrations by the
Institute of Geology and Mineral Exploration (IGME), the Regional Government of Thessaly,
and the Magnesia Prefecture. Last but not least, nitrate concentrations measurements and
chloride observation measurements for the years 2013 to 2015 were performed in the
Almyros aquifer in previous research studies [69,70]. Known locations of pumping wells
were extracted by regional well maps and the National Register of Water Abstraction Points
from Surface and Underground Water Bodies [71].
2.3. UTHBAL–UTHRL Reservoir System–Optimization Module
The surface hydrological model (UTHBAL) and the reservoir’s operation model
(UTHRL) are also connected with the extents of the irrigated areas of the various crop types
that are cultivated in the simulated sub-watersheds of the Almyros basin. A control condition to quantify the adequacy of the water storage works against the crop water demands
has been added to the processes of simulation. At least, the average annual withdrawal
rate of water from the reservoir has to be approximately equal or close to the average
annual irrigation needs of the cultivated crop pattern. The operation of the two models
is optimized by changing the cultivated area of each crop (crop pattern), calculating the
irrigation needs, and setting them as reservoir withdrawals. The loop to find the optimum
water withdrawals is continued until the desirable balance is reached. Then, when the
irrigated area is delineated, the simulation continues deterministically, as described in
the previous paragraph. This module is particularly useful to minimize the designation
time of the appropriate but unknown irrigated extent area by the reservoir and provide
feasible solutions. The UTHBAL–UTHRL Reservoir System-Optimization Module extends
the Integrated Modeling System (IMS) developed in an earlier study [2]. In this study,
the impact of the development and operation of surface storages (i.e., reservoirs) along
with various irrigation and fertilization practices on the water balance and quality of the
Almyros aquifer is assessed. The updated IMS may be used for simulating the impacts of
various agricultural policies and schemes, such as agronomic practices, changes in crop
pattern, implementation of Best Management Practices. Furthermore, the specific module
may also be used for estimating and designing water resources projects for future climate
conditions and scenarios.
This optimization procedure has only been followed for the proposed Klinovos reservoir. For the other two reservoirs, Mavromati and Xirias, the design values have been used
without optimization because they are either constructed and operated (e.g., Mavromati
reservoir) or near construction completion (e.g., Xirias reservoir). The flowchart of the
Integrated Modeling System and the Reservoir Simulation-Optimization Module is shown
in Figure 4.
2.4. Agronomic and Water Resources Management Scenarios
A baseline scenario (S0) has been developed for comparison with other water resources and agronomic scenarios. In this baseline scenario, existing irrigation methods and
fertilization applications are applied for all crops, and the water demands for all water
uses (i.e., irrigation, urban, industrial, and tourism water use) are satisfied by groundwater
pumping. This baseline scenario represents the existing agricultural and water resources
management in the Almyros basin [57].
An alternative scenario (SR0) has been developed to assess the contribution of reservoirs, which is similar to S0, but the water demands for all water uses are satisfied through
withdrawals from the reservoirs and groundwater pumping. This scenario uses, as scenario
S0, the existing irrigation methods and fertilization applications. It is also coupled with
the two proposed irrigated areas of Klinovos reservoir A0 and A1, explained later. Thus,
Water 2022, 14, 1086
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two baseline alternative sub-scenarios are developed SR0-A0 and SR0-A1, for irrigated
areas A0 and A1, respectively. Similar to the logic of SR0, two types of agronomic scenarios
have been combined and simulated for irrigation water and fertilizer applications. Based
on literature field experimental results in the Mediterranean areas and Greece, Crop Water Productivity (CWPI ) can be increased with deficit irrigation, which is the reduction
in applied irrigation at a ratio of crop water demands, and/or with rain-fed cultivation,
which is the absence of irrigation for the crop types that can grow based only on natural
precipitation. Nitrogen Use Efficiency (NUE) can be increased by reducing the nitrogen
fertilization applied to crops. In a previous study for the Almyros basin [57], scenarios of
Deficit Irrigation, Deficit Irrigation and Rain-fed cultivation, and the Reduced Nitrogen
Fertilization amounts for the main crops had been studied and applied based on results of
field experiments in Greece and the Mediterranean area for the major crops of the study
basin [53,54,72–80]. Specifically, irrigation water reduced for alfalfa at 90%, cereals at 80%,
cotton at 80%, maize at 90%, olive groves at 89%, trees at 76%, vegetables at 90%, vineyards
at 60%, and wheat at 80% of crop water demands ensure optimized irrigation and yield.
Nitrogen fertilization, reduced for alfalfa at 93% (28 kg/ha), cereals at 60% (60 kg/ha),
cotton at 78% (109.5 kg/ha), maize at 31% (100 kg/ha), olive groves at 80% (91.2 kg/ha),
trees at 73% (128 kg/ha), vegetables at 80% (120 kg/ha), vineyards at 48% (60 kg/ha) and
wheat at 64% (102 kg/ha), ensure optimized yield. These scenarios are applied during the
implementation of the agronomic and irrigation scenarios, but also in the simulation of the
operation of the reservoirs (Table 3).
Table 3. Summary of the agronomic and water resources management Scenarios.
Scenario
Source of Water
S0
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
Reservoirs and
Groundwater
SR0-A0
SR0-A1
SR1-A0
SR1-A1
SR2-A0
SR2-A1
SR3-A0
SR3-A1
SR4-A0
SR4-A1
Klinovos Reservoir
Irrigated Area
Irrigation
Fertilization
-
Existing
Existing
A0
Existing
Existing
A1
Existing
Existing
A0
Deficit
Existing
A1
Deficit
Existing
A0
A1
Deficit and Rain–fed
cultivation
Deficit and Rain–fed
cultivation
Existing
Existing
A0
Deficit
Reduced
A1
Deficit
Reduced
A0
A1
Deficit and Rain–fed
cultivation
Deficit and Rain–fed
cultivation
Reduced
Reduced
The irrigated area from the Xirias reservoir has been defined by the reservoir project
study and is used in this work [59]. However, the irrigated area from the proposed Klinovos
reservoir is unknown, as the reservoir and surrounding irrigation network have not been
designed yet. Hence, this study examines the reservoir’s efficiency to irrigate two cases
of crop areas. The first case, Area (A0), considers an irrigated area of 23 km2 , which has
been set in past studies [3,60] while the second case, Area (A1), considers an optimized
irrigated extent.
The irrigated croplands from the Xirias and the Klinovos reservoirs are presented in
Table 4 and their location is shown in Figure 3.
Water 2022, 14, 1086
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Table 4. Statistics of area extent of irrigated croplands from the storage water works of Xirias and
Klinovos for the two cases simulated for the period 1991–2018.
Irrigated
Cropland in
km2
Xirias
Klinovos Area
A0
Klinovos Area
A1
Alfalfa
Cereals
Cotton
Maize
Olives
Trees
Vegetables Vineyards Wheat
Sum
0.8
1.9
0.5
0.2
0.6
0
0.3
0.1
2.2
6.5
2.5
2
5.4
0.6
6.3
0.1
0.9
0.2
5.4
23.3
1.1
0.8
1.6
0.2
3.9
0
0.4
0.1
2
10.2
The crop pattern of Area (A1) has been optimized with the use of the UTHBAL–
UTHRL System and Optimization module on known spatial land use data of the region,
and the delineation of the irrigated area includes 10 km2 of arable land. The historical crop
pattern consists of alfalfa, cereals, cotton, maize, olive groves, trees, vegetables, vineyards,
and wheat cultivars.
2.5. Reservoir Reliability and Efficiency Indices
The potential performance of a water resource system can be evaluated with a variety
of performance indices, although the results of the Integrated Modeling System are very
detailed. In this study, the resource efficiency is estimated with several indices, namely, the
Water Exploitation Index (WEI+), the Reservoir Reliability, the Crop Water Productivity
(CWPI ), the Economic Water Productivity (EWPI ), and the Nitrogen Use Efficiency for the
current historical and the agronomic and water resources scenarios.
2.5.1. Water Exploitation Index
Water scarcity is estimated with the Water Exploitation Index (WEI+) for watersheds
and basins or monthly and seasonal timesteps. The index is calculated based on the annual
freshwater abstractions to the long-term average of the renewable resources (LTAA) for
a time of at least 20 years. Based on the data from [81], the WEI+ value for the whole
Thessaly region is, on average, 53% in spring, 35% in summer, 44% in winter, and 10% in
autumn, and the annual average is 33% from 1990 to 2015. A WEI+ score larger than 20%
denotes that a water system is under pressure, and a WEI+ score larger than 40% reveals
tremendous pressure and explicitly inefficient resources use [82]. The Water Exploitation
Index (WEI+) can be estimated for watersheds and aquifers by defining the water demands
and renewable resources, as shown in Equations (1) and (2).
WEI+Watershed =
Water Demand (Irrigation, Urban and other)per year
Renewable Surface Water + Groundwater Volume
WEI+Groundwater =
Groundwater Abstractionsper year
Renewable Groundwater Volume
(1)
(2)
2.5.2. Reservoir Reliability
The reservoirs’ performance has been estimated for their given ability from the surrounding hydrological conditions and regional climate to supply water and fully cover the
drinking and irrigational demands. According to Hashimoto et al. (1982), the reliability of
a water resources system can be defined as “how often the system fails” its purpose. Let Xt
be a random discrete variable in a time step t. The possible system’s response can belong,
in its simplest form, to one of two sets of values that denote either success or satisfactory
result and failure or unsatisfactory result. The reliability is then the possibility a system’s
response falls within the satisfactory set of values. The reliability performance index α, for
Water 2022, 14, 1086
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a number of timesteps n, and a variable Xt that is larger than a specified variable XT , α is
given in Equation (3) [83].
Reliability (α) =
number of times Xt ≥ XT
n
(3)
The reliability performance index has been estimated for the three reservoirs and all
the agronomic and water resources management practices to assess the storage waterworks
capability to cover the water demands.
2.5.3. Agronomic Indices
Indices of the productivity of the simulated water uses, aquifer budget, crop yield,
and nitrogen fertilization have been integrated to assess the efficiency of the agronomic
and water management scenarios in improving the farmland benefits. The scenario results
have been also compared to the baseline (historical) scenario, in which the traditional
practices are implemented. The Crop Water Productivity index (CWPI ) for the applied
irrigation water and the crop yield is estimated based on the simulation results of the
REPIC model, the groundwater abstractions of the MODFLOW model, and the reservoir
withdrawals of the UTHBAL–UTHRL System module for the Almyros cropland. The Crop
Water Productivity for irrigation water is given in Equation (4) [51]:
Crop Water Productivity (CWPI ) =
Crop Yield kg ha−1
Irrigation Water m3 ha−1
(4)
The Economic Water Productivity (EWPI ) index for the applied irrigation water and
Gross Profits of the crop yield is estimated based on the simulation results of the REPIC
model, the groundwater abstractions of the MODFLOW model, and the reservoir withdrawals of the UTHBAL–UTHRL System module and the marketable product prices. The
marketable product prices have been obtained from publicly available data of the Hellenic
Statistical Authority already in the base year of 2015, from 2000 to 2018.
The Economic Water Productivity is given in Equation (5) [51]:
Economic Water Productivity (EWPI ) =
Profit EUR ha−1
Irrigation Water m3 ha−1
(5)
The Nitrogen Use Efficiency index (NUE) for applied nitrogen fertilization and the
crop yield is estimated based on the inputs and simulation results of the REPIC model,
and for the long-term estimation of yields and resources is estimated as partial factor
productivity. The Nitrogen Use Efficiency is given by Equation (6) [56]:
Nitrogen Use Efficiency (NUE) =
Crop Yield (kg ha−1 )
Nitrogen Applied (kg ha−1 )
(6)
3. Application—Results
The results of the Integrated Water Resources Modeling System in the Almyros basin
for the developed agronomic and water resources management scenarios are presented
and discussed in the next paragraphs.
3.1. UTHBAL–UTHRL Surface Hydrology and Reservoir Simulation
The UTHBAL model was used for the simulation of monthly surface runoff and
groundwater recharge of the Almyros basin in the semi-distributed model (sub-watersheds),
for the period October 1991 to September 2018. The results of the UTHBAL model for
the sub-watersheds of the Almyros basin without considering the operation of reservoirs
(baseline scenario) have been acquired in a previous study [2]. In the present study, the
inflows to the existing and proposed reservoirs have been estimated. The monthly areal
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precipitation, temperature, and potential evapotranspiration have been estimated at the
mean elevation of Almyros sub-watersheds following the methodologies of the previous
study by Lyra et al. [2]. Monthly areal precipitation and temperature have been estimated
with the gradient method, while potential evapotranspiration was estimated with the
Thornthwaite method [84]. The summarized results of the hydrological simulation using
the UTHBAL model for the human-modified hydrological systems of Xirias, Mavromati,
and Klinovos of the Almyros basin are presented in Table 5.
Table 5. Mean annual values of input meteorological data and simulated results of the UTHBAL
model for the reservoir sub-watersheds of Almyros basin.
Reservoir
Sub-Watershed
Xirias
Upstream
Xirias
Downstream
Mavromati
Upstream
Mavromati Downstream/Klinovos
Upstream
Klinovos
Downstream
Temperature ◦ C
Precipitation (mm)
PET (mm)
Runoff (mm)
Recharge (mm)
13
697
757
159
75
15.7
539
865
96
35
12.4
727
735
146
78
14
670
798
144
59
16.2
560
889
93
15
The UTHRL model has been applied for the operation of the three reservoirs in the
Almyros basin, namely, Xirias, Mavromati, and Klinovos reservoirs, using as input data the
upstream runoff and meteorological data, the desired reservoir withdrawals on monthly
time step, and the characteristic curves of the reservoirs (water level volume and water
level area of the reservoirs). The net water losses of the reservoir are calculated as the
summation of reservoir evaporation, reservoir precipitation, and losses to the groundwater
through infiltration and integrated over the varied surface area of the reservoir. The output
of the model is, on monthly time step, the feasible water withdrawals, the useable stored
water volume of the reservoir, and the water volume spilled over from the reservoir’s
spillway. The results of the UTHRL model are presented in Figure 5.
Figure 5. Stored Water Volume for (a) Xirias reservoir, (b) Mavromati reservoir, (c) Klinovos reservoir
for Area A0, and (d) Klinovos reservoir for Area A1 for the various irrigation scenarios.
3.2. MODFLOW Model—Groundwater Flow Simulation
From October 1991 until September 2018, the MODFLOW model simulated groundwater levels, pump withdrawals, groundwater storage fluctuation, and seawater flows
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to the coastal aquifer of Almyros on a monthly step. The groundwater flows have been
modelled for the existing irrigation and the operation of the reservoirs, the deficit irrigation
(SR1, SR3), as well as the deficit and rain-fed practices (SR2, SR4), also combined with the
reservoirs’ operation (areas A0 and A1). The modelled hydraulic levels for the different
scenarios have been paired and differentiated from the numerical results of the baseline
water table (S0) (Figure 2) [2]. The changes in the water table are presented in Figure 6.
Figure 6. Hydraulic head changes of Almyros aquifer at the end of the simulation period (September
2018) between the baseline scenario (S0) and the various water resources and fertilization scenarios:
(a) S0—(SR0-A0), (b) S0—(SR1, SR3-A0), (c) S0—(SR2, SR4-A0), (d) S0—(SR0–A1), (e) S0—(SR1,
SR3–A1), and (f) S0—(SR2, SR4–A1).
In all simulated scenarios, the water table is elevated, with a negligible exception
area in the southern-central boundary of the aquifer. In existing irrigation and reservoirs’
operation, the largest increase is noticed in the irrigated areas by Klinovos reservoir. The
largest beneficiary region is noticed in the scenario (SR0-A0) (Figure 5a), but the range
of the elevated water table is similar to the scenario (SR0–A1) (Figure 5d), reaching the
color step of 15 to 18 m. In existing irrigation, 87% of the groundwater table is elevated up
to 3 m for irrigated extent A0 (Figure 5a) and 92% for irrigated extent A0 (Figure 5d). In
deficit irrigation, the largest improvement is also noticed at the southern part of the aquifer.
For irrigated area A0, 76% of the groundwater table is elevated up to 3 m, and 13% up to
6m (Figure 6b), while for irrigated area A1, 81% is elevated up to 3 m and 12% up to 6 m
(Figure 6e). In deficit irrigation and rain-fed cultivars, the water table is elevated across the
aquifer. For irrigated area A0, 20% of the groundwater table is elevated up to 3 m, and 39%
up to 6m (Figure 6c), while for irrigated area A1, 22% is elevated up to 3 m, and 40% up to
6 m (Figure 6f). The average water budget in the existing irrigation scenarios is –12 hm3
(S0), –9 hm3 (SR0-A0), and –10 hm3 (SR0–A1). In deficit irrigation scenarios, the water
balance is –6 hm3 (SR1, SR3-A0) and −7 hm3 in scenario (SR1, SR3–A1), while in deficit
irrigation and rain-fed is 1.6 hm3 (SR2, SR2-A0) and 0.7 hm3 (SR2, SR4–A1).
3.3. REPIC Model—Nitrogen Leaching and Crop Yield Simulation
The nitrates leaching and the crop growth have been simulated with the REPIC
model from October 1991 to September 2018. The nitrates leaching has been modelled for
existing fertilization, with deficit irrigation (SR0-A0, A1, SR1-A0, A1) as well as with deficit
irrigation and rain-fed conditions (SR2-A0, A1), and, respectively, for reduced fertilization
(SR3, SR4-A0, A1). The quantity of the nitrates leached is affected by the alterations in
nitrogen application, irrigation water, and total groundwater recharge. The concentration
of nitrates leached through the vadose zone is dependent on the dilution of the pollutants
by the recharge and irrigation return flow. The time when the nitrogen leachates reach the
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groundwater depends on the rainfall and the infiltrated rainwater in the soil. The spatial
distribution differences of nitrates leaching from the baseline scenario practices for the
simulated scenarios are presented in Figure 7.
Figure 7. Spatial differences of averaged nitrates leached between the baseline scenario (S0) and
the agronomic and water resources management scenarios of the simulation period 1991 to 2018.
(a) S0—(SR1-A0, A1), (b) S0—(SR2-A0, A1), (c) S0—(SR3-A0, A1), and (d) S0—(SR4-A0, A1).
Reducing the nitrogen fertilizer applied in crops and altering the irrigation water
has a direct effect on nitrates concentrations leached in all scenarios. In deficit irrigation,
there is a minor increase in nitrates concentrations leached, which is attributed to irrigation
return flow that transfers less diluted nitrates to groundwater (Figure 7a,c). In deficit
irrigation and rain-fed cultivation, the decrease in nitrates leached is attributed to the
absence of irrigation return flow of olive groves, cereals, and wheat (Figure 7b,d). REPIC
model is capable to calculate the annual yields of various crops for different irrigation
and fertilization scenarios. This simulation has been performed in a previous study [57]
and the results for the simulated annual crop yields of various crops have been compared
to the historical measured crop yields. The averaged yields of the crops for the baseline
conditions and the agronomic and water resources scenarios are presented in Table 6.
Table 6. Mean annual crop yields in (tn/ha) for the various scenarios (period 1991–2018).
Crop Yield (tn/ha)
1
Alfalfa
Cereals
Cotton 1
Maize
Olives 1
Trees
Vegetables 1
Vineyards 1
Wheat
Baseline
SR1
SR2
SR3
SR4
11.4
2.4
3.3
9.0
2.0
1.2
14.5
6.4
2.6
11.3
2.4
3.3
9.0
2.0
1.2
14.4
5.4
2.6
11.3
2.4
3.3
9.0
1.8
1.2
14.4
5.4
2.6
11.3
2.4
3.1
9.0
2.0
1.2
14.4
4.8
2.6
11.3
2.4
3.2
9.0
1.8
1.2
14.4
5.2
2.6
1
sensitive crop.
Alfalfa and vegetables show stable yield and negligible yield reduction in all scenarios.
Cotton shows minimal yield reduction due to reduced fertilization practices, while olive
trees are affected in deficit irrigation and rain-fed conditions. Significantly, vineyards are
the most sensitive to cultivation practices and the results indicate that reduced fertilization
is best combined with rain-fed conditions. On the other hand, the yields of cereals, maize,
trees, and wheat are not affected by changes in fertilization and irrigation practices. These
results have been used for the estimation of the efficiency indices.
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3.4. MT3DMS Model—Nitrate Groundwater Transport Simulation
From October 1991 until September 2018, the MT3DMS model simulated the nitrates
concentrations in the coastal aquifer of Almyros on a monthly step. The modelled nitrates
concentrations for the different scenarios have been paired and differentiated from the
numerical results of the baseline nitrates concentrations (S0) (Figure 2) [2]. The changes
of nitrates concentrations for the various scenarios from the baseline scenario (S0) are
presented in Figure 8.
Figure 8. Nitrates concentration (NO3− ) change in (mg/L) of Almyros aquifer at the end of the simulation period (September 2018) between the baseline scenario (S0) and the various water resources
and fertilization scenarios. (a) S0—(SR0-A0), (b) S0—(SR0-A1), (c) S0—(SR1-A0), (d) S0—(SR1A1), (e) S0—(SR2-A0), (f) S0—(SR2-A1), (g) S0—(SR3-A0), (h) S0—(SR1-A1), (i) S0—(SR4-A0), and
(j) S0—(SR4-A1).
In the existing irrigation and fertilization scenarios, the nitrates are reduced only in
21% and 19% of irrigated areas, respectively, near the operated reservoirs (Figure 8a,b).
In deficit irrigation and existing fertilization for the irrigated Area A0 and A1, 85% of the
area presents a reduction in nitrates concentration up to 10 mg/L (Figure 8c,d). In deficit
irrigation and reduced fertilization, nitrates concentrations are reduced up to 10 mg/L in
95% of the aquifer area (Figure 8g,h). In the scenarios of deficit irrigation with rain-fed
cultivars and existing fertilization, nitrates concentrations are reduced up to 10 mg/L in
90% of the aquifer area in the scenario with area A0 (Figure 8e) and 89% in the scenario
with area A1 (Figure 8f), and up to 20 mg/L in 0.8% and 1.6%, correspondingly. In deficit
irrigation with rain-fed cultivars and reduced fertilization, the nitrates concentrations are
reduced up to 10 mg/L in the 89% and 90% and up to 20 mg/L in the 1.6% and 1.5% of the
aquifer area (Figure 8i,j).
3.5. SEAWAT Model—Seawater Intrusion Simulation
From October 1991 until September 2018, the SEAWAT model simulated the chlorides
concentrations in the coastal aquifer of Almyros on a monthly step. The chlorides fluxes
have been modelled for the various scenarios. The modelled chlorides concentrations for
the different scenarios have been paired and differentiated from the numerical results of the
baseline chlorides concentrations (S0) (Figure 2) [2]. The changes in chlorides concentrations
are presented in Figure 9.
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Figure 9. Chlorides Concentrations Change in (mg/L) of Almyros aquifer at the end of the simulation
period (September 2018) between the baseline scenario (S0) and the various water resources and
fertilization scenarios. (a) S0—(SR0-A0), (b) S0—(SR1, SR3-A0), (c) S0—(SR2, SR4-A0), (d) S0—(SR0A1), (e) S0—(SR1, SR3-A1), and (f) S0—(SR2, SR4-A1).
In existing irrigation and fertilization, the chlorides concentrations are reduced up to
250 mg/L in 94% for the irrigated Area A0 (Figure 9a), and in 93% for the irrigated Area
A1 (Figure 9d). In the location of the Klinovos reservoir, the chlorides concentrations are
increased up to 250 mg/L in the 6% and 7%, respectively. In deficit irrigation, the chlorides
concentrations are reduced up to 250 mg/L in 84% of the aquifer area (Figure 9b,e) while
local increases are noticed in the 15% of the aquifer area. In deficit irrigation with rainfed cultivars, chlorides concentrations are reduced up to 250 mg/L in 78% of the aquifer
area (Figure 9c,f), while local increases are noticed in 19% of the aquifer area. However,
the maximum chlorides concentrations of the coastlines, as compared to the baseline (S0)
scenario, are significantly reduced by 42% in scenarios of deficit irrigation and by 76%
in scenarios of deficit irrigation with rain-fed cultivars. No change is noticed in baseline
irrigation practices with the operation of reservoirs because they are away from the coast.
3.6. Efficiency Indices
3.6.1. Water Exploitation Indices WEI+Watershed and WEI+Groundwater
The Water Exploitation Index (WEI+) has been estimated for the drinking and agricultural demands of the baseline scenario (S0) where no storage works exist and of the
simulated scenarios on a yearly time step. The time series of the WEI+Watershed and
WEI+Groundwater scores are presented in Figure 10.
The average WEI+Watershed of the simulation for the existing irrigation is 0.73, and for
the deficit irrigation scenario is 0.66. Both values are larger than 0.4, indicating unsustainable use of water resources. For the deficit irrigation with rain-fed cultivation, the score is
improved at 0.34, a value larger than 0.2 indicating pressure on water resources.
The average WEI+Watershed of the simulation for the existing irrigation is 0.73, and for
the deficit irrigation scenario is 0.66. Both values are larger than 0.4, indicating unsustainable use of water resources. For the deficit irrigation with rain-fed cultivation, the score is
improved at 0.34, a value larger than 0.2 indicating pressure on water resources.
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Figure 10. Annual values of (a) Basin Water Exploitation Index (WEI+Watershed ) and (b) Aquifer Water
Exploitation Index (WEI+Groundwater ).
The results indicate that the agronomic and water resources management scenarios
have a direct effect on water resources balance. However, the existing crop pattern demands large volumes of water for irrigation that cannot be sustainably supplied by the
groundwater pumping and the existing and proposed reservoirs. A different crop pattern
may alleviate and mitigate the problem of water balance deficit.
3.6.2. Reservoir Reliability
The reservoir reliability of the reservoirs’ operation (α) has been calculated on a
monthly timestep for all agronomic and water resources management scenarios. The
results of the reliability of reservoirs to address to water demands of urban and agricultural
supply are presented in Table 7.
Table 7. Mean monthly reservoir reliability to satisfy water demands.
Reservoir
Reliability α (%)
Existing (SR0)
Deficit (SR1, SR3)
Deficit and Rain-fed
(SR2, SR4)
Mavromati
Xirias
Klinovos Area A0
Klinovos Area A1
97.2
97.2
100
100
68.5
73.8
89.8
100
97.2
100
99.7
100
The irrigation Xirias reservoir, as well as the urban water supply Mavromati reservoir,
cover the monthly water demands with high reliability. The Klinovos reservoir, in the
existing irrigation conditions, has medium reliability. For irrigation area A1, the reservoir is
reliable to operate efficiently. For the scenario of deficit irrigation and rain-fed cultivation,
Klinovos reservoir can cover the water demands of areas A0 and A1 with high reliability.
However, for deficit irrigation scenario, it is evident that it is completely reliable to cover
the water demands of irrigation area A1.
3.6.3. Crop Water Productivity (CWPI )
In light of the results of the REPIC model for grain production and agricultural water
requirements for the different scenarios, the Crop Water Productivity (CWPI ) score has
been calculated for each field crop in the Almyros area, utilizing Equation (2). The averaged
values of the cropland for the simulated scenarios are presented in Figure 11a. CWPI of
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the cropland is more noticeably increased for the deficit irrigation scenario, than for the
deficit irrigation and rain-fed cultivation scenario (Figure 11a). All crops reach maximum
scores of CWPI for the deficit irrigation scenario. Cotton, vegetables, and vineyards show a
slight reduction in CWPI values for the reduced fertilization scenario, but all other crops
maintain stable scores of CWPI for existing conditions and reduced fertilization scenario.
Figure 11. Annual average values of (a) Crop Water Productivity (CWPI ), (b) Nitrogen Use Efficiency
(NUE), and (c) Economic Water Productivity (EWPI ) for the period 1991–2018.
3.6.4. Nitrogen Use Efficiency (NUE)
The Nitrogen Use Efficiency (NUE) score for the scenarios of existing and reduced
nitrogen application has been calculated for each field crop by Equation (3). The averaged
values of NUE of the crops for the simulated scenarios are presented in Figure 11b. An
increase in NUE values is observed for reduced fertilization scenarios (SR3 and SR4, for
both deficit irrigation and deficit irrigation and rain-fed cultivation scenarios) compared
with the existing fertilization conditions. This indicates that the existing fertilization is
larger than an optimum fertilization scheme (Figure 11b). Alfalfa, cereals, cotton, vineyards,
and wheat exhibit the highest scores of NUE, for the reduced fertilization with deficit
irrigation and rain-fed cultivations scenarios. Olive tree cultivation shows the largest
scores of NUE for reduced fertilization with deficit irrigation scenario. Maize, trees, and
vegetables exhibit the highest values of NUE for the reduced fertilization, regardless of the
irrigation practice.
3.6.5. Economic Water Productivity (EWPI )
The Economic Water Productivity (EWPI ) index was estimated using Equation (4)
for the irrigation water applied. The calculation has been performed on the product
prices assuming constant prices that of 2015. The averaged values of the cropland for the
simulated scenarios are presented in Figure 11c. The Economic Water Productivity (EWPI )
follows the trend of (CWPI ) and is greatly increased for the deficit irrigation scenario. EWPI
scores are marginally higher for the scenario of reservoir operation (scenarios SR0-A0, A1)
compared with the baseline scenario (only groundwater pumping, S0). For the deficit
irrigation scenarios either for existing or reduced fertilization, the EWPI values noticeably
increase, especially for irrigated area A0 as less water is pumped out of the aquifer than for
area A1. For the deficit irrigation and rain-fed cultivation scenario, EWPI values decrease
for the irrigated crops. For all scenarios, the contribution of reservoirs for the existing
irrigation and deficit irrigation maximizes the gross profits and EWPI . Deficit irrigation is
the most profitable irrigation scenario.
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4. Discussion
Agronomic and water resources management scenarios have been implemented on
the Almyros watershed in Thessaly, Greece. The analysis took place using an Integrated
Modeling System developed by [2]. The modeling system consists of coupled models
of surface hydrology (UTHBAL), groundwater flow (MODFLOW), crop growth/nitrates
leaching (REPIC), contaminant transport/nitrate pollution (MT3DMS), and seawater intrusion (SEAWAT), integrating the reservoir operation model (UTHRL) developed by [16].
The connection of the UTHBAL and UTHRL models has been configured to optimize the
irrigated extent of croplands, to ensure the reliability of the supplied water volume by
the reservoirs.
The results of groundwater hydraulic heads for existing irrigation indicate that the
operation of reservoirs increases the hydraulic heads near the reservoir locations. The
positive change in the water table continues to improve as groundwater pumping is
reduced with the use of surface water stored in the reservoirs. The results show that, for
the deficit irrigation scenario, the water table elevation increases in the central area and
mostly nearby Klinovos reservoir while, for the deficit irrigation and rain-fed cultivation
scenario, the water table increases over the whole area of the aquifer.
The nitrates leaching, as simulated by the REPIC model, show a small increase in
the deficit irrigation which is attributed to the smaller dilution of the contaminant. In
deficit irrigation and rain-fed practice, the nitrate leaching are reduced significantly in
the southern part of the aquifer, which is attributed to the absence of irrigation return
flow of the rain-fed crop cultivations of olive groves, cereals, and wheat. Regarding
the contaminant species fate under the scenario schemes, the nitrates concentrations, as
simulated by the MT3DMS model, seem to inversely follow the water table’s course of
change for all scenarios. For the existing fertilization and irrigation (baseline) scenario, the
nitrates concentrations are especially reduced in the irrigated areas of Xirias and Klinovos
reservoirs. For the deficit irrigation scenario, the nitrates are reduced across the aquifer
and more in the reservoir irrigated areas. The increases in the west part of the aquifer
are possibly due to the existing fertilization applications and the reduction in pumping
out nitrates through groundwater abstractions. For the deficit irrigation and rain-fed
cultivation scenario, the nitrates concentrations are even more reduced in a wide area, and
larger reductions are found in certain areas.
Similarly, seawater intrusion is greatly alleviated in all scenarios. For the existing
irrigation scenario, no significant decreases occur, while in the southern part of the aquifer,
local assimilations occur that are attributed to the hydrogeological conditions, groundwater
flow regime, and the reduction in chlorides pumped out, along with groundwater abstractions. For the deficit irrigation scenario, the chlorides are increased in the southern area,
but in the coastline the chlorides are extremely reduced. This means that deficit irrigation,
along with the reservoirs’ operation, serves as a seawater intrusion barrier. For the deficit
irrigation and rain-fed cultivation scenario, chloride changes follow the same pattern as
the previous scenarios, but in the coastline the seawater intrusion is more than three times
reduced as compared to the baseline (S0) scenario.
The time-series of the average calculated water table elevation, nitrate concentration,
and chloride concentration are presented in Figure 12 for the Baseline scenario (S0) and
all the agronomic and water resources scenarios. From these graphs, it is evident the
mitigating effect of the scenarios and reservoirs’ operation on the water balance and quality
of groundwater.
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Figure 12. Monthly values of (a) Mean Water Table Elevation (m), (b) Mean Nitrates concentration
(NO3− ) (mg/L), and (c) Chlorides Concentration (mg/L) of Almyros aquifer for the period 1991–2018.
The Water Exploitation Index (WEI+Watershed ) in the baseline scenario, S0, indicates
that the Almyros basin is under severe water stress. However, the operation of reservoirs,
combined with deficit irrigation and/or deficit irrigation and rain-fed cultivations, alleviate
the pressure on water resources to cover the water demands of the area. The Water
Exploitation Index (WEI+Groundwater ) for the baseline scenario (S0) shows that the aquifer
of the Almyros basin is overwhelmingly pressured by groundwater abstractions. However,
with the implementation of irrigation practices, the pressure is reduced but the aquifer
remains under lesser, but still significant, water stress. The WEI+Groundwater scores are
validated by the quality status of the aquifer at the end of simulation, where mostly nitrate
pollution and seawater intrusion are still present, because the WEI+Groundwater scores are
larger than 0.4 and it is considered unsustainable [85].
The CWPI values are larger for deficit irrigation than for deficit irrigation and rain-fed
cultivation. For the deficit irrigation scenarios, all crops exhibit larger CWPI values. Cotton,
vegetables, and vineyards have comparatively lower nutrient management ratings, however other crops have steady values between both existing as well as reduced fertilization.
The results of NUE calculation for the various scenarios indicate that values of NUE are
Water 2022, 14, 1086
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larger for the reduced fertilization scenarios compared with the existing fertilization scenarios. This indicates the existing fertilizer applications may be larger than the optimum
application rates. There is a significant increase in NUE values for the reduced fertilization.
Alfalfa, cereals, cotton, vineyards, and wheat have larger NUE scores for reduced fertilization under deficit irrigation and rain-fed cultivation scenarios. Olive tree cultivation has the
lowest NUE scores for reduced fertilization under deficit irrigation scenarios. Maize, trees,
and vegetables have the lowest NUE scores, compared with the other crops, for reduced
fertilization irrespectively of irrigation practice.
The operation of reservoirs improves the economic efficiency for all the irrigation and
fertilization scenarios. EWPI is considerably increased for deficit irrigation scenarios and
the index values decrease for deficit irrigation and rain-fed scenarios. The results indicate
that deficit irrigation is the most profitable irrigation scenario for the Almyros basin.
5. Conclusions
The simulation of the agronomic and water resources management scenarios with
the Integrated Modeling System for the Almyros basin and its aquifer system proves that
improvement of water resources quantity and quality is likely to occur after decades of
implementation of good agricultural practices.
The presence and operation of reservoirs to replace the groundwater abstractions is
a critical measure in this direction. The target of a good groundwater quantity status is
more easily achieved than a good groundwater quality status during the same duration.
Seawater intrusion can be minimized or reversed if the proposed schemes are implemented,
and nitrate pollution minimization can also be promoted. Nevertheless, local assimilations
of contaminants due to natural reasons, even when good agricultural practices are applied,
indicate the need for drastic interventional approaches, such as techniques of remediation
to be more actively integrated into the policy actions of reaching environmental targets.
The results of the study indicate that priority should be placed upon the deficit
irrigation under the reduced fertilization scenario management scheme for the irrigated
area A1 by the Klinovos reservoir, because the desirable reservoir reliability is achieved,
the economic sustainability is ensured, and the resources sustainability is promoted and
safeguarded to the best possible extent.
The presented approach in this study, using the Integrated Modeling System and
efficiency indices, can be transferred and applied to other coastal agricultural basins if the
necessary data is available. The approach will help to evaluate the water resources status
as well with the aims to assess the status and sustainability of water resources, and also
design, implement, and evaluate Best Management Practices to reach Sustainable Goals
and environmental targets in the modern era.
Author Contributions: Conceptualization, methodology, supervision, writing—original draft-review,
and editing, A.L. (Athanasios Loukas); methodology, software, writing—original draft preparation,
review, and editing, investigation, data curation, and formal analysis, A.L. (Aikaterini Lyra); writing—
original draft preparation, review, and editing, P.S.; writing—original draft preparation, review, and
editing, K.V.; and writing—original draft preparation, review, and editing, N.M. All authors have
read and agreed to the published version of the manuscript.
Funding: This research is co-financed by Greece and the European Union (European Social Fund—
ESF) through the Operational Programme «Human Resources Development, Education and Lifelong
Learning» in the context of the project “Strengthening Human Resources Research Potential via
Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKΥ).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The results of this study are freely available.
Conflicts of Interest: The authors declare no conflict of interest.
Water 2022, 14, 1086
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