Evaluating Impact of Land Use and Land Cover Change under Climate Change on a Lake System

doi:10.21203/rs.3.rs-1712629/v1

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Research Article

Evaluating Impact of Land Use and Land Cover Change under Climate Change on a Lake System

https://doi.org/10.21203/rs.3.rs-1712629/v1

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Persistent pressures that relate to nonstationary climate variations and land use/cover changes (LULCC) keep affecting water resources and their associated environmental services with increasing impact trends worldwide. Lake Marmara, located in Gediz River Basin in Western Turkey, is already considered among the environmentally degraded water resources while counted with a priority wetland due to historical habitats and ecosystem functions. The region is remarkably threatened to its extinction under the combined impacts of both climate and LULCC related concerns. While intensive agricultural practices around the region deplete the lake's inflow sources, low levels of the lake water provide breeding ground for various algae types and cause eutrophication and related massive fish population decreases, in a way restricting local communities' access to fundamental ecosystem services for their survivability. The study investigates the effects of LULCC in the example of Lake Marmara in an environmental scope and provides water balance modelling that is utilized to help examine hydrologic conditions of the lake. Future LULCC and water balance projections are also incorporated to evaluate probable views in near future ahead with the main target of providing a valuable tool for the decision and policy making processes.

Land Use and Land Cover Change (LULCC)

climate change

hydrologic modelling

lake water assessment

The effects of climate change on water resources have been noticeable to various degrees throughout the world in recent years. Such impacts will undoubtedly intensify when other forms of environmental alterations that include land use/cover changes, adapted variations of economic activities (e.g., cropping pattern changes that are deemed necessary for securing economic survivability) may accompany climate impacts, greatly as a widely anticipated consequence of climate drivers on communities’ socio-economic practices and ecosystem functions. It is evident from the observations, predictions and experiences that the greater part of climate change-induced anomalies start with the altered hydro-meteorological characteristics of the regions as most impacts indeed relate to hydrologic variables such as precipitation and temperature (Huntington, 2006). Moving basically from this origin, climate change related concerns emerge on various components of ecosystem structures ranging from food security, human health, economic development, and local/regional conflicts, which may still be connected to the availability and risks on freshwater resources (IPCC 2013). Here, storage systems, which may include natural lakes or artificial ponding structures and which remarkably constitute significant sources for aquatic and terrestrial ecosystems, agriculture and industrial production, are mainly subject to threats of limited potentials and even extinction when considering that their services depend only on hydrologic sustainability.

As one of the research efforts on lake systems under aforementioned negative impacts of climate change and associated change dynamics in forms of LULCC and mismanagement of scarce water resources in the region under emerging threats, the presented study examines the case of Lake Marmara located along the transect in the most western end of Turkey’s territories. Topologically, the lake takes place within the network of Gediz River Basin, where the lake water was greatly supplied by the Gördes Reach until the commissioning of the Gördes Dam in 2011, which was mainly designed for contributing to drinking water supply security aspects for the metropolitan city of Izmir. As a normal perspective of competing sectors for water, i.e. the drinking and utility water needs of a big metropolitan and the ecological water requirements of a lake wetland reserve of inter-/national significance, the dam, unfortunately, left no water to the lake while at the same time failing to retain water in sufficient quantities in the reservoir itself. As a result, Lake Marmara dried up gradually after the year 2011 to present. While the inflows and precipitation to the lake proved to be insufficient for the lake to retain its required storage volume for meeting the environmental needs for its ecological sustainability, further irrigation water withdrawals from the lake even worsened the scarcity conditions.

There are some limited number of research efforts that mainly concentrated on exploring the climate-driven drought appearances of major lakes worldwide (Abd Ellah 2020; Bozorg-Haddad et al. 2022; Hunink et al. 2021; Soonthornrangsan JT and Lowry CS 2021; Zhuang et al. 2018). In most of these, climate impacts on diminishing lake water potentials were explored through various means of hydrologic simulations, water accounting/allocation schemes under water competition conditions, system dynamics modelling again toward assessing the water potentials that may be remained or gained in lake water systems, indicator framework developments also for conducting scenario assessments, etc.

It is obvious from a thorough investigation of previous research that most of these research contributions seem to have conducted analytical tools and approaches, in greatly admirable capacities and with exemplary experiences, on lake water assessment perspectives under non-stationary conditions of the future periods, yet still remaining the needs and gaps for collectively associating climate change impacts on hydro-meteorological conditions, accompanying changes in land use conditions enforced again by the climate dynamics and the emerging adaptation requirements, and the last but not the least, differing foresights in terms of various management rules that may be practiced in operating lake water allocation schemes.

As part of the scientific novelty brought through the framework approach and methods and one of the preliminary outputs of “Safeguarding the livelihood of rural communities and the environment in the Mediterranean through Nature-based Solutions” (Mara-Mediterra) project (2121 Call 2021 Section 1 Water RIA), which involves 8 Mediterranean countries, the presented study explores a wider range of scenarios, as it examines not only the climate change and LULCC and their combined impact, but also the irrigation demand management practices and possible initial storage volume assumptions of the lake as scenario criteria. The demand management practices such as allocating no water to irrigation (thus requiring non-irrigated agriculture applications) were accounted in the study to reflect the dire choices that might have to be considered in order to secure the potential and ecological sustainability of Lake Marmara.

2.1. Materials

The study observes the impacts of LULCC in the case of Lake Marmara, located in the west of Turkey (Fig. 1) within the boundaries of Gediz River Basin. The lake covers an area of 68 km² and ranks among the wetlands that have international importance according to the Ramsar Convention with its ecosystem characteristics that provide hosting a large number of aquatic birds. Around the lake, intensive agricultural and fishery activities supply significant economical benefits for most of the local inhabitants and settlements in/around the lake region.

For modelling the hydrologic system dynamics of the lake watershed, meteorological data were basically obtained from the State Hydraulic Works (DSI). Here, Salihli meteorology station was selected as the most representative station in the vicinity of the lake based on the identification through the Thiessen polygons approach that 97% of the lake drainage area is represented by this station. Thus, a set of daily meteorological data for a 32-year period (1980–2012) recorded at the Salihli station was employed as the input into the hydrological modelling.

In order to represent the terrestrial topography of the model area, ASTER-DEM version 3 data in 30-meter spatial resolution was utilized as the Digital Elevation Model (DEM) of the basin (ASTER 2021). The data on basin topographic and hydro-morphologic characteristics were further processed within the ArcGIS software accordingly. The locally downscaled climate change data of around the representative average year 2050 (RCP 8.5) have been acquired from the Global Climate Model (GCM) Downscaled Data Portal provided by the Research Program on Climate Change, Agriculture and Food Security (CCAFS 2021).

2.2 LULCC Modelling

Assessment of LULCC monitoring has been carried out by the Land Change Modeller (LCM) tool of the TerrSet software (Clark Labs, 2020). Estimation of the land cover classes of the future maps was implemented by employing Artificial Neural Network (ANN) and Cellular Automata (CA) - Markov Chain assessment framework (Ansari and Golabi 2019). Model development processes included; (i) spatial change analysis of the historical LULC data (belonging to the years 2000 and 2012), (ii) selection of driver variables that potentially make substantial impacts to trigger changes, (iii) generation of transition sub-model, (iv) validation of the sub-model, and (v) future predictions. The years 2000 and 2012 CORINE land cover maps were selected for the historical LULC data, whereas the map of the year 2018 was utilized for the validation (Fig. 2). In order to simplify the transition processes, some of the CORINE land cover classes have been thematically grouped under more generic classes (Table 1).

Table 1

Reclassified CORINE land cover classes
CORINE Code	CORINE Land Cover description	Reclassified Land Cover description	CORINE Code	CORINE Land Cover description	Reclassified Land Cover description
111	Continuous urban fabric	Urban	231	Pastures	Pastures
112	Discontinuous urban fabric		321	Natural grassland
121	Industrial or commercial units		323	Sclerophyllous vegetation
133	Construction sites		324	Transitional woodland/shrub
141	Green urban areas		331	Beaches, dunes, sands	Barren land
142	Sport and leisure facilities		332	Bare rock
122	Road and rail networks and associated land	Transportation	333	Sparsely vegetated areas
123	Port areas		334	Burnt areas
124	Airports		131	Mineral extraction sites
211	Non-irrigated arable land	Agriculture	132	Dump sites
212	Permanently irrigated land		411	Inland marshes	Wetlands
242	Complex cultivation patterns		421	Salt marshes
243	Land principally occupied by agriculture, with significant areas of natural vegetation		422	Salines
221	Vineyards	Vineyards, etc.	511	Water courses	Water
222	Fruit trees and berry plantations		512	Water bodies
223	Olive groves		521	Coastal lagoons
311	Broad-leaved forest	Forest	523	Sea and ocean
312	Coniferous forest
313	Mixed forest

As the driver variables, which are the main factors influencing the land cover transitions considered in the LULCC model for LULC transitions, (a) DEM, (b) slope, (c) distance to water bodies, (d) distance to stream network and, (e) distance to urban areas have been chosen (Fig. 3). These mentioned driver variables have been chosen due to their considered relevance to the biophysical (Kim et al. 2014; Tyrrell et al. 2004; Araya and Cabral 2010), agricultural and environmental (Khoi and Murayama 2010) change processes and probable tendencies.

2.3 Hydrologic Modelling

The Hydrologic Modelling System (HEC-HMS, 2001) software was applied to simulate the hydrologic processes in the Lake Marmara watershed. The observed runoff records which cover the data in the period 1980–2012 have been split into 2 subsequent periods as 1980–1996 for calibration and 1997–2012 for validation purposes. In order to reflect the LULCC impacts on the hydrologic simulations, “SCS Curve Number Loss Method” has been basically employed (Gao et al. 2020; Koneti et al. 2018). As it is required in the method to compute Curve Numbers (CNs) for different land segments within the drainage basin, in accordance with the LULC type and hydrologic soil group combinations as given in Table 2 (NRCS 2007), CNs have been computed accordingly. Here, the entire basin has been divided into 6 sub-basins and CNs have been computed for each, rather than a single lumped estimate for CN, to increase further the spatial representative capacity of the hydrologic model (Fig. 4).

Table 2

Cover types and related CNs (NRCS 2007)
LULC Type	Description	A	B	C	D
Agriculture	Row Crops	67	78	85	89
Barren	Wasteland	71	80	85	88
Forest	Oak-aspen-Mountain brush mix. Of brushes	26	40	58	61
Vineyards	Woods-grass combination (orchard)	43	65	76	82
Pasture	Pasture, grassland or range	39	61	74	80
Urban	Developing Urban Areas	77	86	91	94
Water	Reservoir	100	100	100	100
Wetlands	Reservoir	100	100	100	100

After compiling all the meteorological and watershed-related data required for the HEC-HMS model, the model was first run for the 1980–1996 calibration period in order to model the stream runoff. Figure 5 shows the observed and simulated hydrographs and the model diagnostics were given in Table 3. In this table, the coefficient of determination (R²), Nash Sutcliffe Efficiency (NSE), Root Mean Square Error to standard deviation ratio (RSR) and Percent Bias (PBIAS) are the statistical values showing the simulation performance of the model setup. Based on these model-fit statistics, the performance of the model provided sufficient signals that correspond to relatively high performance indications for a good fit (Moriasi et al. 2007). After sufficient degree of fit was achieved, the model was re-operated for the period 1997–2012 using the calibrated model. The validation results are presented in Fig. 6, while again in Table 3 the simulation performances for validation process is shown.

Table 3

Simulation performance statistics of the HEC-HMS model based on the basin runoff outputs.
Statistics	Value for calibration period	Value for validation period
R²	0.90	0.73
NSE	0.87	0.68
RSR	0.36	0.57
PBIAS(%)	-18.20	-10.40

The acquired climate change data (mean precipitation and mean temperature) for the year 2050 according to the RCP 8.5 (Moss et al. 2010; van Vuuren et al. 2011) scenario have been adapted onto the hydro-meteorological data for the year 2012 through the use of the delta change approach. The calculated sets of input were utilized in the rainfall-runoff model to simulate the predicted runoff values in/around the year 2050.

2.4 Scenario Development

In order to evaluate the effects of anticipated LULCC and climate change, Lake Marmara water balance scenarios have been developed in a superposed manner (Table 4). Since the greatest share of the water resources of the lake watershed are mainly demanded by the agriculture sector, agricultural irrigation management scenarios have been generated by considering different management approaches ranging from “irrigated” type (meaning that total irrigation demand is supplied), “50% irrigated” (where half of the irrigation demand can be supplied under potential scarcity conditions of the future) and “non-irrigated” (that associates to the worst cases where no irrigation demand can be supplied, basically due to the fundamental loss of the water resources (i.e., the lake water basically) in the basin).

The initial storage volume of the lake has also been considered as a scenario parameter itself. The Gediz River Basin annual water balance simulations have been the results of SMART (Grant agreement no: 693642) and OPTIMA (Grant agreement no: 509091) projects, which employed the Waterware water balance simulation modelling software. According to Waterware model, observed minimum storage in the Lake Marmara was 72.85 hm³, which was the baseline scenario for SMART and OPTIMA projects. Future predictions of the Waterware model predicted the storage volume of the Lake Marmara as 31.48 hm³ in a wet year and 23.47 hm³ in a dry year. Mentioned storage volumes have been utilized in this study as the initial storage volumes in water balance calculations.

Table 4

Developed scenario descriptions
Agricultural irrigation demand management scenario types	CC	LULCC	CC + LULCC
	Description	Description	Description
	LULC data: year 2012	LULC data: year 2050	LULC data: year 2050
	Meteo data: year 2050	Meteo data: year 2012	Meteo data: year 2050
Initial Storage Volume: Baseline Scenario (72,85 hm³)
Irrigated	M1.1	M1.2	M1.3
50% irrigated	M1.4	M1.5	M1.6
Non-irrigated	M1.7	M1.8	M1.9
Initial Storage Volume: Future Wet Year Scenario (31.48 hm³)
Irrigated	M2.1	M2.2	M2.3
50% irrigated	M2.4	M2.5	M2.6
Non-irrigated	M2.7	M2.8	M2.9
Initial Storage Volume: Future Dry Year Scenario (23.47 hm³)
Irrigated	M3.1	M3.2	M3.3
50% irrigated	M3.4	M3.5	M3.6
Non-irrigated	M3.7	M3.8	M3.9

A total of 27 models have been generated in this manner, where past scientific outcomes and present efforts have been utilized in order to help assess the hydrologic conditions of the Lake Marmara.

Assessment of the LULCC in the study has been carried out on a larger scale than the lake watershed itself. Gediz River Basin, which includes the Lake Marmara watershed, has been assigned as the wider analytical extent for the LULCC analyses. Within the LCM analyses that were conducted with respect to the historical LULC data in the period 2000–2012, a total of 23 transitions have been addressed (Table 3). Out of all these considered transitions, 3 have been chosen for the development of the sub-model: (1) agriculture to urban; (2) vineyards or others to urban and (3) agriculture to barren. The chosen transitions are highly related to the objectives set forth for the presented study, mainly targeting simulations for possible disturbances on agriculture. These disturbances are indeed very significant on different layers, such that the agricultural activities mainly have considerable impact on economic, social and environmental levels. While the sprawl of agriculture could indicate any reduction in quantity and quality aspects for water resources, withdrawal of agricultural lands possibly causes social and economic pressures.

The Multi-Layer Perceptron (MLP) method has been employed to determine the transition potentials between the historical LULC maps and then this has been followed by the future prediction for the year 2018 as part of the validation process. As the validation approach for the LULCC model, Relative Operating Characteristic (ROC) analysis has been utilized (Fig. 7). Calculated area under the curve (AUC) of the total ROC curve yielded the value of 72% in a way to represent fair success rate in the sense of modelling goodness-of-fit statistics (Gorunescu 2011).

Table 5

Land cover transitions between 2000 and 2012 for the extent of Gediz River Basin
Transition No	Transition Name	Area (km²)	Transition No	Transition Name	Area (km²)
1	Agriculture to Barren	1.10	13	Vineyards to Forest	0.24
2	Agriculture to Pasture	0.41	14	Vineyards to Urban	0.57
3	Agriculture to Urban	12.60	15	Pasture to Agriculture	0.95
4	Agriculture to Water	0.60	16	Pasture to Barren	6.44
5	Barren to Agriculture	0.63	17	Pasture to Forest	1.93
6	Barren to Pasture	0.48	18	Pasture to Vineyards	0.22
7	Barren to Urban	1.63	19	Pasture to Urban	2.52
8	Forest to Agriculture	1.20	20	Pasture to Transportation	0.93
9	Forest to Barren	0.41	21	Pasture to Water	0.56
10	Forest to Vineyards	0.32	22	Urban to Agriculture	0.18
11	Forest to Pasture	58.53	23	Water to Agriculture	2.06
12	Forest to Urban	0.38

After the validation process, future prediction for the year 2050 has been carried out (Fig. 8). Results of the LULCC analyses depict that there is considerable increase in urban sprawl and in the rate of agricultural withdrawal in the Lake Marmara extent (Table 6).

Table 6

Land cover type totals and differences through 2000 and 2050
Cover Type	Area (km²)
Cover Type	2000	Difference (%)	2012	Difference (%)	2018	Difference (%)	2050
Agriculture	119.72	-18.09	98.06	-0.06	98.00	-7.81	90.35
Barren	5.99	-69.76	1.81	76.43	3.19	-43.32	1.81
Forest	23.20	-23.94	17.65	-13.14	15.33	15.13	17.65
Vineyards	7.41	258.40	26.54	0.00	26.55	0.01	26.55
Pasture	87.11	12.41	97.92	0.41	98.32	-0.41	97.92
Urban	2.25	47.32	3.32	17.68	3.90	182.22	11.01
Water	60.98	-0.19	60.86	0.00	60.86	0.00	60.86
Wetlands	3.89	12.48	4.38	0.00	4.38	0.00	4.38

The visual comparison of the LULCCs in Lake Marmara watershed through the years between 2000 and 2050 has been presented in the Fig. 9. Noticeable changes have been marked for indication. As given in the Table 6, urban sprawl and withdrawal of barren land are to be expected until 2050 in the Lake Marmara watershed.

According to the LULCCs, area-weighted average values of CNs of the sub-basins have been calculated for the year 2012 and 2050 in order to be utilized as input in the HEC-HMS hydrologic model (Table 7). Since the changes in the cover types are relatively small in the extent of the lake watershed, area-weighted averages of the CNs also differ slightly in the period 2012–2050.

From the hydrological perspective of the projected figures for LULCCs, the runoff rate for the year 2050 has been calculated in accordance with the RCP 8.5 climate scenario (Fig. 10). According to the results, runoff values are estimated to increase by almost 6% until the year 2050 with much clearer differences between the maximum and minimum monthly averages computed periodically.

This result may indicate that the lake ecosystem and surrounding social and economic assets can be prone to increased threats due to the appearance of bigger variation to be observed between the monthly flows throughout the year, by increasing the significance of much improved control operations for water supply and a more detailed planning of water allocation schemes between the water dependent sectors of the lake ecosystem.

Table 7

Area percentages of the cover types and area weighted average CN values of the sub-basins through 2012–2050
Year	2012
Subbasin	SB1	SB2	SB3	SB4	SB5	SB6
Agriculture	33,18	10,39	5,18	3,21	27,94	17,55
Barren	1,53	0,28	0,00	0,00	0,00	0,00
Forest	8,07	0,47	0,00	2,43	6,65	0,00
Vineyards	14,24	0,91	1,24	0,46	9,65	0,00
Pasture	52,83	7,52	8,69	11,45	12,14	5,01
Urban	2,29	0,00	0,13	0,13	0,48	0,28
Water	0,01	0,01	0,06	0,00	0,01	0,11
Wetlands	1,77	0,28	2,24	0,00	0,00	0,00
CN	66,33	70,41	71,59	61,48	67,79	74,50
Year	2050
Subbasin	SB1	SB2	SB3	SB4	SB5	SB6
Agriculture	30,25	10,39	4,52	2,93	26,38	15,35
Barren	1,53	0,28	0,00	0,00	0,00	0,00
Forest	8,07	0,47	0,00	2,43	6,65	0,00
Vineyards	14,24	0,91	1,24	0,46	9,66	0,00
Pasture	52,83	7,52	8,69	11,45	12,14	5,01
Urban	5,21	0,00	0,79	0,41	2,04	2,49
Water	0,01	0,01	0,06	0,00	0,01	0,11
Wetlands	1,77	0,28	2,24	0,00	0,00	0,00
CN	66,53	70,41	71,90	61,61	68,01	75,26

For the water balance simulations; observed and modelled precipitations, evaporation rates calculated according to the Meyer evaporation method and the observed demands have been considered. Since the lake region is located more below the average groundwater level of the river basin and thus the lake area greatly locates on saturated soils, infiltration losses have been considered negligible specifically in the lake watershed. No inflows from the Gediz River to the lake have also been accounted in hydrologic simulations since 2011 that associates to the year when the Gördes Dam started operating and allowing no diversion from the regulator structure that previously supplied flows to the lake.

Generated water balance simulations take count of mainly the satisfaction of the minimum volume requirement for addressing the environmental need of the lake ecosystem, assumed to be in a quantity of 23.47 hm³, which associate to the minimum volume storage amount determined by the Waterware software for these environmental requirements for the future dry year scenario. Time to fail and monthly water deficit volumes to satisfy the environmental needs have been calculated accordingly in the generated simulations as in Table 8 through the Excel software by following the water balance equation given in Eq. 3.1.

\({S}_{t}={P}_{t}+{S}_{t-1}-{E}_{t}-{D}_{t}\) (Eq. 3.1)

where S_t, P_t, S_t−1, E_t and D_t denote the end of month-t storage volume (hm³), the total precipitation in month-t (hm³), the end of month-(t-1) storage volume (hm³), the total evaporation in month-t (hm³) and the total water demand in month-t (hm³), respectively.

According to the results of the water balance simulations, two outcomes are apparent; every scenario model has failed to satisfy the volume required to meet the environmental needs of the lake and the LULCC in the case of Lake Marmara has very little significance on the rainfall-runoff process compared to the climate change. Among the scenarios structured, M1.7, M1.8 and M1.9 give similar results by indicating their relatively better performances than the rest of the scenarios. In these scenarios, while the water volume deficit increases up to the total amount of environmental need, time to fail is 8th month, namely August. On the other hand, scenarios M3.1, M3.2 and M3.3 associate to the worst cases where the volume deficit increases up to almost 5 times the required volume for the environmental need and the time to fail decreases to 6th month, namely June. Deficiency volumes have been plotted below in the Figs. 10 and 11 for irrigated, 50% irrigated and non-irrigated scenario conditions, respectively.

Table 8

Monthly water deficit volumes (hm³) to satisfy the environmental needs of the Lake Marmara
Month	M1.1	M1.2	M1.3	M1.4	M1.5	M1.6	M1.7	M1.8	M1.9
1	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0
7	-9.1	-4.6	-9.1	0	0	0	0	0	0
8	-41.8	-39.2	-41.8	-22.3	-19.8	-22.3	-2.9	-0.3	-2.9
9	-61.3	-58.7	-61.3	-39.9	-37.3	-39.9	-18.5	-15.9	-18.5
10	-67.3	-68.1	-67.3	-45.5	-46.2	-45.5	-23.6	-24.4	-23.6
11	-67.1	-69.8	-67.1	-45.3	-48.0	-45.3	-23.5	-26.2	-23.5
12	-60.5	-61.0	-60.5	-38.7	-39.2	-38.7	-16.9	-17.4	-16.9
Month	M2.1	M2.2	M2.3	M2.4	M2.5	M2.6	M2.7	M2.8	M2.9
1	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0	0	0
6	-15.2	-9.4	-15.2	-8.9	-3.1	-8.9	-2.5	0	-2.5
7	-50.5	-46.0	-50.5	-37.3	-32.8	-37.3	-24.1	-19.6	-24.1
8	-83.1	-80.6	-83.1	-63.7	-61.1	-63.7	-44.2	-41.7	-44.2
9	-102.7	-100.1	-102.7	-81.3	-78.7	-81.3	-59.9	-57.2	-59.9
10	-108.6	-109.4	-108.6	-86.8	-87.6	-86.8	-65.0	-65.8	-65.0
11	-108.4	-111.2	-108.4	-86.6	-89.4	-86.6	-64.8	-67.6	-64.8
12	-101.8	-102.4	-101.8	-80.0	-80.6	-80.0	-58.2	-58.8	-58.2
Month	M3.1	M3.2	M3.3	M3.4	M3.5	M3.6	M3.7	M3.8	M3.9
1	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0	0	0
6	-23.2	-17.4	-23.2	-16.9	-11.1	-16.9	-10.5	-4.7	-10.5
7	-58.5	-54.0	-58.5	-45.3	-40.8	-45.3	-32.1	-27.6	-32.1
8	-91.1	-88.6	-91.1	-71.7	-69.1	-71.7	-52.3	-49.7	-52.3
9	-110.7	-108.1	-110.7	-89.3	-86.7	-89.3	-67.9	-65.3	-67.9
10	-116.6	-117.4	-116.6	-94.8	-95.6	-94.8	-73.0	-73.8	-73.0
11	-116.5	-119.2	-116.5	-94.7	-97.4	-94.7	-72.9	-75.6	-72.9
12	-109.9	-110.4	-109.9	-88.1	-88.6	-88.1	-66.3	-66.8	-66.3

The study shows that since the lateral flows to the Lake Marmara are relatively low and without any supply of the diverted water from the regulator structure in the upstream of the Gördes Dam, Lake Marmara will be more apparent to dry than it is now. This also explains the reason why the LULCCs to be experienced specifically within the lake watershed seem to result in very little impact on the rainfall-runoff process, simply as the lateral flows are computed relatively low in the lake hydrologic system.

According to the water balance simulations, if operations of the Gördes Dam will not be allowing any allocation of water to the lake (due to the utility water concerns that relate to the water supply to the city of Izmir), irrigated agricultural activities also have to be ceased in order to secure sustainable management of the lake ecosystem and the riparian land that are rich in flora and fauna. Even so, due to the detrimental impacts of the climate change that are greatly estimated to be experienced in the region, the Lake Marmara gradually dries and the volume of the lake reduces below the quantity of 23.47 hm³ that is required for meeting the environmental needs. It is certain that the lake is apparently under the emerging needs of increased water allocations, in contrast to the current practices implemented. Options regarding the remedial measures and rehabilitation strategies should be investigated and discussed by the relevant governmental and non-governmental stakeholders as legal governors of the lake system and the main beneficiaries.

Without re-structuring the operation rules for the Gördes Dam to allow supplies of water to the lake, even more significantly between the wet and dry scenario conditions of the future, the lake will be subject to increased risks when solely depends on lateral flow contributions that are estimated to reduce by about 9–15% due to main impacts of climate change. Thus, proper and well-functioning climate change adaptation strategy and the supporting policies should be re-established in a more pro-active manner. For the future studies, Gördes Dam water allocation policies should be reviewed in greater detail as the amounts allocated to domestic and utility purposes, irrigation requirements and environmental/ecologic needs will need to be revised more regularly upon the achievements made toward providing the desired restoration, or on the contrary, the further deteriorating conditions if the planned measures do not sufficiently contribute. The future policies should somehow target introduction of more compromise solutions to address various needs for water between the more competing users of the non-stationary future.

Acknowledgement

The SMART project (ICA3-CT-2002) is funded, in part by the European Commission, DG Research, under FP5 INCO-MED Program. The OPTIMA project (INCO-CT-2004-509091) is funded, in part, by the European Commission, DG Research, under the FW6 INCO-MPC Program. The MARA-MEDITERRA project (2121 Call 2021 Section 1 Water RIA) is funded by the Horizon 2020 European Union Funding for Research & Innovation under Partnership for Research and Innovation in the Mediterranean Area Programme (PRIMA).

Ethical Approval Not applicable.

Consent to Participate Not applicable.

Consent to Publish The authors declare that they consent to publish.

Authors Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M. C. Gunacti, G. Onusluel Gul and C. P. Cetinkaya. The first draft of the manuscript was written by A. Gul and F. Barbaros. All authors commented on previous versions of the manuscript, read and approved the final manuscript.

Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests The authors have no relevant financial or non-financial interests to disclose.

Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Abd Ellah RG (2020) Water resources in Egypt and their challenges, Lake Nasser case study Egypt. J Aquat Res 46(1):1–12. https://doi.org/10.1016/j.ejar.2020.03.001
Ansari A, Golabi MH (2019) Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands–A case study: Meighan Wetland, Iran. Int Soil Water Conserv Res 7:64–70. https://doi.org/10.1016/j.iswcr.2018.10.001
Araya YH, Cabral P (2010) Analysis and modeling of urban land cover change in Setúbal and Sesimbra. Portugal Remote Sensing 2:1549–1563. https://doi.org/10.3390/rs2061549
ASTER (2021) ASTER Global Digital Elevation Model (GDEM) V03 Accessed on: November 2021. https://search.earthdata.nasa.gov/
Bozorg-Haddad O, Dehghan P, Zolghadr-Asli B, Singh VP, Chu X, Loáiciga HA (2022) System dynamics modeling of lake water management under climate change. Sci Rep 12:5828. https://doi.org/10.1038/s41598-022-09212-x
CCAFS (2021) Global Climate Model (GCM) Downscaled Data Portal Accessed on: November 2021. http://www.ccafs-climate.org/
Clark L (2020) TerrSet 2020 Geospatial Monitoring and Modeling Software. Clark University
Gao Y, Chen J, Luo H, Wang H (2020) Prediction of hydrological responses to land use change. Sci Total Environ 708. https://doi.org/10.1016/j.scitotenv.2019.134998
Gorunescu F (2011) Classification Performance Evaluation. In: Kacprzyk J, Jain LC (eds) Data Mining. Springer-Verlag, Berlin, pp 319–330
HEC-HMS (2001) Hydrologic Engineering Center, The Hydrologic Modeling System. US Army Corps of Engineers, Hydrologic Engineering Center
Hunink J, Simons G, Suárez-Almiñana S, Solera A, Andreu J, Giuliani M, Zamberletti P, Grillakis M, Koutroulis A, Tsanis I, Schasfoort F, Contreras S, Ercin E, Bastiaanssen W (2019) A Simplified Water Accounting Procedure to Assess Climate Change Impact on Water Resources for Agriculture across Different European River Basins. Water 11(10):1976. https://doi.org/10.3390/w11101976
Huntington TG (2006) Evidence for intensification of the global water cycle: review and synthesis. J Hydrol 319:83–95
IPCC (Intergovernmental Panel for Climate Change) (2013) Summary for Policymakers. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom; New York, New York, USA
Khoi DD, Murayama Y (2010) Forecasting areas vulnerable to forest conversion in the tam Dao National Park region, Vietnam. Remote Sens 2:1249–1272. https://doi.org/10.3390/rs2051249
Kim I, Le QB, Park SJ, Tenhunen J, Koeller T (2014) Driving forces in archetypical land-use changes in a mountainous watershed in east Asia. Land 3:957–980. https://doi.org/10.3390/land3030957
Koneti S, Sunkara SL, Roy PS (2018) Hydrological Modeling with Respect to Impact of Land-Use and Land-Cover Change on the Runoff Dynamics in Godavari River Basin Using the HEC-HMS Model. ISPRS Int J Geo-Information 7(6):206. https://doi.org/10.3390/ijgi7060206
Moriasi D, Arnold J, Van Liew M, Bingner R, Harmel R, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. http://dx.doi.org/10.13031/2013.23153
Moss R, Edmonds J, Hibbard K, Manning MR, Rose SK, Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756. https://doi.org/10.1038/nature08823
NRCS (Natural Resources Conservation Service, USDA) (2007) Urban hydrology for small watersheds. Accessed December 22, 2021. https://www.wcc.nrcs.usda.gov/ftpref/wntsc/H&H/other/TR55documentation.pdf
Soonthornrangsan JT, Lowry CS (2021) Vulnerability of water resources under a changing climate and human activity in the lower Great Lakes region. Hydrol Process 35(e14440). https://doi.org/10.1002/hyp.14440
Tyrrell ML, Hall MHP, Sampson RN(2004) Dynamics Models of Land-Use Change in Northeastern USA: Developing Tools, Techniques and Talents for Effective Conservation Action; GISF Research Paper 003; Yale University Global Institute of Sustainable Forestry: New Haven, CT, USA
Van Vuuren D, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt G, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith S, Rose S (2011) The representative concentration pathways: an overview. Clim Change 109:5–31. https://doi.org/10.1007/s10584-011-0148-z
Zhuang XW, Li YP, Nie S, Fan YR, Huang GH (2018) Analyzing climate change impacts on water resources under uncertainty using an integrated simulation-optimization approach. J Hydrol 556:523–538. https://doi.org/10.1016/j.jhydrol.2017.11.016

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Evaluating Impact of Land Use and Land Cover Change under Climate Change on a Lake System

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Abstract

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1. Introduction

2. Materials And Method

2.1. Materials

2.2 LULCC Modelling

2.3 Hydrologic Modelling

2.4 Scenario Development

3. Results And Discussion

4. Conclusions

Declarations

References

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Version 1