Human well-being in a large part of the world depends on ecosystem services (ESs). However, humans degrade ESs directly by unsustainable land-use policies and indirectly by climate change. In this regard, how ecosystem services change due to human and climate change is a crucial question to demonstrate the human and climate change effects. This paper aimed to answer this question. The wetland services over 45 years were investigated using remote sensing techniques, InVEST, and Biomod2. Higher restoration priority was determined based on the ES degradation rate, and the importance of each ES. The results of this paper indicated the percentage of ecosystem degradation and the services with the highest priority for restoration measures in arid area. The importance of this paper include applying a functional method to study ESs and running this method by the main components shared between all wetlands. These attributes make this method applicable in all regions to study human and climate change effects and ecosystem restoration prioritization.
Research Article
How ecosystem services change due to human and climate change
https://doi.org/10.21203/rs.3.rs-1535630/v1
This work is licensed under a CC BY 4.0 License
Version 1
posted
You are reading this latest preprint version
InVEST
Spatial modeling
Ecosystem conservation
Restoration measures
Ecosystem services (ESs) are the direct or indirect advantages that natural ecosystems provide for humans (Diaz et al., 2015; Hassan et al., 2020; Gomez Baggethun & Barton, 2013). Change in these services affects human well-being (Wu, 2013). Accordingly, a large number of studies were conducted on ESs (Hassan et al., 2020; Caro et al., 2020; Crespin & Simonetti, 2016; Ramirez-Gomez et al., 2015: Nunez et al., 2015; Fu et al., 2017; Lu et al., 2014; Han et al., 2017; Cord et al., 2017). Despite the importance of ESs, humans degrade these services directly by unsustainable land-use policies and indirectly by climate change (Foley et al., 2005; Abou Rafee et al. 2022). Important evidence about the social effects of ESs degradation is revealed by ecosystem research all over the world. The water resource decline, due to drought and other extremes, leads to extensive reduction of agriculture production and increases poverty, particularly in arid areas (Gowda et al., 2018). Crespin and Simonetti (2016) showed that the hurricane damage reduction by coastal wetlands was the most sensitive ES to ecosystem degradation in El Salvador. Maleki et al. (2019) indicated that the wetland degradation due to human and climate effects caused lower crop production and raised poverty. Hilborn and Beasley (2015), and Galloway et al. (2008) revealed eutrophication in aquatic ecosystems due to fossil fuel combustion and high nitrogen deposition, which threaten human and animal health. Human well-being in dryland ecosystems is more dependent on ES, while these ecosystems are most vulnerable to degradation (Francesconi et al., 2016; Seppelt et al., 2013; Pham et al., 2019). The evidence shows the occurrence of widespread ESs degradation due to human and climate effects, while human suffers from the ESs degradation made by humans. By considering these facts, it is crucial to conserve and restore ESs to provide better conditions for all stockholders. Two groups of policies can rescue natural and human life, including reduction of degraded forces and increase of adaptation and conservation measures.
To design effective adaptation and conservational strategies, it is important to determine which ES is most at risk by human and climate changes. In this case, responding to three questions is essential. How can we quantify the ESs loss? How does ESs change due to human and climate changes? Which ecosystem services are degraded more by human and climate change effects? This paper aims to answer the above questions to quantify the ESs loss, determine the ESs change owing to human and climate changes, and illustrate the most degraded ecosystem services. Since previous studies reported higher sensitivity of drylands to ecosystem degradation (Francesconi et al., 2016), the study was conducted in the Sistan plain located in an arid area. Previous studies in this region confirmed that climate change led to a prolonged drought and human activities intensified its negative effects by unsustainable water resource management and land use policies (Maleki et al., 2019; Miri et al., 2021). The importance of this study is focusing on the ESs of a wetland in an arid area because wetland in this area has the ESs more than other areas, such as reducing the risk of sand and dust storm and soil stabilization.
ESs modeling is the first step in ecosystem studies. The importance of ESs conservation has caused a large number of ecological studies to be devoted to modeling and quantifying ESs. In recent years, useful special models relying on training algorithms have been developed (Conge et al., 2020). InVEST, SolVES, InFOREST, Envision, EcoMetrix, ARIES, EPM, and LUCI are some of the advanced ES models (Hoyer & Chang, 2014; Xu et al., 2018; Pham et al., 2019; Hu et al., 2019).
In this study, the ecosystem services changes due to human and climate effects were investigated. Changes in mean annual temperature and precipitation were detected over 45 years to determine the variation periods. The ecosystem services for the study period were mapped. The trend of changes in each service over the study period was illustrated. The degradation rate of each service was determined over each climate variation period. Finally, higher priority for restoration measures was determined using ESs degradation rates, and the importance of ESs was determined using the AHP method.
2.1 Study area
The study area is the Sistan plain within a vast desert in eastern Iran (Fig. 1) with an average of 475-500 m above sea level (lat 30 25 to 31 27N–long 60 56 to 61 43E). In this plain, the Hamoun wetlands are providing critical water resources for local people and wildlife. The Ramsar Convention introduced a large part of Hamoun as a protected area (Ramsar Convention, 2016). The Hamoun wetlands consist of Hamoun-e Puzak, Hamoun-e Sabari, and Hamoun-e Helmand (In this paper, we used the word “Hamuon” and “wetland” instead of Hamoun wetlands). Under normal conditions, Hamoun covers 300,000 ha of a desert in an arid area that makes a vital water resource for all residents. However, in recent years, climate change as the prolonged drought has led to a decline in precipitation. Humans by unsustainable land and water management policies in upstream cut off the wetland water input in downstream that have made this valuable wetland dry in most time of the year (Maleki et al., 2018; Maleki et al., 2019).
2.2 Data
A time series of Landsat satellite images were used to map the wetland land cover classes. Table 1 presents the acquisition date of these images. Long-term mean annual precipitation and temperature were obtained from Zabol Meteorological Station and gridded data from CRU were employed to investigate the climate variability. The satellite images were achieved from the U.S. Geological Survey (USGS) website, and CRU meteorological data were downloaded from (HTTP:// www.cru.uea.ac.uk/data).
Field data were collected in 2019 based on satellite overpass. Mainland cover classes were identified in field observation, which are described in Table 2. A total of 250 samples were collected by the stratified random sampling method. The effect of neighbor classes was avoided by collecting the samples in the homogeneous area by a random approach in each class (AlaviPanah2003). In addition, the field measurements of our previous studies were used in this study (Maleki et al., 2016; Maleki et al., 2018). The samples were applied in image classification and validation.
Table 1. Landsat satellite image acquisition date were used in this study.
|
Acquisition date |
Acquisition date |
Acquisition date |
Acquisition date |
|
1985/05/21 |
1994/03/19 |
2001/05/10 |
2012/05/31 |
|
1988/05/20 |
1995/07/28 |
2002/02/06 |
2013/07/13 |
|
1989/06/14 |
1996/05/11 |
2004/05/25 |
2015/06/01 |
|
1990/06/12 |
1997/05/14 |
2005/06/12 |
2016/07/21 |
|
1992/05/16 |
1998/06/02 |
2007/07/05 |
2018/06/10 |
|
1993/05/03 |
2000/06/01 |
2009/05/31 |
2019/05/13 |
Table. 2. Hamoun wetland land cover classes
|
Land use |
Code |
Description |
|
Agriculture |
1 |
Agriculture land |
|
Bare-land |
2 |
Bare land, salt land, area without vegetation cover |
|
Pasture |
3 |
Herbs on the ground |
|
Woodlands |
4 |
Tamarix (Main species in study area) |
|
Flooded vegetation |
5 |
Vegetation in shallow |
|
Water |
6 |
Water surface without vegetation |
2.3 Methods
2.3.1 Climate variability (Precipitation and temperature anomaly)
The previous studies in this region indicated that climate change occurred in this basin as a decline of precipitation and an increase in temperature (Maleki et al., 2019). In this paper, the anomaly of annual precipitation and the mean annual temperature over 1977–2019 were calculated to determine the intensity and duration of drought in the study area. The anomaly was calculated by Equation 1.
Pij = Mean i – Mean j,
Py = measurement from year(y),
STD = standard deviation.
2.3.3 ESs mapping
The main ESs of the Hamoun wetland in the Sistan plain were mapped over 45 years included carbon storage, crop production, water birds habitat, water resource providing, and sand and dust storm risk reduction.
To quantify the ESs, the Hamoun wetland land cover map over 45 years (1985-2019) was created and used as the input data for the ESs mapping. Habitat was mapped by the Maxent model using the Biomod2 package in R software. Sand and dust storm risk reduction were quantified by detecting changes in the bare land area and the frequency of dusty days. Water resource providing was achieved by the water body map extracted from the land use/land cover map.
Land cover mapping
The Hamoun wetland land cover maps over 45 years (1985-2019) were created and used as the input data for ESs mapping. The land-sat image of each date was classified using the support vector machine (SVM) method. In this method, a linear separating hyperplane in a multi-dimensional feature space is obtained by the training samples based on field measurements. The SVM algorithm works based on minimizing the error risk and maximizing the margins between the separating hyperplane and the closest training samples (Bigdeli, Samadzadegan, & Reinartz, 2013). The accuracy of the created maps was assessed by 5-fold cross-validation, and the overall accuracy and the kappa coefficient (K) were calculated. In the n-fold cross-validation method, the field samples are divided into n-folds. The classification algorithm is trained by n-1 portions and the remaining partition to assess the result. This process is repeated n times, and a different fold is used each time as the test set, and the remaining partitions are used as the training data (McCauley & Goetz 2004; Maleki et al., 2019).
Carbon storage
The InVEST model was applied to determine carbon storage. Using the Invest model, a raster image was created for each year, showing the amount of carbon stored in mg in each pixel (Sharp et al., 2015). To determine the change in the extent of each class, the area of each class was calculated during the study period.
Total carbon storage is calculated using Equation 2. The area of each pixel is equal to 900 m2, and in each pixel, the carbon storage is equal to 7.5, 9.2, 12.2 mg in classes 1 to 3, respectively.
Total carbon storage of class i = (A * C) / 900 Equation (2)
Class i = The class of carbon storage in Invest output
A= The area of class i
C = The amount of carbon stored in Mg in each pixel
900 = the area of each pixel
Waterbirds habitat suitability map
The MaxEnt model using the Biomod2 package in the R software was applied to create the suitable habitat map for four waterbird species in 2019, including Botaurus stellaris, Limosa limosa, Charadrius hiaticula, and Podiceps grisegena. The ecological maps as input layers included distance to road and residential area, tasseled cap wetness-greenness difference (TCWGD) map, normalized difference vegetation index (NDVI) map, and land cover map. The correlation among variables was tested to eliminate highly correlated variables (r ≥ 0.85 Pearson correlation coefficient). Since there was no significant correlation among the variables, all of them were used in creating the habitat suitability map. The MaxEnt Software provides a 10-percentile training presence logistic threshold to determine suitable classes. Using this threshold, the suitable class for each waterbird species (Botaurus stellaris, Limosa limosa, Charadrius hiaticula, Podiceps grisegena) in the Hamoun wetland was mapped. The suitable range for each species was extracted from the ecological maps in each year of the study period, and it was used to map the suitable habitat for water birds.
2.3.2 How ESs were changed due to human and climate changes?
The changes in the mean annual temperature and precipitation were detected. Based on changes in the climate variables, the study period was divided into two sections: before and after changes in climate variables. The changes in ecosystem services after and before human and climate effects were compared to each other.
2.3.3 Which ecosystem services are degraded more by human and climate change effects?
The trend of changes in each service over the study period was illustrated. Then, the degradation rate of each service was determined over each climate variation period using Equation 3.
2.3.4 Which ecosystem services have a higher priority for restoration measures?
To determine the ESs with priority for restoration measures, the weighted linear combination (WLC) method was used (Equation 4).
Where, Mi is the overall score for restoration measures for ith gird cell;
wj is the weight of jth variable
xij is the degradation rate of ith gird cell for jth variable.
The weight of ESs was assessed using the AHP method. The degradation rate of ESs was achieved in Section 2.3.3.
3.1 Climate variability (Precipitation and temperature anomaly)
The anomaly of climate parameters over a 45-year period indicates a decline in precipitation and an increase in temperature (Fig. 2). Since Hamoun is an inland wetland that the precipitation in upstream is its only water source, it is clear that a decline in precipitation leads to water limitation, and an increase in temperature exacerbates the negative effects of water resources limitations. Previous studies in this region confirmed that climate change led to a prolonged drought in this region, and human activities intensified its negative effects by unsustainable water resource management (Maleki et al., 2019; Miri et al., 20201). Based on changes in climate variables, the study period was divided into two sections: before and after changes in climate variables. As Figure 2. shows, the year 2000 is the first year of the dry section of the study period. To determine the changes in ESs in relation to human and climate changes, the year 2000 was selected as the base year.
3.2 Land use/land cover map
The land use/land cover maps over the study period were created with acceptable accuracy (table3). These maps are applied as the input layer to quantify ESs. Figure 3 shows all land use/land cover maps. These maps reveal widespread changes in this ecosystem after and before the base year (2000). As observed, Hamoun as a permanent wetland in an arid area provides water for all stockholders. Vast agricultural lands, natural vegetation cover, and water resources were attained when Hamoun was not dry. However, this plain was degraded when Hamoun was dry.
Table 3. The overall accuracy and Kappa coefficient of land use/land cover.
|
Acquisition date |
Accuracy % |
Kappa |
Acquisition date |
Accuracy % |
Kappa |
Acquisition date |
Accuracy % |
Kappa |
Acquisition date |
Accuracy % |
Kappa |
|
|
1985/05/21 |
67.8 |
0.52 |
1988/05/20 |
68.2 |
0.52 |
1989/06/14 |
70.6 |
0.57 |
1990/06/12 |
75.8 |
0.63 |
|
|
1992/05/16 |
77.1 |
0.70 |
1993/05/03 |
73.8 |
0.59 |
1994/03/19 |
76.1 |
0.68 |
1995/07/28 |
73.2 |
0.59 |
|
|
1996/05/11 |
72.9 |
0.58 |
1997/05/14 |
81.3 |
0.75 |
1998/06/02 |
79.6 |
0.72 |
2000/06/01 |
85.4 |
0.82 |
|
|
2001/05/10 |
83.6 |
0.78 |
2002/02/06 |
80.2 |
0.74 |
2004/05/25 |
82.2 |
0.77 |
2005/06/12 |
84.8 |
0.79 |
|
|
2007/07/05 |
87.9 |
0.86 |
2009/05/31 |
83.4 |
0.8 |
2012/05/31 |
84.1 |
0.8 |
2013/07/13 |
88.7 |
0.87 |
|
|
2015/06/01 |
88 |
0.86 |
2016/07/21 |
85.9 |
0.85 |
2018/06/10 |
86.2 |
0.84 |
2019/05/13 |
89.2 |
0.9 |
|
3.3 How ESs was changed due to human and climate changes?
Carbon storage
The resulted carbon storage analysis in the Hamoun wetland consists of three classes, including 7.2, 9.2, and 12.2 mg carbon per pixel. Figure 4 shows the area of regions, where the amount of carbon storage in each pixel (900 m2) is equal to 7.5, 9.2, and 12.2 mg per pixel. Before the drought, the main part of the wetland stores carbon equal to 9.2 mg in 900 m2. While after the drought, the carbon storage of the main part of the wetland is equal to 7.5 mg in 900 m2. Furthermore, a decline occurred in the area of regions, where the amount of carbon in 900 m2 is equal to 12.2 mg.
By increasing the climate change effects on the global community, it is worthwhile to quantify the change in total carbon storage by a wetland in an arid area. Figure 5 presents the anomaly of carbon storage in the Hamoun wetland over 43 years. This figure demonstrates the significant carbon decline in this wetland after the year 2000 (the first year of the dry period). The carbon storage after the year 2000 was below the mean of the study period (43 years). In 2000 and 2005 when the wetland was dried, the carbon storage was approximately 60 units below the mean of the study period, indicating how wetland degradation affects carbon storage and revealing their crucial role in the carbon cycle.
Crop production
Changes in crop production are crucial ESs to support life and the local human well-being, particularly in an arid area (Maleki et al., 2019; Miri et al., 2009; Pahlavanravi et al., 2012). Clearly, after each prolonged drought, crop production is decreased. Results of change in the agriculture area as the index of change in crop production demonstrate that in the Sistan plain, the crops were reduced to ¼ compared to the normal years (Fig. 6). The agriculture area in 1985 was 200,000 ha, while it was reduced to 50,000 ha in 2004, and it was even reduced more in 2018. As observed, the lower crop production occurred when Hamoun was dried. This reduction of crops in connection with the water limitation could lead to poverty in this region (Maleki et al., 2019).
Water birds’ habitat and providing water resource
Figure 7 reveals the changes in water birds’ habitat and water bodies over the study period in the Hamoun wetland. Under normal conditions, the largest water body over 43 years is equal to 350,000 ha. Furthermore, a large part of Hamoun is a suitable habitat for water birds. In 1992, the suitable habitat was 450,000 ha which is a worthwhile habitat to support biodiversity in arid regions. This figure demonstrates the important role of Hamoun to support water birds’ life. The suitable habitat loss occurred in connection with the water decline. A widespread habitat degradation occurred after 2000. In 2002, 2004, and 2018, the water birds’ habitat was lost completely because of vanishing the Hamoun wetland.
Sand and dust storm risk reduction
Soil stabilization and reduction of the risk of sand and dust storms are the most important services of the Hamoun wetlands. Figure 8 displays the increased number of dusty days and the bare land area in relation to water body changes. When the wetland was dried, the number of dusty days doubled or tripled. In 1990, the number of dusty days was 10 days, but in 2002, it was increased to 80 days. As observed, a clear pattern of change in the area of bare land and the number of dusty days are presented after 2000, when the dry period was started.
3.4 Which ecosystem services are degraded more by human and climate change effects?
The degradation rate of the Hamoun wetland ESs shows the widespread loss in all ecosystem services (Fig. 9). During 43 years, this wetland lost its crucial advantages for all residents. The degradation rate of Hamoun services shows the highest loss in reducing the risk of dust storms. Water resource and water birds habitat are the services with the second and third highest degradation rate, respectively. These services play a crucial role in supporting life in this arid region. The carbon storage of the Hamoun wetland was halved for 43 years. Since the natural vegetation cover vanished in a large part of the wetland due to drought, the carbon storage was lost dramatically. Nevertheless, agriculture experienced lower degradation by 10%. The slower decline in the agriculture area is a logical change owing to applying agriculture conservational plans by the local government. These results confirmed the highest effect of human and climate on sand and dust storm risk reduction.
3.5 Which ecosystem services have a higher priority for restoration measures?
Figure 10 shows the priority for restoration measures to restore each ESs. As observed, dust storm risk reduction has the highest priority for restoration in the study area. The Hamoun wetland locates in an arid region where dust storms occurred, particularly during the dry period (Miri et al., 2021; Maleki et al., 2021). Since during a storm, sand and dust are moved thousands of kilometers far from their hotspots, the negative effects of dust storms involve all parts of the ecosystem. For this reason, dust storm risk reduction is the most important service in the Hamuon wetland that all residents encounter. Water resource providing is the second priority for ecosystem services restoration. Agriculture achieved the lowest priority that could be owing to lower degradation and capability of improving agriculture by increasing the available water. In other words, by restoring one ES such as water resource, other ESs can be improved.
This paper aims to show how ecosystem services change due to human and climate effects. Responding to these questions is important to mitigate the negative effects of the ESs loss on local stockholders. Priority for implementing the restoration and conservation purposes is identified using the results of studying ESs (Cerretelli et al., 2018). In addition, inaccuracy and limitations constraining adoption masseurs and decision-making can be defined by ESs assessments (Nelson et al., 2010).
Quantification of ESs is the first step to assess these services. Pandeyal et al. (2016) emphasized that the lack of locally ecosystem data was the main constraint for the application of ESs frameworks in decision-making. In this paper, remote sensing as the most important tool in ecosystem modeling (Wood et al., 2010; Martínez-Harms et al., 2016; Choudhary et al., 2018) was used to map the ecological parameters that can be used as the indicator of ESs (Burkhard et al., 2012; Van Oudenhoven et al., 2012). For instance, the change in bare land can be an indicator of the change in dust hotspots that in each storm move thousands of kilometers. Therefore, changes in a bare land area cause changes in the reduction of sand and dust storm risk as to the important ES of Hamoun in this arid area (Vrieling et al., 2008; Del Río-Mena et al., 2020; Onyando et al., 2005). Del Río-Mena et al. (2020) showed the ability of RS-based data to assess ecosystem services.
The results of this paper demonstrated the reduction of sand and dust storm risk as the main ES that was lost by human and climate change effects. The study was conducted in an arid area that the wetland provides additional ESs compared to other regions. Reduction of sand and dust storm risk is the ES that benefits all inhabitants in the arid region and avoids various socio-economic, human health, and agricultural difficulties (Miri et al., 2021; Prospero & Mayol-Bracero, 2013; Middleton, 2017). Providing water resources is the second-highest ES degraded. Both are the ESs affecting the well-being of local communities, and both of them were affected more by human and climate impacts on the ecosystem. Both ESs (reduction of sand and dust storm risk and providing water resource) could be protected by water and land management (e.g. changes in cropping patterns, balance between water reversion in upstream and downstream, optimization of irrigation, and land use planning). Sustainable management policies were also suggested by other studies (El-Khoury et al., 2015; Van der Pol et al., 2015; Zimmerman & Faris, 2011; Langerwisch et al., 2018; Scullion et al., 2014).
The results of the carbon storage analysis demonstrate that storing carbon as an important global service of the wetland is degraded after the water limitation in the Hamoun wetland. Carbon storage is a crucial service that has changed to the most important ES on a global scale owing to the acceleration of climate change (Pham et al., 2019; Goldstein et al., 2012). Previous studies revealed that wetlands carbon storage was decreased due to higher temperatures, lower precipitation, and increased water stress (Oswalt et al., 2014; Pugh et al., 2019; Wear & Coulston, 2015; USDA, 2016). Since wetlands are highly productive ecosystems that store carbon, wetland degradation not only affects the local region but also has global negative effects (Davis et al., 2015; Howard et al., 2017).
Crop production has a lower degradation rate that could be because of applying the agriculture conservational plans by the local government, demonstrating the importance of conservation and restoration measures to raise the quality of life in a region encountering the negative effects of human and climate (Maleki et al., 2019; El-Khoury et al., 2015; van der Pol et al., 2015; Langerwisch et al., 2018; Scullion et al., 2014).
To plan conservation and mitigation measures, decision-makers need to be aware of the sensitivity of ecosystem services to degradation as well as the priority of ecosystem services restoration. In this paper, a method was used to determine this priority based on the degradation rate and the importance of each ecosystem service. The degradation rate shows the sensitivity of ecosystem services to destructive forces, and the higher importance of each ecosystem service demonstrates higher positive effects of this service in the region. Weiskopf et al. (2020) stated that effective adaptation strategies could be designed by determining which ES is most at risk. Results showed the higher ES restoration priority for dust storm risk reduction that is vital in an arid region. When Hamoun is dry, not only is the soil stabilization by water body and vegetation cover lost but also the dry body of dried Hamoun change to the main hotspot for sand and dust causing to move the sand and dust thousands of kilometers far from their hotspots. Sand and dust storms impacts affect all parts of the ecosystem (Ghaljahi et al., 2019; Miri et al., 2007; Javan et al., 2020; Malkei et al.,2021). For this reason, dust storm risk reduction is the most important service. Water resource providing is the second priority for ecosystem services restoration. By providing water resources, other ecosystem services, including carbon storage, crop production, and habitat change will be restored. For this reason, restoration of this ecosystem service is crucial to restoring other services.
This paper responded to three questions about ecosystem management: How we can quantify ESs? How does ESs change due to human and climate changes? Which ecosystem services are degraded more by human and climate change effects? Each question is applicable for ecosystem management and conservation, particularly by considering the widespread ESs degradation in the world owing to human and climate effects. Six main ESs for the Hamoun wetland were investigated. The ESs included carbon storage, crop production, water birds’ habitat, water resource providing, and sand and dust storm risk reduction. Conducting this paper in arid regions caused to investigate the special ESs for a wetland that is not in other regions, including reduction of sand and dust storm risk. In this paper, remote sensing as the most important tool in ecosystem modeling was used to map the ecological parameters as the indicator of ESs. For example, the area of bare land and the number of dusty days were applied as indicators of reduction of sand and dust storm risk. Using the time series of the satellite image, valuable information about the ESs over 43 years was achieved. The information is useful for ecological studies over a long period.
Acknowledgement
This research was supported by the University of Zabol, under Grant UOZ-GR-1348.
Funding
This work was supported by University of Zabol (Grant numbers UOZ-GR-1348). Saeideh Maleki has received research support.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Author Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Saeideh Maleki, Vahid Rahdari and Ahmad Pahlevanravi. The first draft of the manuscript was written by Saeideh Maleki and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on request.
Conflict of interest
The authors declare that they have no conflict of interest with respect to this study and publication.
- Abou Rafee SA, Uvo CB, Martins JA, Machado CB, Freitas ED (2022) Land Use and Cover Changes versus climate shift: Who is the main player in river discharge? A case study in the Upper Paraná River Basin. J Environ Manage 309:114651
- AlaviPanah SK (2003) The Applications of Remote Sensing in the Earth Sciences. University of Tehran. Tehran, Iran
- Bigdeli B, Samadzadegan F, Reinartz P (2013) A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data. J Indian Soc Remote Sens 41:763–776
- Burkhard B, Kroll F, Nedkov S, Müller F (2012) Mapping ecosystem service supply, demand and budgets. Ecol Ind 21:17–29. https://doi.org/10.1016/j.ecolind.2011. 06.019
- Choudhary K, Boori MS, Kupriyanov A (2018) Spatial modelling for natural and environmental vulnerability through remote sensing and GIS in Astrakhan, Russia.Egypt. J. Remote Sens. Sp. Sci. 21, 139–147. https://doi.org/10.1016/J.EJRS.2017. 05.003
- Caro C, Marques JC, Cunha PP, Teixeira Z (2020) Ecosystem services as a resilience descriptor in habitat risk assessment using the InVEST model. Ecol Ind 115:106426
- Cord AF, Bartkowski B, Beckmann M, Dittrich A, Hermans-Neumann K, Kaim A, Lienhoop N, Locher-Krause K, Priess J, Schröter-Schlaack C, Schwarz N, Seppelt R, Strauch M, Václavík T, Volk M (2017) Towards systematic analyses of ecosystem service trade-offs and synergies: main concepts, methods and the road ahead. Ecosyst Serv 28:264–272
- Crespin SJ, Simonetti JA (2016) Loss of ecosystem services and the decapitalization of nature in El Salvador. Ecosyst Serv 17:5–13
- Cerretelli S, Poggio L, Gimona A, Yakob G, Boke S, Habte M, Black H (2018) Spatial assessment of land degradation through key ecosystem services: the role of globally available data. Sci Total Environ 628:539–555
- Diaz S, Lavorel S, Mcintyre SFalczukV, Casanoves F, Milchunas DG Skarpe,C.,etal.,2007.Planttraitresponsestograzing–aglobalsynthesis.GlobalChangeBiology13 (2),313–341
- Del Río-Mena T, Willemen L, Tesfamariam GT, Beukes O, Nelson A (2020) Remote sensing for mapping ecosystem services to support evaluation of ecological restoration interventions in an arid landscape. Ecol Ind 113:106182
- El-Khoury A, Seidou O, Lapen D, Que Z, Mohammadian M, Sunohara M, Bahram D (2015) Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin. J Environ Manage 151:76–86. https://doi.org/10.1016/j.jenvman.2014.12.012
- Ghaljahi M, Bagheri S, Keykhaei KR (2019) The effects of haze on general health of women employed in Zabol University of Medical Sciences in 2018. Asian J Water Environ Pollut 16(2):59–64
- Goldstein JH, Caldarone G, Duarte TK, Ennaanay D, Hannahs N, Mendoza G, Polasky S, Wolny S, Daily GC (2012) Integrating ecosystem-service tradeoffs into land-use decisions. Proc. Natl. Acad. Sci. USA 109, 7565–7570. https://doi.org/10. 1073/pnas.1201040109
- Galloway JN, Townsend AR, Erisman JW, Bekunda M, Cai Z, Freney JR, Martinelli L, Seitzinger SP, Sutton M (2008) Transformation of the nitrogen cycle: recent trends, questions, adn potential solutions. Sci (80-) 320:889–892
- Gómez-Baggethun E, Barton DN (2013) Classifying and valuing ecosystem services for urban planning. Ecol Econ 86:235–245
- Francesconi W, Srinivasan R, Pérez-Miñana E, Willcock SP, Quintero M (2016) Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review. J Hydrol 535:625–636
- Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Helkowski JH (2005) Global consequences of land use. Science 309(5734):570–574
- Fu Q, Li B, Hou Y, Bi X, Zhang X (2017) Effects of land use and climate change on ecosystem services in Central Asia's arid regions: a case study in Altay Prefecture. China Sci Total Environ 607–608:633–646
- Gómez-Baggethun E, Barton DN (2013) Classifying and valuing ecosystem services for urban planning. Ecol Econ 86:235–245
- Han Z, Song W, Deng XZ, Xu XL (2017) Trade-offs and synergies in ecosystem service within the three-rivers headwater region. China Water 9(8):558–572
- Hasan SS, Zhen L, Miah MG, Ahamed T, Samie A (2020) Impact of land use change on ecosystem services: A review. Environmental Development, p 100527
- Hilborn ED, Beasley VR (2015) One health and cyanobacteria in freshwater systems: animal illnesses and deaths are sentinel events for human health risks. Toxins (Basel) 7:1374–1395. https://doi.org/10.3390/toxins7041374
- Hoyer R, Chang H (2014) Assessment of freshwater ecosystem services in the tualatin and Yamhill basins under climate change and urbanization. Appl Geogr 53:402–416. https://doi.org/10.1016/j.apgeog.2014.06.023
- Hu T, Wu J, Li W (2019) Assessing relationships of ecosystem services on multi-scale: A case study of soil erosion control and water yield in the Pearl River Delta. Ecol Indic 99:193–202. https://doi.org/10.1016/j.ecolind.2018.11.066
- Javan S, Rahdar S, Miri M, Djahed B, Kazemian H, Fakhri Y, Eslami H, Fallahzadeh RA, Gholizadeh A, Taghavi M (2020) Modeling of the PM10 pollutant health effects in a semi-arid area: a case study in Zabol,Iran. Model. Earth Syst. Environ.1–9
- Langerwisch F, Vaclavi T, Bloh W, Vetter T, Thonicke K (2018) Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems. Environ Res Lett 13:015003. https://doi.org/10.1088/1748-9326/ aa954d
- Lu N, Fu BJ, Jin TT, Chang RY (2014) Trade-off analyses of multiple ecosystem services by plantations along a precipitation gradient across Loess Plateau landscapes. Landsc Ecol 29(10):1697–1708
- McCauley S, Goetz SJ (2004) Mapping Residential Density Patterns Using Multi-temporal Landsat Data and a Decision-tree Classifier. Int J Remote Sens 25(6):1077–1094. doi:10.1080/0143116031000115102
- Maleki S, Soffianian AR, Koupaei SS, Pourmanafi S, Saatchi S (2018) Wetland Restoration Prioritizing, a Tool to Reduce Negative Effects of Drought, an Application of Multicriteria-spatial Decision Support System MC-SDSS. Ecol Eng 112:132–139. 10.1016/j. ecoleng.2017.12.031
- Maleki S, A.R.Soffianian SS (2016) Habitat Mapping as a Tool for Water BirdsConservation PlanninginanArid ZoneWetland:The Case Study Hamoun Wetland. Ecol Eng 95:594–603. doi:10.1016/j.ecoleng.2016.06.115
- Maleki S, Koupaei SS, Soffianian A, Saatchi S, Pourmanafi S, Rahdari V (2019) Human and climate effects on the Hamoun wetlands. Weather Clim Soc 11(3):609–622
- Maleki S, Baghdadi N, Soffianian A, El Hajj M, Rahdari V (2020) Analysis of multifrequency and multi-polarization SAR data for wetland mapping in Hamoun-e- Hirmand wetland. Int J Remote Sens 41(6):2277–2302
- Martínez-Harms S, Quijas AM, Merenlender P (2016) Balvanera Enhancing ecosystem services maps combining field and environmental data 2016 Serv Ecosyst 10.1016/j. ecoser.09.007
- Middleton NJ (2017) Desert dust hazards: a global review. Aeolian Res 24:53–63
- Miri A, Ahmadi H, Ghanbari A, Moghaddamnia A (2007) Dust storms impacts on air pollution and public health under hot and dry climate. Int J Energy Environ 1(2):101–105
- Miri A, Ahmadi H, Ekhtesasi MR, Panjehkeh N, Ghanbari A (2009) Environmental and socio-economic impacts of dust storms in Sistan Region, Iran. Int J Environ Stud 66:343–355
- Miri A, Maleki S, Middleton N (2020) An investigation into climatic and terrestrial drivers of dust storms in the Sistan region of Iran in the early twenty-first century.Science of The Total Environment,143952
- Nelson E, Sander H, Hawthorne P, Conte M, Ennaanay D, Wolny S, Manson S, Polasky S (2010) Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models. PLoS ONE 5:1–22. https://doi. org/10.1371/journal.pone.0014327
- Nunez D, Nahuelhual L, Oyarzu ÂC (2015) Forests and water: The value of native temperate forests in supplying water for human consumption. Ecol Econ 58(3):606–616
- Onyando JO, Kisoyan P, Chemelil MC (2005) Estimation of potential soil erosion for river Perkerra catchment in Kenya. Water Resour Manag 19:133–143. https://doi. org/10.1007/s11269-005-2706-5
- Oswalt SN, Smith WB, Miles PD, Pugh SA (2014) Forest resources of the United States, 2012: a technical document supporting the Forest Service 2015 update of the RPA assessment. General technical report GTR WO-91, U.S. Department of Agriculture, Forest Service, Washington Office, Washington, DC, 218 pp
- Pahlavanravi A, Miri A, Ahmadi H, Ekhtesasi MR (2012) The impacts of different kinds of dust storms in hot and dry climate, a case study in sistan region. Desert 17(1):15–25
- Paudyal K, Baral H, Bhandari SP, Bhandari A, Keenan RJ (2019) Spatial assessment of the impact of land use and land cover change on supply of ecosystem services in Phewa watershed. Nepal Ecosyst Serv 36:100895. https://doi.org/10.1016/j.ecoser.2019.100895
- Prospero JM, Mayol-Bracero OL (2013) Understanding the transport and impact of African dust on the Caribbean basin. Bull Am Meteorol Soc 94:1329–1337
- Pham HV, Sperotto A, Torresan S, Acuña V, Jorda-Capdevila D, Rianna G, Critto A (2019) Coupling scenarios of climate and land-use change with assessments of potential ecosystem services at the river basin scale. Ecosyst Serv 40:101045
- Pugh TA, Lindeskog M, Smith B, Poulter B, Arneth A, Haverd V, Calle L (2019) Role of forest regrowth in global carbon sink dynamics. Proceedings of the National Academy of Sciences, 116(10), 4382–4387
- Ramirez-Gomez S, Torres-Vitolas C, Schreckenberg K, Honza M, Cruz-Garcia G, Willcock S et al (2015) Analysis of ecosystem services provision in the Colombian Amazon using participatory research and mapping techniques. Ecosyst Serv 13:93–107
- Reyers B, O'farrell PJ, Cowling RM, Egoh BN, Le Maitre DC, Vlok JHJ, Silvestri L, Zaibet S, Said L, Kifugo MY (2009) S.C., 2013. Valuing ecosystem services for conservation and development purposes: a case study from Kenya. Environ. Sci. Policy 31, 23–33
- Scullion J, Vogt K, Sienkiewicz A, Gmur S, Trujillo C (2014) Assessing the influence of land-cover change and conflicting land-use authorizations on ecosystem conversion on the forest frontier of Madrede Dios. Peru Biol Conserv 171:247–258. https://doi.org/10.1016/j.biocon.2014.01.036
- Seppelt R, Dormann CF, Eppink FV, Lautenbach S, Schmidt S (2011) A quantitative review of ecosystem service studies: approaches, shortcomings and the road ahead. J Appl Ecol 48(3):630–636
- Sharp R, Tallis H, Ricketts T, Guerry A, Wood S, Chaplin Kramer R, Nelson E, Ennaanay D, Wolny S, Olwero N et al (2015) InVEST 3.1.2 User’s Guide. The Natural Capital Project, Stanford, CA, USA
- USDA Forest Service (2016) Future of America’s forests and rangelands: update to the 2010 resources planning act assessment. General technical report WO-GTR-94, Washington, DC, 250 pp
- Vrieling A, de Jong SM, Sterk G, Rodrigues SC (2008) Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping. Int J Appl Earth Obs Geoinf 10:267–281. https://doi.org/10.1016/j.jag.2007.10.009
- Van der Pol T, Ierland E, Gabbert S, Weikard H, Hendrix E (2015) Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change. J Environ Manage 154:40–47. https:// doi.org/10.1016/j.jenvman.2015.02.016
- Van Oudenhoven APE, Petz K, Alkemade R, Hein L, De Groot RS (2012) Framework for systematic indicator selection to assess effects of land management on ecosystem services. Ecol Ind 21:110–122. https://doi.org/10.1016/j.ecolind.2012.. 01.012
- Wear DN, Coulston JW (2015) From sink to source: regional variation in US forest carbon futures. Scientific reports 5
- Weiskopf SR, Rubenstein MA, Crozier LG, Gaichas S, Griffis R, Halofsky JE, Pershing AJ (2020) Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of the Total Environment, 137782
- Wu J (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landscape Ecol 28(6):999–1023
- Wood E, Tappan G, Hadj A (2010) Use of remote sensing and GIS for improved natural resources management: case study from different agroecological zones of West Africa. Int J Remote Sens 31:6115–6141. https://doi.org/10.1080/01431160903376415
- Xu X, Jiang B, Tan Y, Costanza R, Yang G (2018) Lake-wetland ecosystem services modeling and valuation: Progress, gaps and future directions. Ecosyst Serv 33:19–28. https://doi.org/10.1016/j.ecoser.2018.08.001