Socioeconomic benefits of conserving Iran's water resources through modifying agricultural practices and water management strategies

The monetary facilities payment for ecosystem services as an approach to restore the Degraded Urmia Lake in Iran

This study analyzed the potential use of Payment for Ecosystem Services (PES) as a strategy for improving water supply management. This study focused on the Siminehroud Sub-basin due to its high importance to the Basin of Urmia Lake (UL). Siminehroud is the second provider of water (by volume) to Urmia Lake. To evaluate the technical and economic feasibility of a PES scheme, the current land use map was extracted using satellite imagery. In addition, the two algorithms of Support Vector Machines (SVMs) and Maximum Likelihood (ML) are used for Landsat images classification, rather than analyzing the relationship between land use and ecosystem services. Then, the most relevant ecosystem services provided in the region were evaluated using the Benefit Transfer Method. In the last step, by designing and implementing a survey, on the one hand, the local farmers' Willingness to Accept (WTA) cash payments for reducing the area they cultivate, and on the other hand, the farmers' Willingness to Pay (WTP) for managing the water consumption were determined. The results illustrated that the WTA program is more acceptable among the beneficiaries. It is also notable that this program needs very high governmental funding. Furthermore, the results of the program indicate that the land area out of the cultivation cycle will gradually increase while the price of agricultural water will also increase.

Agricultural Adaptation to Reconcile Food Security and Water Sustainability Under Climate Change: The Case of Cereals in Iran

In this study, we simulate the crop yield and water footprint (WF) of major food crops of Iran on irrigated and rainfed croplands for the historical and the future climate. We assess the effects of three agricultural adaptation strategies to climate change in terms of potential blue water savings. We then evaluate to what extent these savings can reduce unsustainable blue WF. We find that cereal production increases under climate change in both irrigated and rainfed croplands (by 2.6-3.1 and 1.4-2.3 million t yr-1, respectively) due to increased yields (6.6%-78.7%). Simultaneously, the unit WF (m3 t-1) tends to decrease in most scenarios. However, the annual consumptive water use increases in both irrigated and rainfed croplands (by 0.3-1.8 and 0.5-1.7 billion m3 yr-1, respectively). This is most noticeable in the arid regions, where consumptive water use increases by roughly 70% under climate change. Off-season cultivation is the most effective adaptation strategy to alleviate additional pressure on blue water resources with blue water savings of 14-15 billion m3 yr-1. The second most effective is WF benchmarking, which results in blue water savings of 1.1-3.5 billion m3 yr-1. The early planting strategy is less effective but still leads to blue water savings of 1.7-1.9 billion m3 yr-1. In the same order of effectiveness, these three strategies can reduce blue water scarcity and unsustainable blue water use in Iran under current conditions. However, we find that these strategies do not mitigate water scarcity in all provinces per se, nor all months of the year.

A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin

We implemented the semantically open conceptual framework ‘Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism’ (MuSIASEM) to deal with nexus challenges in agricultural production systems in transboundary river basins, using the Iranian Aras River Basin as a case study. The performance of the agricultural sector was characterized for relevant typologies of crop production using metabolic profiles, i.e., inputs and outputs per ton of crop produced, per hectare of land use, and per hour of labor. This analysis was contextualized across hierarchical levels of analysis, including the agronomic context at the regional level (rainfed versus irrigated cultivation), the socio-economic and political context at the national level (food sovereignty; urbanization), and the hydro-ecological context of the larger transboundary river basin (water constraints, GHG emissions). We found that the simultaneous use of two different interrelated logics of aggregation—the productivity of land and labor (relevant for the agronomic and socio-economic dimension) and the density of flows under different land uses (relevant for the hydrological and ecological dimension)—allowed for the identification of trade-offs in policy deliberations. In the case of Iran, it showed that striving for strategic autonomy will exacerbate the current water crisis; with the current cropping patterns, agronomic improvements will not suffice to avert a water crisis. It was concluded that the proposed approach fills an important gap in nexus research, but to effectively guide nexus governance in the region, a co-production of the analysis with social actors as well as more complete data sets at the river basin level would be essential.

Assessing agro-environmental sustainability of intensive agricultural systems

Sustainable production in water-scarce regions entails not to overshoot the sustainable blue water availability (BWA), which in turn requires addressing environmental flow requirements (EFRs). We explored the long-term effects of agricultural development, before (1984-1997) and during (1998-2018) the operation of the modern irrigation and drainage network of Tajan (TIDN), northern Iran, on the sustainability of blue water consumptions. A combination of different methods were applied to estimate hydrological EFRs of rivers, ab-bandans (traditional water reservoirs), and groundwater resources. Three major pollutants in the region's water resources, including nitrogen, phosphorus, and salinity, were used to estimate water quality EFR. Monthly agriculture water footprints (WFs) were calculated using the AquaCrop model, and then were compared with the region's BWA, which was calculated by subtracting monthly EFRs from monthly natural runoff. When WF exceeded BWA, the production system includes unsustainable water consumption. The EFR satisfaction of surface water decreased after TIDN operation by about 19%. Unmanaged nitrogen application and post-TIDN overexploitation of groundwater resulted in substantial increase in groundwater EFR violation. The TIDN led to more water consuming cropping pattern resulting in increased agricultural water consumption by about 73%. Overall, agricultural development in TIDN was beyond the capacity of the area, which resulted in up to about 167 MCM y^-1 unsustainable blue water consumption. Based on the results, the new framework presented for assessing agro-environmental sustainability could assist managers and policy makers to modify agricultural systems according to environment resilience.

A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery

Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types.

Spatio-Temporal Assessment of Global Gridded Evapotranspiration Datasets across Iran

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ETwb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ETERA5 provides higher base average values and lower maximum annual average values than ETGLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ETERA5 has a higher correlation with available energy than available water, while ETGLEAM has higher correlation with available water, and ETGLDAS does not correlate with none of these drivers. Based on the comparison of ETERA5 and ETGLEAM with ETwb, both have similar errors in spatial distribution, while ETGLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ETERA5 and ETGLEAM). All three datasets provide better ET estimates (values closer to ETWB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran.