Urmia Lake Research Articles

Study and analysis of the Chalcolithic Period of the Mughan Plain based on the archaeological data of Yataq-Tepesi, Northwest of Iran

The rivers, including Aras, in the Mughan region played an important role in the formation of the prehistoric sites. The Mughan Plain has not received particular attention in terms of the archaeological investigations, as the extensive scientific activities concentrated on the Lake Urmia basin. Yataq-Tepesi is a prehistoric site in the city of Germi (Mughan Region) that became the subject of the scientific research. The main aim of this paper is to discuss the pottery traditions at the site and to determine the chronological sequence of the studied region. In view of further detailed studies on the cultural relations of Mughan population with other territories, and to establish its chronological sequence, two main questions are posed: how do we date Yataq-Tepesi on the basis of cultural information, including pottery traditions? The main hypothesis suggested here is that the site development took place during the Middle Chalcolithic and Late Chalcolithic periods, somewhere between 4500 and 3700 / 3600 BC. The second question relates to the geographical regions that Yataq-Tepesi was in contact with through cultural relations and trade with other regions of northwestern Iran, especially with the region of Qaradagh and the Lake Urmia basin, as well as with the Southern Caucasus.

Field scale spatio-temporal variability of wind erosion transport capacity and soil loss at Urmia Lake

Urmia Lake has been known as the second hypersaline lake in the world, with the surface area of approximately 5200 km2. With decreasing the water input of the lake due to anthropogenic activities, the susceptible areas to wind erosion and dust emission were extended during the last decades. The present study attempted to measure wind erosion on the edge of Urmia Lake for three years since 2017. In order to provide a quantitative understanding of wind erosion parameters in the dried up Urmia Lake area, and to prioritize different areas in terms of wind erosion intensity, it was necessary to establish wind erosion measurement and monitoring stations in different areas of dried up shores. Wind erosion measurement and monitoring stations were established in six erodible areas such as Salmas, Jabal Kandi, Soporghan, Miandoab, Khaselou and Ajabshir. Wind erosion parameters such as transport capacity and soil loss in the dried margin of Urmia Lake were determined. For this purpose, BSNE traps were used in the layout of two circles having an identical center. After each wind erosion event, sediment traps were emptied and weighted; then, the vertical and horizontal distribution of the particulate matters was calculated. Comparison of the values of maximum transport capacity-fmax (kg/m.yr) and soil loss- SL (ton/ha.yr) of aeolian particulate in 2017 showed that the two main centers of wind erosion on the edge of Urmia Lake were Ajabshir and Jabal Kandi. The stations of Khaselou, Salmas, Soporghan and Mianduab were in the declined ranking. Results showed that the transfer capacity values were 351.97 and 297.30 kg/m/year and soil losses were 18.04 and 35.4 ton/ha/year, respectively, for the stations with high wind erosion potential, i.e., Ajab Shir and Jabal Kandi, in 2017. Furthermore, these values were significantly reduced for the mentioned stations in 2019, so that the values obtained from the transfer capacity reached 54.93 and 40.39 kg/m/year and soil losses reached 3.70 and 2.43 ton/ha. Investigating the results of transport capacity and soil loss showed the decreasing trend in wind erosion rate due to the increasing water level of the lake as well as biological and engineering conservation practices (non-live windbreaks) from 2017 to 2019.

What says the chalcidoid (Hymenoptera) diversity in the Lake Urmia basin?

What says the chalcidoid (Hymenoptera) diversity in the Lake Urmia basin?

Forecasting groundwater anomaly in the future using satellite information and machine learning

• A machine learning model based remote sensing and terrestrial data was developed. • Fill the data gap in GRACE and GFO satellites data by machine learning. • Soil Moisture, temperature and evapotranspiration was estimated by the CanESM2. • Terrestrial water storage and groundwater anomaly were predicted for the future. By applying Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) satellites, this study estimates the value of Terrestrial Water Storage Anomaly (TWSA) and GroundWater Anomaly (GWA) in Lake Urmia Basin (LUB), Iran. TWSA changes are first calculated using GRACE/GFO satellites where the best Gaussian Filter Radius in Kilometer (GFRK) with the least error is obtained. To provide a model and a practical convenient equation for modeling the value of TWSA in the study area, six parameters affected the value of TWSA is used. These data are obtained from two data centers: Global Land Data Assimilation System (GLDAS) and fifth generation (V5) European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). This information is obtained in the Google Earth Engine (GEE) environment by developing JavaScript code for the study area and on a monthly basis for 107 points that have a suitable distribution in the area. The TWSA value is also calculated using GRACE/GFO data satellites for 10 pixels that completely cover the area. Next, Machine Learning (ML) is used to model the TWSA value. The best model is obtained for 2–6 inputs in calculating the TWSA value. The results indicate that the use of three inputs of Soil Moisture in 10–40 cm (SM 2 ), Average Temperature (AT), and Evapotranspiration (ET) present the best performance compared to other models. Finally, GRACE/GFO missing data are obtained using this equation. To calculate the TWSA value in the future, three input parameters are downscaled using the second-generation Canadian Earth System Model (CanESM2) to predict the TWSA value. The results of climate change show that the value of TWSA in the two periods 2021–2040 and 2041–2060 has a reduction compared to the average measurement period. Also, due to TWSA changes, the value of GWA would be drastically reduced in the future.

Monitoring and Prediction of the Urmia Lake Drying Trend Based on Time-Series Remotely Sensed Images and Artificial Neural Networks

Urmia Lake, which is the second largest permanent hypersaline lake in the world, is shrinking in recent decades. Since accurate spatial information about the lake is essential to managing the current and emerging crises of the lake, this study is used Urmia Lake satellite images time-series to investigate drought trends by analyzing via Artificial Neural Networks (ANN). The proposed approach is comprising the following four steps. First, yearly time-series Landsat images (2000-2022) are corrected geometrically and radiometrically. Then, time-series images of 2000-2020 are classified into five land cover classes, including; deep water, shallow water, salt, soil, and vegetation. In the third step, ANN trained for 2000-2019 as input and tested for 2020 as an output. Finally, the trained ANN is proposed to predict the future land covers of the lake (for 2021 and 2022 years). In order to evaluate the proposed model, the predicted maps of 2021 and 2022 were compared with their corresponding ground truth maps and quantitative criteria were calculated. The overall accuracy of the prediction for 2021 and 2022 is equal to 92.75% and 90.62%, respectively, which indicates the high capability of the proposed method for modeling and predicting changes in Urmia Lake and its shores.

Prevention of Wind Erosion in Salty Lands of Urmia Lake with Butadiene Carboxylic Latex and Cement Grout

Prevention of Wind Erosion in Salty Lands of Urmia Lake with Butadiene Carboxylic Latex and Cement Grout

Satellite based lake bed elevation model of Lake Urmia using time series of Landsat imagery

This study presents a lake bed elevation model of Lake Urmia. In the course of model generation, a time series of the extent of the lake surface was derived from 129 satellite images with different acquisition dates based on the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Due to the rapid shrinking of the lake during the last two decades, lake surface areas ranging from 890 km2 to 6125 km2 could be covered. The water edge of the various lake extents was then linked to the observed water level on the day of the satellite image acquisition. The resulting contour lines, covering water levels between 1270.04 m and 1278.42 m a.s.l. and thus representing the lakebed morphology in its shallow parts, were merged with existing data (deeper parts) and interpolated to generate a lake bed elevation model. Finally, Lake Urmia’s Level-Area-Volume relationships were derived from the lake bed elevation model and compared to bathymetric data previously published.

Multi-step Lake Urmia water level forecasting using ensemble of bagging based tree models

Multi-step Lake Urmia water level forecasting using ensemble of bagging based tree models

Long-Term Investigation of Aerosols in the Urmia Lake Region in the Middle East by Ground-Based and Satellite Data in 2000–2021

Dried lake beds are some of the largest sources of dust in the world and have caused environmental problems in the surrounding areas in recent decades. In the present work, we studied the monthly and annual occurrence of dust storms at selected weather stations around Urmia Lake in northwestern (NW) Iran. Furthermore, we investigated the variations in the daily aerosol optical depth (AOD at 550 nm) and the Ångström exponent (at 412/470 nm), as well as the vertical profile of the total aerosol extinction coefficient and AOD at 532 nm, using space-borne MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and CALIPSO Satellite LiDAR data over the Urmia Lake region (36–39°N, 44–47°E). The monthly variations of AOD550 and AOD532 for the regions 37–39°N and 46–59°E were compared, and it was found that the CALIPSO AOD532 and MODIS AOD532 (reconstructed using the Ångström exponent) were in good agreement. In general, the dust storms during 2000–2021 increased the AOD550 above average around the Urmia Lake. The vertical profile of aerosols showed that the largest contribution to total aerosol loading over the Urmia Lake was from 1.5–3 km, 1.5–4 km, 1.5–5 km, and 1.5–3 km during winter, spring, summer, and autumn seasons, respectively.

Ranking and optimizing the rain-gauge networks using the entropy–copula approach (Case study of the Siminehrood Basin, Iran)

The existence of reliable rain-gauge networks is a necessity in managing water sources and the relevant environmental issues in any basin. The present study aimed to investigate the rain-gauge network and rank the rain-gauge stations in the Tazehkand sub-basin in the southwest of Lake Urmia, Iran, using the entropy–copula approach using the data obtained from six rain-gauge stations in the Siminehrood basin in Northwestern Iran between 1981 and 2019. The interaction effects of the stations were investigated using the copula simulation approach instead of the multivariate regression analysis. The R-vine was selected as the most convenient copula after investigating various vines using the statistics related to error investigation. Then, the interaction effects of the stations were investigated using the R-vine. The rainfall in each station was estimated by considering the rainfall of other stations. Moreover, the more prevalent multivariate regression analysis was implemented, and its results were compared with the results obtained from the vine simulation approach. The results of the comparison according to the NSE statistic showed that the vine simulation approach was above 90% across all stations. The estimates for the ITI and N(i) indices of each station using the entropy theory showed the shortage of stations in the investigated area. The stations were in average, above-average, and surplus modes according to the ITI index. The rating concerning the N(i) index indicated that the Dashband station was the most appropriate one in terms of communicating with other stations and conveniently covering the plain.

The challenge of reference evapotranspiration between the WaPOR data set and geostatistical methods

AbstractThis study aimed evaluating the ability of the FAO's WaPOR Product (FWP) to estimate reference evapotranspiration (RET) based on lysimetric data and RET equations and compare its accuracy with geostatistical methods in the Lake Urmia basin. Lysimetric RET was collected at two stations for 4 years. RET equations were then evaluated using the lysimetric data; the best equation was determined. The RET data obtained from FWP were evaluated at daily, monthly and annual scales using the results of the selected equation for the years 2010–2020. Finally, the accuracy of FWP in the spatial estimation of RET was compared with geostatistical methods. The results show that the FAO Penman–Monteith (FPM) equation was more accurate at both stations. Therefore, the results of the FPM were used to evaluate the reference evapotranspiration of FWP. The average nRMSE of the FWP to daily, monthly and annual data was 31, 25 and 16%, respectively. In general, 15% overestimation was observed in the FWP. Comparison of the FWP with geostatistical methods showed that the highest and lowest accuracy was observed in the experimental kriging method and FWP with an nRMSE value of 4.6 and 18%, respectively.

Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images

Crop classification provides essential information for ensuring global food security, allowing early crop monitoring practices and water irrigation management. Despite of high importance of satellite imagery in identifying agricultural products, a significant number of optical images could become inaccessible due to cloud cover, which makes mapping agricultural products difficult. Therefore, the provision of up-to-date data integration methods and the availability of time series of optical and radar images such as Sentinel-1 data have provided new opportunities for mapping agricultural products with high spatial and temporal resolution. In this study, the results of classification accuracy were investigated based on the phenology of agricultural products and using time series of Landsat 8, Sentinel-1 and the digital elevation model (DEM). To do so, the key phenological parameters of the products such as start of season, length of season and end of season were first extracted using time series of the NDVI vegetation index calculated from the optical images and the TIMESAT model. Then, based on vegetation phenology of crops using optical (Landsat-8) and radar (Sentinel-1) time series data along with Digital Elevation Model (DEM) to increase the accuracy of classification, we applied the support vector machine method based on machine learning. The performance of this model was investigated on a part of Miandoab catchment basin located in the catchment of Lake Urmia in the northwest of Iran. Also, five major agricultural products of the study area, such as alfalfa, wheat, sugar beet, apple and grape were identified with overall accuracy of 89% using the support vector machine algorithm. Classification results demonstrated that using a combination of different data (overall accuracy (OA) = 89%, Kappa = 0.78) were more accurate than the only single-sensor inputs (OA = 77%, Kappa = 0.64), and when differences between crop types were largest, classification performance increased throughout the season until July.

Halophytes play important role in phytoremediation of salt-affected soils in the bed of Urmia Lake, Iran

Soil salinity is a major threat in agriculture even in semi-arid regions of the world which can accelerate land degradation and desertification and decrease agricultural productivity and consequently jeopardize environmental and food security. Halophytes play important role in phytoremediation. This study is assessed the potential of Halocnemum strobilaceous, Atriplex verruciferae, Salsola crassae, and Salicornia europaeae in phytoremediation of saline soils occurred after water level desiccation of Urmia Lake. Three distances from the water body (500, 1000, and 1500 m) was selected for evaluating. Soils and plants were analyzed using standard methods. The mean values of salinity indices of the saline-sodic soil samples were identified as pH 8.6 and electrical conductivity (ECe) 65.34 dS m−1, also sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) were higher than 13 and 15%, respectively. The maximum soil exchangeable Na+, K+, and Ca2+ concentrations (7200, 1900, and 1400 mg kg−1, respectively), also the concentrations of Mn2+ (12.5 mg kg−1), Fe2+ (5.5 mg kg−1), and Cu2+ (1.5 mg kg−1), were significantly different at various distances. However, the highest amounts were obtained at 500 m. In addition the concentration of Fe2+ (511.85 mg kg−1), Zn2+ (99.97 mg kg−1), and Na+ (25.65 mg kg−1) was the highest, especially in shoots. Furthermore, Salicornia and Halocnemum were more effective in salinity-remediation in comparison to other halophytes. The maximum dry matter (38%), protein (16%), and oil percentage (3.5%) were found in Salicornia, followed by Halocnemum. The findings indicated that salt-accumulating halophytes could be considered as the suggestions for phytoremediation saline soils and desalinating soil in arid and semi-arid regions.

THE IMPORTANCE OF MANNA COUNTRY IN FOREIGN POLITICS OF THE URARTU STATE

The Manna State was established in the south of Lake Urmia in the 9th century BC. The name of this state is mentioned in Urartian and Assyrian cuneiform sources from the 1st millennium BC. The region where the land of Manna located is particularly important as it is on trade routes when a great drought and thus a wave of migration from Central Asia began during the Iron Age. Urmia Lake Basin, which has attracted the attention of the Urartian Kingdom from the early period, is one of the regions that could not be dominated and therefore almost all Urartian kings organized expeditions. The attempt to subdue Manna increased during the reign of the Urartian King Menua and the Assyrian threat was faced during the intense campaigns during the reign of Menua’s successor, Argišti I. During the Sarduri II period, expeditions continued and tribute was taken from the Manna principalities. Manna King Iranzu, who was an Assyrian vassal, took advantage of Assyrians’ successful war against Urartu, followed a policy of centralization and created a principality system dependent on the central authority in the country. Although Manna became a powerful state around the Urmia Lake Basin during Iranzu’s reign, some rulers and princes who did not like Iranzu’s centralization policy gave Urartu the opportunity to interfere in Manna’s internal affairs. Urartian King Rusa I used the internal political crisis among the Mannas for his own purposes, provoked some principalities against the central authority, and a pro-Urartian group emerged in the country. Thus, the Lake Urmia Basin became the subject of an intense struggle for dominance between the Urartu and Assyrian States during the reign of Rusa I. With the Assyrian King Sargon II, the pressure of the Urartians in the region decreased. After the defeat of Urartian Kingdom by Sargon II, the balance of power in the region changed, and Manna was not mentioned in Urartian written sources after that. In this study, the importance of the Manna State, which has an important place in the history of Azerbaijan, in terms of the foreign policy of the Urartian State is discussed.

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.

Spatial prioritization of wind-erosion-prone areas in the dried-up beds of Lake Urmia; using field sampling and in-vitro measurement

Preparing wind erosion susceptibility maps and prioritizing the susceptible areas in the dried-up lakebeds is essential through field sampling data and in-vitro measurement, which has so far received less attention. Accordingly, this study aimed to identify and prioritize the wind-erosion-prone dried-up beds of Lake Urmia, Iran. Therefore, 190 composite soil samples (from 1520 points) were taken. Then the soil texture, organic matter (OM), and calcium carbonate (CaCO3) content of the samples were measured. Afterward, the equation-based wind-erodible fraction of soil (EF-e) and soil crust factor (SCF) were used to calculate the wind-erosion susceptibility. Sieving-based wind-erodible fraction of soil (EF-s) obtained from the flat sieve. Wind-induced soil loss (WE) measured using a fixed wind tunnel to validate the results of EF-e, SCF and EF-s methods. Then, the spatial distribution maps of EF-e, EFs, SCF and WE values, and OM and CaCO3 content in the non-sampled beds were estimated using the ordinary Kriging method. The results indicated that, in the dried-up beds of Lake Urmia, the EF-e, EF-s, SCF and WE indices varied from 1% to 55.98%, 4.16% to 55.25%, 0.034 to 0.996, and 1.27 to 15.56 g m−2 min−1, respectively. There was a strong significant correlation (P < 0.01) between EF-e and EF-s with WE. The high content of CaCO3 had the most effect on decreasing the susceptibility to wind erosion on the dried-up beds of Lake Urmia. We also found that the dried-up beds located in the southeastern (WE = 9.03–15.56 g m−2 min−1) and northwestern (WE = 7.89–15.56 g m−2 min−1) parts of Lake Urmia had the highest susceptibility to wind erosion. In addition, the southwestern (WE = 5.72–7.89 g m−2 min−1) of Lake Urmia had the lowest susceptibility. The technical and managerial measures can be developed the priorities highlighted in this study to stabilize dust-salt hotspots in the dried-up beds of Lake Urmia.

Studying the Changes in the Hydro‐Meteorological Components of Water Budget in Lake Urmia

AbstractAbrupt changes in the Lake Urmia water level have been addressed in many studies, and yet the link between the water level decline and hydro‐meteorological variables in the basin is a major topic for debate between researchers. In this study, a set of data‐driven techniques is used to investigate the components of the water budget in Lake Urmia. Then, the rate of monthly depth differences (DD), precipitation (P), evaporation (E), potential groundwater head (G), and streamflow (Q) time series between 1974 and 2014 are used in the analysis. Several scenarios and strategies are developed by considering the major changes in the year‐2000, which is believed to be the initiation of the hydrological encroachment in the basin. Simple water budget (WB), dynamic regression (DR), and symbolic regression (SR) techniques are used to simulate the DD with consideration to P, E, G, and Q. Alternatively, the effect of the year 1997 as the potential base‐line for the initiation of significant meteorological trends in the basin is investigated. Conducted analysis showed that the DR models of an autoregressive moving average together with multiple exogenous inputs provide an approximate R2: 0.7 as the best alternative among the selected models. It is shown that the Q and G depict abrupt changes compared to the P and E, while either the year 1997 (climate effect) or the year 2000 (encroachment effect) is considered as the baseline in the study.

Monitoring of aquatic birds and surveillance of avian influenza and Newcastle disease of waterfowls at the National Park of Urmia Lake

BackgroundUrmia lake, as a national park, is one of the most valuable aquatic ecosystems in the Middle East and quatitative and qualitative changes in Urmia lake water have a great impact on its ecological performance and in the region.ObjectivesThis project was designed to study the effects of the extent of Urmia lake water surface area on the area size and on the number of aquatic birds of the six selected habitats in 2011–2019. The presence of avian influenza (AI) and Newcastle disease (ND) viruses in migratory aquatic birds together with their impacts on poultry farms as well as on rural birds was also under surveillance in 2018–2019.MethodsChanges of Urmia lake and its impacts on area size of the six selected birds habitats were monitored by GIS. The small monitoring program with circular plot point counts was used for counting of the number of birds of the six selected habitats. At least, 100 samples (oropharyngeal and cloacal swabs) were collected. each sample was placed in a sterile plastic tube containg transport media and assigned with an number and store untill used. Reverse transcription‐polymerase chain reaction (RT‐PCR) and real‐time RT‐PCR test were used for detection of AI and ND viruses in the samples.ResultsThe results revealed that changes in the water surface area of Urmia lake had a gsignificat impacts on area size and the number of aquatic birds of the six selected habitats. The surveillance results showed that 5% of the samples were AIV positvie while 25% of the samples were pasitive for NDV including 20% for non‐virulent NDV (lNDV) and 5% for virulent NDV (vNDV) strains.ConclusionThis study showed that fluctuation of Urmia lake's water surface area influenced (p < 0.05) the area size of the six selected aquatic birds' habitats and had a great impacts on the number of the migratory birds. Detection of AIV and vNDV emphesises that the seasonal migratory waterfowls spread AI and vND viruses to the ponds and estuaries as well as to the rural birds and industrialised poultry units around the Urmia lake. Potential public health treats were also discussed.

Soil wind erodibility and erosion estimation using Landsat satellite imagery and multiple-criteria decision analysis in Urmia Lake Region, Iran

Assessing variations in soil wind erosion (SWE) is critical for identifying key change areas and formulating desertification control strategies. Satellite images with an expansive spatial coverage and temporal repeatability make it possible to monitor the process of soil degradation and its consequences such as SWE. This research aims to model SWE in the eastern shoreline of Urmia Lake in the 2005–2017 period through multiple-criteria decision analysis (MCDA). Soil moisture, soil erodibility (SE), soil crust index, number of snow cover days, wind field intensity, and vegetation fraction were determined as critical factors affecting SWE. The analytic hierarchy process (AHP) method was applied to determine the weight of each factor. High SE and poor vegetation were the most important factors in the developed SWE model. The SE was precisely estimated (relative percent deviation (RPD)=2.01) by the support vector regression (SVR) method using Landsat-8 image. The developed SWE estimation method had an overall accuracy of 81%. Most of the eastern shoreline of Urmia Lake Region was classified in the severe SWE class. Results showed a declining erosion intensity trend from central parts with high wind erosion (47% of the region) to northern and southern parts of the region. Increasing the distance from the lake led to an increase in SWE.

ERA5 and ERA-Interim Data Processing for the GlobWat Global Hydrological Model

The reproducibility of computational hydrology is gaining attention among hydrologists. Reproducibility requires open and reusable code and data, allowing users to verify results and process new datasets. The creation of input files for global hydrological models (GHMs) requires complex high-resolution gridded dataset processing, limiting the model’s reproducibility to groups with advanced programming skills. GlobWat is one of these GHMs, which was developed by the Food and Agriculture Organization (FAO) to assess irrigation water use. Although the GlobWat code and sample input data are available, the methods for pre-processing model inputs are not available. Here, we present a set of open-source Python and YAML scripts within the Earth System Model Evaluation Tool (ESMValTool) that provide a formalized technique for developing and processing GlobWat model weather inputs. We demonstrate the use of these scripts with the ERA5 and ERA-Interim datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). To demonstrate the advantage of using these scripts, we ran the GlobWat model for 30 years for the entire world. The focus of the evaluation was on the Urmia Lake Basin in Iran. The validation of the model against the observed discharge in this basin showed that the combination of ERA5 and the De Bruin reference evaporation method yields the best GlobWat performance. Moreover, the scripts allowed us to examine the causes behind the differences in model outcomes.