Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia

Lake Urmia is one of the largest hypersaline lakes on earth with a unique biodiversity. Over the past two decades the lake water level declined dramatically, threatening the functionality of the lake’s ecosystems. There is a controversial debate about the reasons for this decline, with either mismanagement of the water resources, or climatic changes assumed to be the main cause. In this study we quantified the water budget components of Lake Urmia and analyzed their temporal evolution and interplay over the last five decades. With this we can show that variations of Lake Urmia’s water level during the analyzed period were mainly triggered by climatic changes. However, under the current climatic conditions agricultural water extraction volumes are significant compared to the remaining surface water inflow volumes. Changes in agricultural water withdrawal would have a significant impact on the lake volume and could either stabilize the lake, or lead to its complete collapse

A System Dynamics- Based Analysis of Operation Policies for Water Resources at River Basin Scale

There are many natural and human subsystems in a watershed with their special interrelationships. These interrelationships must be duly considered for the integrated and comprehensive management of the water resources in a water basin. One example of such interrelationships includes upstream water development and utilization projects which adversely affect downstream water quality and quantity. Within the framework of an integrated water resources management, various water resources development and operation policies must be analyzed to select the most convenient one securing the benefits of all the stakeholders in the watershed. In this study, various operation policies in theUrmiahLakeBasinand theAjiChaiRiverBasin on the east of the lake are analyzed to determine their impacts on the water level in the lake. For this purpose, the Aji Chai Basin is subdivided into three sub-basins and the System Dynamics, which is a feedback–based object–oriented simulation approach, is used to develop the dynamic model of the region. To investigate the present scenarios, the ARMA (1, 1) model is used to generate 10 different time series for each sub-basin and the lake water level is accordingly determined for each case

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%

Assessment of Residential Water Conservation due to Using Low-Flow Fixtures

Increasing population and socioeconomic development have led to increased urban water demand. Residential use forms the principal portion of urban water consumption. One of the most effective residential water conservation measures is using low-flow fixtures and devices designed for this purpose. In this paper, conservation results of using low-flow fixtures including low-flow showerheads and faucet aerators are evaluated in the city of Kashan. For this purpose, two groups of 40 households were randomly selected as experimental and control groups. The fixtures were installed in the houses of the experimental group and water consumption was measured over one month. Results indicate that retrofitting with these fixtures reduces residential water consumption by about 22 percent. Projections of Kashan’s future water demand and supply indicate that using these fixtures by Kashan residents can delay the need for new water supply projects by up to 6 years. Cost-benefit ratio of this conservation measure for Kashan is estimated to be 5.8 to 1. Finally, user satisfaction of retrofitting with these fixtures is evaluated

River Stream-Flow and Zayanderoud Reservoir Operation Modeling Using the Fuzzy Inference System

The Zayanderoud basin is located in the central plateau of Iran. As a result of population increase and agricultural and industrial developments, water demand on this basin has increased extensively. Given the importance of reservoir operation in water resource and management studies, the performance of fuzzy inference system (FIS) for Zayanderoud reservoir operation is investigated in this paper. The model of operation consists of two parts. In the first part, the seasonal river stream-flow is forecasted using the fuzzy rule-based system. The southern oscillated index, rain, snow, and discharge are inputs of the model and the seasonal river stream-flow its output. In the second part, the operation model is constructed. The amount of releases is first optimized by a nonlinear optimization model and then the rule curves are extracted using the fuzzy inference system. This model operates on an "if-then" principle, where the "if" is a vector of fuzzy permits and "then" is the fuzzy result. The reservoir storage capacity, inflow, demand, and year condition factor are used as permits. Monthly release is taken as the consequence. The Zayanderoud basin is investigated as a case study. Different performance indices such as reliability, resiliency, and vulnerability are calculated. According to results, FIS works more effectively than the traditional reservoir operation methods such as standard operation policy (SOP) or linear regression

Improving the bathymetric model of Lake Urmia in Iran using multispectral satellite data

Improved bathymetric model of Lake Urmia derived from satellite images from Landsat sensors TM, ETM+, and OLI and recent bathymetric model, based on an echo sounding survey in 2017. The existing bathymetric data shows some inaccuracies in the shallow areas of the lake and was therefore replaced by satellite-based remote sensing derived data above an elevation of 1270.04 m. For this purpose, the extent of the lake's surface was extracted from 129 satellite images with different acquisition dates covering water levels ranging from 1270.04 m to 1278.42 m a.s.l. The resulting contour lines, were merged with existing data (deeper parts) and interpolated to generate the improved bathymetric model with a resolution of 30 × 30 m

Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran

Study region: This study develops the first daily runoff forecast system for Bukan reservoir in Urmia Lake basin (ULB), Iran, a region suffering from water shortages and competing water demands. Study focus: A weather forecast downscaling model is developed for downscaling large-scale raw weather forecasts of ECMWF and NCEP to small-scale spatial resolutions. Various downscaling methods are compared, including deterministic Artificial Intelligence (AI) techniques and a Bayesian Belief Network (BBN). Downscaled precipitation and temperature forecasts are then fed into a rainfall-runoff model that accounts for daily snow and soil moisture dynamics in the sub-basins upstream of Bukan reservoir. The multi-objective Particle Swarm Optimization (MOPSO) method is used to estimate hydrological model parameters by maximizing the simulation accuracy of observed river flow (NSEQ) and the logarithm of river flow (NSELogQ) in each sub-basin. New hydrological insights for the region: Results of the weather forecast downscaling model show that the accuracy of the BBN is greater than the various deterministic AI methods tested. Calibration results of the rainfall-runoff model indicate no significant trade-off between fitting daily high and low flows, with an average NSEQ and NSELogQ of 0.43 and 0.63 for the calibration period, and 0.54 and 0.57 for the validation period. The entire forecasting system was evaluated using inflow observations for years 2020 and 2021, resulting in an NSE of 0.66 for forecasting daily inflow into Bukan reservoir. The inflow forecasts can be used by policymakers and operators of the reservoir to optimize water allocation between agricultural and environmental demands in the ULB.Water Resource

Effects of water level decline in Lake Urmia, Iran, on local climate conditions

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and the overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake's impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into periods 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.</p

Effects of water level decline in Lake Urmia, Iran, on local climate conditions

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and the overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake's impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into periods 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.Water Resource