Spatiotemporal variation and driving forces of reference evapotranspiration in Jing River Basin,northwest China

Evapotranspiration is an important component of the hydrological cycle, which integrates atmospheric demands and surface conditions. Research on spatial and temporal variations of reference evapotranspiration (ET_o) enables understanding of climate change and its effects on hydrological processes and water resources. In this study, ET_o was estimated by the FAO‐56 Penman–Monteith method in the Jing River Basin in China, based on daily data from 37 meteorological stations from 1960 to 2005. ET_o trends were detected by the Mann–Kendall test in annual, seasonal, and monthly timescales. Sensitivity coefficients were used to examine the contribution of important meteorological variables to ET_o. The influence of agricultural activities, especially irrigation on ET_o was also analyzed. We found that ET_o showed a decreasing trend in most of the basin in all seasons, except for autumn, which showed an increasing trend. Mean maximum temperature was generally the most sensitive parameter for ET_o, followed by relative humidity, solar radiation, mean minimum temperature, and wind speed. Wind speed was the most dominant factor for the declining trend in ET_o. The more significant decrease in ET_o for agricultural and irrigation stations was mainly because of the more significant decrease in wind speed and sunshine hours, a mitigation in climate warming, and more significant increase in relative humidity compared with natural stations and non‐irrigation stations. Changes in ET_o and the sensitivity coefficient of meteorological variables in relation to ET_o were also affected by topography. Better understanding of ET_o response to climate change will enable efficient use of agricultural production and water resources, which could improve the ecological environment in Jing River Basin. Copyright © 2015 John Wiley & Sons, Ltd.     

Summer evapotranspiration in western Siberia: a comparison between eddy covariance and Penman method formulations

Evapotranspiration is difficult to quantify because of the many factors and complex processes that influence it. Several empirical methods have been developed over the years to estimate potential evapotranspiration based on easily available parameters. Directly measured data of actual evapotranspiration have been rather sparse in the past and still need to be improved in particular regions like western Siberia. The transition zone between the warm temperate and cold temperate continental climates is very sensitive to climate change, and water stress is an increasingly important issue in these regions with a highly dynamic agricultural activity. So there is a growing need to estimate actual evapotranspiration. Widely usable approximations are needed. In this study, the values of potential evapotranspiration computed with the original version, and eight modifications of the Penman formulation were compared and related to the actual evapotranspiration measured by eddy covariance over a grassland area in western Siberia. The original 1948 and 1963 Penman formulations are best for estimating potential evapotranspiration in the transition zone between the forest steppes and the pre‐taiga. A nearly linear relationship between the potential and actual evapotranspiration was found. A simple modification of the Penman equation (i.e. the multiplication of the result by a factor of 0.47) is suggested for approximating the actual evapotranspiration based on standard meteorological data for the region. The original Penman formulation is most robust and will provide the widest applicability in the future under changing climate and environmental conditions. In this context, it is further recommended not to neglect the ventilation term of the Penman equation, which is often assumed to be negligibly small. A detailed correlation analysis showed that under dry soil conditions, the vegetation largely contributed to the actual evapotranspiration and, in contrast to widely held expectations, that the Penman equation is best adapted to vegetated surfaces. Copyright © 2015 John Wiley & Sons, Ltd.     

On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment

In this paper, we present a genetic algorithm-based methodology to quantify agricultural and water management practices from remote sensing (RS) data in a mixed-pixel environment. First, we formulated a linear mixture model for low spatial resolution RS data where we considered three agricultural land uses as dominant inside the pixel—rainfed, irrigated with two, and three croppings a year; the mixing parameters we considered were the sowing dates, area fractions of agricultural land uses in the pixel, and their corresponding water management practices. Then, we carried out numerical experiments to evaluate the feasibility of the proposed approach. In the process, the mixing parameters were parameterized by data assimilation using evapotranspiration and leaf area index as conditioning criteria. The soil–water–atmosphere–plant system model SWAP was used to simulate the dynamics of these two biophysical variables in the pixel. The results of our numerical experiments showed that it is possible to derive some sub-pixel information from low spatial resolution data e.g. the existing agricultural and water management practices in a region, which are relevant for regional agricultural monitoring programs.     

Evaluating different GCMs for predicting spatial recharge in an irrigated arid region

Groundwater systems in arid regions will be particularly sensitive to climate change owing to the strong dependence of rates of evapotranspiration on temperature, and shifts in the precipitation regimes. Irrigation use in these arid regions is typically a large component of the water budget, and may increase due to changes in soil moisture resulting from higher temperatures and changes in the timing of precipitation events. In this study, future predicted climate change scenarios from three global climate models (CGCM1 GHG+A1, CGCM3.1 A2, and HadCM3 A2) are used to determine the sensitivity of recharge to different climate models in an irrigated agricultural region. The arid Oliver region (annual precipitation 300 mm) in the Okanagan Basin, British Columbia, is used to demonstrate the approach. Irrigation return flow, as a contribution to total diffuse recharge, is simulated by calculating the daily applied irrigation based on estimates of seasonal crop water demand and the forecasted precipitation and evaporation data. The relative contribution of irrigation return flow to groundwater recharge under current and future climate conditions is modelled. Temperature data were downscaled using Statistical Downscaling Model (SDSM), while precipitation and solar radiation changes were estimated directly from the GCM data. Shifts in climate, from present to future predicted, were applied to a stochastic weather generator, and used to force a one-dimensional hydrologic model, HELP 3.80D. Results were applied spatially, according to different soil profiles, slope and vegetation, over a 22.5 km by 8.6 km region. Changes to recharge in future time periods for each GCM result in modest increases of recharge with the peak recharge shifting from March to February. Lower recharge rates and higher potential evapotranspiration rates are similarly predicted by all three models for the summer months. All scenarios show that the potential growing season will expand between 3 and 4 weeks due to increases in temperature. However, the magnitude of the change varies considerably between models. CGCM3.1 has the largest increases of recharge rates, CGCM1 has very minor increases, and HadCM3 is relatively stable (as indicated by the near-zero changes between climate states). The significant differences between these three models indicate that prediction of future recharge is highly dependent on the model selected. The minor increase of annual recharge in future predicted climate states is due the shift of peak recharge from increased temperature. Irrigation rates dominate total recharge during the summer months in this arid area. Recharge in irrigated areas is significantly higher than natural recharge, with irrigation return flow between 25% and 58%. A comparison of recharge results for the least efficient and the most efficient irrigation systems indicates that the latter are more sensitive to choice of GCM.     

Optimal estimation of irrigation schedule – An example of quantifying human interferences to hydrologic processes

Reliable records of water use for irrigation are often lacking. This presents a difficulty for a qualified water use and water availability assessment. Quantification of the hydrologic cycle processes in regions of intensive agricultural practice requires irrigation as an input to hydrologic models. This paper presents a coupled forward-inverse framework to estimate irrigation schedule using remote-sensed data and data assimilation and optimization techniques. Irrigation schedule is treated as an unknown input to a hydro-agronomic simulation model. Remote-sensed data is used to assess actual crop evapotranspiration, which is used as the “observation” of the computed crop evapotranspiration from the simulation model. To handle the impact of model and observation error and the unknown biased error with irrigation inputs, a coupled forward-inverse approach is proposed, implemented and tested. The coupled approach is realized by an integrated ensemble Kalman filter (EnKF) and genetic algorithm (GA). The result from a case study demonstrates that the forward and inverse procedures in the coupled framework are complementary to each other. Further analysis is provided on the impact of model and observation errors on the non-uniqueness problem with inverse modeling and on the exactness of irrigation estimates.     

Exploring spatiotemporal relationships among meteorological,agricultural, and hydrological droughts in Southwest China

Each type of drought has different characteristics in different regions. It is important to distinguish different types of droughts and their correlations. Based on gauged precipitation, temperature, simulated soil moisture, and runoff data during the period 1951–2012, the relationships among meteorological, agricultural, and hydrological droughts were analyzed at different time scales in Southwest China. The standardized precipitation evapotranspiration index (SPEI), soil moisture anomaly percentage index (SMAPI), and standardized runoff index (SRI) were used to describe meteorological, agricultural, and hydrological droughts, respectively. The results show that there was a good correlation among the three indices. SMAPI had the best correlation with the 3 month SPEI and SRI values. It indicates that agricultural drought was characterized by a 3-month scale. The three drought indices displayed the similar special features such as drought scope, drought level, and drought center during the extreme drought of 2009–2010. However, the scope and level of SPEI were bigger than those of SMAPI and SRI. The propagation characteristics of the three types of droughts were significantly different. The temporal drought process in typical grids reflect that the meteorological drought occurred ahead of agricultural and hydrological droughts by about 1 and 3 months, respectively. Agricultural drought showed a stable drought process and reasonable time periods for the drought beginning and end. These results showed the quantitative relationships among three types of drought and thus provided an important supporting evidence for regional drought monitoring and strategic decisions.     

A distributed approach for estimating catchment evapotranspiration: comparison of the combination equation and the complementary relationship approaches

In large river basins, there may be considerable variations in both climate and land use across the region. The evapotranspiration that occurs over a basin may be drastically different from one part of the region to another. The potential influence of these variations in evapotranspiration estimated for the catchment is weakened by using a spatially based distributed hydrological model in such a study. Areal evapotranspiration is estimated by means of approaches requiring only meteorological data: the combination equation (CE) model and the complementary relationship approach—the complementary relationship areal evapotranspiration (CRAE) and advection–aridity (AA) models. The capability of three models to estimate the evapotranspiration of catchments with complex topography and land‐use classification is investigated, and the models are applied to two catchments with different characteristics and scales for several representative years. Daily, monthly, and annual evapotranspiration are estimated with different accuracy. The result shows that the modified CE model may underestimate the evapotranspiration in some cases. The CRAE and AA models seem to be two kinds of effective alternatives for estimating catchment evapotranspiration. Copyright © 2003 John Wiley & Sons, Ltd.     

Effects of Planting Field on Groundwater and Surface Water Pollution in China

Water forms an essential resource for life on earth because all living things on earth depend on water for life activities. However, with the increase in the human population, which is coupled with intense urbanization and agricultural activities, global water pollution has increased over the past decades. In China, agricultural activities that occure mainly in the planting fields have been listed as the main source of surface water and groundwater pollution. This review focuses on the major factors that influence pollution from planting fields in China mainly as a result of farming activities such as flood irrigation, excessive application of fertilizers and pesticides, and poor management practices. At present, good results are achieved by adopting soil fertilization test formula, biodegradable pesticides, proper irrigation, and agroforestry interventions. In the future, pollution from planting fields as a non‐point source of water pollution can be improved and resolved by perfect nutrient management, best management practices, organic amendments, restoring water environment, and intelligent assessment management.     

Urban irrigation in the modeling of a semi-arid urban environment: Ballona Creek watershed,Los Angeles,California

ABSTRACT

Ballona Creek watershed in Los Angeles, California provides a unique combination of heterogeneous urban land cover, a semi-arid environment, and a large outdoor water-use flux that presents a challenge for physically-based models. We ran simulations using the Noah Land Surface Model and Parflow-Community Land Model and compared to observations of evapotranspiration (ET), runoff, and land surface temperature (LST) for the entire 11-year study period. Both models were systematically adjusted to test the impact of land cover and urban irrigation on simulation results. Monthly total runoff and ET results are greatly improved when compared to an in-situ stream gauge and meteorological tower data: from 0.64 to 0.81 for the Nash–Sutcliffe efficiency (NSE) for runoff and from a negative NSE to 0.82 for ET. The inclusion of urban irrigation in semi-arid urban environments is found to be vital, but not sufficient, for the accurate simulation of variables in the studied models.     

Evaluating actual evapotranspiration and impacts of groundwater storage change in the North China Plain

As a critical water discharge term in basin‐scale water balance, accurate estimation of evapotranspiration (ET) is therefore important for sustainable water resources management. The understanding of the relationship between ET and groundwater storage change can improve our knowledge on the hydrological cycle in such regions with intensive agricultural land usage. Since the 1960s, the North China Plain (NCP) has experienced groundwater depletion because of overexploitation of groundwater for agriculture and urban development. Using meteorological data from 23 stations, the complementary relationship areal evapotranspiration model is evaluated against estimates of ET derived from regional water balance in the NCP during the period 1993–2008. The discrepancies between calculated ET and that derived by basin water balance indicate seasonal and interannual variations in model parameters. The monthly actual ET variations during the period from 1960 to 2008 are investigated by the calibrated model and then are used to derive groundwater storage change. The estimated actual ET is positively correlated with precipitation, and the general higher ET than precipitation indicates the contributions of groundwater irrigation to the total water supply. The long term decreasing trend in the actual ET can be explained by declining in precipitation, sunshine duration and wind speed. Over the past ~50 years, the calculated average annual water storage change, represented by the difference between actual ET and precipitation, was approximately 36 mm, or 4.8 km³; and the cumulative groundwater storage depletion was approximately 1700 mm, or 220 km³ in the NCP. The significantly groundwater storage depletion conversely affects the seasonal and interannual variations of ET. Irrigation especially during spring cause a marked increase in seasonal ET, whereas the rapid increasing of agricultural coverage over the NCP reduces the annual ET and is the primary control factor of the strong linear relationship between actual and potential ET. Copyright © 2013 John Wiley & Sons, Ltd.     

Regional impacts of climate change on irrigation water demands

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U₂), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U₂. Consequently, the reference evapotranspiration, modeled by the Penman–Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U₂). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd.     

Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring,and numerical modelling

Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modelling using Hydrus. Energy balance data and routine weather data serve to estimate ET₀. Surface reflectance data from satellite images (Sentinel-2) are used to derive leaf area indices, which help to partition ET₀ into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ET_act, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ET_act and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ET_act and groundwater recharge of 769 and 297 mm over a period of 281 days, respectively. The total estimated water inflow accounts for 946 mm, of which 77% originates from irrigation.     

M5 model tree based modelling of reference evapotranspiration

This paper investigates the potential of M5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation Management Information System (CIMIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration calculated using a relation provided by the CIMIS was used as output. To compare the performance of M5 model tree in predicting the reference evapotranspiration, FAO–56 Penman–Monteith equation and calibrated Hargreaves–Samani relation was used. A comparison of results suggests that M5 model tree approach works well in comparison to both FAO–56 and calibrated Hargreaves–Samani relations. To judge the generalization capability of M5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study suggest that M5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with M5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright © 2009 John Wiley & Sons, Ltd.     

Cotton canopy reflectance under variable solar zenith angles: Implications of use in evapotranspiration models

Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ET_c) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices.     

The first results of the pilot project on complex diagnosing earthquake precursors on Sakhalin

The results of the first stage of the pilot project on the complex monitoring of the atmospheric and ionospheric parameters, conducted on the instructions of the Russian Federation Government in order to decrease risk of destructive earthquakes in the Far East, are presented. The experiment was performed before and during a strong (M = 6.3) earthquake that occurred on August 2, 2007, on Sakhalin. The meteorological data (relative humidity and temperature), cloudiness anomalies according to the TERRA and AQUA satellite data, thermal anomalies of outgoing IR radiation according to the NOAA satellite data, variations in the total electron content according to the GPS data, and tomographic reconstructions of the ionosphere vertical structure according to the TRANSIT satellite data have been analyzed. The indications, typical of earthquake preparation and previously presented in the publications devoted to studying earthquake precursors, have been detected in all analyzed parameters. Synchronism and localization of the anomalies, registered using different methods in different geophysical fields, make it possible to assume that these anomalies have a common source, which could be the earthquake preparation process that is explained using the developed complex model of the lithosphere-atmosphere-ionosphere coupling (LAIC).     

Estimating irrigation inputs for distributed hydrological modelling: a case study from an irrigated catchment in southeast Australia

Adequate irrigation inputs are essential for the application of hydrological models in irrigated catchments, but reliable data on both the amount and the frequency of irrigation applications are often missing at an appropriate spatial scale. In this paper, we demonstrate and test approaches to estimate irrigation inputs for distributed hydrological modelling. In this context, the Soil and Water Assessment Tool was applied to simulate water balances for an irrigated catchment in southeast Australia during the period 2008–2010. Two methods for estimating irrigation inputs were tested. One method was based on a fixed irrigation application rate, whereas the other one had variable irrigation rates depending on season and the irrigated crop. These two approaches were also compared with the ‘auto‐irrigation’ method within the Soil and Water Assessment Tool model. The method with variable irrigation rates resulted in the most reasonable interpretation of the readily available irrigation data, consistent estimates of irrigation runoff coefficients throughout the year and the best fit to observed data on both drain flows at the catchment outlet and spatial evapotranspiration patterns. We also found that the different irrigation inputs significantly affected simulated water balances, in particular deep percolation under relatively dry climatic conditions. All these results suggest that it is possible to infer irrigation inputs from readily available data and local knowledge, adequate for hydrological modelling in irrigated catchments. Our study also demonstrates that, in order to predict reliable water balances in irrigated catchments, an accurate knowledge of irrigation scheduling and irrigation runoff is required. Copyright © 2015 John Wiley & Sons, Ltd.     

Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment

In temperate humid catchments, evapotranspiration returns more than half of the annual precipitation to the atmosphere, thereby determining the balance available to recharge groundwaters and support stream flow and lake levels. Changes in evapotranspiration rates and, therefore, catchment hydrology could be driven by changes in land use or climate. Here, we examine the catchment water balance over the past 50 years for a catchment in southwest Michigan covered by cropland, grassland, forest, and wetlands. Over the study period, about 27% of the catchment has been abandoned from row‐crop agriculture to perennial vegetation and about 20% of the catchment has reverted to deciduous forest, and the climate has warmed by 1.14 °C. Despite these changes in land use, the precipitation and stream discharge, and by inference catchment‐scale evapotranspiration, have been stable over the study period. The remarkably stable rates of evapotranspirative water loss from the catchment across a period of significant land cover change suggest that rainfed annual crops and perennial vegetation do not differ greatly in evapotranspiration rates, and this is supported by measurements of evapotranspiration from various vegetation types based on soil water monitoring in the same catchment. Compensating changes in the other meteorological drivers of evaporative water demand besides air temperature—wind speed, atmospheric humidity, and net radiation—are also possible but cannot be evaluated due to insufficient local data across the 50‐year period. Regardless of the explanation, this study shows that the water balance of this landscape has been resilient in the face of both land cover and climate change over the past 50 years.     

Meteorological fields from remote infrared sensing

Modern numerical weather prediction techniques require global observations of the atmospheric state and structure parameters. The current meteorological observing system, which is based on radiosonde balloon observations, has extensive gaps. Remote sensing of the Earth atmosphere emission spectrum from satellites can fill these gaps. The physical basis for extracting information on meteorological fields from such remote observations is explained. The problem reduces to that of solving a linear Fredholm equation of the first kind in the presence of noisy data. There is no unique solution to such a problem. The mathematical techniques-inversion techniques-that are currently used to solve the problem are reviewed. Examples are given of meteorological fields obtained from remote infrared sensing from satellites. Results indicate that meteorological parameters such as temperature and geopotential height of constant pressure surfaces can be measured-in conditions of clear skies-to accuracies approaching that of the radiosonde system. Other meterological variables, e.g., water vapor and ozone, can be determined to a lesser degree of accuracy. Applications of the remotely sensed fields are described. Problem areas and suggested solutions are discussed.     

Parametric calibration of the Hargreaves–Samani equation for use at new locations

The Hargreaves–Samani (HS) evapotranspiration equation is very useful for the on‐site irrigation management in data‐short situations such as small and midsize farms and landscaped areas. Although much work has been performed to improve the precision of the evapotranspiration (ET_o) estimates for use at new locations, the results have not been consistent and many have not been confirmed by other works. The purpose of this study was to review and to evaluate the seven most promising parameters used for the calibration of the HS evapotranspiration equation, using two different regions: California and Bolivia. The results of this study show that annual correlations between HS and Penman–Monteith can be misleading because the correlation is poor in the humid months and improves progressively along the dry season until the first rains. The average monthly wind speed can be used for both spatial and seasonal calibration of the HS equation, especially during the irrigation season. Elevation and precipitation can be used to calibrate the HS equation when no reference ET_o values are available at nearby stations. The monthly value of K_T calculated from solar radiation follows a parabolic function along the year and should not be used for improving the estimates of the HS equation because the clearness index produces better results than actual solar radiation measurements. The results also indicate that the use of distance to coast, temperature range and temperature parameter does not improve the precision of the HS equation. Copyright © 2012 John Wiley & Sons, Ltd.     

Mapping of climatic parameters under climate change impacts in Iran

ABSTRACT

This research aims to provide a comprehensive evaluation of climate change effects on temperature, precipitation and potential evapotranspiration over the country of Iran for the time periods 2010–2039, 2040–2069 and 2070–2099, and under scenarios A2 and B2. After preparation of measured temperature and precipitation data and calculation of potential evapotranspiration for the base time period of 1960–1990 for 46 meteorological stations (with a nationwide distribution), initial zoning of these three parameters over the country was attempted. Maximum and minimum temperatures and values of precipitation were obtained from the HadCM3 model under scenarios A2 and B2 for the three time periods, and these data were downscaled. Corresponding maps were prepared for the three parameters in the three time periods, and spatial and temporal variations of these climatic parameters under scenarios A2 and B2 were extracted and interpreted. Results showed that the highest increase in temperature would occur in western parts of the country, but the highest increase of potential evapotranspiration would occur in the central region of Iran. However, precipitation would vary temporally and spatially in different parts of the country depending on the scenario used and the time period selected.

Editor Z. W. Kundzewicz; Associate editor not assigned