Monthly Evaporation Research Articles

Catchment response to climatic variability: implications for root zone storage and streamflow predictions

Abstract. This paper investigates the influence of multi-decadal climatic variability on the temporal evolution of root zone storage capacities (Sr,max) and its implications for streamflow predictions in the Meuse basin. Through a comprehensive analysis of 286 catchments across Europe and the US that are hydro-climatically comparable to the Meuse basin, we construct inter-decadal distributions of past deviations in evaporative ratios (IE) from expected values based on catchment aridity (IA). These distributions of ΔIE were then used to estimate inter-decadal changes in Sr,max and to quantify the associated consequences for streamflow predictions in the Meuse basin. Our findings reveal that, while catchments do not strictly adhere to their specific parametric Budyko curves over time, the deviations in IE are generally very minor, with an average ΔIE=0.01 and an interquartile range (IQR) of −0.01 to 0.03. Consequently, these minor deviations lead to limited inter-decadal changes in Sr,max, mostly ranging between −10 and +21 mm (−5 % to +10 %). When these changes (ΔSr,max) are accounted for in hydrological models, the impact on streamflow predictions in the Meuse basin is found to be marginal, with the most significant shifts in monthly evaporation and streamflow not exceeding 4 % and 12 %, respectively. Our study underscores the utility of parametric Budyko-style equations for first-order estimates of future Sr,max in hydrological models, even in the face of climate change and variability. This research contributes to a more nuanced understanding of hydrological responses to changing climatic conditions and offers valuable insights for future climate impact studies in hydrology.

Modeling Monthly Evaporation in Different Climates Using ANN–COOT Hybrid Algorithm

Modeling Monthly Evaporation in Different Climates Using ANN–COOT Hybrid Algorithm

Contrasting Drought Propagation Into the Terrestrial Water Cycle Between Dry and Wet Regions

AbstractDrought's intensity and duration have increased in many regions over the last decades. However, the propagation of drought‐induced water deficits through the terrestrial water cycle is not fully understood at a global scale. Here we study responses of monthly evaporation (ET) and runoff to soil moisture droughts occurring between 2001 and 2015 using independent gridded datasets based on machine learning‐assisted upscaling of satellite and in‐situ observations. We find that runoff and ET show generally contrasting drought responses across climate regimes. In wet regions, runoff is strongly reduced while ET is decoupled from soil moisture decreases and enhanced by sunny and warm weather typically accompanying soil moisture droughts. In drier regions, ET is reduced during droughts due to vegetation water stress, while runoff is largely unchanged as precipitation deficits are typically low in these regions and ET decreases are buffering runoff reductions. While these water flux drought responses are controlled by the large‐scale climate regimes, they are additionally modulated by local vegetation characteristics. Land surface models capture the observed water cycle responses to drought in the case of runoff, but not for ET where the ET deficit (surplus) is overestimated (underestimated), related to a misrepresentation of the general soil moisture‐evaporation interplay. In summary, our study illustrates how the joint analysis of machine learning‐enhanced Earth observations can advance the understanding of global eco‐hydrological processes, as well as the validation of land surface models.

Study on the Monthly Scale Evaporation Conversion Coefficient of Φ20 and E-601 Evaporation Dishes in Dingbian County Meteorological Station

To explore the relationship between Φ20 and E-601 Evaporation Dishes, the temporal and spatial variation of monthly evaporation conversion coefficient and water surface evaporation of was studied by collecting the data of typeΦ20 and E-601. The conversion coefficient K of two kinds of evaporating dishes in Dingbian County is explored by using the linear fitting method with the simultaneous observation data of Φ20 and E-601 evaporating dishes, and the annual and interannual variation laws of water surface evaporation in Dingbian County are analyzed. The monitoring data ofΦ20 and E-601 evaporating pan has a good correlation in Dingbian County, with a conversion coefficient of 0.6421, and the conversion coefficient of the water surface evaporation of the two evaporating pans increases month by month from May to September; The water surface evaporation monitored by the Dingbian Meteorological Station has the same change rule in the year, showing periodic changes. There is a difference between the change rule of water surface evaporation and air temperature in the year, and the maximum value of water surface evaporation in the year has a difference of one month compared with the air temperature; The interannual evaporation of water surface fluctuates and has a significant downward trend, with an average decline rate of 197.6 mm/10a, showing a general trend of gradually increasing from southwest to northwest in spatial distribution. The conversion coefficient in Dingbian County shows significant monthly differences, with a small value in spring and a large value in autumn; Under the background of climate warming, the evaporation of water surface in Dingbian County is declining, which indicates that human activities such as ecological engineering have improved the ecological environment of Dingbian County.

Fitting statistical probability distribution for evaporation in western Brazil

This study aims to find evaporation (ETo) probabilities for the central west region of Brazil, including Campo Grande, Cuiabá, Goiânia and Brasilia. In this case, a maximum likelihood estimation was used for the probability distributions adjusting to the monthly ETo from 1960 to 2020. The work estimates three probability distributions, including the Generalized Extreme Values (GEV), Gumbel (GUM) and Log-Normal (LN). The Kolmogorov-Smirnov test, the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the root mean square error (RMSE) and the coefficient of determination (R2) were applied to measure the quality of the adjustment. The evapotranspiration forecasts were made for the return periods of 10, 30, 50, and 100 years. The GEV and GUM distributions are advised for use during the summer. Altering LN distribution to a sequence of ETo throughout the winter months is the best option. Moreover, deforestation, combustion, extensive fires, aerosol pollutants, and climate change are the primary causes of excessive ETo in the area under study.

Climate change effect on water temperature and evaporation in Lake Qarun

ABSTRACT Climate change can have significant effects on the water and heat budgets of lakes and reservoirs. This study examined the effect of climate change on Lake Qarun through analyzing the temporal variation in lake surface temperature and evaporation from the lake. The analysis was based on the remote-sensing dataset MOD11A1 and the ERA5 climate reanalysis dataset. Daily surface temperatures for 1-km MOD11A1 pixels were filtered to remove outliers, averaged to obtain mean lake temperature, and then aggregated to compute the monthly average lake temperature. Lake evaporation was estimated by applying an un-mixing algorithm to ERA5 evaporation fluxes for mixed land-lake pixels and land-only pixels. The resulting monthly evaporation from the lake was corrected using a regression model based on evaporation estimates available in the literature for Lake Qarun. The regression model performance was confirmed through the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and root mean square error. Over the period 1980 to 2019, findings show that annual evaporation ranged between 140 and 190 cm/year. Maximum average monthly evaporation for the study period is 21 cm in July while the minimum is 6.7 cm in February from 1980 to 2019. For the average lake surface temperature, it varied between ~31.9 and 33.9°C over the period 2000 to 2019. For the same time period, average maximum and minimum monthly temperatures were 44°C in August and 20.1°C in January. It is concluded that the average increase in evaporation and lake surface temperature is 37 mm/decade and 0.4°C/decade, respectively. The increase in both lake surface temperature and evaporation is likely due to climate change effects. However, using datasets with a larger temporal record for lake surface temperature, further studies can be applied for confirmation. Also, further confirmation can be obtained by applying the same analysis to other lakes in Egypt.

Deterministic and Stochastic Generation of Evaporation Data for Long-Term Mine Pit Lake Water Balance Modelling

Lakes commonly form in mine pits following the end of mining. A good understanding of the pit lake water balance over future decades to centuries is essential to understand and manage environmental risks from the lake. Evaporation is often the major or only outflow from the lake, thus being an important determinant of equilibrium lake level and environmental risks. A general lack of in situ measurements of pit lake evaporation has meant that estimates have usually been based on pan coefficients derived for other contexts or on alternative unvalidated evaporation models. Our research used data from an evaporation pan and weather station that were floated on a pit lake in semi-arid central Queensland, Australia. A deterministic aerodynamic evaporation model was developed from these data to infill missing values, and an adjusted aerodynamic model was used to reconstruct long-term historical daily evaporation data. With an average bias of 6.5% during the measurement period, this long-term model was found to be more accurate than alternative simple models (e.g., using the commonly used pan coefficient of 0.7 gave a bias of 45%). The reconstructed data were then used to fit and assess a stochastic model for the generation of future evaporation and rainfall realisations, assuming a stationary climate. Fitting stochastic models at a monthly time step was found to accurately represent the monthly evaporation statistics. For example, the cross-correlation between historical rainfall and evaporation was within the 25 and 75 percentiles of the modelled values in 11 of 12 months and always within the 2.5 and 97.5 percentiles. However, the stationary nature of the model presented limitations in capturing interannual anomalies, with continuous periods of up to 6 years, where the modelled annual rainfall was consistently lower and modelled annual evaporation consistently higher than the historical values. Fitting stochastic models at a daily time step had problems capturing a range of statistics of both rainfall and evaporation. For example, in 6 of the 12 months, the cross-correlation between historical rainfall and evaporation was outside the modelled 2.5 and 97.5 percentiles. This likely arises from the complex patterns in transitions from wet to dry days in the semi-arid climate of the case study. While the long-term model and monthly stochastic model are promising, further work is needed to understand the significance of the observed errors and refine the models.

Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley

The use of traditional methods to predict evaporation may face many obstacles due to the influence of many factors on the pattern of evaporation's shape. Therefore, the use of existing methods of artificial intelligence is a reliable prediction model in many applications in engineering. Monthly measurements were employed in the present work to predict for duration eighteen years, from beginning of January 2000 until December 2017. The best model was chosen using ANNs (MLP, RBF) and AI (SVM) techniques. The best evaporation model prediction was made using ANNs (MLP, RBF) and AI (SVM) technologies, with temperature, wind speed, relative humidity, and sunshine hours used as independent variables. Several statistical metrics have been used to evaluate the effectiveness of the proposed model to other popular artificial intelligence models. The obtained result denotes the superiority of the MLP models over the RBF and SVM models. It is concluded that the MLP model is better than RBF and SVM for evaporation prediction for both groups. A comparison of the model performance between MLP, RBF, and SVM models indicated that the MLP-ANN method presents the best estimates of monthly evaporation rate with minimum RMSE 0.033, minimum MAE 0.026, and maximum determination coefficient 0.967.

The Evaporation on the Tibetan Plateau Stops Increasing in the Recent Two Decades

AbstractAs a crucial variable in hydrological and climate modeling, a lot of efforts have been made to estimate the evaporation (E, including soil evaporation, canopy interception, and plant transpiration) on the Tibetan Plateau (TP). Most studies showed that the evaporation on the TP stops increasing or even decreases in the recent two decades. However, the spatio‐temporal variability of this phenomenon and its underlying mechanism remains uncertain. Here, we utilized a state‐of‐the‐art version of the sigmoid generalized complementary equation to estimate the monthly evaporation over the TP for the period of 1979–2018. The results show that the annual E increases significantly during 1979–1998 with a rate of 1.94 mm yr−1 (based on the total E over the TP), however, it decreases during 1999–2018 with a rate of −1.06 mm yr−1. The most significant decreasing trend of E occurs in the spring. The areas with the most significant decrement of E include the central and western Qiangtang Plateau, the middle reaches of Yarlung Zangbo River, and the Yangtze River basin. By introducing a new aridity index Rwe, we identified that the decrement of E mainly occurs in the water‐limited regions, which contribute to 60% of the total decreasing trend of the whole region; at the same time, the energy‐limited regions also contribute to 40% of the decreasing trend. This study is important for the hydrological researches and decision making because the trend of E can influence the water resources and agriculture.

Modification and Validation of Ritchie Model for Evaporation of Maize Field in Saline Alkali Soil

In order to study the evaporation law of the soil between the corn trees of Daqing salt-alkali soil, the evaporation simulation value of different growth stages in the experimental period was compared with the measured value according to the Ritchie model theory, and the model fit was not well matched by using a homemade microsteaming seepage instrument to study the evaporation of the whole fertility period of corn in the green wind garden test area of Daqing High-tech Zone, as well as rainfall and soil moisture content. The accuracy of the two parameters in Ritchie’s model directly determines the accuracy of the model’s prediction of evaporation. The first U value and the second-stage α values were revised, respectively, and the model verification was based on the different growth-and-long-term values of corn, and the analysis showed that the average monthly rainfall was a function relationship with the average monthly evaporation. The modified Ritchie model has a maximum evaporation fit of 0.96 for maturity and a minimum fit of 0.91 for seedling. It provides an accurate evaporation prediction model for the accurate prediction of evaporation between saline soil corn classes in Daqing area and provides a theoretical basis for water quantity management and soil salinization prevention and control in the corn planting process.

Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley

Evaporation is influenced by several meteorological parameters, evaporation data are usually difficult to obtain compared to rainfall data, especially in arid regions. Developing a monthly evaporation prediction model in arid regions in terms of available meteorological data is a significant step. The data used in this study for modeling are monthly measurements to cover substantial continuity over a period of 18 years between January 2000 and December 2017. Stepwise and backward multiple linear regression techniques were used with a new procedure of variable selection to select the best model. Temperature, wind speed, relative humidity and sunshine hours were used as a independent variables in the multiple linear regression (MLR) technique to establish the best prediction of the evaporation model. To examine the MLR evaporation developed model in the current study, MLR results were compared with the most common evaporation models commonly used in arid regions such as Kharufa and Khosla methods. The results of performance indicators shows that the R2 values are approximately 0.937, 0.90 and 0.85 for MLR evaporation developed model, Kharufa and Khosla methods, respectively. Moreover, the values of the error measures, namely RMSE and NAE for MLR evaporation developed model were 36.3 and 0.123, Kharufa model 71.22 and 0.241 and Khosla model was and 173.7 and 0.581 respectively. Based on the foregoing, the results of the MLR developed evaporation model in the current study outperforms in all performance indicators and proves to be better than the Kharufa and Khosla models.

Estimating Dam Reservoir Level Change of Istanbul Alibey Dam with The Fuzzy SMRGT Method

Accurate estimation of the dam reservoir level in a dam reservoir is very important for the planning and operation of water structures. In this study, monthly dam reservoir level data between 1989 and 2020 obtained from the State Hydraulic Works (DSI) was used to estimate the monthly dam reservoir level change. For the monthly dam reservoir level estimation, it has been tried to be estimated using the Simple Membership Functionsand Fuzzy Rules Generation Technique (Fuzzy-SMRGT), Artificial Neural Network (ANN) and the classical Multiple Linear Regression (MLR) methods. Alibey Dam located in Sultangazi district of Istanbul was chosen as the study area. The monthly evaporation, water entering into the lake, consumption of drinking water and amount of water discharged from the dam amounts were used to estimate the monthly Alibey Dam reservoir average dam reservoir level. The model results were compared with the actual observation dam reservoir levels. When the results were evaluated, it was observed that the models were successful in estimating the dam reservoir level and gave results close to each other

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.

Time Series Analysis of Evaporation Duct Height over South China Sea: A Stochastic Modeling Approach

The evaporation duct could significantly affect the work status of maritime microwave communication systems in the South China Sea. Therefore, the exact forecasting of the evaporation duct is vital for the normal operation of the systems. This study presents a stochastic modeling approach to predict the future trends of the evaporation duct over the South China Sea. The autoregressive integrated moving average (ARIMA) model has been used for modeling the monthly evaporation duct height estimated from the Climate Forecast System Reanalysis dataset released by the National Centers for Environment Prediction. The long-term evaporation duct height data were collected for a period of 10 years from 2008 to 2017. The analysis of correlation function reveals the existence of seasonality in the time series. Therefore, a seasonal ARIMA model with the form as ARIMA (0,0,1) × (0,1,2)12 is proposed by fitting the monthly data optimally. The fitted model is further used to forecast the evaporation duct variation for the year 2018 at 95% level of confidence, and high-accuracy results are obtained. Our study demonstrates the feasibility of the proposed stochastic modeling technique to predict the future variations of the evaporation duct over South China Sea.

Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm.

Evaporation is a crucial factor in hydrological studies; its precise measurement has always been challenging due to the costly recording tolls. Therefore, machine learning models that can give reliable predictive results with the least information available have been recommended for evaporation prediction. This study was conducted in the central of Iran using the data related to the Doroudzan dam. Several hydrological and meteorological variables, including inflow and outflow of the reservoir, lake area behind the dam, temperature, overflow from the reservoir, precipitation, and evaporation at the previous month, were considered input data to predict the evaporation at the current month. Monthly data from October 1999 to September 2020 were used during the modeling. First, the single adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector regression (LS-SVR) models were evaluated for predicting the amount of evaporation using different scenarios defined based on the different combinations of input variables. The results showed that LS-SVR with RMSE = 2.77, MAPE = 2.48, and NSE = 0.93 provided a better prediction than ANFIS. Second, the Harris hawks optimization (HHO) algorithm was used to optimize the parameters of ANFIS to check for the possibility of performance improvement. The hybrid ANFIS-HHO model predicted the evaporation with RMSE = 2.35, MAPE = 1.55, and NSE = 0.95, respectively. The Taylor's diagram also demonstrated the superior performance of the hybrid ANFIS-HHO model than the LS-SVR and ANFIS models. The best scenario for all three models included all input variables but the area behind the dam into the models. The methodology proposed in this study is useful for predicting the evaporation from dam reservoirs under the influence of various dam variables.

Modeling of pan evaporation based on the development of machine learning methods

For effective planning and management of water resources and implementation of the related strategies, it is important to ensure proper estimation of evaporation losses, especially in regions that are prone to drought. Changes in climatic factors, such as changes in temperature, wind speed, sunshine hours, humidity, and solar radiation can have a significant impact on the evaporation process. As such, evaporation is a highly non-linear, non-stationary process, and can be difficult to be modeled based on climatic factors, especially in different agro-climatic conditions. The aim of this study, therefore, is to investigate the feasibility of several machines learning (ML) models (conditional random forest regression, Multivariate Adaptive Regression Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K- nearest neighbor, and the weighted K- nearest neighbor) for modeling the monthly pan evaporation estimation. This study proposes the development of newly explored ML models for modeling evaporation losses in three different locations over the Iraq region based on the available climatic data in such areas. The evaluation of the performance of the proposed model based on various evaluation criteria showed the capability of the proposed weighted K- nearest neighbor model in modeling the monthly evaporation losses in the studies areas with better accuracy when compared with the other existing models used as a benchmark in this study.

Spatiotemporal distributions of pan evaporation and the influencing factors in China from 1961 to 2017

Pan evaporation (EVP) is an important element of the hydrological cycle and exhibits a close relationship with climate change. In this study, the generalized regression neural network (GRNN) model and extreme gradient boosting (Xgboost) model were applied to estimate the monthly EVP. The spatiotemporal distributions of EVP and influencing factors in China and eight subregions from 1961 to 2017 were analyzed. The root mean square error (RMSE) of all GRNN models was approximately 10%, and the Nash-Sutcliffe efficiency (NSE) coefficient was larger than 0.94 in different subregions. The annual mean EVP in all subregions and throughout China showed decreasing trends before 1993, while EVP increasing trends occurred in East China (EC), South China (SC), Southwest China (SWC), west of Northwest China (WNC), and throughout China after 1994. Subsequently, the variable importance in projection (VIP) between EVP and climatic factors obtained by partial least squares (PLS) regression and the relative contribution calculated by Xgboost stepwise regression analysis (SRA) were used to investigate the climatic parameter sensitivity to EVP. The results indicated that the combined effects of the vapor pressure deficit (VPD), sunshine duration (SSD), and wind speed (WIN) were the main reasons for the variations in EVP across China. At the seasonal scale, SSD, WIN, relative humidity (RHU), and VPD were the most sensitive climatic factors to EVP in different seasons. In addition, the Pacific decadal oscillation (PDO) index showed a significant negative correlation with EVP, and the El Niño 3.4 (N3.4) and East Atlantic/Western Russia (EA/WR) indices revealed positive correlations in most regions from 1961 to 1993, while the North Atlantic oscillation (NAO) was negatively correlated with EVP. Moreover, N3.4 and Atlantic multidecadal oscillation (AMO) were positively correlated with EVP from 1994 to 2017. Finally, the yearly number of heatwave events (HWN) was highly correlated with EVP because of the high VPD and SSD levels during the heatwave event periods.

Records of Surface Meteorological Elements and their Applications in Alexandria Eastern Harbour, Egypt

The surface meteorological elements, namely: air temperature, wind regime (speed and direction), dew point temperature, relative humidity and atmospheric pressure were considered; to describe the general meteorological conditions over Alexandria Eastern Harbour (AEH). These conditions were then applied to calculate some important physical properties of the Harbour’s surface waters: sea surface temperature (SST), the evaporation rates and the net surface heat flux. The period of investigation extended from 01.01.2019 to 31.12.2020. The air temperature distribution over AEH reflected a seasonal variability with higher values in spring/summer and lower records in autumn/winter. The dominant wind direction was NNW with more than 40% occurrence and the average wind speed was 4.11 m/s. The inverse relationship between atmospheric pressure and air temperature was clear. The SST over the study period showed the same seasonal pattern in the air temperature. The mean daily evaporation from AEH surface water follows the pattern of air temperature variations to a considerable extent. The maximum mean monthly evaporation of 14 cm (0.14 m) occurred in July and August, while the minimum of 6 cm (0.06 m) occurred in January. This was associated with a volume loss of 0.389x106 m3 in July and of 0.171x106 m3 in January from AEH surface waters. The solar radiation (Qs) reached its maximum value in May (383 Wm-2) and its lowest value in September (258 Wm-2). The net surface heat in AEH (Qnet) showed a gain in four successive months: March-June, and a loss in the remaining period: July-September. Keywords: Alexandria Eastern Harbour

Monitoring and Forecasting Kenya’s Fluctuating Hydroclimate

AbstractThis study reviews Kenya’s fluctuating hydroclimate (3°S–4°N, 35°–40°E) and evaluates products that describe its area-averaged daily rainfall during 2008–18, monthly evaporation during 2000–18, and catchment hydrology via gauge, satellite, and model hindcast/forecast. Using the correlation of rainfall as a metric of skill we found daily satellite versus model hindcasts achieved 75%, while model forecasts at 2–6-day lead achieved 55%–58%. The daily satellite versus model soil moisture had a significant correlation (84%), and model runoff versus gauge streamflow reached 61%. A 2-day delay was noted between rainfall and streamflow response in recent flood events; however, long-range predictability was found to be poor (35%). These outcomes were considered at a local workshop, and ways to sustainably improve the real-time reporting of key hydroclimate parameters for operational data assimilation were suggested as steps toward better monitoring and forecast services in Kenya.

TREND ANALYSIS OF EVAPORATION AND SOLAR RADIATION USING INNOVATIVE TREND ANALYSIS METHOD

Analysis of trends in monthly evaporation and solar radiation in the face of climate change gives useful information for better planning and management of water resources. This paper examines the monthly evaporation and solar radiation trend using the recently developed innovative trend analysis method (ITAM). The monthly evaporation trend result shown that 75% of the months indicated decreasing trend with the month of February, March, August and April decreased significant at 0.1%, 10%, 10% and 5% significance level respectively. As regards solar radiation all the months indicated decreasing trend with January, July and October shown a decreasing trend at 5% significant level. By comparing the Mann-Kendall method with the ITAM the reliability of ITAM was ascertained. Hence, ITAM can be effectively utilized in climate change scenarios where useful information is needed for accurate management and planning of water resources.