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This research addresses the growing complexity and urgency of climate change's impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash-Sutcliffe efficiency - NSERCP2.6 = 0.99, root mean square error - RMSERCP2.6 = 1.1, and coefficient of determination RRCP2:6² = 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays a key role in the transfer of heat to the atmosphere and to the subsurface layers of soil. This study aims at examining the determination coefficient of MODIS LST on air temperature and soil temperature at different depths of Iran. A new method was employed to create a time consistent LST from Terra and Aqua MODIS products, to eliminate the observation differences in local solar time. Preceding the production of time consistent MODIS LST for 12:30 PM, a comparison was carried out with temperature measurements of meteorological stations. The correlation of MODIS LST and Meteorological Station Measurement (hereafter MSM) demonstrate high values, especially for air temperature and 5cm-deep subsurface soil temperature (R2>0.95). The lowest value was obtained for 100cm-deep soil temperature (R2=0.83). The results of intra annual analysis revealed significant relationship between MODIS LST and MSM temperatures. In the comparison of MODIS LST with subsurface soil temperatures, the scatter plot changes from 1:1 to fusiform due to the delay in heat transfer from surface to the subsurface of soil layers. This result postulates that MODIS LST is consonant with MSM temperatures in arid and semiarid regions of Iran. Spatial variation of correlation is higher for 100cm-deep soil temperature (16%). On the contrary, for air temperature and 5cm-deep soil temperature showing the highest correlation, the spatial variation is negligible throughout Iran (6.2%). However, Root Mean Square Error (RMSE) analysis revealed LST differences from 2.43 to 24.88 °C throughout Iran rather than MSM temperatures. [ABSTRACT FROM AUTHOR]

• The exponent of the power-function complementary relationship is mapped globally. • A new isenthalpic index yields the exponent better physical interpretation. • The mapped values improve global land evaporation estimates. • Spatial distribution of the exponent aligns with theory. The complementary relationship (CR) of evaporation is one of widely-used approaches in estimating terrestrial evapration (E). However, the determination of the parameter values in CR-based models remains a difficult task. This is especially true for the new power-function complementary relationship (PCR) model developed recently, in which the key parameter that accounts for the effect of moisture advection over the drying land, b , must be well calibrated with consideration of an approporiate physical basis. Here, by proposing a new isenthalpic index that can be used for determining b directly without resorting to any a priori information of global E , this study found that the resulting b values (median = 1.64, standard deviation = 0.73) are generally smaller and display a narrower range than those reported recently by Kim et al. (2024). When applied with monthly input variables of 124 FLUXNET stations, the gridded b values improve upon E estimates that employ either i) a standard value of b (=2), or; ii) a calibrated value (same for all stations). The global distribution of the gridded b values –in contrast to those obtained by Kim et al. (2024) –aligns with its physical interpretation: low values (1 < b < 2) where the effect of moisture advection is weak (i.e., permanently/periodically wet, humid regions or regions far away from any significant external moisture source) and high ones (b > 2) when strong (i.e., warm arid/semi arid regions near a sea or high mountains of abundant moisture). The gridded b values improve the calibration-free PCR that employs a constant b (=2) and a Priestley-Taylor coefficient linked to the wet-environment air temperature. [ABSTRACT FROM AUTHOR]

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP's results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is an important variable to estimate all the forms of evaporation and transpiration from crops and vegetation. An accurate estimation of ET0 plays a critical role in crop water management. The Food and Agriculture Organization of the United Nations (FAO) has recommended the Penman Monteith (PM) model as the standard to estimate the ET0. However, the PM model requires a large number of input parameters, including the solar radiation (Rs) or sunshine hours (n), the maximum and minimum air temperatures (Tmax and Tmin), Relative Humidity (RH), and wind speed at 2m high (u2). In addition to temperature data, most datasets are often unavailable, incomplete, or of uncertain quality in practice. Thus, the ET0 estimation using temperature data has drawn much attention in recent years. In this study, the recent estimation models were compared to improve the high accuracy of ET0 using the temperature data. The traditional Hargreves-Samani (HS) model (HS1), the improved HS model (HS2), and the PM temperature approach (PMTPP) were recently proposed by Paredes and Pereira. Some latest models were also introduced, including the ea estimation model proposed by Qiu et al., and the temperature-based Rs model (Rs-K) proposed by Korachagaon to improve the PM temperature approach (PMTNew). All models were calibrated and tested using the dataset collected from 51 radiation stations in China during the period 1967-2016. The results showed that the Rs-K model performed a better accuracy for the ET0 estimation, compared with the Rs model proposed by Paredes and Pereira (Rs-HS). For instance, the values of modelling efficiency (EF) were much closer to 1.00, the correlation coefficient (r) increased from 0.92-0.99 to 0.94-0.99, the values of Root Mean Square Error (RMSE), and the Relative Root Mean Square Error (RRMSE) were reduced by 0.08-0.25 mm/d and 0.03-0.09, respectively, at different climate zones. Except for the arid and hyper-arid climate zones, the average values of RMSE and RRMSE in the HS2 model decreased by 0.05-0.09 mm/d and 0.02-0.03, respectively, and the average values of EF increased by 0.03-0.10, compared with the HS1 model. Although the PMTPP model produced a similar r, the mean values of RMSE and RRMSE from the dry sub-humid and humid climate zones were reduced by 0.01-0.15 mm/d and 0-0.05, respectively, while the EF values increased by 0.01-0.06, compared with the HS2 model. Therefore, the PMTPP model presented a better accuracy than the HS model in estimating the ET0 at various climate zones. Except for the arid and semi-arid climate zones, the PMTNew model further reduced the RMSE and RRMSE by 0.04-0.12 mm/d and 0-0.04, respectively, indicating the higher EF and r values, compared with the PMTPP. In addition, the HS2 model presented a higher Global Performance Index (GPI) value than the HS1 model, indicating a better GPI value in the humid zone. Except for the humid climate zones, the PMTPP model presented a higher GPI value at various climate zones, the mean value of GPI range -0.06-0.42, compared with the HS2 model. The PMTNew model also presented a better GPI of each station, 90% of which ranked the first. Therefore, it is strongly recommended to use the PMTNew model to estimate the ET0 of each station, when the limited data is available. [ABSTRACT FROM AUTHOR]

The determination of the PV module temperature is a key parameter for the assessment of the actual performance of the PV systems. The application of available models for PV module temperature estimation in literature can be verified, but the application of these correlations for different climate conditions does not lead to unequivocal results. The main objective of this study is to suggest new empirical models for estimating the back surface module temperature under outdoor hot dry climatic conditions of Adrar province (Algerian Sahara) and to compare the developed models to different existing models in the literature. The models are developed based on meteorological and irradiance data collected from two different plants with different module technologies. The best site-specific approach uses a simple formula to derive the PV-back module temperature from the meteorological variables such as ambient temperature, and irradiance. The relative root mean square error and the Pearson's correlation coefficient of the best developed model are 10.662% and 0.955, respectively. In addition, MAPE and RMSE values are considerably small for the studied stations. A general model for predicting the PV-back temperature was also recommended for simple PV modules or open rack systems in rural locations with no measurement equipment nearby. The results are quite useful for studying PV system performance and estimating its energy output. Image 1 • Empirical models of PV back temperature are developed for Algerian Sahara Region. • PV temperature is correlated to air temperature and global irradiance. • Used data from two stations at Adrar city of hot and arid climate. • Best performing models are identified based on statistical analysis. [ABSTRACT FROM AUTHOR]

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. The FAO-56 Penman-Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that the ANN estimated ET better than the M5 model tree but both models performed well for the study area and yielded results close to the FAO56-PM method. Root mean square error and R for the comparison between reference and estimated ET for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the M5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available. [ABSTRACT FROM AUTHOR]

• The performances of CatBoost, GRNN and RF were assessed to estimate daily ET 0. • CatBoost model provided the most accurate results among the considered models. • GRNN model showed comparable estimates of daily ET 0 to the RF model. • The generalized analysis confirmed the results obtained in the local analysis. Establishing a computational model for accurate prediction of reference crop evapotranspiration (ET 0) is critical for regional water resources planning and irrigation scheduling design. FAO Penman-Monteith equation is recommended as the standard model to predict ET 0. However, its application is restricted by lack of complete meteorological data in many regions. This study evaluated the performance of CatBoost, an algorithm for gradient boosting on decision trees, for estimating daily ET 0 using limited meteorological data in arid and semi-arid regions of Northern China. The CatBoost model was further compared with their corresponding generalized regression neural network (GRNN) and random forests (RF) models. Eight input combinations of daily meteorological data including daily maximum air temperature (T max), daily minimum air temperature (T min), wind speed at 2 m height (u 2), relative humidity (RH) and net radiation (R n) from 15 weather stations during 1996–2015 were used to train and test the models. Four statistical indicators were used to evaluate the accuracy and performance of the models, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE). The results showed that all the three models using T max , T min , u 2 and R n could obtain satisfactory ET 0 estimates in arid and semi-arid regions of Northern China with incomplete sets of data. For the local models, CatBoost (on average RMSE ranging 0.096–0.821 mm d−1) was superior to GRNN (on average RMSE ranging 0.206–0.847 mm d−1) and RF (on average RMSE ranging 0.169–0.866 mm d−1) under the same meteorological parameters as input. The results of the generalized models were similar to the local models, but the former ones performed worse than the latter ones. Overall, CatBoost is observed to be the best alternative for estimating ET 0 , which is helpful for irrigation scheduling in arid and semi-arid regions of Northern China and maybe elsewhere with similar climates. [ABSTRACT FROM AUTHOR]