GPS-derived precipitable water vapour and its comparison with MODIS data for Almora, Central Himalaya, India

The present study is an attempt to analyse the precipitable water vapour (PWV) derived from Global Positioning System (GPS) and observed meteorological data over Almora, Central Himalayan Region. The PWV values derived using GPS study is compared with the corresponding moderate resolution imaging spectro-radiometer (MODIS) data. The statistical analysis reveals a positive correlation between both methods. Moderate resolution imaging spectroradiometer near-infrared (MODIS NIR) clear column water vapour product shows a higher correlation (R ² = 90–93 %) with GPS-derived precipitable water vapour on annual scale as compared to the seasonal scale (R ² = 62–87 %). MODIS is found to be overestimating in NIR clear column where the magnitude of bias and RMSE show systematic changes from season to season. Monsoon is an important phenomenon in the Indian weather context and holds significant importance in Central Himalayan ecosystem. The monthly and seasonal variation in precipitable water vapour is related with monsoon onset in the region. Diurnal variations in precipitable water vapour are studied with other meteorological data over Almora during dry and wet season. The precipitable water vapour had minimum value in the morning, increases in the afternoon to evening and again decreases to the midnight in both the dry and wet seasons. These results suggest that diurnal variation of water vapour is caused by the transport of water vapour by thermally induced local circulation.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. 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 ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

Atmospheric abundance of HULIS during wintertime in Indo-Gangetic Plain: impact of biomass burning emissions

This study reports for the first-time the ambient concentrations of HULIS mass (HULIS-OM, Humic-like substances) and HULIS-C (carbon) in PM₁₀ (particulate matter with aerodynamic diameter?≤?10 μm) from the Indo-Gangetic Plain (IGP at Kanpur, wintertime). HULIS extraction followed by purification and isolation protocol with methanol: acetonitrile (1:1 v/v) on HLB (Hydrophilic-Lipophilic Balanced) cartridge has been established. Quantification of HULIS-C was achieved on a total organic carbon (TOC) analyser whereas HULIS-OM was determined gravimetrically. Consistently high recovery (> 90%) of HULIS-C based on analysis of Humic standard (sodium salt of Humic acid) suggested suitability of our established analytical protocol involving solvent extraction, purification and accurate quantification of HULIS. HULIS-OM varied from 17.3–38 μg m^?3 during daytime and from 19.8–40.6 μg m^?3 during night in this study. During daytime the HULIS-OM constituted 20–30% mass fraction of OM_Total and 10–15% of PM₁₀ mass. However, a relatively low contribution of HULIS-OM has been observed during the night. This observation has been attributed to higher concentrations of OM and PM₁₀ in night owing to nighttime chemical reactivity and condensation of organics in conjunction with shallower planetary boundary layer height. Strong correlation of HULIS-C with K⁺_BB (R²?>?0.80) and significant day-night variability of HULIS-C/WSOC ratio in conjunction with air-mass back trajectories (showing transport of pollutants from upwind IGP) suggest biomass burning emission and secondary transformations as important sources of HULIS over IGP. High-loading of atmospheric PM₁₀ (as high as 440 μg m^?3) with significant contribution of water-soluble organic aerosols (WSOC/OC: ~ 0.40–0.80) during wintertime highlights their plausible potential role in fog and haze formation and their impact on regional-scale atmospheric radiative forcing over the IGP.     

Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z _H), differential reflectivity (Z _DR), co-polar correlation coefficient (ρ _HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R ² and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R ² and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.     

Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

In this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-ε support vector regression (PSO-εSVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R ²) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-εSVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; minR ² = 0.9169, maxR ² = 0.9995, medianR ² = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations.     

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

Seasonal and annual trends of carbonaceous species of PM<Subscript>10</Subscript> over a megacity Delhi,India during 2010–2017

PM₁₀ samples were collected to characterize the seasonal and annual trends of carbonaceous content in PM₁₀ at an urban site of megacity Delhi, India from January 2010 to December 2017. Organic carbon (OC) and elemental carbon (EC) concentrations were quantified by thermal-optical transmission (TOT) method of PM₁₀ samples collected at Delhi. The average concentrations of PM₁₀, OC, EC and TCA (total carbonaceous aerosol) were 222?±?87 (range: 48.2–583.8 μg m^?3), 25.6?±?14.0 (range: 4.2–82.5 μg m^?3), 8.7?±?5.8 (range: 0.8–35.6 μg m^?3) and 54.7?±?30.6 μg m^?3 (range: 8.4–175.2 μg m^?3), respectively during entire sampling period. The average secondary organic carbon (SOC) concentration ranged from 2.5–9.1 μg m^?3 in PM₁₀, accounting from 14 to 28% of total OC mass concentration of PM₁₀. Significant seasonal variations were recorded in concentrations of PM₁₀, OC, EC and TCA with maxima during winter and minima during monsoon seasons. In the present study, the positive linear trend between OC and EC were recorded during winter (R²?=?0.53), summer (R²?=?0.59) and monsoon (R²?=?0.78) seasons. This behaviour suggests the contribution of similar sources and common atmospheric processes in both the fractions. OC/EC weight ratio suggested that vehicular emissions, fossil fuel combustion and biomass burning could be the major sources of carbonaceous aerosols of PM₁₀ at the megacity Delhi, India. Trajectory analysis indicates that the air mass approches to the sampling site is mainly from Indo Gangetic plain (IGP) region (Uttar Pradesh, Haryana and Punjab etc.), Thar desert, Afghanistan, Pakistan and surrounding areas.     

Long-term variation of rainfall erosivity in Calabria (Southern Italy)

The changes in rainfall erosivity have been investigated using the rainfall erosivity factor (R) proposed for USLE by Wischmeier and Smith (R _W-S ) and some simplified indexes (the Fournier index modified by Arnoldus, F, a regional index spatial independent, R _Fr , and a regional index spatial dependent, R _Fs ) estimated by indirect approaches. The analysis has been carried out over 48 rainfall stations located in Calabria (Southern Italy) using data collected in the period 1936–2012 and divided in three sub-periods. The series of the erosivity indexes and of some precipitation variables have been analyzed for evidence of trends using standard methods. The simplified indexes suggested a general underestimation of the rainfall erosivity with respect to R _W-S . The mean underestimation ranged between 23 and 54 % for R _Fr and from 10 to 15 % for R _Fs . Both the sign and the magnitude of the trends were different for the different stations depending on the variable and sub-period considered. In general, the erosivity increased during the period 1936–1955 (1^st sub-period) and during the more recent sub-period (1992–2012, 3^rd sub-period), whereas it decreased during 1958–1977 (2^nd sub-period). The evidence of trends was generally higher for R _W-S than for R _Fr and R _Fs . Focusing on the most recent sub-period (3^rd sub-period), all the variables analyzed showed mainly increasing trends but with different magnitude. More particularly, R _W-S showed a mean increment of 29 %; F, R _Fr and R _Fs increased by 11, 15 and 18 %, respectively; the maximum intensity of 0.5-h precipitation increased by 5 %; and the annual precipitation increased by 22 %. Consequently, it remains difficult to define which precipitation variable plays the dominant role in the temporal variation of rainfall erosivity in the region. However, the overall results suggest that the indexes estimated by indirect procedures (F, R _Fr , and R _Fs ) should be used with caution for climate change analysis, despite they are used for practical purposes considering they are based on easily available information.     

Simulation of rice brown planthopper, Nilaparvata lugens (Stal.) population and crop-pest interactions to assess climate change impact

Brown planthopper (BPH), Nilaparvata lugens (Stal.) development studied at six constant temperatures, 19, 22, 25, 28, 31 and 33 ±1 °C on rice plants revealed that developmental period from egg hatching to adult longevity decreased from 46.8 to 18.4 days as temperature increased from 19 to 31 °C. Through regression of development rate on temperature, thermal constant of small nymph (1st-2nd instar), large nymph (3rd–5th instar) and adult were determined to be 126.6, 140.8 and 161.3 degree days (DD), respectively with corresponding development threshold being 8.8, 9.5 and 9.6 °C. A thermal constant-based mechanistic-hemimetabolous-population model was adapted for BPH and linked with InfoCrop, a crop simulation model to simulate climate change impact on both the pest population and crop-pest interactions. The model was validated with field data at New Delhi and Aduthurai (Tamil Nadu, India), (R ²?=?0.96, RMSE?=?1.87 %). Climate-change-impact assessment through coupled BPH-InfoCrop model, in the light of the projected climate-change scenario for Indian subcontinent, showed a decline of 3.5 and 9.3–14 % in the BPH population by 2020 and 2050, respectively, during the rainy season at New Delhi, while the pest population exhibited only a small decline of 2.1–3.5 % during the winter at Aduthurai by 2050. BPH population decline is attributed to reduction in fecundity and survival by simulation model, which otherwise was not possible to account for with an empirical model. Concomitant to its population decline, BPH-induced yield loss also indicated a declining trend with temperature rise. However, the study considered the effect of only CO₂ and temperature rise on the BPH population and crop yield, and not that of probable changes in feeding rate and adaptive capacity of the pest.     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Parameterisation of incoming longwave radiation over glacier surfaces in the semiarid Andes of Chile

A good understanding of radiation fluxes is important for calculating energy, and hence, mass exchange at glacier surfaces. This study evaluates incoming longwave radiation measured at two nearby glacier stations in the high Andes of the Norte Chico region of Chile. These data are the first published records of atmospheric longwave radiation measurements in this region. Nine previously published optimised parameterisations for clear sky emissivity all produced results with a root mean square error (RMSE) ~20 W?m^?2 and bias within ±5 W m^?2, which is inline with findings from other regions. Six optimised parameterisations for incoming longwave in all sky conditions were trialled for application to this site, five of which performed comparably well with RMSE on daytime data <18 W?m^?2 and bias within ±6 W?m^?2 when applied to the optimisation site and RMSE <20 W?m^?2 and bias within ±10 W m^?2 when applied to the validation site. The parameterisation proposed by Mölg et al. (J Glaciol 55:292-302, 2009) was selected for use in this region. Incorporating the proposed elevation modification into the equation reduced the bias in the modelled incoming longwave radiation for the validation site. It was found that applying the parameterisation optimised in the original work at Kilimanjaro produced good results at both the primary and validation site in this study, suggesting that this formulation may be robust for different high mountain regions.     

Estimating net radiation at surface using artificial neural networks: a new approach

This study describes the results of artificial neural network (ANN) models to estimate net radiation (R _n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R _n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R _n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m^?2 and correlation coefficient (R ²) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m^?2 and R ² between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R _n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R _n prediction using commonly measured meteorological parameters.     

Estimation of atmospheric surface layer parameters and numerical simulation using MM5 at Mangalore, West Coast of India

Atmospheric surface layer meteorological observations obtained from 20-m-high meteorological tower at Mangalore, situated along the west coast of India are used to estimate the surface layer scaling parameters of roughness length (z _o) and drag coefficient (C _D), surface layer fluxes of sensible heat and momentum. These parameters are computed using the simple flux–profile relationships under the framework of Monin–Obukhov (M–O) similarity theory. The estimated values of z _o are higher (1.35–1.54 m) than the values reported in the literature (>0.4–0.9 m) probably due to the undulating topography surrounding the location. The magnitude of C _D is high for low wind speed (<1.5 m s^?1) and found to be in the range 0.005–0.03. The variations of sensible heat fluxes (SHF) and momentum fluxes are also discussed. Relatively high fluxes of heat and momentum are observed during typical days on 26–27 February 2004 and 10–11 April 2004 due to the daytime unstable atmospheric conditions. Stable or near neutral conditions prevail after 1700 h IST with negative SHF. A mesoscale model PSU/NCAR MM5 is run using a high-resolution (1 km) grid over the study region to examine the influence of complex topography on the surface layer parameters and the simulated fluxes are compared with estimated values. Spatial variations of the frictional velocity (u _*), C _D, surface fluxes, planetary boundary layer (PBL) height and surface winds are noticed according to the topographic variations in the simulation.     

Soil CO2 efflux, carbon dynamics, and change in thermal conditions from contrasting clear-cut sites during natural restoration and uncut larch forests in northeastern China

With the implementation of the Chinese Natural Forest Conservation Program (NFCP) in 1998, over millions of hectares of forest in northeastern China have been protected through natural restoration (closure of hills). The impact of this program on the carbon budget of soil has not been evaluated until now. This paper presents results from a 6-year study of total CO₂ efflux from both soil and litter (R _total), CO₂ flux from soil (R _soil), soil organic matter (SOM), soil microbe density, and litter input and root biomass at an uncut larch (Larix gmelinii) forest and at a natural restoration site. The natural restoration area is a clear-cut site that was formerly part of a continuous portion of the uncut larch forest. Our objectives were to: (1) quantify the magnitude of CO₂ efflux from typical sites in northeastern China; (2) explore the changes in thermal conditions, SOM, and annual CO₂ flux during the 6-year natural restoration, and (3) evaluate the impact of NFCP on soil carbon processes. The annual R _soil at the clear-cut site (58.6–68.2 mol m^???2 year^???1) was 113.6–228.4% (mean 141.5%) higher than that at the uncut larch site (29.6–58.4 mol m^???2 year^???1). At the same time, annual CO₂ from litter at the clear-cut site (2.0–14.2 mol m^???2 year^???1) was only 23.5–84.5% (mean 52.5%) of that at the uncut larch site (5.4–16.8 mol m^???2 year^???1). SOM at the surface layer of the clear-cut site was 75% of that at the uncut larch site, but the soil microbial biomass (carbon) at the clear-cut site was much higher than that at the larch site (p?<?0.05). The percentage of bacteria, fungi and actinomycetes also were largely different between both sites. Natural restoration at the clear-cut site strongly affected thermal conditions. Although the soil temperature (T _soil) and effective accumulated $T_{\rm soil} > 0^{\circ}$ C at the clear-cut site was much higher, the temperature sensitivity (Q ₁₀) was much lower than that at the uncut larch site, and their differences decreased linearly from 2001 to 2006 (p?<?0.05). Moreover, Q ₁₀ at the clear-cut site significantly increased with the progress of natural restoration, which diminished the Q ₁₀ difference between the two sites (slope?=???0.2792, r ²?=?0.4744, p?<?0.05). These data imply that the NFCP natural restoration process has positively recovered the thermal condition of the clear-cut site to the level of uncut larch forest during the 6-year period. However, linear regression analysis showed that the 6-year natural restoration only slightly affected the annual soil CO₂ efflux and SOM at both sites, and also did not diminish the differences between the two sites (p?>?0.10), indicating that a much longer time is necessary to restore the soil carbon in the clear-cut site.     

Long-lasting floods buffer the thermal regime of the Pampas

The presence of large water masses influences the thermal regime of nearby land shaping the local climate of coastal areas by the ocean or large continental lakes. Large surface water bodies have an ephemeral nature in the vast sedimentary plains of the Pampas (Argentina) where non-flooded periods alternate with flooding cycles covering up to one third of the landscape for several months. Based on temperature records from 17 sites located 1 to 700 km away from the Atlantic coast and MODIS land surface temperature data, we explore the effects of floods on diurnal and seasonal thermal ranges as well as temperature extremes. In non-flooded periods, there is a linear increase of mean diurnal thermal range (DTR) from the coast towards the interior of the region (DTR increasing from 10 to 16 K, 0.79 K/100 km, r ² = 0.81). This relationship weakens during flood episodes when the DTR of flood-prone inland locations shows a decline of 2 to 4 K, depending on surface water coverage in the surrounding area. DTR even approaches typical coastal values 500 km away from the ocean in the most flooded location that we studied during the three flooding cycles recorded in the study period. Frosts-free periods, a key driver of the phenology of both natural and cultivated ecosystems, are extended by up to 55 days during floods, most likely as a result of enhanced ground heat storage across the landscape (~2.7 fold change in day-night heat transfer) combined with other effects on the surface energy balance such as greater night evaporation rates. The reduced thermal range and longer frost-free periods affect plant growth development and may offer an opportunity for longer crop growing periods, which may not only contribute to partially compensating for regional production losses caused by floods, but also open avenues for flood mitigation through higher plant evapotranspirative water losses.     

MLP-based drought forecasting in different climatic regions

Water resources management is a complex task and is further compounded by droughts. This study applies a multilayer perceptron network optimized using Levenberg–Marquardt (MLP) training algorithm with a tangent sigmoid activation function to forecast quantitative values of standardized precipitation index (SPI) of drought at five synoptic stations in Iran. The study stations are located in different climatic regions based on De Martonne aridity index. In this study, running series of total precipitation corresponding to 3, 6, 9, 12, and 24?months were used and the corresponding SPIs were calculated: SPI3, SPI6, SPI9, SPI12, and SPI24. The multilayer perceptrons (MLPs) for SPIs with the 1-month lead time forecasting, were tested and validated. Four different input vectors were considered during network development. In the first model, MLP constructed by importing antecedent SPI with 1-, 2-, 3-, and 4-month time lags and antecedent precipitation with 1- and 2-month time lags (MLP1). Addition of antecedent North Atlantic Oscillation or antecedent Southern Oscillation Index with 1-month time lag or both of them to MLP1 led to MLP2, MLP3, and MLP4, respectively. The MLP models were evaluated using the root mean square error (RMSE) and the coefficient of determination (R ²). The results showed that MLP4 had a higher prediction efficiency than the other MLPs. The more satisfactory results of RMSE and R ² values of MLP4 for 1-month lead time for validation phase were equal to 0.35 and 0.92, respectively. Also, results indicated that MLPs can forecast SPI24 and SPI12 more accurately than the other SPIs.     

Available energy and energy balance closure at four coniferous forest sites across Europe

The available energy (AE), driving the turbulent fluxes of sensible heat and latent heat at the earth surface, was estimated at four partly complex coniferous forest sites across Europe (Tharandt, Germany; Ritten/Renon, Italy; Wetzstein, Germany; Norunda, Sweden). Existing data of net radiation were used as well as storage change rates calculated from temperature and humidity measurements to finally calculate the AE of all forest sites with uncertainty bounds. Data of the advection experiments MORE II (Tharandt) and ADVEX (Renon, Wetzstein, Norunda) served as the main basis. On-site data for referencing and cross-checking of the available energy were limited. Applied cross checks for net radiation (modelling, referencing to nearby stations and ratio of net radiation to global radiation) did not reveal relevant uncertainties. Heat storage of sensible heat J _H, latent heat J _E, heat storage of biomass J _veg and heat storage due to photosynthesis J _C were of minor importance during day but of some importance during night, where J _veg turned out to be the most important one. Comparisons of calculated storage terms (J _E, J _H) at different towers of one site showed good agreement indicating that storage change calculated at a single point is representative for the whole canopy at sites with moderate heterogeneity. The uncertainty in AE was assessed on the basis of literature values and the results of the applied cross checks for net radiation. The absolute mean uncertainty of AE was estimated to be between 41 and 52 W m^?2 (10–11 W m^?2 for the sum of the storage terms J and soil heat flux G) during mid-day (approximately 12% of AE). At night, the absolute mean uncertainty of AE varied from 20 to about 30 W m^?2 (approximately 6 W m^?2 for J plus G) resulting in large relative uncertainties as AE itself is small. An inspection of the energy balance showed an improvement of closure when storage terms were included and that the imbalance cannot be attributed to the uncertainties in AE alone.     

Seasonal variation of carbon dioxide fluxes over irrigated soybean (Glycine max L.)

In this study, variations in carbon dioxide (CO₂) fluxes resulting from gross primary production (GPP), net ecosystem exchange (NEE), and respiration (R _e) of soybean (Glycine max L.) were investigated by the Eddy Covariance method during the growing period from June to November 2005 on an irrigated sand field at the Arid Land Research Center, Tottori University in Tottori, Japan. Although climatic conditions were humid and temperate, the soybeans required frequent irrigation because of the low water holding capacity of the sandy soil at the field site. Finally, it has been found that the accumulated NEE, GPP, and R _e fluxes of soybean over 126 days amount to ?93, 319, and 226 gC m^?2, respectively. Furthermore, the average ratio of GPP to R _e was 1.4 and the average ratio of NEE to GPP was about ?0.29 for the growth period of soybean. Daily maximum NEE of ?3.8 gC m^?2 occurred when LAI was 1.1.