Soil hydraulic conductivity and water retention model parameters are crucial in Land Surface Models (LSMs). This study evaluates soil hydraulic parameters derived from 23 widely used pedotransfer functions (PTFs) and their influence on simulated soil hydraulic behavior in soils of the Gadanki region (13.4593° N, 79.1684° E), Andhra Pradesh, India, using soil property inputs obtained from the global SoilGrids database. Volumetric water content was measured at different depths at this site during an isolated thunderstorm at 0500 h IST on October 18, 2018 (23:30 UTC October 17, 2018), followed by a two-week dry spell, to determine soil suction and unsaturated hydraulic conductivity. The results showed good agreement in this region. The Brooks-Corey model consistently predicts higher hydraulic conductivity and faster changes in soil suction than the Van Genuchten -Mualem model across multiple depths. These findings highlight that model choice substantially affects soil water flow simulations and should be carefully considered in light of soil characteristics and study objectives.

Accurate simulation of the Indian Summer Monsoon (ISM) is frequently constrained by uncertainties in land-surface initialization and the representation of land–atmosphere feedback. This study investigates the impact of high-resolution land surface initialization on ISM characteristics using the Weather Research and Forecasting (WRF) model. Two numerical experiments were conducted for the 2007 monsoon season: a Control run initialized with ERA5 reanalysis data, and an Experimental run (MWF) initialized with soil moisture and temperature states, that are at equilibrium, generated via the NASA Land Information System (LIS). The LIS-based initialization reduced systematic warm and dry biases in the planetary boundary layer, particularly over the arid Northwest and Central India. These improvements are driven by the correction of systematic biases in the antecedent subsurface soil states, which act as a persistent boundary condition regulating the surface energy partition and the bowen ratio throughout the season. Consequently, the MWF simulation demonstrated a distinct 8.0

Agricultural irrigation in India is often based on subjective and regionally variable practices, leading to poor representation in land surface models (LSMs). Data assimilation (DA) is an effective method to integrate soil moisture (SM) observations into model predictions, to produce an observation based, spatially distributed SM estimates over Indian domian. Previous studies have employed DA to incorporate unmodeled processes, such as irrigation, into model SM estimates. However, these studies had limited success, primarily due to ineffective bias correction methods. To overcome the above limitation, the present study employed and tested an anomaly-based bias correction method prior to DA, for the first time over the Indian domain. The present study assimilated Soil Moisture Active Passive (SMAP) satellite retrievals into the Noah LSM, utilizing Global Data Assimilation System (GDAS) atmospheric forcings data and precipitation data from the following three data sets such as, TRMM, GDAS, and IMERG-GPM over the Indian domain. DA with the anomaly correction method performs better, as compared to DA with the cumulative distribution function (CDF) matching method, particularly during the winter and pre-monsoon seasons. RMSE values for DA with the anomaly correction are lower during pre-monsoon and winter seasons for all the three different precipitation-forced SM estimates. This improvement is attributed to the significant irrigation over India during pre-monsoon and winter seasons. The comparison with the Global Map of Irrigation Area (GMIA) showed that improvements of GDAS forced DA with anomaly correction method are significant over highly irrigated regions. This study highlights the effectiveness of assimilating SMAP data with anomaly-based bias correction in improving soil moisture estimates from the Noah LSM over India. The approach not only enhances model accuracy but also helps reveal irrigation signals, particularly during high-irrigation seasons.

The land-atmosphere interaction processes are crucial for the atmospheric modelling studies as it influences the interchange of energy and matter between the land surface and atmosphere and can impact climate and weather patterns on regional and global scales. The feedback between the soil moisture (SM) and precipitation (PR) is the key factor for land surface and atmosphere interactions; however the above feedback is not well understood. The present study employs Event Coincidence Analysis (ECA) method for investigating the influence of SM on extreme PR over the Indian domain. In this study, 21 years (2000-2020) of Global Land Evaporation Amsterdam Model (GLEAM) surface SM data and root zone ( R_z ) SM data together with India Meteorological Department PR data are considered. The findings indicate that West central India (WCI) region has a higher long term relationship between SM and PR over the surface as well as R_z , than the rest of the Indian regions. The higher long term relationship between SM and PR over WCI region is associated since the WCI region is a transition region, where the land-atmosphere interaction is more pronounced as compared to either the wet or the dry regions. The results of the ECA method also shows that the number of grid points having higher trigger coincidence rate (TCR) for the highest time lag (30 days), is lower for R_z SM as compared to the surface SM. Additionally, seasonal TCR analyses are performed, using the 21 years (2000-2020) data. The results of the seasonal TCR analysis indicate that the monsoon season (June to September) shows reduced TCR magnitudes as compared to annual analyses; however the TCR results during monsoon provides similar spatial distributions as to the results of the annual analysis. The results of TCR monsoon season shows higher TCR values over in Northwest and WCI regions. Furthermore, the study employed the PCMCI causality test to examine the dynamic causal relationships between surface SM and PR over different time lags. The results of the PCMCI test shows strong short-term causal links between SM and PR in South Peninsular India, especially for lags of 1 and 2 days, while showing weaker long-term relationship between SM and PR over Northeast region. The strongest long-term causal relationship between SM and PR is observed in the Northwest India (NWI) region, with a time lag of 24 days. The above is attributed to the fact that the NWI region experiences very little impact from synoptic level weather systems that form over India. Furthermore, additionally, the PCMCI analysis reveals that the long-term causal relationships between SM and PR are significant over WCI and Central Northeast India. The results of this study demonstrate that both the ECA and PCMCI methods are effective in capturing the complex relationships between extreme SM and PR events across the Indian domain, and hence provide for deeper insights into land-atmosphere feedback mechanisms.

Cloudburst events are characterized by very high rainfall rates (100 mm h−1) over a small area ( 10×10 km). It catastrophically damages the properties and causes loss of lives. Therefore, it is essential to understand the nature of the microphysical processes that contributed to producing such a high rain rate. Collision processes inside the cloud are vital in determining the rainfall rate. Hence, in the present study, we have evaluated a comparative performance of disparate collision efficiencies based on different formulations during the cloud burst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 AM IST (06:15 UTC 10 June 2021). The lifting condensation level (LCL), lifting deposition level (LDL), and lifting freezing levels (LFL) have been calculated using the ERA5 dataset. The collision efficiencies were determined on 100 levels between the lifting condensation and freezing levels. The appraisal of eight formulations shows that Onishi, Beard and Grover, Bohm, and Jin formulations yielded higher collision efficiencies during the cloudburst event, which is expected. Meanwhile, the estimated collision efficiency based on Ahmad’s formulation is high for specific bins only. The formulations by Barnet, Long, and Lee and Baik, however, yielded very low values of collision efficiencies during the cloud burst event.

Cloud bursts have become a pressing concern with their devastating impact and increasing frequency over the Himalayan region. Therefore, understanding the physical mechanisms associated with their occurrence is essential and urgent. Our investigation into the physical mechanisms of a cloud burst over Sauni Binsar, Uttarakhand, India, which occurred on 10 June 2021 around 06 UTC, is a step towards addressing this urgency. On the day of the cloud burst, there was a continuous accumulation/stagnation (at 850 hPa) of moist air over Sauni Binsar due to the northward propagation of the southwest monsoon, leading to atmospheric column supersaturation. The advection of warm air (dry) from the monsoon heat low at higher (700 hPa) caused potential instability over this region. Orographic lifting and a gradual increase in convergence over this region caused moist convection. Further, the analysis of Richardson's number indicated that turbulence was maximum in the middle and upper troposphere. Just a few hours before the cloud burst event (02-06 UTC 10 June 2021), the decrease in potential vorticity indicates the squashing of the moist columns and the decrease in the vorticity. The sudden squashing of the supersaturated atmospheric column might have caused enormous rainfall (Cloud burst) over the Sauni Binsar region.

Analyzing the land surface states and their interlinkages with the atmosphere, improves the land-atmosphere interactions primarily. In this study, the initial state of the soil is generated using NASA’s Land Information System (LIS). The ARW-WRF model (WRF), a widely employed mesoscale model is utilized to simulate the evolution of two Deep Depressions and two Monsoon Depressions, and their associated landfall in the coast adjoining the Head Bay of Bengal, and these simulations are called the “control run”. Furthermore, the NASA Unified WRF (NUWRF) modeling system, that utilizes the initial equilibrium soil state, is also employed to simulate the aforementioned four depression systems, which are known as the “experimental run”. While the accumulated precipitation values of both simulations are validated with the 3-hour accumulated precipitation data extracted from Indian Meteorological Department (IMD) daily accumulated precipitation data, the values of the top layer soil moisture of both simulations are compared with Indian Monsoon Data Assimilation and Analysis (IMDAA), Global Land Evaporation Amsterdam Model (GLEAM), and ESA’s Climate Change Initiative soil moisture datasets (ESA-CCI). Both the ARW-WRF and NUWRF model simulations are analysed to investigate differences, if any, between the two model outputs and hence the effect of the improved soil state on the atmospheric variables. The results of the validation of top layer soil moisture (SM) with GLEAM, IMDAA and ESA-CCI datasets, clearly indicate that while the SM values of WRF run abruptly drops over the first two days, the abrupt drop in SM is not observed in the NUWRF run. Furthermore, none of the three SM datasets, show any abrupt drop in SM values, rather they show an increase in the SM values with time and then mostly little change of SM with time. The results also indicate that the WRF model typically shows higher values of the top layer SM as compared to NUWRF. Furthermore, at the time of the system’s landfall, the output of both NUWRF and WRF models are compared for all four depressions. The variations in heat fluxes, surface air temperature, and planetary boundary layer height for the four systems are consistent with the differences shown in soil moisture for both WRF and NUWRF model simulations. Contiguous Rain Area (CRA) Verification method is utilized for verifying the gridded quantitative precipitation results with IMD’s daily accumulated precipitation; which yielded mixed results.

The collection mechanism inside the cloud is associated with the coalescence process. It controls the rainfall rate in the presence of high moisture content and low cloud condensation nuclei concentrations. The effectiveness of the coalescence mechanism is determined by the magnitude of the coalescence efficiency. This study compared the effectiveness of six coalescence efficiencies to identify the appropriate formulation, which can simulate the cloudburst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 IST (06:15 UTC). The coalescence efficiencies have been estimated between the lifting condensation levels and freezing levels over 100 equidistance intervals, with variation in the bins of colliding droplet pairs. The comparative analysis shows that Ochs’s formulation estimated high values of coalescence efficiencies ranging between 60 and 100

Wavelets are employed to study atmospheric turbulent data of three wind components, temperature and passive scalars CO _2 and H _2 O. The multiresolution analysis (MRA) based on maximal overlap discrete wavelet transform (MODWT) is used to separate turbulent fluctuations from the mean flow. These turbulent fluctuations are further partitioned into small scales x'_s and large scales x'_L , and the fluxes are calculated by averaging over the given time interval. The large scales are responsible for much of the flux transport, while the small scales are fine scales consisting of non-transporting, nearly isotropic motions. The velocity spectrum for both small (non-coherent) and large scale (coherent) follow -5/3 scaling, and the transfer efficiency R_wa similarity laws are better satisfied for the large scales. The velocity probability distribution of partitioned signals shows a narrower distribution for small scales compared to large ones. However, the flatness factor indicates deviation from Gaussianity. The joint probability distribution for large scales is skewed, suggesting the dominance of ejections and sweeps. Despite their wave-like nature, the large scales are not linear waves as indicated by the phase spectrum. The large scales are subjected to the continuous wavelet transform (CWT) to detect and isolate the strong localized events. The Mexican Hat (MHAT) wavelet transform and zero-crossing method is used to estimate the duration, separation, and frequency of occurrence of the detected events.

Abstract The present study has employed a regional Land Surface Model (LSM) to investigate the impact of historical land cover changes on land surface characteristics over the Indian subcontinent for the period of 1930-2013. Four simulations that include a control run and three experiment runs are performed with the Noah 3.6 LSM within the Land Information System (LIS). The control run is performed with a MODIS-IGBP land cover map, while the three experimental runs are performed with three different potential land cover maps for the years 1930, 1975, and 2013. The potential land cover maps for the above three simulations are developed by blending the MODIS-IGBP data set with the fractional forest cover data set; the latter data is available for the years 1930, 1975, and 2013. Results indicate that the historical land cover change (1930 to 2013) has reduced the annual mean of latent heat flux and net surface radiation over the Indian domain by -24.74 W/m 2 and -14.18 W/m 2 respectively, while the sensible heat flux and the soil temperature has increased by 4.97 W/m 2 and 2.78 K. The annual mean change in latent heat flux, sensible heat flux, and soil temperature demonstrate that the largest changes occur when the land cover changes from forest to urban land as compared to forest to cropland, forest to grassland and forest to open shrubland. The annual mean change in latent heat flux is moderately large for the land cover change from forest to open shrubland when compared to forest to grassland and forest to cropland. The above is attributed to the effects of evapotranspiration, which has high values for the cropland followed by grassland and open shrubland. Furthermore, the triple collocation method is employed to assess the impact of historical land cover change on soil moisture. Results indicate that the triple collocation method effectively demonstrates the impact of land cover change on soil moisture.

Odhisa, a state of India, bore the disastrous consequences of two consequent Tropical Cyclones (TCs), TC 04B and TC 05B (Odisha 1999 supercyclone), which formed over the Bay of Bengal and experienced landfall in October 1999, with a time gap of fewer than two weeks, over the same region. It is suggested that the first TC, TC 04B, provided an “ocean‐like situation” over the coastal land region, thus delivering the appropriate land conditions that would facilitate the intensification of the following second tropical cyclone, TC 05B; a clear illustration of the “Brown Ocean effect.” Two Weather Research Forecasting (WRF) simulations were conducted, with the control and experimental runs differing solely in the following aspect: the initial cyclonic vortex corresponding to the first TC at the initial time was removed in the experimental run, whereas it was retained in the control run. Both simulations were analyzed to reveal the “Brown Ocean Effect” role. The experimental run result indicates that the minimum central sea level pressure of the second TC was 35 hPa higher than the second TC simulation in the control run. The heavy rainfall associated with TC 04B led to increased soil moisture conditions, providing the second TC (TC 05B) with the necessary conditions for its intensification by the “Brown Ocean Effect.” The results of this study appear to strongly suggest that the “Brown Ocean Effect” could provide one of the main reasons for the extraordinary intensities associated with the 1999 Odisha supercyclone.

A comprehensive investigation is undertaken to discern the structure of momentum flux, turbulent kinetic energy, and scalar fluxes like heat, CO $$_2$$ , and H $$_2$$ O in the atmospheric surface layer (ASL) at the Thumba Equatorial Rocket Launching Station—a coastal station on the west coast of southern peninsular India. The vertical transport, transfer efficiency, and dissimilarity between flux transport are studied as a function of stability using data collected over 1 year. The transfer efficiency for heat fluxes and momentum exhibits a strong dependence on stability ( $$\zeta $$ ). However, the transfer efficiency of passive scalars CO $$_2$$ and H $$_2$$ O displays no apparent dependence on $$\zeta $$ . The correlation between fluxes and squared coherence estimates is evaluated to study the dissimilarity between flux transport. The correlation is strongest among momentum and heat fluxes and between CO $$_2$$ and H $$_2$$ O fluxes and shows a dependence on the prevailing stability conditions. However, the influence of stability is not evident for the various other combinations. The momentum and heat flux transport is dissimilar for unstable conditions, and it becomes similar during the transition from unstable to near-neutral conditions. The quadrant analysis is employed to study the contribution of different fluid motions to the aforementioned turbulent fluxes. Except for CO $$_2$$ and H $$_2$$ O fluxes, where all the quadrants have an equal contribution, ejections and sweeps are the dominating contributors for momentum and heat fluxes. The stability conditions greatly determine the ejection-sweep balance for heat flux, while some changes in duration and impact fraction are also detectable for momentum flux. Furthermore, contour maps of joint-probability function (JPDF) of vertical velocity fluctuations ( $$w'$$ ) with streamwise velocity fluctuation ( $$u'$$ ), temperature fluctuation ( $$T'$$ ), and scalar fluctuations, respectively, are also presented. The dominance of the ejection and sweep cycles for turbulent fluxes provide evidence for the presence and importance of coherent structures in ASL.

Determining the number concentration of minor constituents in the atmosphere is very important as it determines the whole tropospheric chemistry process. These constituents may act as cloud condensation nuclei (CCN) and ice nuclei (IN), impacting heterogeneous nucleation inside the cloud. However, the estimations of the number concentration of CCN/IN in cloud microphysical parameters are associated with uncertainties. In the present work, a hybrid Monte Carlo Gear solver has been developed to retrieve profiles of CH4, N2O, and SO2. The idealized experiments have been carried out using this solver for retrieving vertical profiles of these constituents over four megacities, viz., Delhi, Mumbai, Chennai, and Kolkata. Community Long-term Infrared Microwave Coupled Atmospheric Product System(CLIMCAPS) dataset around 0800 UTC (2000UTC) has been used for initializing the number concentration of CH4, N2O, and SO2 for daytime (nighttime). The daytime (nighttime) retrieved profiles have been validated using 2000 UTC (next day 0800 UTC) CLIMCAPS products. ERA5 temperature dataset has been used to estimate the kinematic rate of reactions with 1000 perturbations determined using Maximum Likelihood Estimation (MLE). The retrieved profiles and CLIMCAPS products are in very good agreement, as evidenced by the percentage difference between them within the range of 1.3x10(-5)-60.8 % and the coefficient of determination mainly within the range between 81 and 97 %. However, during the passage of tropical cyclone and western disturbance, its value became as low as 27 and 65% over Chennai and Kolkata, respectively. The enactment of synoptic scale systems such as western disturbances, tropical cyclone Amphan, and easterly waves caused disturbed weather over these megacities-the retrieved profiles during disturbed weather cause large deviations of vertical profiles of N2O. However, the profiles of CH4 and SO2 have less deviation. It is inferred that incorporating this methodology in the dynamical model will be useful to simulate the realistic vertical profiles of the minor constituents in the atmosphere.

The turbulent flow over a coastal region is investigated to study the drag coefficient ( C_D ) behavior during on-shore and off-shore winds. The analysis of turbulent data over 2 years is carried out to examine the dependence of C_D on mean wind speed ( U ) and stability parameter ( ζ ). The drag coefficient is found to show a parabolic dependence with wind speed for on-shore flows and a slightly linear trend for off-shore flows for neutral and weakly unstable cases. Only for strongly unstable and stable cases ( ζ >0 ), high values of C_D are observed for low wind speed ( U<2ms^-1 ). The likely cause for high values of C_D during low wind speed is attributed to an increase in turbulent intensity caused due to the presence of coherent structures. On further analysis of C_D with ζ , it is found that under stable conditions ( ζ >0 ), C_D shows a systematic decrease with increasing ζ . On the contrary, for unstable cases ( ζ <0 ), the values of C_D peaks around ζ≈ -0.13 , before decreasing with increasing -ζ .

The accurate forecast of the diurnal cycle of the number concentration of trace gases is vital due to their influence on precipitation processes by controlling the number concentration of cloud condensation nuclei (CCN). 1-D hybrid Monte Carlo-Gear solver developed to retrieve vertical profiles of the number concentration of CCNs for microphysics modeling has been tested for representation of the diurnal cycle in the present paper. The retrieved profiles of CH4 and SO2 have been tested with the Copernicus Atmosphere Monitoring Service (CAMS) model at 3-hour time intervals for four megacities: Delhi, Kolkata, Chennai, and Mumbai for rainy and non-rainy days. The retrieved profiles have shown diurnal variation up to 18 UTC at all pressure levels with lead or lag with that of the CAMS model. After 18 UTC there was a furious increase in the number concentrations. During non-rainy days, the 1-D model slightly overestimated (underestimated) the maximum (minimum) number concentrations of CH4 over Delhi whereas concentrations are overestimated over Kolkata, Chennai, and Mumbai. Forecasted CH4 has a good (weak) correlation over Chennai (Mumbai) respectively. The 1-D model overestimated (overestimated) the maximum (minimum) number concentrations of SO2 over Delhi but the maximum (minimum) concentrations are underestimated (overestimated) over Kolkata, Chennai, and Mumbai. The number concentrations of SO2 have shown a good correlation for all megacities except Delhi. CH4 number concentration is overestimated during rainy days. Delhi and Kolkata show a good correlation of CH4 during rainy days. SO2 during rainy days is underestimated except over Chennai and both models show a good correlation except over Mumbai. Overall, it can be stated that the 1-D hybrid solver is successful in simulating the monthly mean diurnal variation of vertical profiles of CH4 and SO2, and its implementation in the global model may estimate the number concentrations with better accuracies.

A hybrid Monte-Carlo Gear solver developed earlier has been improvised to retrieve the vertical profiles of CH4 and N2O during disturbed weather situations such as western disturbances, tropical cyclones, and heavy rainfall events over the megacities: Delhi, Kolkata, Chennai, and Mumbai. Due to rapid changes in the temperature during the passage of these systems over megacities, the percentage differences of CH4 and N2O number concentrations were more compared to the Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS). Therefore, the hybrid solver has been modified by improving maximum likelihood estimates of vertical temperature profiles. The number concentrations of CH4 and N2O during these weather events since 2012 are obtained from the CLIMCAPS dataset for bias correction. It is found that the modified methodology has improved the retrieval of CH4 and N2O vertical profiles by reducing the error percentages during daytime and nighttime over these megacities. The percentage error in estimated number concentrations of CH4 and N2O significantly reduced during (i) the passage of the western disturbance and rainy days of August 2020 over Delhi; (ii) the rainy days of June 2020 over Kolkata; (iii) the influence of supercyclonic storm Amphan (24 and 25 Nov 2020) over Chennai and (iv) rainy days of July 2020 over Mumbai. Implementing this solver in the global model may retrieve the number concentrations more accurately.

The main objective of this study is to analyse the near-surface soil moisture fields over the Indian region from two gridded soil moisture datasets and to compare the soil moisture from the above-mentioned datasets with the soil moisture data obtained from the advanced scatterometer (ASCAT). The two soil moisture datasets considered in this study are (i) global land evaporation Amsterdam model (GLEAM) and (ii) Indian monsoon data assimilation and analysis (IMDAA). The IMDAA soil moisture is obtained from modelled output that assimilates soil moisture, while the soil moisture estimates from the GLEAM dataset are derived from both satellite and modelled observations. The results of this study indicate that the differences of ASCAT soil moisture with GLEAM soil moisture are consistently lower than the differences of ASCAT soil moisture with IMDAA soil moisture over all the four seasons in the period 2008–2012. Also, quantitative measures such as improvement parameter (IP), forecast parameter (FP), spatial and temporal correlation are obtained using the two datasets and the ASCAT data to further quantify the relative closeness of the datasets with ASCAT data. The results of these quantitative measures clearly indicate that over the Indian region, the GLEAM near-surface soil moisture data are closer to the ASCAT soil moisture data when compared to the IMDAA near-surface soil moisture data over all seasons for the period 2008–2012. Also, GLEAM soil moisture dataset has lower root mean square error value as compared to IMDAA soil moisture dataset over all seasons and for the period 2008–2012. Also, the results of the IP and FP indicate that the largest percentage of grid cells over which GLEAM data are closer to ASCAT are in the post-monsoon season (October and November). Based on the spatial correlation of near-surface soil moisture between IMDAA, GLEAM and ASCAT, the largest spatial correlation values are observed during the south-west Indian monsoon. The results of temporal correlation reveal that the ASCAT and GLEAM datasets have higher correlation coefficient (CC) values as compared to the CC values corresponding to the ASCAT and IMDAA datasets over most regions of India and over most of the seasons considered.

The impact of different formulations of background error covariances (BECs) is examined for three heavy rainfall episodes over north India with a regional 4-dimensional variational (4DVar) data assimilation (DA) system. Three BEC formulations are analyzed, in which two of them employ stream function and velocity potential (psi and chi) and the third one uses zonal and meridional velocity components (v and v) as momentum control variables. The uv-based formulation is completely univariate whereas, the correlations among the control variables are taken into account in the psi chi -based formulations through linear regression relations. Among the two psi chi-based BECs, one uses univariate relation and the other one uses multivariate relations for the moisture field. The multivariate relationship allows for impacting the moisture analysis through the assimilation of temperature or wind observations. Three experiments are carried out for each case with cyclic 4DVar assimilation. The conventional surface and upper-air observations are assimilated in combination with atmospheric motion vectors (AMVs) and ocean surface winds. Free forecast for 48 h is performed from respective final analysis fields for all the experiments. The results indicate that the uv-based analysis fields are closer to the observations. A comparative analysis of the 4DVar experiments with the 3DVar DA system provided a critical insight on the role of the 4DVar DA system on implicitly accounting for the multivariate correlations. The precipitation forecasts confirm the improved performance of the psi chi-based experiment, when multivariate nature of the humidity is incorporated. The time evolution of the intense rainfall episodes over the location of maximum rainfall are relatively well reproduced in the uv-based experiment. The results indicate that the inclusion of multivariate humidity variable in the BEC formulation does have a significant impact on suppressing the excessive overestimation in rainfall intensity.

Among all the variables that relate to the water cycle, precipitation is considered the most important variable for streamflow modeling and forecasting. Uncertainty in the precipitation measurement is one of the chief limitations in the hydrological modeling where the hydrology model is run in a stand-alone mode. In this study, the WRF-Hydro model is used for modeling streamflow over Godavari basin. This study explores the effect of uncertainty in the precipitation forcing in simulating the streamflow by the uncoupled WRF-Hydro model. For the present study, satellite-based [Global Precipitation Measurement (GPM), Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)] precipitation products are used. Furthermore, the present study also utilized gauge-based [Indian Meteorological Department (IMD) gridded] and reanalysis-based [Global Land Data Assimilation System (GLDAS)] precipitation products. The uncertainty in the satellite-based and reanalysis-based precipitation is evaluated quantitatively by using the gauge-based IMD gridded dataset as the reference dataset. The results indicate that the IMD gridded rainfall forced model output streamflow is in agreement with the observed streamflow at four stations having positive Nash–Sutcliffe coefficient of efficiency (NSCE) values. The results indicate the importance of utilizing precipitation datasets (satellite/reanalysis) having similar spatial and temporal variability of precipitation as the observed rainfall for accurate simulation of streamflow using hydrological models.

Changes in the nature of vegetation imply changes in land surface parameters such as surface albedo, roughness length, root depth, and surface emissivity which results in alteration of land surface conditions. This study aims to understand the effect of enhanced forest conditions on land surface states and land surface fluxes over central India during different seasons. In this study, the following simulations were carried out using Noah3.6 Land Surface Model (LSM) in Land Information System (LIS) for 5 years from May 2013 to May 2018 over the Indian region: (i) Control (Ctl) run, that represents the cropland(normal) scenario with Modis IGBP land cover map, (ii) Experimental (Exp) run, that represents enhanced forest conditions, where the cropland cover was replaced by evergreen broadleaf forest over the targeted region (central India). The results of this study indicate that when the model outputs were averaged over the targeted region, enhanced forest conditions led to increased soil moisture content as compared to the Cropland scenario due to dense cover of the surface and higher water holding capacity of the soil. Due to the canopy interception of solar radiation and increased soil moisture availability in the Exp-run, a decrease in both soil temperature and daily soil temperature range was seen as compared to Ctl-run. Net surface radiation increased during all the seasons in Exp-run as compared to Ctl-run due to a decrease of surface albedo. On conversion from cropland to the evergreen broadleaf forest, an increase in the latent heat flux was observed during all the seasons due to an increase in soil moisture and an increase in surface roughness length. A reduction in the sensible heat flux was observed during the post-monsoon season and beginning of winter season, however, for other seasons, an increase in the sensible heat flux was seen. The above behavior of sensible heat flux is due to variation of precipitation and the varying absorbed surface energy during different seasons. Furthermore, the results suggest that the effect of this enhanced forest conditions on land surface characteristics is more pronounced during the drier periods. A similar behavior of land surface characteristics due to enhanced forest conditions is observed for all the seasons during different years from 2013 to 2018.