Eight rainfall bias correction techniques were compared over the Chindwin River basin in Myanmar to improve hydrological simulation at multiple timescales using two approaches, viz. monthly and annual. The techniques included linear scaling, parametric quantile mapping using linear, scale, power and exponential assymptotic transfer functions and nonparametric quantile mapping using empirical, robust regression and smoothing splines interpolation methods. Three global climate models (GCMs), wet, near‐normal and dry in nature to estimate mean rainfall at the country and the basin scales were selected from a set of 13 GCMs. The rainfall bias correction factors for each GCM were generated from the control period 1981–1999 and verified over 2000–2005.Application of bias correction techniques resulted in reduction of biases and improved the flow simulations. These techniques showed better performance statistics in simulating daily, monthly and seasonal flows under the monthly approach, where correction factors were generated and applied separately for different months. The inconsistencies in magnitude and seasonality of flows were addressed under the monthly approach while only the biases related to magnitude were corrected under the annual approach. Linear scaling followed by parametric (linear and power transformation) and nonparametric empirical quantile mapping methods yielded a very good hydrological performance at all temporal scales when applied under the monthly approach. Parametric quantile mapping with scaling function yielded least efficiency under the annual approach for all temporal scales. These results are expected to be valid for other river basins in the region showing similar strong rainfall seasonality.

A representative of meteorological data-constrained basin, Ayeyarwady, in Myanmar, Southeast Asia, is set for flow simulation and forecasting at 15 locations using a range of hydrological modeling approaches: conceptual lumped (GR4J), hybrid-lumped [Identification of unit Hydrographs And Component flows from Rainfall Evapotranspiration and Streamflow Catchment Wetness Index (IHACRES CWI)], semidistributed [Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS)], and relatively distributed [Soil and Water Assessment Tool (SWAT)]. Using daily rainfall data from 51 surface rainfall stations (over an area of approximately $\mathrm{400,000}{\mathrm{km}}^2$ ) and coarse monthly evaporation inputs from global sources, the models are calibrated (validated) against observed flows for 2001–2009 (2010–2014) using the performance indicators Nash-Sutcliffe efficiency (NSE), percentage bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and volumetric efficiency . The developed models were then integrated with rainfall forecasts from the Weather Research and Forecasting Model for 2015–2018 and assessed for biases against observed flows. The NSE values were favorable for GR4J (median $\mathrm{NSE}=0.9$ ), followed by IHACRES ($\mathrm{NSE}=0.86$ ), SWAT ($\mathrm{NSE}=0.81$ ) and HEC-HMS ($\mathrm{NSE}=0.77$ ) during calibration and GR4J ($\mathrm{NSE}=0.87$ ) and the latter three ($\mathrm{NSE}=0.83$ ) during validation. Lumped models were found to have comparable, albeit better in simulating low, median, and high quantiles of flows during both calibration and validation periods, compared to other models of varying complexity set for the study basin. The hydrometeorological coupling also revealed that GR4J yielded the least while HEC-HMS yielded the highest biases (up to 30-fold at some stations) in daily flow forecasting. The analysis suggested that while process-based and relatively complex models may exhibit better performance in data-rich basins, simple conceptual models like GR4J are useful for daily flow simulation and forecasting in data-constrained basins of the region.

Crops can uptake only a fraction of nitrogen from nitrogenous fertilizer, while losing the remainder through volatilization, leaching, immobilization and emissions from soils. The emissions of nitrogen in the form of nitrous oxide (N₂O) have a strong potency for global warming and depletion of stratospheric ozone. N₂O gets released due to nitrification and denitrification processes, which are aided by different environmental, management and soil variables. In recent years, researchers have focused on understanding and simulating the N₂O emission processes from agricultural farms and/or watersheds by using process-based models like Daily CENTURY (DAYCENT), Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT). While the former two have been predominantly used in understanding the science of N₂O emission and its execution within the model structure, as visible from a multitude of research articles summarizing their strengths and limitations, the later one is relatively unexplored. The SWAT is a promising candidate for modeling N₂O emission, as it includes variables and processes that are widely reported in the literature as controlling N₂O fluxes from soil, including nitrification and denitrification. SWAT also includes three-dimensional lateral movement of water within the soil, like in real-world conditions, unlike the two-dimensional biogeochemical models mentioned above. This article aims to summarize the N₂O emission processes, variables affecting N₂O emission and recent advances in N₂O emission modeling techniques in SWAT, while discussing their applications, strengths, limitations and further recommendations.

Topography affects soil hydrological, pedological, and biochemical processes and may influence nitrous oxide (N₂O) emissions into the atmosphere. While N₂O emissions from agricultural fields are mainly measured at plot scale and on flat topography, intrafield topographical and crop growth variability alter soil processes and might impact N₂O emissions. The objective of this study was to examine the impact of topographical variations on crop growth period dependent soil N₂O emissions at the field scale. A field experiment was conducted at two agricultural farms (Baggs farm; BF and Research North; RN) with undulating topography. Dominant slope positions (upper, middle, lower and toeslope) were identified based on elevation difference. Soil and gas samples were collected from four replicated locations within each slope position over the whole corn growing season (May–October 2019) to measure soil physio-chemical properties and N₂O emissions. The N₂O emissions at BF ranged from −0.27 ± 0.42 to 255 ± 105 g ha⁻¹ d⁻¹. Higher cumulative emissions were observed from the upper slope (1040 ± 487 g ha⁻¹) during early growing season and from the toeslope (371 ± 157 g ha⁻¹) during the late growing season with limited variations during the mid growing season. Similarly, at RN farm, (emissions ranged from −0.50 ± 0.83 to 70 ± 15 g ha⁻¹ d⁻¹), the upper slope had higher cumulative emissions during early (576 ± 132 g ha⁻¹) and mid (271 ± 51 g ha⁻¹) growing season, whereas no impact of slope positions was observed during late growing season. Topography controlled soil and environmental properties differently at different crop growth periods; thus, intrafield variability must be considered in estimating N₂O emissions and emission factor calculation from agricultural fields. However, due to large spatial variations in N₂O emissions, further explorations into site-specific analysis of individual soil properties and their impact on N₂O emissions using multiyear data might help to understand and identify hotspots of N₂O emissions.