Integrating Remotely Sensed Leaf Area Index with Biome-BGC to Quantify the Impact of Land Use/Land Cover Change on Water Retention in Beijing

Maintaining or increasing water retention in ecosystems (WRE) can reduce floods and increase water resource provision. However, few studies have taken the effect of the spatial information of vegetation structure into consideration when assessing the effects of land use/land cover (LULC) change on WRE. In this study, we integrated the remotely sensed leaf area index (LAI) into the ecosystem process-based Biome-BGC model to analyse the impact of LULC change on the WRE of Beijing between 2000 and 2015. Our results show that the volume of WRE increased by approximately 8.58 million m3 in 2015 as compared with 2000. The volume of WRE in forests increased by approximately 26.74 million m3, while urbanization, cropland expansion and deforestation caused the volume of WRE to decline by 11.96 million m3, 5.86 million m3 and 3.20 million m3, respectively. The increased WRE contributed by unchanged forests (14.46 million m3) was much greater than that of new-planted forests (12.28 million m3), but the increase in WRE capacity per unit area in new-planted forests (124.69 ± 14.30 m3/ha) was almost tenfold greater than that of unchanged forests (15.60 ± 7.85 m3/ha). The greater increase in WRE capacity in increased forests than that of unchanged forests was mostly due to the fact that the higher LAI in unchanged forests induced more evapotranspiration to exhaust more water. Meanwhile, the inverted U-shape relationship that existed between the forest LAI and WRE implied that continued increased LAI in forests probably caused the WRE decline. This study demonstrates that integrating remotely sensed LAI with the Biome-BGC model is feasible for capturing the impact of LULC change with the spatial information of vegetation structure on WRE and reduces uncertainty.

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Numerical Simulation of Topography Impact on Transport and Source Apportionment on PM2.5 in a Polluted City in Fenwei Plain

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control.

High-Resolution Regional Climate Modeling and Projection of Heatwave Events over the Yangtze River Basin

Heatwave events (HWEs) have strong impacts on human health, ecosystems, and sustainable social development. Using a gridded observation dataset and a high-resolution regional climate model (RCM), this study analyzed the characteristics of HWEs over the Yangtze River Basin (YRB) in eastern China during the historical period and projected the changes in HWEs over the YRB in the future. The daily maximum temperature (Tmax), long-lived (≥6 days) HWEs, and total (≥3 days) HWEs in the YRB all showed an obvious upward trend from 1981 to 2018, while the increase in short-lived (≥3 days and <6 days) HWEs was relatively moderate overall. The RCM of the Weather Research and Forecasting (WRF) model can simulate the characteristics of Tmax and HWEs in the historical period very well, and the projection results showed that Tmax, total HWEs, and long-lived HWEs will all increase obviously in both the SSP245 and SSP585 scenarios. Short-lived HWEs will also increase rapidly under SSP585, but they will rise slowly overall under SSP245. The changes in HWEs had distinct regional differences, and the intensity and coverage area of HWEs were greater under SSP585 overall. In the future, the increase in HWEs over the YRB region is likely to be associated with the enhancement of the western-Pacific subtropical high (WPSH) and South-Asian high (SAH), and this enhancement was also greater under SSP585. The results from the high-resolution simulation of the RCM can provide an important reference for disaster prevention and mitigation in the future.

Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes

The Pantanal, the largest contiguous wetland in the world with a high diversity of ecosystems and habitat for several endangered species, was impacted by record-breaking wildfires in 2020. In this study, we integrate satellite and modeling data that enable exploration of natural and human contributing factors to the unprecedented 2020 fires. We demonstrate that the fires were fueled by an exceptional multi-year drought, but dry conditions solely could not explain the spatial patterns of burning. Our analysis reveals how human-caused fires exacerbated drought effects on natural ecosystem within the Pantanal, with large burned fractions primarily over natural (52%), and low cattle density areas (44%) in 2020. The post-fire ecosystem and hydrology changes also had strong ecological effects, with vegetation productivity less than − 1.5 σ over more than 30% of the natural and conservation areas. In contrast to more managed areas, there was a clear decrease in evaporation (by ~ 9%) and an increase in runoff (by ~ 5%) over the natural areas, with long-term impacts on ecosystem recovery and fire risk. This study provides the first tropical evidence outside rainforests of the synergy between climate, land management and fires, and the associated impacts on the ecosystem and hydrology over the largest contiguous wetlands in the world.

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States

Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions - most notably in the central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO2 conditions.

The Effects of Topography and Urban Agglomeration on the Sea Breeze Evolution over the Pearl River Delta Region

Sea breezes are one of the most important weather processes affecting the environmental and climatic features over coastal areas, and the sea breeze from the Pearl River Estuary (PRE) has significant effects on the Pearl River Delta (PRD) region. We simulated a typical sea breeze process that occurred on 27 December 2020 in the PRD region using the Weather Research and Forecasting (WRF) model to quantify the effects of topography and city clusters on the development of the sea breeze circulation. The results show that: (1) the topography on the west coast of the PRD tends to block the intrusion of the sea breeze and detour it along the eastern part of the terrain in the southeast of Jiangmen. The depth of sea breeze along the position of the detour is increased by 120 m, the penetration distance is increased by 40 km, the maximum intensity of sea breeze decreases by ~0.4 m/s, and the time of maximum speed delays for 4 h. However, on the east coast, the topography promotes the sea breeze, resulting in an occurrence about 4 h earlier due to the heating effects. The depth and the speed of the sea breeze are increased by 466 m and 1.2 m/s, respectively. (2) Under the influence of Urban Heat Island Circulation (UHIC), the sea breezes reach cities near the coast an hour earlier and are later inhibited from propagating further inland. Moreover, a wind convergence zone with a speed of 3–5 m/s and a width of about 25 km is formed along the boundary of suburbs and cities in the PRD region. As a result, two important convergence areas: Foshan–Guangzhou, and Dongguan–Shenzhen are formed. (3) Overall, the topography has a more remarkable impact on the mesoscale wind field especially in the mountain and bay areas, resulting in an average speed disturbance of 2.8 m/s. The urban heat island effect is relatively small and on average it causes only ± 0.9–1.8 m/s wind speed perturbations in the periphery of two convergence areas and over PRE.

Sensitivity of Summertime Convection to Aerosol Loading and Properties in the United Arab Emirates

The Weather Research and Forecasting (WRF) model is used to investigate convection–aerosol interactions in the United Arab Emirates (UAE) for a summertime convective event. Both an idealized and climatological aerosol distributions are considered. The convection on 14 August 2013 was triggered by the low-level convergence of the cyclonic circulation associated with the Arabian Heat Low (AHL) and the daytime sea-breeze circulation. Numerical experiments reveal a high sensitivity to aerosol properties. In particular, replacing 20% of the rural aerosols by carbonaceous particles has a comparable impact on the surface radiative fluxes to increasing the aerosol loading by a factor of 10. In both cases, the UAE-averaged net shortwave flux is reduced by ~90 W m−2 while the net longwave flux increases by ~51 W m−2. However, when the aerosol composition is changed, WRF generates 20% more precipitation than when the aerosol loading is increased, due to a broader and weaker AHL. The surface downward and upward shortwave and upward longwave radiation fluxes are found to scale linearly with the aerosol loading. An increase in the amount of aerosols also leads to drier conditions and a delay in the onset of convection due to changes in the AHL.

Impact of Fully Coupled Hydrology-Atmosphere Processes on Atmosphere Conditions: Investigating the Performance of the WRF-Hydro Model in the Three River Source Region on the Tibetan Plateau, China

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region.

Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

Early warm-season mesoscale convective systems dominate soil moisture-precipitation feedback for summer rainfall in central United States

Soil moisture can significantly influence precipitation through soil moisture–precipitation feedback. Previous studies of soil moisture–precipitation feedback focused on the total precipitation, confounding the distinct roles of different storm types. Mesoscale convective systems (MCSs) are the largest form of deep convective storms, contributing 30 to 70% of warm-season rainfall in the central United States. Using unique datasets of MCS and non-MCS rains and soil moisture sourced from these rains, analyses revealed the dominant role of early warm-season (April to June) MCS rainfall in summer (July) MCS and non-MCS rainfall through positive and negative soil moisture–precipitation feedback, respectively. These results underscore the importance of understanding and modeling MCSs in the significant grain growing region of the central United States.

Land–atmosphere interactions play an important role in summer rainfall in the central United States, where mesoscale convective systems (MCSs) contribute to 30 to 70% of warm-season precipitation. Previous studies of soil moisture–precipitation feedbacks focused on the total precipitation, confounding the distinct roles of rainfall from different convective storm types. Here, we investigate the soil moisture–precipitation feedbacks associated with MCS and non-MCS rainfall and their surface hydrological footprints using a unique combination of these rainfall events in observations and land surface simulations with numerical tracers to quantify soil moisture sourced from MCS and non-MCS rainfall. We find that early warm-season (April to June) MCS rainfall, which is characterized by higher intensity and larger area per storm, produces coherent mesoscale spatial heterogeneity in soil moisture that is important for initiating summer (July) afternoon rainfall dominated by non-MCS events. On the other hand, soil moisture sourced from both early warm-season MCS and non-MCS rainfall contributes to lower-level atmospheric moistening favorable for upscale growth of MCSs at night. However, soil moisture sourced from MCS rainfall contributes to July MCS rainfall with a longer lead time because with higher intensity, MCS rainfall percolates into deeper soil that has a longer memory. Therefore, early warm-season MCS rainfall dominates soil moisture–precipitation feedback. This motivates future studies to examine the contribution of early warm-season MCS rainfall and associated soil moisture anomalies to predictability of summer rainfall in the major agricultural region of the central United States and other continental regions frequented by MCSs.

Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

Implementation and Impacts of Surface and Blowing Snow Sources of Arctic Bromine Activation Within WRF-Chem 4.1.1

Elevated concentrations of atmospheric bromine are known to cause ozone depletion in the Arctic, which is most frequently observed during springtime. We implement a detailed description of bromine and chlorine chemistry within the WRF-Chem 4.1.1 model, and two different descriptions of Arctic bromine activation: (1) heterogeneous chemistry on surface snow on sea ice, triggered by ozone deposition to snow (Toyota et al., 2011 https://doi.org/10.5194/acp-11-3949-2011), and (2) heterogeneous reactions on sea salt aerosols emitted through the sublimation of lofted blowing snow (Yang et al., 2008, https://doi.org/10.1029/2008gl034536). In both mechanisms, bromine activation is sustained by heterogeneous reactions on aerosols and surface snow. Simulations for spring 2012 covering the entire Arctic reproduce frequent and widespread ozone depletion events, and comparisons with observations of ozone show that these developments significantly improve model predictions during the Arctic spring. Simulations show that ozone depletion events can be initiated by both surface snow on sea ice, or by aerosols that originate from blowing snow. On a regional scale, in spring 2012, snow on sea ice dominates halogen activation and ozone depletion at the surface. During this period, blowing snow is a major source of Arctic sea salt aerosols but only triggers a few depletion events.

Hindcasts of Sea Surface Wind around the Korean Peninsula Using the WRF Model: Added Value Evaluation and Estimation of Extreme Wind Speeds

Sea surface wind plays an essential role in the simulating and predicting ocean phenomena. However, it is difficult to obtain accurate data with uniform spatiotemporal scale. A high-resolution (10 km) sea surface wind hindcast around the Korean Peninsula (KP) is presented using the weather research and forecasting model focusing on wind speed. The hindcast data for 39 years (1979–2017) are obtained by performing a three-dimensional variational analysis data assimilation, using ERA-Interim as initial and boundary conditions. To evaluate the added value of the hindcasts, the ASCAT-L2 satellite-based gridded data (DASCAT) is employed and regarded as “True” during 2008–2017. Hindcast and DASCAT data are verified using buoy observations from 1997–2017. The added value of the hindcast compared to ERA-Interim is evaluated using a modified Brier skill score method and analyzed for seasonality and wind intensity. Hindcast data primarily adds value to the coastal areas of the KP, particularly over the Yellow Sea in the summer, the East Sea in the winter, and the Korean Strait in all seasons. In case of strong winds (10–25 m·s−1), the hindcast performed better in the East Sea area. The estimation of extreme wind speeds is performed based on the added value and 50-year and 100-year return periods are estimated using a Weibull distribution. The results of this study can provide a reference dataset for climate perspective storm surge and wave simulation studies.

Impacts of Non-Local versus Local Moisture Sources on a Heavy (and Deadly) Rain Event in Israel

Motivated by poor forecasting of a deadly convective event within the Levant, the factor separation technique was used to investigate the impact of non-local versus local moisture sources on simulated precipitation and lightning rates in central and southern Israel on 25 and 26 April 2018. Both days saw unusually heavy rains, and it was hypothesized that antecedent precipitation on 25 April contributed to the development of deadly flooding late morning on the 26th, as well as strong lightning and heavy rains later the same day. Antecedent precipitation led to an increase in the precipitable water content and an overall increase in instability as measured by the Convective Available Potential Energy (CAPE). The deadly flood occurred in the area of the Tzafit river gorge (hereafter, Tzafit river), about 25 km southeast of the city of Dimona, a semi-arid region in the northeastern Negev desert. The heavy rains and strong lightning occurred throughout the Levant with local peaks in the vicinity of Jerusalem. Factor separation conducted in model simulations showed that local ground moisture sources had a large impact on the CAPE and subsequent precipitation and lightning rates in the area of Jerusalem, while non-local moisture sources enabled weak convection to occur over broad areas, with particularly strong convection in the area of the Tzafit river. The coupled impact of both moisture sources also led to localized enhanced areas of convective activity. The results suggest that forecast models for the Levant should endeavor to incorporate an accurate depiction of soil moisture to predict convective rain, especially during the typically drier spring-time season.

Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

Soil moisture (SM) plays a significant role in determining the probability of flooding in a given area. Currently, SM is most commonly modeled using physically-based numerical hydrologic models. Modeling the natural processes that take place in the soil is difficult and requires assumptions. Besides, hydrologic model runtime is highly impacted by the extent and resolution of the study domain. In this study, we propose a data-driven modeling approach using Deep Learning (DL) models. There are different types of DL algorithms that serve different purposes. For example, the Convolutional Neural Network (CNN) algorithm is well suited for capturing and learning spatial patterns, while the Long Short-Term Memory (LSTM) algorithm is designed to utilize time-series information and to learn from past observations. A DL algorithm that combines the capabilities of CNN and LSTM called ConvLSTM was recently developed. In this study, we investigate the applicability of the ConvLSTM algorithm in predicting SM in a study area located in south Louisiana in the United States. This study reveals that ConvLSTM significantly outperformed CNN in predicting SM. We tested the performance of ConvLSTM based models by using a combination of different sets of predictors and different LSTM sequence lengths. The study results show that ConvLSTM models can predict SM with a mean areal Root Mean Squared Error (RMSE) of 2.5% and mean areal correlation coefficients of 0.9 for our study area. ConvLSTM models can also provide predictions between discrete SM observations, making them potentially useful for applications such as filling observational gaps between satellite overpasses.

Characteristics of Historical Precipitation in High Mountain Asia Based on a 15-Year High Resolution Dynamical Downscaling

The mountains of High Mountain Asia serve as an important source of water for roughly one billion people living downstream. This research uses 15 years of dynamically downscaled precipitation produced by the Weather Research and Forecasting (WRF) model to delineate contrasts in precipitation characteristics and events between regions dominated by the Indian Summer Monsoon (ISM) versus westerly disturbances during the cool season (December to March). Cluster analysis reveals a more complex spatial pattern than indicated by some previous studies and illustrates the increasing importance of westerly disturbances at higher elevations. Although prior research suggests that a small number of westerly disturbances dominate precipitation in the western Himalaya and Karakoram, the WRF-downscaled precipitation is less dominated by infrequent large events. Integrated vapor transport (IVT) and precipitation are tightly coupled in both regions during the cool season, with precipitation maximizing for IVT from the south-southwest over the Karakoram and southeast-southwest over the western Himalaya. During the ISM, Karakoram precipitation is not strongly related to IVT direction, whereas over the western Himalaya, primary and secondary precipitation maxima occur for flow from the west-southwest and northwest, respectively. These differences in the drivers and timing of precipitation have implications for hydrology, glacier mass balance, snow accumulation, and their sensitivity to climate variability and change.

Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

Contribution of South Asian biomass burning to black carbon over the Tibetan Plateau and its climatic impact

This study used a regional climate-chemistry transport model, WRF-Chem v3.9.1, to evaluate the impact of South Asian biomass burning on black carbon (BC) over the Tibetan Plateau (TP) and its climatic effects for an entire year. The simulation, which was validated by comparing surface meteorological parameters and BC concentration against in-situ observations over South Asia and the TP, provided a perspective on the seasonal variations and regional spatial patterns of BC concentration. Using a sensitivity simulation where BC emissions from biomass burning were removed from South Asia, this study found South Asian biomass burning emissions contributed up to 90% of BC mass over the TP during the pre-monsoon season, specifically emissions from western India for the simulated year. The emissions led to reduced surface radiative forcing, causing the temperature to decrease accordingly. However, column cloud water was increased. This study suggested that the biomass burning emissions from South Asia have significant impact on atmospheric BC over the TP, especially during the pre-monsoon season. Therefore, reducing biomass burning emissions from South Asia is potentially important for alleviating the effects of BC on climatic and environmental conditions over the TP and surrounding regions.

Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression

The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions.

Quantifying Uncertainty in the Modelling Process; Future Extreme Flood Event Projections Across the UK

With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK by the 2080s (2069–2098). Capturing uncertainty in the hydroclimatic modelling chain, flow projections were extracted from the EDgE (End-to-end Demonstrator for improved decision-making in the water sector in Europe) multi-model ensemble: five Coupled Model Intercomparison Project (CMIP5) General Circulation Models and four hydrological models forced under emissions scenarios Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 (5 × 4 × 2 chains). Uncertainty in extreme value parameterisation was captured through consideration of two methods: generalised extreme value (GEV) and generalised logistic (GL). The method was applied across 192 catchments and aggregated to eight regions. The results suggest that, by the 2080s, many regions could experience large increases in extreme runoff, with a maximum mean change signal of +34% exhibited in East Scotland (1:2-year RP). Combined with increasing urbanisation, these estimates paint a concerning picture for the future UK flood landscape. Model chain uncertainty was found to increase by the 2080s, though extreme value (EV) parameter uncertainty becomes dominant at the 1:30-year RP (exceeding 60% in some regions), highlighting the importance of capturing both the associated EV parameter and ensemble uncertainty.

Large-eddy simulation of foehn-cold pool interactions in the Inn Valley during PIANO IOP 2

Processes of cold-air pool (CAP) erosion in an Alpine valley during south foehn are investigated based on a real-case large-eddy simulation (LES). The event occurred during the second Intensive Observation Period (IOP 2) of the PIANO field experiment in the Inn Valley, Austria, near the city of Innsbruck. The goal is to clarify the role of advective versus turbulent heating, the latter often being misrepresented in mesoscale models. It was found that the LES of the first day of IOP 2 outperforms a mesoscale simulation, is not yet perfect, but is able to reproduce the CAP evolution and structure observed on the second day of IOP 2. The CAP exhibits strong heterogeneity in the along-valley direction. It is weaker in the east than in the west of the city with a local depression above the city. This heterogeneity results from different relative contributions and magnitudes of turbulent and advective heating/cooling, which mostly act against each other. Turbulent heating is important for faster CAP erosion in the east and advective cooling is important for CAP maintenance to the west of Innsbruck. The spatial heterogeneity in turbulent erosion is linked to splitting of the foehn into two branches at the mountain range north of the city, with a stronger eastward deflected branch. Intensification of the western branch at a later stage leads to complete CAP erosion also to the west of Innsbruck. Above the city centre, turbulent heating is strongest, and so is advective cooling by enhanced pre-foehn westerlies. These local winds are the result of CAP heterogeneity and gravity-wave asymmetry. This study emphasizes the importance of shear-flow instability for CAP erosion. It also highlights the large magnitudes of advective and turbulent heating compared to their net effect, which is even more pronounced for individual spatial components.

Estimation of the Qinghai-Tibetan Plateau runoff and its contribution to large Asian rivers

The Qinghai-Tibetan Plateau (QTP), named the Asian Water Towers, feeds more than 2.5 billion people in downstream regions. It is still unknown how much water outflows from this region owing to lack of observations. The main objective of this study is to clarify availability of water flowed out of this region and its contribution to large Asian rivers. The Global Land Data Assimilation System (GLDAS) products are evaluated with the help of observations of the QTP. In addition, a velocity-based routing method is embedded into the GLDAS model to route runoff products to the basin outlet in this study. The results show that the simulated dry season runoff in the GLDAS model is generally lower than the observed value, which is mainly because most hydrological models only consider the potential evapotranspiration (ET) when simulating ET, while ignoring the water constraint factor. Noah10_v2.0 has the highest precision at the QTP. For the monthly precipitation and runoff series, the relative error is within 5%, the correlation coefficient is greater than 0.90, and the Nash-Sutcliffe efficiencies are 0.95 and 0.76, respectively. Glacier melt runoff plays an important role in the QTP runoff, with a proportion of approximately 22%. It is relatively high in the Tarim River basin (83%), Syr Darya River and Amu Darya River basins (69%), and Indus River basin (60%). The contribution ratio also reaches 23% in the Yarlung Zangbo-Brahmaputra River and Ganges River basins, whereas it is the lowest in the Irrawaddy River basin (2%). According to the Noah10_v2.0 simulations, the mean annual runoff provided by the QTP exceeds 620 billion cubic metres, of which approximately 440 billion cubic metres flow out of the QTP and supply downstream regions of international rivers. The contribution ratio of the QTP runoff to the total runoff of its affected basins is approximately 16%.

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

A high-resolution Asia-Pacific regional coupled prediction system with dynamically downscaling coupled data assimilation

A regional coupled prediction system for the Asia-Pacific (AP-RCP) (38°E-180°, 20°S-60°N) area has been established. The AP-RCP system consists of WRF-ROMS (Weather Research and Forecast, and Regional Ocean Model System) coupled models combined with local observational information through dynamically downscaling coupled data assimilation (CDA). The system generates 18-day forecasts for the atmosphere and ocean environment on a daily quasi-operational schedule at Pilot National Laboratory for Marine Science and Technology (Qingdao) (QNLM), consisting of 2 different-resolution coupled models: 27 km WRF coupled with 9 km ROMS, 9 km WRF coupled with 3 km ROMS, while a version of 3 km WRF coupled with 3 km ROMS is in a test mode. This study is a first step to evaluate the impact of high-resolution coupled model with dynamically downscaling CDA on the extended-range predictions, focusing on forecasts of typhoon onset, improved precipitation and typhoon intensity forecasts as well as simulation of the Kuroshio current variability associated with mesoscale oceanic activities. The results show that for realizing the extended-range predictability of atmospheric and oceanic environment characterized by statistics of mesoscale activities, a fine resolution coupled model resolving local mesoscale phenomena with balanced and coherent coupled initialization is a necessary first step. The next challenges include improving the planetary boundary physics and the representation of air-sea and air-land interactions to enable the model to resolve kilometer or sub-kilometer processes.

The Met Office Operational Soil Moisture Analysis System

In this study, the current Met Office operational land surface data assimilation system used to produce soil moisture analyses is presented. The main aim of including Land Surface Data Assimilation (LSDA) in both the global and regional systems is to improve forecasts of surface air temperature and humidity. Results from trials assimilating pseudo-observations of 1.5 m air temperature and specific humidity and satellite-derived soil wetness (ASCAT) observations are analysed. The pre-processing of all the observations is described, including the definition and construction of the pseudo-observations. The benefits of using both observations together to produce improved forecasts of surface air temperature and humidity are outlined both in the winter and summer seasons. The benefits of using active LSDA are quantified by the root mean squared error, which is computed using both surface observations and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses as truth. For the global model trials, results are presented separately for the Northern (NH) and Southern (SH) hemispheres. When compared against ground-truth, LSDA in winter NH appears neutral, but in the SH it is the assimilation of ASCAT that contributes to approximately a 2% improvement in temperatures at lead times beyond 48 h. In NH summer, the ASCAT soil wetness observations degrade the forecasts against observations by about 1%, but including the screen level pseudo-observations provides a compensating benefit. In contrast, in the SH, the positive effect comes from including the ASCAT soil wetness observations, and when both observations types are assimilated there is a compensating effect. Finally, we demonstrate substantial improvements to hydrological prediction when using land surface data assimilation in the regional model. Using the Nash-Sutcliffe Efficiency (NSE) metric as an aggregated measure of river flow simulation skill relative to observations, we find that NSE was improved at 106 of 143 UK river gauge locations considered after LSDA was introduced. The number of gauge comparisons where NSE exceeded 0.5 is also increased from 17 to 28 with LSDA.

Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Impacts of tiled land cover characterization in the Model for Prediction Across Scales-Atmosphere (MPAS-A)

Parameterization of subgrid-scale variability of land cover characterization (LCC) is an active area of research, and can improve model performance compared to the dominant (i.e., most abundant tile) approach. The "Noah" land surface model implementation in the global Model for Predictions Across Scales-Atmosphere (MPAS-A), however, only uses the dominant LCC approach that leads to oversimplification in regions of highly heterogeneous LCC (e.g., urban/suburban settings). Thus, in this work we implement a subgrid tiled approach as an option in MPAS-A, version 6.0, and assess the impacts of tiled LCC on meteorological predictions for two gradually refining meshes (92-25 and 46-12 km) focused on the conterminous U.S for January and July 2016. Compared to the dominant approach, results show that using the tiled LCC leads to pronounced global changes in 2-m temperature (July global average change ~ -0.4 K), 2-m moisture, and 10-m wind speed for the 92-25 km mesh. The tiled LCC reduces mean biases in 2-m temperature (July U.S. average bias reduction ~ factor of 4) and specific humidity in the central and western U.S. for the 92-25 km mesh, improves the agreement of vertical profiles (e.g., temperature, humidity, and wind speed) with observed radiosondes; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic temperature biases found in numerical weather prediction models, particularly those that employ a dominant LCC approach.

Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

Hydropower dam operation strongly controls Lake Victoria's freshwater storage variability

River impoundments strongly modify the global water cycle and terrestrial water storage (TWS) variability. Given the susceptibility of global water cycle to climate change and anthropogenic influence, the synthesis of science with sustainable reservoir operation strategy is required as part of an integrated approach to water management. Here, we take advantage of new approaches combining state-of-the-art computational models and a novel satellite-based reservoir operation scheme to spatially and temporally decompose Lake Victoria's TWS, which has been dam-controlled since 1954. A ground-based lake bathymetry is merged with a global satellite-based topography to accurately represent absolute water storage, and radar altimetry data is integrated in the hydrodynamic model as a proxy of reservoir operation practices. Compared against an idealized naturalized system (i.e., no anthropogenic impacts) over 2003-2019, reservoir operation shows a significant impact on water elevation, extent, storage and outflow, controlling lake dynamics and TWS. For example, compared to Gravity Recovery and Climate Experiment (GRACE) data, reservoir operation improved correlation and root mean square error of basin-wide TWS simulations by 80% and 54%, respectively. Results also show that lake water storage is 20% higher under dam control and basin-wide surface water storage contributes 64% of TWS variability. As opposed to existing reservoir operation schemes for large-scale models, the proposed model simulates spatially distributed surface water processes and does not require human water demand estimates. Our proposed approaches and findings contribute to the understanding of Lake Victoria's water dynamics and can be further applied to quantify anthropogenic impacts on the global water cycle.

Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California

Precipitation extremes will likely intensify under climate change. However, much uncertainty surrounds intensification of high-magnitude events that are often inadequately resolved by global climate models. In this analysis, we develop a framework involving targeted dynamical downscaling of historical and future extreme precipitation events produced by a large ensemble of a global climate model. This framework is applied to extreme "atmospheric river" storms in California. We find a substantial (10 to 40%) increase in total accumulated precipitation, with the largest relative increases in valleys and mountain lee-side areas. We also report even higher and more spatially uniform increases in hourly maximum precipitation intensity, which exceed Clausius-Clapeyron expectations. Up to 85% of this increase arises from thermodynamically driven increases in water vapor, with a smaller contribution by increased zonal wind strength. These findings imply substantial challenges for water and flood management in California, given future increases in intense atmospheric river-induced precipitation extremes.

A Process-Based, Fully Distributed Soil Erosion and Sediment Transport Model for WRF-Hydro

A soil erosion and sediment transport model (WRF-Hydro-Sed) is introduced to WRF-Hydro. As a process-based, fully distributed soil erosion model, WRF-Hydro-Sed accounts for both overland and channel processes. Model performance is evaluated using observed rain gauge, streamflow, and sediment concentration data during rainfall events in the Goodwin Creek Experimental Watershed in Mississippi, USA. Both streamflow and sediment yield can be calibrated and validated successfully at a watershed scale during rainfall events. Further discussion reveals the model’s uncertainty and the applicability of calibrated hydro- and sediment parameters to different events. While an intensive calibration over multiple events can improve the model’s performance to a certain degree compared with single event-based calibration, it might not be an optimal strategy to carry out considering the tremendous computational resources needed.

Analog Site Experiment in the High Andes-Atacama Region: Surface Energy Budget Components on Ojos del Salado from Field Measurements and WRF Simulations

Remote sensing data are abundant, whereas surface in situ verification of atmospheric conditions is rare on Mars. Earth-based analogs could help gain an understanding of soil and atmospheric processes on Mars and refine existing models. In this work, we evaluate the applicability of the Weather Research and Forecasting (WRF) model against measurements from the Mars analog High Andes-Atacama Desert. Validation focuses on the surface conditions and on the surface energy budget. Measurements show that the average daily net radiation, global radiation, and latent heat flux amount to 131, 273, and about 10 W/m², respectively, indicating extremely dry atmospheric conditions. Dynamically, the effect of topography is also well simulated. One of the main modeling problems is the inaccurate initial soil and surface conditions in the area. Correction of soil moisture based on in situ and satellite soil moisture measurements, as well as the removal of snow coverage, reduced the surface skin temperature root mean square error from 9.8°C to 4.3°C. The model, however, has shortcomings when soil condition modeling is considered. Sensible heat flux estimations are on par with the measurements (daily maxima around 500 W/m²), but surface soil heat flux is greatly overestimated (by 150-500 W/m²). Soil temperature and soil moisture diurnal variations are inconsistent with the measurements, partially due to the lack of water vapor representation in soil calculations.

Changes in South American hydroclimate under projected Amazonian deforestation

Continued deforestation in the Amazon forest can alter the subsurface/surface and atmospheric branches of the hydrologic cycle. The sign and magnitude of these changes depend on the complex interactions between the water, energy, and momentum budgets. To understand these changes, we use the weather research and forecasting (WRF) model with improved representation of groundwater dynamics and the added feature of Amazonian moisture tracers. The control simulation uses moderate resolution imaging spectroradiometer (MODIS) based observations of land use, and the deforestation simulations use a "business-as-usual" scenario projected for 2040-2050. Our results show that deforestation leads to changes that are seasonally very different. During the dry season, deforestation results in increased albedo and less available net radiation. This change, together with reduced leaf area, results in decreased evapotranspiration (ET), less atmospheric moisture of Amazonian origin, and an increase in temperature. However, we find no changes in precipitation over the basin. Conversely, during the wet season, surface winds increase significantly due to decreased surface roughness. Vapor transport increases throughout the deforested region and leads to an increase in easterly moisture export, and significant decrease in precipitation within the deforested regions of Eastern Amazon. Contrary to expectations, the moisture tracers in WRF show no evidence that precipitation decreases are due to recycling or changes in stability.

Determination of earthquake epicentres based upon invariant quantities of GRACE strain gravity tensors

Investigation of regional and temporal variations in Earth’s gravitational field that are detected by the Gravity Recovery and Climate Experiment (GRACE) twin-satellites may be useful in earthquake epicentre determinations. This study focuses on monthly spherical harmonic coefficients that were extracted from GRACE observations, which were corrected for hydrological effects to determine earthquake epicentres. For the first time, we use the concept of deformation of Earth’s gravity field to estimate invariant components of strain tensors. Four different earthquakes (Iran, China, Turkey, Nepal) were analysed that occurred between 2003 and 2015 and under different hydrological regimes. Wavelet analysis was explored as a means of refining and reconstructing tectonic signals forming the disturbance gravitational potential tensor in the GRACE gravity field models. Dilatation and maximum shear were extracted from these tensors and used to map earthquake epicentre locations. Both components reached their maxima during months of the earthquakes (respectively, 11.78 and 4.93, Bam earthquake; 61.36 and 169.10, Sichuan-Gansu border earthquake; 2415.80 and 627.93, Elazig earthquake; 98.71 and 157.37, Banepa earthquake). For the aforementioned earthquakes, we estimated their respective epicentres in the ranges: φ = 29°–29.5° λ = 58.5°–59°; φ = 32.5°–33° λ = 105.5°–106°; φ = 38.5°–39° λ = 39.5°–40°; and φ = 27.5°–28° λ = 85°–85.5°. Overall, these results agree well with values from other sources. The advance that is provided by our method compared to other research is the ability of determining earthquake epicentres with magnitudes ≤7.5 based upon GRACE observations. However, the approach is of limited use for very deep earthquakes.

An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area

The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.

Evaluation of Noah-MP Land-Model Uncertainties over Sparsely Vegetated Sites on the Tibet Plateau

The water budget and energy exchange over the Tibetan Plateau (TP) region play an important role on the Asian monsoon. However, it is not well presented in the current land surface models (LSMs). In this study, uncertainties in the Noah with multiparameterization (Noah-MP) LSM are assessed through physics ensemble simulations in three sparsely vegetated sites located in the central TP. The impact of soil organic matter on energy flux and water cycles, along with the influence of uncertainties in precipitation are explored using observations at those sites during the third Tibetan Plateau Experiment from 1August2014 to31July2015. The greatest uncertainties are in the subprocesses of the canopy resistance, soil moisture limiting factors for evaporation, runoff (RNF) and ground water, and surface-layer parameterization. These uncertain subprocesses do not change across the different precipitation datasets. More precipitation can increase the annual total net radiation (Rn), latent heat flux (LH) and RNF, but decrease sensible heat flux (SH). Soil organic matter enlarges the annual total LH by ~26% but lessens the annual total Rn, SH, and RNF by ~7%, 7%, and 39%, respectively. Its effect on the LH and RNF at the Nagqu site, which has a sand soil texture type, is greater than that at the other two sites with sandy loam. This study highlights the importance of precipitation uncertainties and the effect of soil organic matter on the Noah-MP land-model simulations. It provides a guidance to improve the Noah-MP LSM further and hence the land-atmosphere interactions simulated by weather and climate models over the TP region.

Applying Infrared Thermography to Soil Surface Temperature Monitoring: Case Study of a High-Resolution 48 h Survey in a Vineyard (Anadia, Portugal)

The soil surface albedo decreases with an increasing biochar application rate as a power decay function, but the net impact of biochar application on soil temperature dynamics remains to be clarified. The objective of this study was to assess the potential of infrared thermography (IRT) sensing by monitoring soil surface temperature (SST) with a high spatiotemporal and thermal resolution in a scalable agricultural application. We monitored soil surface temperature (SST) variations over a 48 h period for three treatments in a vineyard: bare soil (plot S), 100% biochar cover (plot B), and biochar-amended topsoil (plot SB). The SST of all plots was monitored at 30 min intervals with a tripod-mounted IR thermal camera. The soil temperature at 10 cm depth in the S and SB plots was monitored continuously with a 5 min resolution probe. Plot B had greater daily SST variations, reached a higher daily temperature peak relative to the other plots, and showed a faster rate of T increase during the day. However, on both days, the SST of plot B dipped below that of the control treatment (plot S) and biochar-amended soil (plot SB) from about 18:00 onward and throughout the night. The diurnal patterns/variations in the IRT-measured SSTs were closely related to those in the soil temperature at a 10 cm depth, confirming that biochar-amended soils showed lower thermal inertia than the unamended soil. The experiment provided interesting insights into SST variations at a local scale. The case study may be further developed using fully automated SST monitoring protocols at a larger scale for a range of environmental and agricultural applications.

An Evaluation Study of the Fully Coupled WRF/WRF-Hydro Modeling System for Simulation of Storm Events with Different Rainfall Evenness in Space and Time

With the aim of improving the understanding of water exchanges in medium-scale catchments of northern China, the spatiotemporal characteristics of rainfall and several key water cycle elements e.g., soil moisture, evapotranspiration and generated runoff, were investigated using a fully coupled atmospheric-hydrologic modeling system by integrating the Weather Research and Forecasting model (WRF) and its terrestrial hydrologic component WRF-Hydro (referred to as the fully coupled WRF/WRF-Hydro). The stand-alone WRF model (referred to as WRF-only) is also used as a comparison with the fully coupled system, which was expected to produce more realistic simulations, especially rainfall, by allowing the redistribution of surface and subsurface water across the land surface. Six storm events were sorted by different spatial and temporal distribution types, and categorical and continuous indices were used to distinguish the applicability in space and time between WRF-only and the fully coupled WRF/WRF-Hydro. The temporal indices showed that the coupled WRF-Hydro could improve the time homogeneous precipitation, but for the time inhomogeneous precipitation, it might produce a larger false alarm than WRF-only, especially for the flash storm that occurred in July, 2012. The spatial indices showed a lower mean bias error in the coupled system, and presented an enhanced simulation of both space homogeneous and inhomogeneous storm events than WRF-only. In comparison with WRF-only, the fully coupled WRF/WRF-Hydro had a closer to the observations particularly in and around the storm centers. The redistributions fluctuation of spatial precipitation in the fully coupled system was highly correlated with soil moisture, and a low initial soil moisture could lead to a large spatial fluctuated range. Generally, the fully coupled system produced slightly less runoff than WRF-only, but more frequent infiltration and larger soil moisture. While terrestrial hydrologic elements differed with relatively small amounts in the average of the two catchments between WRF-only and the fully coupled WRF/WRF-Hydro, the spatial distribution of elements in the water cycle before and after coupling with WRF-Hydro was not consistent. The soil moisture, runoff and precipitation in the fully coupled system had a similar spatial trend, but evapotranspiration did not always display the same.

Quantifying the Impact of Excess Moisture From Transpiration From Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top-Down Constraint From Moderate Resolution Imaging Spectroradiometer Water Vapor Retrieval

The primary focus of this study is to understand the contribution from excess moisture from crop transpiration to the severity of a heat wave episode that hit the Midwestern U.S. from 16 to 20 July 2011. To elucidate this, we first provide an optimal estimate of the transpiration water vapor flux using satellite total column water vapor retrievals whose accuracy and precision are characterized using independent observations. The posterior transpiration flux is estimated using a local ensemble transform Kalman filter that employs a mesoscale weather model as the forward model. The new estimation suggests that the prior values of transpiration flux from crops are biased high by 15%. We further use the constrained flux to examine the sensitivity of meteorology to the contributions from crops. Over the agricultural areas during daytime, elevated moisture (up to 40%) from crops not only increases humidity (thus the heat index) but also provides a positive radiative forcing by increasing downward longwave radiation (13 ± 4 W m-2) that results in even higher surface air temperature (+0.4 °C). Consequently, we find that the elevated moisture generally provides positive feedback to aggravate the heat wave, with daytime enhancements of heat index by as large as 3.3 ± 0.8 °C. Due to a strong diurnal cycle in the transpiration, the feedback tends to be stronger in the afternoon (up to 5 °C) and weaker at night. Results offer a potential basis for designing mitigation strategies for the effect of transpiration from agriculture in the future, in addition to improving the estimation of canopy transpiration.

A New Coupled Modeling Approach to Simulate Terrestrial Water Storage in Southern California

The study introduces a new atmosphere-land-aquifer coupled model and evaluates terrestrial water storage (TWS) simulations for Southern California between 2007 and 2016. It also examines the relationship between precipitation, groundwater, and soil moisture anomalies for the two primary aquifer systems in the study area, namely the Coastal Basin and the Basin and Range aquifers. Two model designs are introduced, a partially-coupled model forced by reanalysis atmospheric data, and a fully-coupled model, in which the atmospheric conditions were simulated. Both models simulate the temporal variability of TWS anomaly in the study area well (R2 ≥ 0.87, P < 0.01). In general, the partially-coupled model outperformed the fully-coupled model as the latter overestimated precipitation, which compromised soil and aquifer recharge and discharge. Simulations also showed that the drought experienced in the area between 2012 and 2016 caused a decline in TWS, evapotranspiration, and runoff of approximately 24%, 65%, and 11%, and 20%, 72% and 8% over the two aquifer systems, respectively. Results indicate that the models first introduced in this study can be a useful tool to further our understanding of terrestrial water storage variability at regional scales.

The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

A Vision for Hydrological Prediction

IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society’s ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

Impact of Surface Albedo Assimilation on Snow Estimation

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.

Meteorological modeling relevant to mesoscale and regional air quality applications: a review

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed.Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.