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The assessment of future extremes is hindered by the lack of long time series. Weather generators can alleviate this problem, but easily become complex. In this study, a weather generator combining Bartlett-Lewis models and vine copulas is presented. This combination allows for the generation of time series with statistics similar to those of the input. This model chain has never been assessed on the basis of future simulations. However, it could have value for extending climate simulations. The model chain was applied to historical observations and one climate model time series. The statistical moments and the correlation on the basis of the future simulations were comparable to those on basis of the historical observations. The results for the extremes were ambiguous, but still provided valuable information. The adequate performance for the statistical moments and the correlation indicates that the weather generator might be useful for the characterization of future extremes.

The population in the Lake Victoria Basin (LVB) is affected by extreme weather both on land, where flooding regularly occurs and on the lake, where nightly storms often catch fishermen by surprise. The CORDEX Flagship Pilot Study ELVIC investigates how extreme weather events will evolve in this region of the world and to provide improved information for the climate impact community. Here we evaluate the performance of five regional climate models at convection-permitting resolution and present projections for the future using COSMO-CLM in a pseudo global warming approach. Most substantial systematic improvements were found in metrics related to deep convection in convection-permitting models compared to their coarser scale counterparts. For the future, extreme precipitation and wind gusts are expected to increase over the lake due to an thermodynamically induced increase in water vapor whereas the impacts of weaker meso-scale circulation over the lake and stronger thunderstorm dynamics compensate each other. More compound events are expected for the future during which both rainfall and wind gusts are intense. Interestingly, the mean precipitation is strongly affected by uncertainties in large-scale dynamics whereas thermodynamics dominate extreme precipitation. This might imply that uncertainties in future projected extremes are smaller than those in mean precipitation.

Effective representation of soil heterogeneity in land surface models is crucial for accurate weather and climate simulations. The NOAH-MP land surface model uses dominant soil texture from State Soil Geographic (STATSGO)/Food and Agriculture Organization (FAO) datasets, considerably introducing uncertainty in the simulation of soil hydrothermal changes and terrestrial water and energy fluxes, at a fine scale. This study investigates the likely added value of incorporating an alternative high resolution soil grid data at different depths, for a better representation of soil hydrothermal dynamics in NOAH-MP v4.3. The model is set up at 1 km grid space over all Ireland domain and soil layer thicknesses of 0.07, 0.21, 0.72 and 1.55 m, with a cummulative soil depth of 2.55 m. The thicknesses are selected to match the layers of initial soil input fields. Model experiments are carried out based on two soil data options namely, (1) the STATSGO/FAO dominant soil texture and (2) the 250 m global soil grid textural compositions from the International Soil Reference and Information Centre (ISRIC), in combination with PedoTransfer Functions (PTFs). The current model integration is applied within the high resolution land data assimilation (HRLDAS) framework to simulate soil temperature and soil liquid water, and evaluated for wet and dry periods using observations from the newly established Terrain-AI data platforms (terrainai.com). Ultimately, the study highlights the importance of using realistic dynamic soil information, which could provide insightful scientific contributions to better monitor surface climate and the influences on land use and land management under climate change.

In recent years it has become increasingly evident that the solutions to climate changes, both mitigationand adaptation, must pay greater attention to the places where most people on the planet live. Moreover,the demand for weather services at urban scales is increasing in line with the ability to modelatmospheric processes at these finer scales; these advances could herald more resilient cities with theevidence to support planning and design at appropriate time scales. In this context, the lack ofinformation on urban landscapes and the dearth of urban observations represent a major obstacle toprogress.This work explores the possibility to create the scientific infrastructure needed to incorporate climateknowledge into urban decision-making quickly. By combining globally available but locally appropriatedatasets (global map of LCZs, ERA5, Copernicus global land cover layers) and cloud computing (GoogleEarth Engine), we created a seamless, holistic and user-friendly SUPY-based urban modelling frameworkthat can be applied anywhere at any time. Results are evaluated using the Urban Plumber flux towerobservations and a large unique database crowdsourced weather-station observations. The widerpurpose of the project is to develop a pathway to the creation of a universal ‘toolbox’ for baseliningclimate-related data for use in any city.

The CORDEX Flagship Pilot Study ELVIC (climate Extremes in the Lake VICtoria basin) was recently established to investigate how extreme weather events will evolve in this region of the world and to provide improved information for the climate impact community. Here we assess the added value of the convection-permitting scale simulations on the representation of moist convective systems over and around Lake Victoria. With this aim, 10-year present-day model simulations were carried out with five regional climate models at both PARameterized (PAR) scales (12-25km) and Convection-Permitting (CP) scales (2.5-4.5km), with COSMO-CLM, RegCM, AROME, WRF and UKMO. Most substantial systematic improvements were found in metrics related to deep convection. For example, the timing of the daily maximum in precipitation is systematically delayed in CP compared to PAR models, thereby improving the agreement with observations. The large overestimation in the total number of rainy events is alleviated in the CP models. Systematic improvements were found in the diurnal cycle in Top-Of-Atmosphere (TOA) radiation and in some metrics for precipitation intensity. No unanimous improvement nor deterioration was found in the representation of the spatial distribution of total rainfall and the seasonal cycle when going to the CP scale. Furthermore, some substantial biases in TOA upward radiative fluxes remain. Generally our analysis indicates that the representation of the convective systems is strongly improved in CP compared to PAR models, giving confidence that the models are valuable tools for studying how extreme precipitation events may evolve in the future in the Lake Victoria basin and its surroundings.

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale. These data can support a range of environmental services, since cities are places of intense resource consumption and waste generation and of concentrated infrastructure and human settlement exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data are also required to explore future urbanization pathways and urban design strategies in order to lock in long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability and mitigation role. To serve this purpose, we present a 100 m-resolution global map of local climate zones (LCZs), a universal urban typology that can distinguish urban areas on a holistic basis, accounting for the typical combination of micro-scale land covers and associated physical properties. The global LCZ map, composed of 10 built and 7 natural land cover types, is generated by feeding an unprecedented number of labelled training areas and earth observation images into lightweight random forest models. Its quality is assessed using a bootstrap cross-validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. As each LCZ type is associated with generic numerical descriptions of key urban canopy parameters that regulate atmospheric responses to urbanization, the availability of this globally consistent and climate-relevant urban description is an important prerequisite for supporting model development and creating evidence-based climate-sensitive urban planning policies. This dataset can be downloaded from https://doi.org/10.5281/zenodo.6364594 (Demuzere et al., 2022a).

Managing greenhouse gas (GHG) emissions at a landscape scale requires a framework that can account for local and advected sources. TerrainAI is a funded project that is designed to provide the scientific infrastructure needed to make policy decisions at a national scale to mitigate net GHG emissions from the Irish landscape. The project has established new observation platforms over a variety of natural (e.g., grasslands, peatlands, and forests) and urbanised landscapes and uses satellite and drone technologies to capture detailed spatial and temporal data on the physical properties of land-cover. A core part of the work uses the Weather Research Forecasting (WRF) model coupled with chemistry (WRF-Chem) to simulate the net emissions and transport of GH gases. WRF-Chem has been set up for double nested domain with the entire country of Ireland as inner domain at 1 km resolution; a third nest will be established for the Dublin metropolitan area at a resolution of 250 m. The current model incorporates better resolved model input fields (e.g., land use, meteorology, and emissions) and is evaluated for two 15-day periods in 2018, representing a dry and wet periods dominated by anticyclonic and cyclonic activity respectively. The Dublin nest will employ detailed descriptions of the urban canopy layer and examine their influence on the net emission and dispersion of GHGs. Currently, a WRF-Greenhouse option is used to simulate CO2, CO and CH4 over the study region using the EDGAR emission inventory, which can capture hot-spots and general patterns. More precise emission and sequestration data based on socio-economic profiles will be generated to match the detailed land-cover database used in WRF. These data will permit us to evaluate the efficacy of mitigation policies.

With ongoing climate change and rapid urbanization, exposure to severe heat is expected to accelerate in tropical East African cities. Yet not all parts of the city are equally vulnerable. The present-day intra-urban heat stress variation in Kampala, the capital city of Uganda, is quantified by deriving the daily mean, minimum and maximum Humidex Index from a network of low-cost temperature and humidity sensors operational in 2018-2019. Heat is shown to be heterogeneously distributed over the city, with a daily maximum intra-urban Humidex Index deviation of 6.4°C averaged over the observational period, but reaching 14.5°C on the most extreme day.Also extreme heat is heterogeneously distributed over the city, putting local populations at risk of great discomfort or health danger. One station in a dense settlement reports a daily maximum Humidex Index above 40°C in 68% of the observation days, a level which was never reached at the nearby campus of the Makerere University, and only a few times at the city outskirts. About 75% of this intra-urban heat stress variability is explained by the Normalized Difference Vegetation Index (NDVI), though strong collinearity is found with other variables like impervious surface fraction and population density.Overall, our results highlight the importance of (i) including both temperature and humidity in heat stress studies, (ii) urban greening in city planning, and (iii) large intra-urban heat stress variations in heat action planning in tropical humid cities.

Future urban climates are likely to warm substantively in coming decades as a result of climate change, and greater heat wave severity is anticipated. Moreover, the urban heat island contributes additional heat, especially during evening and night. Infrastructure-based heat reduction strategies can reduce canopy air temperatures during daytime, and to some extent at night. These strategies also have several additional effects beyond air temperature reduction. Here, we apply an early coupling of the WRF mesoscale model with the BEP-Tree urban canopy model to simulate extreme heat events representative of both contemporary and projected future climates for the metropolitan region of Toronto, Canada. Urban and non-urban land cover is derived using the state-of-the-art LCZ Generator methodology. Subsequently, the effectiveness of heat mitigation strategies, including highly reflective surfaces and vegetation, is quantified for the future scenario in the context of the increase in heat wave intensity. Specifically, the neighbourhood- and city-scale climate impacts of street trees across the diurnal cycle are quantified, and the diurnal progression of their local climate effects is discussed with reference to their modifications to multiple physical processes in the canopy. Effects of all heat mitigation strategies on canopy climate, building energy use, and thermal comfort indices are evaluated.

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale to support a range of environmental services, as cities are acknowledged as places of: intense resource consumption and waste generation and foci of population and infrastructure that are exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data is also required for future urbanisation pathways and urban design strategies, in order to “lock in” long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability. Eventually, these global form-based, contextually specific urban planning and urban design strategies are the ultimate guarantors of successful life cycle costs, payback, and liveability. Moreover, these strategies are needed to identify the relevant data for planning and climate on neighbourhood, city and global scales, and to become part of a basic infrastructure to support a host of studies on exposure to environmental hazards, energy demand, climate adaptation and mitigation solutions and human health, as examples. Therefore, a more holistic urban landscape description is required, that goes beyond the urban mask and that enables the assessment of the spatial impact of urban planning decisions that will alter urban canopy parameters (UCPs) and their climate outcome. The global Local Climate Zone (LCZ) map presented here serves this purpose, as the LCZ typology is the only universal classification that can distinguish urban surfaces on a holistic basis, accounting for the typical combination of micro-scale land-covers and associated physical properties, all being the consequence of historic urbanisation patterns that reflect local terrain, culture, economy, etc. The 100m resolution global LCZ map is generated by feeding an unprecedented amount of labelled training areas (partly sourced from the LCZ Generator - https://lcz-generator.rub.de/) and earth observation imagery into lightweight random forest models. Its quality is assessed using the default bootstrap-based cross validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open- source data on surface cover, surface imperviousness, anthropogenic heat and building height.Complementing the single cities‘ LCZ maps accessible via the LCZ Generator, the global LCZ map for the first time reveals the world‘s intra-urban heterogeneity heterogeinity. In addition, as each LCZ type is associated with generic numerical descriptions of key UCPs, parameters critical to model atmospheric responses to urbanisation, the availability of this globally consistent and climate-relevant urban description is an essential prerequisite for developing fit-for-purpose integrated climate-sensitive urban planning policies.

This study uses the statistical and meta-analysis methods to comprehensively review 324 LCZ papers during 2012-2020, 202 of which are categorized as LCZ mapping papers. We present a bibliometric analysis of LCZ mapping papers from literature statistics, research topics, city distribution, institutions and cooperation, and research projects.

Nowadays, cities are the preferred location for more than half of the human population and the places where major human-perceived climate change impacts occur. In an increasingly urbanized world, it is essential to represent such areas adequately in Numerical Weather Prediction (NWP) models, not only to correctly forecast air temperature, but also the human heat stress and the micro-climate phenomena induced by the cities. Among them, the best known is the Urban Heat Island (UHI) effect, which refers to the significantly higher temperatures experienced by a metropolitan area than its rural surroundings. Currently, the COSMO model employs a zero-order urban description, which is unable to correctly reproduce the UHI effect: cities are simply represented as natural lands with increased surface roughness length and reduced vegetation cover. However, the reproduction of the urban climate features in NWP and regional climate models is possible with the use of the so-called urban canopy models, that are able to parameterize the interaction between the urbanized surface and the overlying atmosphere. In this context, a new bulk parameterization scheme, TERRA_URB (TU), has been developed within the COSMO Consortium. TU offers an intrinsic representation of urban physics: the effect of buildings, streets and other man-made layers on the surface-atmosphere interaction is described by parameterizing the impervious water balance, translating the 3D urban-canopy parameters into bulk parameters with the Semi-empirical Urban canopy parameterization (SURY) and using the externally calculated anthropogenic heat flux as additional heat source. In this work, we present high-resolution simulations with the TU scheme, for different European cities, Turin, Naples and Moscow. An in-depth evaluation and verification of the performances of the recent COSMO version with TU scheme and new implemented physical parameterizations, such the ICON-like surface-layer turbulence scheme and the new formulation of the surface temperature, have been carried out. The validation concerned the 2-meter temperature and was performed for 1- or 2-week selected periods over the 3 European cities characterized by different environment and climate, namely the Moscow megacity in Russia and Turin and Naples in Italy. Even if the three domains are morphologically different, the results follow a common behavior. In particular, the activation of TERRA_URB provides a substantial improvement in capturing the UHI intensity and improving air temperature forecasts in urban areas. Potential benefits in the model performance also arise from a new turbulence scheme and the representation of skin-layer temperature (for vegetation). Our model framework provides promising perspectives for enhancing urban climate modelling, although further investigations are needed.

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change., Remote sensing observations of mountain snow depth are still lacking for the Northern Hemisphere mountains. Here authors use Sentinel-1 satellite radar measurements to assess the snow depth in mountainous areas at 1 km² resolution and show that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences.

Using the cloud-computing resources of Google's Earth Engine (EE) and a range of satellite sensors (input features) this paper for the first time explores the potential of up-scaling the current Local Climate Zone mapping efforts to regional and global scales. Using a transferability framework, we test whether information from one city contains valuable information to categorise a different city, simultaneously exploring the role of the input features and the characteristics of individual cities. It was found that the accuracies of the EE approach are comparable to the standard WUDAPT method, making EE a viable alternative approach. The results from the city-to-city experiments are generally poor when compared to the single city benchmark experiments, indicating that the collection of site-specific training areas remains relevant. However, LCZ mapping accuracies are considerably improved when a) the source of the training data is from a city in the same ecoregion as the city of interest and b) if the training areas from several cities are combined. These results support the claim that the LCZ framework is a universal urban typology and indicate that, provided a continued optimisation of input features and quality of training areas, up-scaling to regional or global levels is feasible.

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. In 2015, as part of the community-based World Urban Database and Access Portal Tools (WUDAPT) project, a protocol was developed that enables the mapping of cities into LCZs, using freely available data and software packages, yet performed on local computing facilities. The LCZ Generator described here further simplifies this process, providing an online platform that maps a city of interest into LCZs, solely expecting a valid training area file and some metadata as input. The web application (available athttps://lcz-generator.rub.de) integrates the state-of-the-art of LCZ mapping, and simultaneously provides an automated accuracy assessment, training data derivatives, and a novel approach to identify suspicious training areas. As this contribution explains all front- and back-end procedures, databases, and underlying datasets in detail, it serves as the primary “User Guide” for this web application. We anticipate this development will significantly ease the workflow of researchers and practitioners interested in using the LCZ framework for a variety of urban-induced human and environmental impacts. In addition, this development will ease the accessibility and dissemination of maps and their metadata.

Extreme weather events, such as heavy precipitation, hail storms, heat waves, droughts and strong wind gusts have a detrimental impact on East African societies. The Lake Victoria Basin (LVB) is especially vulnerable, due to a large and growing population at risk from flooding. Moreover, nightly storms on the lake often catch fishermen by surprise. As the frequency and intensity of weather and climate extremes in the region is projected to further increase substantially with climate change, so do the risks. This has potentially major consequences for livelihoods and policy. The ultimate aim of the CORDEX Flagship Pilot Study ELVIC (Climate Extremes in the Lake Victoria Basin) is therefore to investigate how extreme weather events will evolve in the future in the LVB and to provide improved information to the impact community. ELVIC brings together different research groups that perform simulations with multiple high-resolution regional climate models operating at the convection-permitting scale (CPS) (https://ees.kuleuven.be/elvic). As a first step towards this overall goal, the added value of the CPS on the representation of deep convective systems in Equatorial Africa is assessed. For this purpose, 10-year present-day model simulations were carried out with five regional climate models at both parameterized and convection-permitting scales, namely COSMO-CLM, RegCM, AROME, WRF and MetUM . Similarly to other regions in the world, there is no unanimous improvement nor deterioration in the representation of the spatial distribution of total rainfall and the seasonal cycle when going to the CPS. Moreover, substantial biases in the multi-annual averages (up to 30 W m-2) and seasonal cycle in Top-Of-Atmosphere (TOA) upward radiative fluxes remain, both in some models with parameterized and with explicitly resolved convection. Most substantial systematic improvements were found in the representation of the diurnal cycle in precipitation, the diurnal cycle in TOA radiation, some metrics for precipitation intensity and number of rain events. More specifically, the timing of the daily maximum in precipitation is systematically delayed when going to the CPS, thereby improving the agreement with observations. In particular, peaktime of precipitation strongly improves over land, especially at the shores of the lake, indicative of a better representation of the impact of the lake-land-mountain circulations on the convection at CPS. The underestimation in the 90th rainfall quantile of three-hourly precipitation in the parameterized models is alleviated. For the 95th and 99th percentile of precipitation no clear improvement nor deterioration is found, which might be related to poor observational constraints on extreme precipitation. The large overestimation in the total number of rainy events is alleviated when going to the CPS. The diurnal range in the radiative fluxes at the TOA strongly improves when going to CPS, especially for the longwave. All this indicates that the representation of the convective systems is strongly improved when going to CPS, giving confidence that the models are a valuable tool for studying how extreme precipitation events evolve in the future in the LVB.

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterising urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. In 2015, as part of the community-based World Urban Database and Access Portal Tools (WUDAPT) project, a protocol was developed that enables the mapping of cities into LCZs, using freely available data and software packages, yet performed on local computing facilities. The ‘LCZ Generator’ described here further simplifies this process, providing an online platform that maps a city of interest into LCZs, solely expecting a valid training area file and some metadata as input. The tool integrates the state-of-the-art of LCZ mapping, and simultaneously provides an automated accuracy assessment, training data derivatives and a novel approach to identify suspicious training areas. In addition, this development will ease the dissemination of maps and metadata. We anticipate this development will significantly ease the accessibility and workflow of researchers and practitioners interested in using the LCZ framework for a variety of urban-induced human and environmental impacts.

In order to build resilient cities in face of climate change in Sub-Saharan Africa, much is to be done to understand the impact of rapid and uncontrolled urbanization on the local climate in the region. Recent efforts by Brousse et al. (2019, 2020) demonstrated that using generic urban parameter information derived out of Local Climate Zones (LCZ ; Stewart and Oke, 2012) maps created through the World Urban Database and Access Portal Tool framework (Ching et al. 2018) may be used to model the impact of Sub-Saharan African cities on their local climate – using the case of Kampala, the capital city of Uganda. These studies showed that despite the characteristic data scarcity on urban typologies that is present in Sub-Saharan Africa, LCZ could be used as a solution for modelling and studying the urban climates in the region.Yet these conclusions were only obtained through the use of the bulk-level urban canopy model TERRA_URB, embedded in the COSMO-CLM regional climate model. We therefore test the applicability of a more complex urban canopy models – the Building Effect Parameterization coupled to the Building Energy Model (BEP-BEM) – over the region. To do so, we focus on short periods with specific meteorological conditions during the dry season spanning from December 2017 to February 2018. These are obtained through a k-means clustering over hourly weather measurements given by the automatic weather station located at the Makerere University, in the city-center of Kampala. Wind direction and speed, 2-meter air temperature, incoming short-wave radiation, precipitation, daily temperature range, 2-meter air relative humidity and near-surface pressure are used to depict 5 weather typologies (ie. clusters) during the dry season. We chose to keep only periods with 5 consecutive days of one weather typology, which results in three 5-day periods of distinct typology. We then run the model for these periods and evaluate its outputs against the state-of-the-art simulation by Brousse et al. (2020) as well as in-situ and satellite observations for certain meteorological variables. After that, we show the effect of the recent urbanization on the local climate for each of those three periods and relate it to the variability in urban heat.This study is the first to model a tropical African city at 1 km horizontal resolution using the BEP-BEM model embedded in WRF. The latter could have major implications as more complex urban canopy models coupled to building energy models could shed light on the impact of the built environment on the livability of indoor and outdoor environments in these cities. Furthermore, insights could indeed be gained on the contribution of air conditioning heat fluxes to outdoor temperatures and the energetic consumption needed to keep indoor environments at an optimal temperature. Additionally, by resolving the urban environment in three dimensions, BEP-BEM could help increase our understanding of how specific urban planning and architectural adaptation strategies (like green or cool roofs, roof top solar panel, new building materials, urban greening etc.) may increase the citizens’ thermal comfort and reduce negative health impacts under specific weather conditions.

Bias adjustment of climate model simulations is a common step in the climate impact assessment modeling chain. For precipitation intensity, multiple bias-adjusting methods have been developed, but less so for precipitation occurrence. Intensity-bias-adjusting methods such as ‘Quantile Delta Mapping’ can adjust too many wet days, but not too many dry days. Some occurrence-bias-adjusting methods have been developed to resolve this by the addition of the ability to adjust too dry simulations. Earlier research has shown this to be important when adjusting on a continental scale, when both types of biases can occur. However, the newer occurrence-bias-adjusting methods have their weakness: they might retain a bias in the number of dry days when adjusting data in a region that only has too many wet days. Yet, if this bias is small enough, it is more practical and economical to apply the newer methods when data in the larger region are adjusted. In this study, we consider two recently introduced occurrence-bias-adjusting methods, Singularity Stochastic Removal and Triangular Distribution Adjustment, and compare them in a region with only wet-day biases. This bias adjustment is performed for precipitation intensity and precipitation occurrence, while the evaluation is performed on precipitation intensity, precipitation occurrence and discharge, which combines the former two variables. Despite theoretical weaknesses, we show that both Singularity Stochastic Removal and Triangular Distribution Adjustment perform well. Thus, the methods can be applied for both too wet and too dry simulations, although Triangular Distribution Adjustment may be preferred as it was designed with a broad application in mind.