The socioeconomic challenges associated with the effects of global warming are such that there is a clearly expressed need for tailored climate information to support the implementation of mitigation and/or adaptation strategies, as articulated by economic sectors (e.g., agriculture, energy, tourism, land and maritime infrastructure, etc.) and by territories/regions that fully understand their vulnerabilities. In response to these demands, numerous national, European, and international research projects have enabled the funding of “climate services.”Several national and European operational actors are developing and making “climate services” available via platforms that are often freely accessible; finally, consulting firms are emerging on the market whose commercial activity focuses on developing “climate services” tailored to clients’ specific needs.The range of “climate services” is therefore diverse today, both in terms of the information provided (“simple” climate data, indicators, decision-support tools) and in terms of how they are developed. Given this diversity, the challenges lie in documenting and understanding the current landscape of climate services, identifying needs, and equipping ourselves with the means to characterize the success of climate services, to evaluate existing offerings, and to guide the development of new projects. The aim of this presentation has four objectives:i) To document the current landscape of climate services in France and worldwide (as identified by our TRACCS community), presenting them by use and target audience;ii) Identify unmet needs regarding climate services;iii) Identify a set of success criteria for climate services to evaluate them;iv) Propose best practices for meeting these success criteria. This work is based on a combination of collaborative research and the collection of statements from stakeholders in “climate services”. This study has received funding from Agence Nationale de la Recherche - France 2030 as part of the PEPR TRACCS programme under grant number ANR-22-EXTR-0002 and ANR-22-EXTR-0004.

Modelling options play a key role in the reliability of flood estimates, especially of extreme ones. Here, we investigate the influence of two components in a hydrometeorological modelling chain using long continuous simulations, under a stationary climate. We employed two parameterizations of the stochastic weather generator GWEX, and two model structures of the bucket-type hydrological model HBV that were configured, calibrated, and run for three representative model parameter sets. We analyzed the impact of these modelling options on the magnitude and uncertainty of floods for return periods of 1-1000 years for selected large Swiss catchments with diverse physiographic characteristics. We found that uncertainty increases with return period, while the main source of uncertainty depends on catchment characteristics and return period. In higher elevation catchments, the hydrological model parameters were the dominant source of uncertainty, whereas in lower-elevation and rainfall-dominated catchments, the weather generator parameterizations and stochasticity were critical. Furthermore, we investigated the effect of the selected model structures on the identification of a threshold return period beyond which precipitation is the main driver of floods. No substantial differences were found between the threshold return periods for the two model structures. These findings highlight that physiographic characteristics affect the identification of the threshold return period and the contribution of the components to the uncertainty of flood estimates, challenging our ability to make a priori generalizations. Overall, this work underscores the importance of diversity in modelling options and uncertainty decomposition as a tool for informed decision-making in flood risk management.

Multi-scenario, multi-model ensembles of hydrological projections are widely used to describe possible futures of regional hydrology and inform adaptation strategies. The Explore2 dataset is such an ensemble of river flow projections in Metropolitan France. It provides future simulations for 1735 catchments with modeling chains composed of different hydrological models forced by 36 regional climate projections based on bias-adjusted EUROCORDEX simulations. This study assesses the uncertainties of this ensemble with QUALYPSO, a method specifically designed to deal with incomplete ensembles and to disentangle and quantify all uncertainty sources, including that due to internal variability.Focusing on results obtained at the end of the century, this study shows a strong agreement between modeling chains towards decreases in low flows in a large southern part of France for a high-emission scenario, and very uncertain changes for the annual mean and high flows. Emission scenario uncertainty is the dominant source of uncertainty for low flows over the whole of France, and for mean annual flows in southeastern France. The contribution of the global and regional climate models is important for mean and high flows, especially in rainfall-dominated areas. Regional climate models contribute considerable uncertainty to low flows, much more than global models. The contribution of hydrological model uncertainty is large for low flows, moderate for mean annual flows, and small for high flows. For all climate and hydrological indicators, internal variability is often large and cannot be overlooked. It is often of the same order and sometimes larger than the uncertainty on the climate change response.

A large transient multi-scenario and multi-model ensemble of future streamflow and groundwater projections in France developed in a national project named Explore2 was recently made available. The main objective of Explore2 is to provide rich and spatially-consistent information for the future evolution of hydrological (surface and groundwater) resources and extremes in France to support adaptation strategies. The Explore2 dataset was obtained using a nested multi-scenario multi-model approach to estimate future uncertainty and to assess local climate at the catchment scale: three greenhouse gas (GHG) emission scenarios, a set of 17 combinations of Global Climate Models and Regional Climate Models (GCM/RCM), and two bias correction methods provide the meteorological forcing for nine surface hydrology models and four groundwater hydrology models (one to simulate groundwater recharge and three to simulate groundwater level). In this paper, we present the methodology underlying the dataset, the evaluation of the hydrological models against daily observations of streamflow and groundwater level, and the key messages on the impact of climate change on both mean river flows and groundwater recharge. This large set of hydrological projections shows a high model agreement on the decrease in seasonal flows in the South of France under the RCP8.5 high-emission scenario, confirming its hotspot status. The surface hydrological models agree on the decrease in summer flows across France under the RCP8.5 scenario, with the exception of northern part France. This area may indeed benefit from more active winter recharge that may counterbalance decrease in summer precipitation and increase in evapotranspiration. In addition to northern France, annual groundwater recharge is projected to increase slightly in the north-east while remaining unchanged elsewhere by the end of the century, according to the RCP8.5 scenario. In the mountainous areas, winter flows will increase as a result of higher air temperature and the high degree of agreement between the models holds regardless of the RCP considered. Unsurprisingly, the higher the GHG emission scenario, the higher the median changes. Most of these changes are organised in France along a north-south gradient, regardless of the RCP considered.

In this study, we analyze how precipitation, antecedent conditions, and their spatial patterns and interactions lead to extreme floods in a large catchment. The analysis is based on 10 000 years of continuous simulations from a hydro-meteorological modelling chain for a large catchment, the Aare River basin, Switzerland. To account for different flood-generating processes, we based our work on simulations with hourly time resolution. The hydro-meteorological modelling chain consisted of a stochastic weather generator (GWEX), a bucket-type hydrological model (HBV), and a routing system (RS MINERVE), providing the hydrological basis for flood protection management in the Aare River basin. From the long continuous simulations of runoff, snow, soil moisture, and dynamic storage, we were able to assess which combinations of antecedent conditions and triggering precipitation lead to extreme floods in the sub-basins of the Aare catchment. We found that only about 18 % to 44 % (depending on the sub-catchment) of annual maximum precipitation (AMP) and simulated annual maximum flood (AMF) events occurred simultaneously, highlighting the importance of antecedent conditions for the generation of large floods. For most sub-catchments in the 200–500 km2 range, after return periods greater than 500 years we found only AMF caused by triggering AMP, which is notably higher than the return periods typically used for design floods. Spatial organization within a larger area is complicated. After routing the simulated runoff, we analyzed the important patterns and drivers of extreme flooding at the outlet of the Aare River basin using a random forest. The different return period classes had distinct key predictors and showed specific spatial patterns of antecedent conditions in the sub-catchments, leading to different degrees of extreme flooding. While precipitation and soil moisture conditions from almost all sub-catchments were important for more frequent floods, for rarer events only the conditions in specific sub-catchments were important. Snow conditions were important only from specific sub-catchments and for more frequent events.

To achieve universal electricity access and comply with Paris Agreement, one large-scale objective of the Economic Community of West African States (ECOWAS) is the deployment of +8 to +20 GWp of solar energy systems by 2030 (IRENA, 2018). ECOWAS is located south of the Saharan region and close to the Bodélé depression, which has been observed to have the largest atmospheric dust production activity on Earth (Isaacs et al., 2023). Once deposited on panels, dust reduces the transmission of solar radiation to the panels and, consequently, the energy production (Sarver et al., 2013). Annual losses of solar energy production of up to 54% have been observed in the region due to dust (Chanchangi et al., 2022). These production losses can be mitigated by regularly cleaning solar panels. In West Africa, cleaning operations commonly use water but many areas are water-scarce. It is thus important to ensure that water resources are not further strained by water cleaning operations associated with the expected large-scale deployment of solar energy systems in the region.In the present work, we aim to assess the water footprint of different cleaning strategies of virtual solar plants in the ECOWAS region. A first step towards this aim consists in regionally assessing how dust would accumulate on Photovoltaic (PV) panels and, in turn, what the associated production losses would be. We present a dust accumulation model allowing to simulate, over a long time period and across the region, the temporal sub daily variations of dust accumulation on virtual PV panels. The model uses as input the particulate matter concentration of different particle sizes. Dust data from the CAMS and MERRA2 reanalyses are considered. Both datasets are first compared to observations of regional particulate matter concentration available from a set of four stations from the INDAAF network. CAMS data were found to better agree with observations (> 0.8 correlation for a 1-week temporal resolution). Time series of dust accumulation simulated from CAMS data were then compared to time series of dust deposit observations available for the same four INDAAF stations. Results show fair agreement but highlight significant differences, likely due to uncertainties in various variables and model assumptions. Lastly, simulated accumulated dust amounts are used as input to a PV soiling loss model to derive the transmission reduction and the mean PV production losses for different cleaning operation strategies.ReferencesChanchangi et al., 2022. Soiling mapping through optical losses for Nigeria. Renewable Energy, 197, 995–1008. https://doi.org/10.1016/j.renene.2022.07.019IRENA (2018), Renewable Energy Statistics 2018, The International Renewable Energy Agency, Abu Dhabi.Isaacs et al., 2023. Dust soiling effects on decentralized solar in West Africa. Applied Energy, 340, 120993. https://doi.org/10.1016/j.apenergy.2023.120993Sarver et al.,2013. A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches. Renewable and Sustainable Energy Reviews, 22, 698–733. https://doi.org/10.1016/j.rser.2012.12.065

The estimation of extreme floods using long continuous simulations is linked to uncertainties which are inherent in different components of the modeling chain. The main objective of this study was to investigate the role of precipitation input data from a weather generator for extreme flood estimates. A hydrometeorological modeling chain consisting of a multi-site weather generator (GWEX) at an hourly time scale, a rainfall-runoff model (HBV) and a hydrologic routing model (RS Minerve), was implemented, using different parameterizations of GWEX. While the sensitivity to the altered precipitation inputs was not uniform across the selected catchments due to their different physiographic characteristics, we found that the uncertainty of flood estimates increased with increasing return period. In addition, the flood peaks were strongly affected when a bootstrapping of precipitation was performed and to a lesser extent when weather types (WT) were used to condition the parameters of GWEX. However, the latter seemed to reduce the spread of the uncertainty both in generated precipitation and simulated floods. Therefore, results suggested that precipitation inputs strongly contribute to the uncertainties of extreme floods. Accounting for uncertainty information enhances the usefulness of long continuous simulations and is essential as a context for applications including hydraulic engineering, spatial planning and safety assessments.

This study examines changes in extreme precipitation over the Greater Antilles and their correlation with large-scale sea surface temperature (SST) for the period 1985 to 2015. The data used for this study were derived from two satellite products, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1, with resolutions of 5 and 25 km, respectively. Then, changes in the characteristics of six extreme precipitation indices defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) are analyzed, and Spearman's correlation coefficient is used and evaluated by t test to investigate the influence of a few large-scale SST indices: (i) Caribbean Sea Surface Temperature (SST-CAR), (ii) Tropical South Atlantic (TSA), (iii) Southern Oscillation Index (SOI), and (iv) North Atlantic Oscillation (NAO). The results show that, at the regional scale, +NAO contributes significantly to a decrease in heavy precipitation (R95p), daily precipitation intensity (SDII), and total precipitation (PRCPTOT), whereas +TSA is associated with a significant increase in daily precipitation intensity (SDII). On an island scale, in Puerto Rico and southern Cuba, the positive phases of +TSA, +SOI, and +SST-CAR are associated with an increase in daily precipitation intensity (SDII) and heavy precipitation (R95p). However, in Jamaica and northern Haiti, the positive phases of +SST-CAR and +TSA are also associated with increased indices (SDII, R95p). In addition, the SST warming of the Caribbean Sea and the positive phase of the Southern Oscillation (+SOI) are associated with a significant increase in the number of rainy days (RR1) and the maximum duration of consecutive wet days (CWD) over the Dominican Republic and in southern Haiti.

Mini-grids with a low carbon footprint are a promising solution for providing electricity in rural areas, while being compatible with the objectives of the Paris Agreement. Public policies are needed to encourage their development and their design should consider the different point of view from each stakeholder involved in minigrid projects (State, developer, users). We propose a multi-criteria approach to evaluate a set of policies to limit the carbon footprint of mini-grids. Our method is based on the simulation of fictitious mini-grids and on the calculation of four indicators: the mitigation cost, the policy cost, the average levelized cost of energy (LCOE) at the national level, and the disparity of individual mini-grid LCOE within the country. We applied the methodology to Senegal, Madagascar, Kenya and Nigeria chosen for the diversity in solar resource and fuel price. Our results advocate for the combination of fuel tax and subsidy on solar panels and batteries to further reduce the carbon footprint of mini-grids. Using fuel tax revenues to equalize the LCOE of mini-grids within a country allows a cost-efficient reduction of the carbon footprint while reducing the cost disparities between mini-grid projects.

Study RegionSwitzerlandStudy FocusStochastic weather generators (WGENs) are a common tool for generating long precipitation scenarios, also needed at ungauged sites. This study evaluates different methods for obtaining the parameters of a hybrid at-site WGEN at any location within the study area. The hybrid GWEX-MRC model is composed of GWEX, a daily WGEN, and MRC, a disaggregation model based on multiplicative random cascades. Two approaches are considered for obtaining parameter maps. The first approach applies classical spatial interpolation techniques, kriging and thin-plate splines, to parameter estimates derived from rain gauge data. The second approach uses CombiPrecip, an hourly gridded precipitation product from MeteoSwiss, to estimate parameters at grid scale. New Hydrological Insights for the RegionWe find that the parameters of GWEX-MRC can be interpolated with satisfactory results across Switzerland. Among interpolation techniques, kriging with elevation as an external drift performs best for GWEX, while thin-plate spline with elevation gives better results for MRC. The comparison of the two approaches, interpolation of site-based estimates and direct parameter estimation using CombiPrecip, showed comparable or slightly different performance depending on the precipitation statistic and season. These findings reveal the feasibility of both approaches and provide insights into their relative strengths and limitations. In addition, this study demonstrates that long precipitation scenarios can be reliably generated throughout Switzerland, which can later be used to feed a hydrological model.

To address the growing electricity demand driven by population growth and economic development while mitigating climate change, West and Central African countries are increasingly prioritizing renewable energy as part of their Nationally Determined Contributions (NDCs). This study evaluates the implications of climate change on renewable energy potential using ten downscaled and bias-adjusted CMIP6 models (CDFt method). Key climate variables—temperature, solar radiation, and wind speed—were analyzed and integrated into the Teal-WCA platform to aid in energy resource planning. Projected temperature increases of 0.5–2.7 °C (2040–2069) and 0.7–5.2 °C (2070–2099) relative to 1985–2014 underscore the need for strategies to manage the rising demand for cooling. Solar radiation reductions (~15 W/m2) may lower photovoltaic (PV) efficiency by 1–8.75%, particularly in high-emission scenarios, requiring a focus on system optimization and diversification. Conversely, wind speeds are expected to increase, especially in coastal regions, enhancing wind power potential by 12–50% across most countries and by 25–100% in coastal nations. These findings highlight the necessity of integrating climate-resilient energy policies that leverage wind energy growth while mitigating challenges posed by reduced solar radiation. By providing a nuanced understanding of the renewable energy potential under changing climatic conditions, this study offers actionable insights for sustainable energy planning in West and Central Africa.

Background Activities embedded in academic culture (international conferences, field missions) are an important source of greenhouse gas emissions. For this reason, collective efforts are still needed to lower the carbon footprint of Academia. Serious games are often used to promote ecological transition. Nevertheless, most evaluations of their effects focus on changes in knowledge and not on behaviour. The main objectives of this study are to 1) Evaluate the feasibility of a control and an experimental behaviour change intervention and, 2) Evaluate the fidelity (the extent to which the implementation of the study corresponds to the original design) of both interventions. Methods People employed by a French research organisation (N = 30) will be randomised to one of the two arms. The experimental arm consists in a 1-hour group discussion for raising awareness about climate change, carrying out a carbon footprint assessment and participating to a serious game called “Ma terre en 180 minutes.” The control arm consists of the same intervention (1h discussion + carbon footprint assessment) but without participating to the serious game. On two occasions over one month, participants will be asked to fill in online surveys about their behaviours, psychological constructs related to behaviour change, sociodemographic and institutional information. For every session of intervention, the facilitators will assess task completion, perceived complexity of the tasks and the perceived responsiveness of participants. Descriptive statistics will be done to analyse percentages and averages of the different outcomes. Discussion Ma-terre EVAL pilot study is a 1-month and a half pilot randomised controlled trial aiming to evaluate the feasibility and the fidelity of a 24-month randomised controlled trial. This study will provide more information on the levers and obstacles to reducing the carbon footprint among Academia members, so that they can be targeted through behaviour change interventions or institutional policies.

Continuous hydrological simulation is a powerful approach for generating long-term series of river discharges used for hydrological analyses. This approach requires as inputs precipitation time series generated by a stochastic weather generator (WGEN) to simulate discharge time series. For small catchments where a lumped hydrological model is suitable, the weather generator needs to generate time series of mean areal precipitation (MAP). Here we assess the ability of an at-site hybrid WGEN to generate time series of MAP for a set of test areas ranging from 9 to 1,089 km ^2 . The generator is composed of a model based on a Markov chain model used to generate time series of daily MAP, and a multiplicative random cascade used to disaggregate them to an hourly resolution. The work is carried out at several test locations in Switzerland with different precipitation regimes. The parameters of the model are estimated on the observed MAP time series extracted from CombiPrecip, a 1 km ^2 resolution radar-gauge product of precipitation assimilating rain gauges and radar data. For each test location and each test area, 100-year time series are generated and compared with the observed MAP time series. Whatever the location and spatial scale considered, the performance of the WGEN is satisfactory. The model reproduces the observed standard statistics and extreme precipitation of observed MAP very well. At an hourly resolution, better results are obtained at larger spatial scales, while no difference is noticed at a daily resolution. The study shows that using this hybrid WGEN is possible to model and generate MAP for areas ranging from 9 to 1,089 km ^2 . Moreover, this particular WGEN is easy to implement for end-user applications. The modelling approach is even more promising as high-resolution gridded precipitation data are expected to become increasingly available worldwide, offering a source of data to calibrate the hybrid model.

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The increasing frequency and intensity of precipitation extremes poses a serious challenge for societies that must adapt to a changing climate. Communicating these changes in terms of their magnitude at a given lead time (e.g., 2100) or at a given level of global warming (e.g., +2 °C) can give the misleading impression that climate change is a distant issue; yet, adaptation measures to cope with future hydro-climatic conditions may be designed and implemented today. Contextualizing the potential future consequences of precipitation intensification in a current temporal frame of reference may help perceive climate change as an ongoing phenomenon, in turn encouraging adaptation planning. Using an ensemble of climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in a non-stationary extreme value framework, we quantify the time it takes for the frequency of extreme 1-day and 7-day precipitation accumulations –as estimated in the current climate– to double; the frequency double time (FDT) is estimated for a range of event rarities over land regions. Vast parts of the Northern Hemisphere high-latitudes are found to have FDT < 80 years. Substantial parts of some densely populated mid-latitude regions have FDT in the next 5–6 decades for some rare events. The fastest frequency doubling, in the coming two decades, is found in the highly vulnerable tropical regions of Western and South Eastern Africa, with strong implications for hydrological risk management there. In addition, the rarest events are found to have smaller FDT compared to more ‘common’ extremes; infrastructures designed to withstand the strongest events are thus more exposed to premature obsolescence.

With growing gas and oil prices, electricity generation based on these fossil fuels is becoming increasingly expensive. Furthermore, the vision of natural gas as a transition fuel is subject to many constraints and uncertainties of economic, environmental, and geopolitical nature. Consequently, renewable energies such as solar and wind power are expected to reach new records of installed capacity over the upcoming years. Considering the above, North Africa is one of the regions with the largest renewable resource potential globally. While extensively studied in the literature, these resources remain underutilized. Thus, to contribute to their future successful deployment and integration with the power system, this study presents a spatial and temporal analysis of the nature of solar and wind resources over North Africa from the perspective of energy droughts. Both the frequency and maximal duration of energy droughts are addressed. Both aspects of renewables’ variable nature have been evaluated in the North Atlantic Oscillation (NAO) context. The analysis considers the period between 1960 and 2020 based on hourly reanalysis data (i.e., near-surface shortwave irradiation, wind speed, and air temperature) and the Hurrel NAO index. The findings show an in-phase relationship between solar power and winter NAO index, particularly over the coastal regions in western North Africa and opposite patterns in its eastern part. For wind energy, the connection with NAO has a more zonal pattern, with negative correlations in the north and positive correlations in the south. Solar energy droughts dominate northern Tunisia, Algeria, and Morocco, while wind energy droughts mainly occur in the Atlas Mountains range. On average, solar energy droughts tend not to exceed 2–3 consecutive days, with the longest extending for five days. Wind energy droughts can be as prolonged as 80 days (Atlas Mountains). Hybridizing solar and wind energy reduces the potential for energy droughts significantly. At the same time, the correlation between their occurrence and the NAO index remains low. These findings show the potential for substantial resilience to inter-annual climate variability, which could benefit the future stability of renewables-dominated power systems.

The massive development of mini-grids (MGs) is seen as a promising alternative to the extension of national grids to achieve universal access to electricity. MGs based on solar photovoltaic are often recognized fully consistent with net-zero CO2 emissions objectives. However, if they have low or even no direct emissions from diesel consumption, they embed indirect carbon emissions due to solar panels and batteries manufacturing. Electrification policies, mainly based on the levelized costs of electricity (LCOE), should likely account for the carbon footprints (CFP) of possible electrification strategies.In this work, we assess the CFP of hybrid MGs (solar, battery, diesel) for rural electrification in Africa. We consider a large number of MG configurations for many locations across the continent. For each location, we identify the lowest CFP and LCOE, and estimate their dependency to meteorological and socio-economic factors.Our results show that: (i) the lowest CFP depends on location and is around 200gCO2/kWh; (ii) it can be higher than the CFP of certain African national grids; (iii) the CFP of hybrid MGs can be lower than the CFP of MGs relying only on solar PV; (iv) for most techno-economic and environmental assumptions, moderate LCOE increases allow significant CFP reductions.

We assess the ability of two modelling chains to reproduce, over the last century (1902–2009) and from large-scale atmospheric information only, the temporal variations in river discharges, low-flow sequences and flood events observed at different locations of the upper Rhône River catchment, an alpine river straddling France and Switzerland (10 900 km2). The two modelling chains are made up of a downscaling model, either statistical (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions – SCAMP) or dynamical (Modèle Atmosphérique Régional – MAR), and the Glacier and SnowMelt SOil CONTribution (GSM-SOCONT) model. Both downscaling models, forced by atmospheric information from the global atmospheric reanalysis ERA-20C, provide time series of daily scenarios of precipitation and temperature used as inputs to the hydrological model. With hydrological regimes ranging from highly glaciated ones in its upper part to mixed ones dominated by snow and rain downstream, the upper Rhône River catchment is ideal for evaluating the different downscaling models in contrasting and demanding hydro-meteorological configurations where the interplay between weather variables in both space and time is determinant. Whatever the river sub-basin considered, the simulated discharges are in good agreement with the reference ones, provided that the weather scenarios are bias-corrected. The observed multi-scale variations in discharges (daily, seasonal, and interannual) are reproduced well. The low-frequency hydrological situations, such as annual monthly discharge minima (used as low-flow proxy indicators) and annual daily discharge maxima (used as flood proxy indicators), are reproduced reasonably well. The observed increase in flood activity over the last century is also reproduced rather well. The observed low-flow activity is conversely overestimated, and its variations from one sub-period to another are only partially reproduced. Bias correction is crucial for both precipitation and temperature and for both downscaling models. For the dynamical one, a bias correction is also essential for getting realistic daily temperature lapse rates. Uncorrected scenarios lead to irrelevant hydrological simulations, especially for the sub-basins at high elevation, due mainly to irrelevant snowpack dynamic simulations. The simulations also highlight the difficulty in simulating precipitation dependency on elevation over mountainous areas.