Statistical Downscaling of Precipitation for Mahanadi Basin in India - Prediction of Future Streamflows

Climate change has long-term impacts on precipitation patterns, magnitude, and intensity, affecting regional water resources availability. Besides, understanding the interannual to decadal variations of streamflows in a river basin is paramount for watershed management, primarily extreme events such as floods and droughts. This study investigates impact of climate change in streamflows estimation for four sub-basins of the Mahanadi River, in India. The study includes three major components: (i) Historical trend analysis of hydroclimatic variables, using Mann-Kendall test; (ii) Statistical downscaling of GCM generated precipitation using change factor method and KnnCAD V4 stochastic weather generator; (iii) Dependable flow analysis of future streamflows predicted using Soil Water Assessment Tool (SWAT) model for various future GCM scenarios. It is observed that during the historical period, there is a decrease in number of rainy days and total annual precipitation in all sub-basins. However, the analysis also indicates an increase in flood intensity in two of the sub-basins. The decadal future precipitation has a marginal decrease in precipitation (up to 10%) for future scenarios; however, the precipitation events with high intensities increases. The results indicate that the magnitudes of 5% and 10% dependable flows are higher than the historically observed streamflows, for all future scenarios. This indicates a significant increase in extreme flood events in the basin. Further, only one of the sub-basins has shown an increase in water availability for agriculture and drinking water purposes (75% and 95% dependable flows) in the future. Understanding future flood events in the Mahanadi basin can help decision-makers to implement appropriate mitigation strategies.

Holistic approaches to palaeohydrology: Reconstructing and modelling the Neolithic River Çarşamba and the riverscape of Çatalhöyük, Turkey

Alluvial landscapes have long been considered optimal locations for Neolithic settlement because of the availability of water and nutrient-bearing silts. However, the dynamics of these landscapes have often been underestimated in relation to the interactions of precipitation, temperature and vegetation at catchment scale, along with flow and geomorphic processes in the channel and adjacent areas. In this paper we employ a multi-method approach to model the alluvial landscape around Çatalhöyük in central Turkey to develop a more nuanced understanding of the potential interactions between the Neolithic population and its environment. Starting from detailed sedimentological reconstructions of the area surrounding the site, we use climate and vegetation proxies to estimate past climate scenarios. Four temperature and five precipitation scenarios and two vegetation endmember scenarios were constructed for the Neolithic. These scenarios are coupled with a stochastic weather generator to simulate past flows using the HEC-HMS rainfall-runoff model. Impacts and extents of past flooding are then estimated using bankfull flow estimates from the modelled time series. The model results suggest that crops at Çatalhöyük were less vulnerable to flooding than has previously been supposed, with flooding spread more evenly through the year and with relatively unerosive flows. Spatial variability suggests a range of wet and dry conditions would have been available at different times of the year near the site. Interannual and decadal variability was important and so resilience against drought is also a significant consideration and so subsistence patterns must have been resilient to this variability to enable the settlement to continue for over a millennium. This interpretation of the riverscape of Neolithic Çatalhöyük as a mosaic of wet and dry conditions is compatible with the range of plant and animal remains excavated from the site.

CLIGEN parameter regionalization for mainland China

The stochastic weather generator CLIGEN can simulate long-term weather sequences as input to WEPP for erosion predictions. Its use, however, has been somewhat restricted by limited observations at high spatial–temporal resolutions. Long-term daily temperature, daily, and hourly precipitation data from 2405 stations and daily solar radiation from 130 stations distributed across mainland China were collected to develop the most critical set of site-specific parameter values for CLIGEN. Ordinary kriging (OK) and universal kriging (UK) with auxiliary covariables, i.e., longitude, latitude, elevation, and the mean annual rainfall, were used to interpolate parameter values into a 10 km×10 km grid, and the interpolation accuracy was evaluated based on the leave-one-out cross-validation. Results showed that UK generally outperformed OK. The root mean square error between UK-interpolated and observed temperature-related parameters was ≤0.88 ∘C (1.58 ∘F). The Nash–Sutcliffe efficiency coefficient for precipitation- and solar-radiation-related parameters was ≥0.87, except for the skewness coefficient of daily precipitation, which was 0.78. In addition, CLIGEN-simulated daily weather sequences using UK-interpolated and observed parameters showed consistent statistics and frequency distributions. The mean absolute discrepancy between the two sequences for temperature was <0.51 ∘C, and the mean absolute relative discrepancy for solar radiation, precipitation amount, duration, and maximum 30 min intensity was <5 % in terms of the mean and standard deviation. These CLIGEN parameter values at 10 km resolution would meet the minimum data requirements for WEPP application throughout mainland China. The dataset is available at http://clicia.bnu.edu.cn/data/cligen.html (last access: 20 May 2021) and https://doi.org/10.12275/bnu.clicia.CLIGEN.CN.gridinput.001 (Wang et al., 2020).

Agricultural ecosystem services under climate change – Modelling grassland biodiversity

&lt;p&gt;European permanent grasslands not only represent a backbone for dairy and meet production, but also are hotspots of biodiversity, providing important ecosystem services to society. Understanding how climate variability and change affect the botanical composition of permanent grasslands is therefore essential for informing adaptation and helping farmers targeting sustainable development goals. It is also a key requirement for gauging climate change effects on forage quality, an aspect often overlooked in impact assessments. In this contribution, we present results of a modelling effort to understand short- and long-term changes in grassland biodiversity in response to climatic variations. We use &lt;em&gt;DynaGraM&lt;/em&gt;, a recently developed process-based model for simulating community dynamics in multi-species managed grasslands. Earlier we demonstrated that &lt;em&gt;DynaGraM&lt;/em&gt; is capable of representing the composition of permanent grasslands in the French Jura Mountains inferred from floristic relev&amp;#233;s. In these earlier investigations, we also showed that the model predicts highest, resp. lowest vegetation diversity for extensive grazing, resp. extensive mowing. We further found that the time scales of responses to external perturbations largely dependent on management, with shorter time scales (of the order of 5 to 10 years) under grazing than under mowing (of the order of 50 years).&lt;/p&gt;&lt;p&gt;Here we apply the model to examine how increasing summer aridity affects the species composition of pastures in the same geographic area. To drive the model, we use a set of climate change scenarios obtained from the CMIP5 repository, which we downscaled with the help of the LARS-WG stochastic weather generator. The results underline that management intensity modulates the impact of summer drought on both yield as well as botanical diversity, with largest changes over time in the latter under extensive grazing. Apart from presenting the results in more detail, we also discuss their practical implications and opportunities to extend in future the scope of this work.&lt;/p&gt;

Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation

In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill scores (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance.

Assessing the impact of rainfall intensification on ecosystem productivity

&lt;p&gt;Increasing atmospheric CO2 levels, temperature, and atmospheric drought as well as changes in rainfall structure (event frequency, storm intensity) are expected to jointly alter ecosystem responses in the future. High-resolution convection-permitting models have been recently employed at continental scales to develop robust projections of climate, and particularly precipitation, at fine spatiotemporal scales (~1km, ~1hour) overcoming the documented limitations of larger scale general circulation models.&lt;/p&gt;&lt;p&gt;Climate projections at fine spatiotemporal scale can support robust quantification of ecosystem responses under a changing climate when used in conjunction with state-of-the-art terrestrial biosphere models resolving the soil &amp;#8211; vegetation &amp;#8211; atmosphere continuum processes at those scales. In this study, we assess the changes in ecosystem functioning for multiple biomes in North America. We use the 4km, 1-h future WRF continental-wide simulation over the US together with a state-of-the-art stochastic weather generator and the Tethys &amp; Chloris ecohydrological model to investigate ecosystem responses for 33 sites where eddy covariance data exist (i.e., FLUXNET sites).&lt;/p&gt;&lt;p&gt;We designed a series of numerical experiments tuned to disentangle the roles of CO2, temperature and the structure of precipitation, while considering the effect of natural weather variability.&lt;/p&gt;&lt;p&gt;Our results reveal that the impact of mean annual rainfall is dominant in more arid sites, while sites of intermediate wetness are more sensitive to the temporal structure of precipitation at fine scales. Wet sites, which are energy limited, are more sensitive to temperature increase instead. The impact of rainfall is partly offset by increases in atmospheric drought. The fertilization effect of elevated CO2 levels is strong in this high-end (RCP 8.5) scenario across all sites. Fertilization is more pronounced for sites of low and intermediate wetness, where stomatal closure allows for positive feedbacks through water savings.&lt;/p&gt;

Quantification of the relation between dynamical properties of meteorological variables and their predictability

&lt;p&gt;Local properties of chaotic systems can be summarized by dynamical indicators, that describe the recurrences of all states in phase space. Faranda et al. (2017) defined such indicators with the local dimension (d, approximating the local number of degrees of freedom of the system) and the inverse of persistence (&amp;#952;, approximating the time it takes to leave a local state). It has been conjectured that such indicators give access to the local predictability of systems. The aim of this study is to evaluate how the predictability of climate variables such as temperature and precipitation is related to dynamical properties of the atmospheric flow.&lt;/p&gt;&lt;p&gt;The predictability of a chaotic system can be evaluated through ensembles of simulations, with probability scores (e.g. Continuous Rank Probability Score, CRPS). In this work, we consider ensembles of climate forecasts with a stochastic weather generator (SWG) based on analogs of atmospheric circulation (Yiou and D&amp;#233;andr&amp;#233;is, 2019). We are interested in relating predictability scores of European temperatures and precipitation, obtained with this SWG, and the local dynamical properties of the synoptic atmospheric circulation, obtained from the NCEP reanalysis. We show experimentally that the CRPS of local climate variables can be predicted from large-scale (d, \ &amp;#952;) values of geopotential height fields, for time leads of 5 to 10 days. A practical application is that the predictability of local variables (in Europe) can be anticipated from large-scale dynamical quantities, which can help to dimension the size of ensemble forecasts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Faranda, D., Messori, G., Yiou, P., 2017. Dynamical proxies of North Atlantic predictability and extremes. Sci. Rep. 7, 41278. https://doi.org/10.1038/srep41278&lt;/p&gt;&lt;p&gt;Caby, T. Extreme Value Theory for dynamical systems, with applications in climate and neuroscience. Mathematics [math]. Universit&amp;#233; de Toulon Sud; Universita dell&amp;#8217;Insubria, 2019. English.tel-02473235v1&lt;/p&gt;&lt;p&gt;Yiou, P., D&amp;#233;andr&amp;#233;is, C., 2019. Stochastic ensemble climate forecast with an analogue model. Geosci. Model Dev. 12, 723&amp;#8211;734. https://doi.org/10.5194/gmd-12-723-2019&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&amp;#160;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgments&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This project has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation programme under the Marie Sk&amp;#322;odowska-Curie grant agreement No 813844.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Contribution of remote sensing and auxiliary variables in the study of the evolution of periods of droughts

&lt;p&gt;In semi-arid areas, plant water use and plant water stress can be derived over large&lt;br&gt;areas from remotely sensed evapotranspiration estimates. Those can help us to monitor the&lt;br&gt;impact of drought on the agro- and ecosystems. Both variables can be simulated by a dual&lt;br&gt;source energy balance model that relies on meteorological variables (air temperature, relative&lt;br&gt;humidity, wind speed and global radiation) and remote sensing data (surface temperature,&lt;br&gt;NDVI, albedo and LAI). Surface temperature acquired in the Thermal InfraRed (TIR) domain&lt;br&gt;is particularly informative for monitoring agrosystem health and adjusting irrigation&lt;br&gt;requirements. However, available meteorological observations period may often be&lt;br&gt;insufficient to account for the variability present in the study area. Statistical downscaling&lt;br&gt;methods applied to reanalysis data can serve to generate surrogate series of meteorological&lt;br&gt;variables that either fill the gaps in the observation period or extend the observation period in&lt;br&gt;the past. For this aim, a stochastic weather generator (SWG) is adapted in order to compute&lt;br&gt;temporal extension of multiple meteorological variables. This surrogate series is then used to&lt;br&gt;constrain the dual-source model Soil Plant Atmosphere and Remote Evapotranspiration&lt;br&gt;(SPARSE). Stress index anomalies retrieved from SPARSE are then compared to anomalies in&lt;br&gt;other wave lengths in order to assess their capacity to detect incipient water stress and early&lt;br&gt;droughts at the kilometer resolution. Those are the root zone soil moisture at low resolution&lt;br&gt;derived from the microwave domain, and active vegetation fraction cover deduced from&lt;br&gt;NDVI time series.&lt;/p&gt;

Reparametrizing rainfall generators with convective-permitting models to generate high-resolution rainfall for climate impact studies

&lt;p&gt;A high-resolution rainfall data at a km and sub-hourly scales provides a powerful tool for hydrological risk assessment in the current and the future climate. Global circulation models or regional circulation models generally provide projections at much coarser space-time resolutions of 10-100 kilometres and daily to monthly. In the recent decade, convection-permitting models (CPM) have been developed and enable the projection at a kilometre and sub-hourly scales. CPMs, due to their very high computational demand, are still limited to a small number of ensemble simulations. This limits their skill in hydrology, where quantification of extremes and their variability is essential for risk assessment and design. In this project, we propose the combined use of CPMs with stochastic rainfall generators to simulate ensemble of climate change at hydrologically relevant scales.&lt;/p&gt;&lt;p&gt;To achieve this, we used the STREAP space-time stochastic rainfall generator, a 1 km resolution composite rain radar data and a 2.2km CPM dataset from the UK Met Office. For the mid-land region of the UK, we parameterised STREAP for the present climate using rainfall observations. CPM simulations were used to derive the change of STREAP parameters with a changing climate. These parameters describe the change in weather patterns, the rainfall intensification, and changes in the structure of rainfall. Our results show that by combining a physics-based model and a stochastic weather generator we can simulate robust ensemble of rainfall at a minimal computational cost while preserving all physical attributes from climate change projections.&lt;/p&gt;