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The updated Köppen-Geiger climate classification for continental Chile is a cartographic product of great interest for climate research in the South American context. This study included 200 weather stations and climate surfaces at a scale of 1:1,500,000. The results indicate that the climates of continental Chile are essentially arid (B), temperate (C) and polar (E), the latter due to the elevation of the Andes. The predominant climates are high tundra (ET) and mediterranean (Cs). We have concluded that the use of climate surfaces enables the development of new classifications and indices as a function of scale. With respect to latitude, the climates of northern Chile are arid due to the Atacama Desert, and those of southern Chile are temperate, ranging from mediterranean to marine west coast.

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Significant changes in regional climates have been observed at the end of the twentieth century, taking place at unprecedented rates. These changes, in turn, lead to changes in global climate zones with pace and amplitude varying from one region to another. Algeria, a country characterized by climate conditions ranging from relatively wet to very dry (desert-like), has also experienced changes in its climate regions, notably in the country’s wet region, which represents about 7% of its total surface area, but is home to 75% of its population. In this study, the pace of climate zone changes as it is defined by Koppen–Geiger was analyzed for the period from 1951 to 2098 using climate data from observation and regional climate simulations over Algeria. The ability of the CORDEX-Africa regional climate models simulations to reproduce the current observed climate zones and their shifts was first assessed. Future changes over the whole of the twenty-first century were then estimated based on two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Analysis of the shift rate of climate zones from 1951 to 2005 found a gradual but significant expansion of the surface area of the desert zone at an approximate rate of 650 ± 160 km 2 /year along with the abrupt shrinking, by approximately 30%, at a rate of 1086 ± 270 km 2 /year, of the warm temperate climate zone surface area. According to projections for the RCP8.5 scenario, the rate of expansion of desert climate will increase in the future (twenty-first century), particularly during the period from 2045 to 2098.

Climate change represents a profound threat to the diversity and stability of global climate zones. However, the complex interplay between climate change and elevation in shaping climate heterogeneity is not yet fully understood. Here, we combine Shannon's diversity index (SHDI) with the Köppen‐Geiger climate classification to explore the altitudinal distributions of global climate heterogeneity; and their responses to climate change. The study reveals a distinctive pattern: SHDI, a proxy for climate heterogeneity tends to slow down or decline at lower elevations with increasing temperatures, while at higher elevations, it continues to rise due to continuing cold conditions. Examination of climate simulations, both with and without anthropogenic forcing, confirms that observed changes in climate heterogeneity are primarily attributable to anthropogenic climate change within these high‐elevation regions. This study underscores the importance of high‐elevation regions as not only custodians of diverse climate types but also potential refuges for species fleeing warmer climates. Plain Language Summary Climate change is threatening the diversity and stability of global climate patterns. But we're still not completely sure how climate change interacts with elevation to affect climate heterogeneity. In this study, we looked at how climate heterogeneity changes with altitude and responds to climate change. We found that as temperatures rise, the climate diversity tends to decrease at lower elevations, but it increases at higher elevations. We used climate simulations to show that these changes can be attributed to anthropogenic climate change. This study shows that high‐elevation regions are important because they can sustain diverse climates and are likely to be a safe haven for plants and animals when climate diversity continues to decline at lower elevations. Key Points We employed a high‐resolution climate data set to analyze changes in global climate heterogeneity With increasing temperatures, global climate heterogeneity amplifies at higher elevations, while diminishing at lower altitudes Anthropogenic climate change primarily drives alterations in climate heterogeneity at higher elevations

Thornthwaite climate classification indices are essential to interpret climate types in Bartin City, simplifying calculation process and interpretation of climatological water balance by forests. Thus, we aimed to develop a climatological water balance by the Thornthwaite method, as well as climatic characterization using the indices proposed by Thornthwaite for Bartin (including measurement points are Amasra, Arit, Hasankadi, Kozcagiz, Kurucasile, Ulus, Ulus Cubukeli, Ulus Ceyupler Koyu). We used historical series of climate data from all 15 metrological stations of Bartin between 1988 and 2019, which were divided into measurements regions. Air temperature and precipitation were collected on a daily scale. Precipitation and potential evapotranspiration data allowed calculating water balance by the Thornthwaite method. We characterized all locations as wet and dry using aridity indices proposed by Thornthwaite. Maps were generated from climate indices of Bartin using kriging interpolation method with spherical model, one neighbor, and 0.25° resolution. The measurement regions showed different patterns regarding water balance components and humidity index. Humidity index had a mean of 16.97. The prevailing climate in Bartin is moist subhumid. Bartin has two well-defined periods during the year: a dry and a rainy period. Three climate types predominate in Bartin and, according to the Thornthwaite classification, are humid, moist subhumid, and dry subhumid. Water characterization in Bartin showed 123.67 mm year−1 of water surplus, 79.7 mm year−1 of water deficit, and 1003.9 mm year−1 of potential evapotranspiration. Water deficit and potential evapotranspiration decrease as latitude increases.

The study investigates the future climate change in the Jammu and Kashmir (J&K) Himalaya, India, by the end of the twenty-first century under 3 emission scenarios and highlights the changes in the distribution of the prevalent climate zones in the region. The multi-model climate high-resolution projections for the baseline period (1961–1990) are validated against the observed climate variables from 8 meteorological stations in the region. The temperature projections from the GFDL CM2.1 model are found in good agreement with the observations; however, no single model investigated in the present study reasonably simulates precipitation and therefore multi-model ensemble is used for precipitation projections. The average annual temperature is projected to increase by 4.5 °C, 3.98 °C, and 6.93 °C by the end of the twenty-first century under A1B, RCP4.5, and RCP8.5 scenarios, respectively. In contrast, an insignificant variation in precipitation projection is observed under all the 3 scenarios. The analysis indicates that, unlike the 13 climate zones under the updated Köppen-Geiger climate classification scheme, the J&K Himalaya broadly falls into 10 main climate zones only namely, “3 subtropical (~ 11%), 4 temperate (~ 19%), and 3 cold desert (~ 70%) zones”. The projected climate change under the 3 emission scenarios indicates significant changes in the distribution of prevalent climate zones. The cold desert climate zone in the Ladakh region would shrink by ~ 22% and correspondingly the subtropical and temperate zones would expand due to the projected climate change. This information is vital for framing robust policies for adaptation and mitigation of the climate change impacts on various socio-economic and ecological sectors in the region.

The behavior of every catchment is unique. Still, we seek for ways to classify them as this helps to improve hydrological theories. In this study, we use hydrological signatures that were recently identified as those with the highest spatial predictability to cluster 643 catchments from the CAMELS dataset. We describe the resulting clusters concerning their behavior, location and attributes. We then analyze the connections between the resulting clusters and the catchment attributes and relate this to the co-variability of the catchment attributes in the eastern and western US. To explore whether the observed differences result from clustering catchments by either climate or hydrological behavior, we compare the hydrological clusters to climatic ones. We find that for the overall dataset climate is the most important factor for the hydrological behavior. However, depending on the location, either aridity, snow or seasonality has the largest influence. The clusters derived from the hydrological signatures partly follow ecoregions in the US and can be grouped into four main behavior trends. In addition, the clusters show consistent low flow behavior, even though the hydrological signatures used describe high and mean flows only. We can also show that most of the catchments in the CAMELS dataset have a low range of hydrological behaviors, while some more extreme catchments deviate from that trend. In the comparison of climatic and hydrological clusters, we see that the widely used Köppen–Geiger climate classification is not suitable to find hydrologically similar catchments. However, in comparison with novel, hydrologically based continuous climate classifications, some clusters follow the climate classification very directly, while others do not. From those results, we conclude that the signal of the climatic forcing can be found more explicitly in the behavior of some catchments than in others. It remains unclear if this is caused by a higher intra-catchment variability of the climate or a higher influence of other catchment attributes, overlaying the climate signal. Our findings suggest that very different sets of catchment attributes and climate can cause very similar hydrological behavior of catchments – a sort of equifinality of the catchment response.

Cultivation of Coffea arabica is highly sensitive to and has been shown to be negatively impacted by progressive climatic changes. Previous research contributed little to support forward-looking adaptation. Agro-ecological zoning is a common tool to identify homologous environments and prioritize research. We demonstrate here a pragmatic approach to describe spatial changes in agro-climatic zones suitable for coffee under current and future climates. We defined agro-ecological zones suitable to produce arabica coffee by clustering geo-referenced coffee occurrence locations based on bio-climatic variables. We used random forest classification of climate data layers to model the spatial distribution of these agro-ecological zones. We used these zones to identify spatially explicit impact scenarios and to choose locations for the long-term evaluation of adaptation measures as climate changes. We found that in zones currently classified as hot and dry, climate change will impact arabica more than those that are better suited to it. Research in these zones should therefore focus on expanding arabica's environmental limits. Zones that currently have climates better suited for arabica will migrate upwards by about 500m in elevation. In these zones the up-slope migration will be gradual, but will likely have negative ecosystem impacts. Additionally, we identified locations that with high probability will not change their climatic characteristics and are suitable to evaluate C. arabica germplasm in the face of climate change. These locations should be used to investigate long term adaptation strategies to production systems.

The impact of large dams is a popular topic in environmental science, but the importance of altered water quality as a driver of ecological impacts is often missing from such discussions. This is partly because information on the relationship between dams and water quality is relatively sparse and fragmentary, especially for low-latitude developing countries where dam building is now concentrated. In this paper, we review and synthesize information on the effects of damming on water quality with a special focus on low latitudes. We find that two ultimate physical processes drive most water quality changes: the trapping of sediments and nutrients, and thermal stratification in reservoirs. Since stratification emerges as an important driver and there is ambiguity in the literature regarding the stratification behavior of water bodies in the tropics, we synthesize data and literature on the 54 largest low-latitude reservoirs to assess their mixing behavior using three classification schemes. Direct observations from literature as well as classifications based on climate and/or morphometry suggest that most, if not all, low-latitude reservoirs will stratify on at least a seasonal basis. This finding suggests that low-latitude dams have the potential to discharge cooler, anoxic deep water, which can degrade downstream ecosystems by altering thermal regimes or causing hypoxic stress. Many of these reservoirs are also capable of efficient trapping of sediments and bed load, transforming or destroying downstream ecosystems, such as floodplains and deltas. Water quality impacts imposed by stratification and sediment trapping can be mitigated through a variety of approaches, but implementation often meets physical or financial constraints. The impending construction of thousands of planned low-latitude dams will alter water quality throughout tropical and subtropical rivers. These changes and associated environmental impacts need to be better understood by better baseline data and more sophisticated predictors of reservoir stratification behavior. Improved environmental impact assessments and dam designs have the potential to mitigate both existing and future potential impacts.

Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings.