Droughts can have serious negative impacts on the water quality needed for irrigated agriculture. The Metropolitan region of Chile is a relevant producer of high-value crops and is prone to droughts. Standardized Drought Indices were used to characterize meteorological and hydrological droughts for the period from 1985 to 2015. To understand the relationship between droughts and water quality, we evaluated the correlations between daily discharge and surface water quality observations. The threshold level method was used to compare physicochemical parameters during hydrological drought periods with the Chilean water quality thresholds for agricultural uses. A significant (p < 0.05) negative relationship between discharge and electrical conductivity and major ions was found in most of the basin. Hydrological stations located in irrigation districts exceeded the official thresholds for these parameters during hydrological drought periods seriously threatening irrigated agriculture of the region.

Accurate and reliable precipitation data with high spatial and temporal resolution are essential in studying climate variability, water resources management, and hydrological forecasting. A range of global precipitation data are available to this end, but how well these capture actual precipitation remains unknown, particularly for mountain regions where ground stations are sparse. We examined the performance of three global high‐resolution precipitation products for capturing precipitation over Central Asia, a hotspot of climate change, where reliable precipitation data are particularly scarce. Specifically, we evaluated MSWEP, CHIRPS, and GSMAP against independent gauging stations for the period 1985–2015. Our results show that MSWEP and CHIRPS outperformed GSMAP for wetter periods (i.e., winter and spring) and wetter locations (150–600 mm·year−1), lowlands, and mid‐altitudes (0–3,000 m), and regions dominated by winter and spring precipitation. MSWEP performed best in representing temporal precipitation dynamics and CHIRPS excelled in capturing the volume and distribution of precipitation. All precipitation products poorly estimated precipitation at higher elevations (>3,000 m), in drier areas (<150 mm), and in regions characterized by summer precipitation. All products accurately detected dry spells, but their performance decreased for wet spells with increasing precipitation intensity. In sum, we find that CHIRPS and MSWEP provide the most reliable high‐resolution precipitation estimates for Central Asia. However, the high spatial and temporal heterogeneity of the performance call for a careful selection of a suitable product for local applications considering the prevailing precipitation dynamics, climatic, and topographic conditions.

Water withdrawals for irrigated crop production constitute the largest source of freshwater consumption on Earth. Monitoring the dynamics of irrigated crop cultivation is crucial for tracking crop water consumption, particularly in water-scarce areas. We analyzed changes in water-dependent crop cultivation for 650 000 km2 of Central Asian drylands, including the entire basin of the Amu Darya river, once the largest tributary to the Aral Sea before large-scale irrigation projects grossly reduced the amount of water reaching the river delta. We used Landsat time series to map overall cropland extent, dry season cropping, and cropping frequency in irrigated croplands annually from 1987 to 2019. We scrutinized the emblematic change processes of six localities to discern the underlying causes of these changes. Our unbiased area estimates reveal that between 1988 and 2019, irrigated dry season cropping declined by 1.34 million hectares (Mha), while wet season and double cropping increased by 0.64 Mha and 0.83 Mha, respectively. These results show that the overall extent of cropland in the region remained stable, while higher cropping frequency increased harvested area. The observed changes’ overall effect on water resource use remains elusive: Following the collapse of the Soviet Union, declining dry season cultivation reduced crop water demand while, more recently, increasing cropping frequency raised water consumption. Our analysis provides the first fine-scale analysis of post-Soviet changes in cropping practices of the irrigated areas of Central Asia. Our maps are openly available and can support future assessments of land-system trajectories and, coupled with evapotranspiration estimates, changes in crop water consumption.

Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains.

No abstract

Discrete time structured population projection models are an important tool for studying population dynamics. Within this field, Integral Projection Models (IPMs) have become a popular method for studying populations structured by continuously distributed traits (e.g. height, weight). Databases of discrete time, discrete state structured population models, for example DATLife (life tables) and COMPADRE & COMADRE (matrix population models), have made quantitative syntheses straightforward to implement. These efforts allow researchers to address questions in both basic and applied ecology and evolutionary biology. There are now over 300 peer-reviewed publications containing IPMs. We describe a novel framework to quickly reconstruct these models for subsequent analyses using Rpadrino R package, which serves as an interface to PADRINO, a new database of structured population models.We introduce an R package, Rpadrino, which enables users to download, subset, reconstruct, and extend published IPMs. Rpadrino makes use of recently created software, ipmr, to provide an engine to reconstruct a wide array of IPMs from their symbolic representations and conduct subsequent analyses. Rpadrino and ipmr are extensively documented to help users learn their usage.Rpadrino currently enables users to reconstruct 280 IPMs from 40 publications that describe the demography of 14 animal and 26 plant species. All of these IPMs are tested to ensure they reproduce published estimates. Rpadrino provides an interface to augment PADRINO with external data and modify parameter values, creating a platform to extend models beyond their original purpose while retaining full reproducibility.PADRINO and Rpadrino provide a toolbox for asking new questions and conducting syntheses with peer-reviewed published IPMs. Rpadrino provides a user-friendly interface so researchers do not need to worry about the database structure or syntax, and can focus on their research questions and analyses. Additionally, Rpadrino is thoroughly documented, and provides numerous examples of how to perform analyses which are not included in the package’s functionality.

&lt;p&gt;Water withdrawals for irrigated crop production constitute the largest global consumer of blue water resources. Monitoring the dynamics of irrigated crop cultivation allows to track changes in water consumption of irrigated cropping, which is particularly paramount in water-scarce arid and semi-arid areas. We analyzed changes in irrigated crop cultivation along with occurrence of hydrological droughts for the Amu Darya river basin of Central Asia (534,700 km&lt;sup&gt;2&lt;/sup&gt;), once the largest tributary river to the Aral Sea before large-scale irrigation projects have grossly reduced the amount of water that reaches the river delta. We used annual and seasonal spectral-temporal metrics derived from Landsat time series to quantify the three predominant cropping practices in the region (first season, second season, double cropping) for every year between 1988 and 2020. We further derived unbiased area estimates for the cropping classes at the province level based on a stratified random sample (n=2,779). Our results reveal a small yet steady decrease in irrigated second season cultivation across the basin. Regionally, we observed a gradual move away from cotton monocropping in response to the policy changes that were instigated since the mid-1990s. We compared the observed cropping dynamics to the occurrence of hydrological droughts, i.e., periods with inadequate water resources for irrigation. We find that areas with higher drought risks rely more on irrigation of the second season crops. Overall, our analysis provides the first fine-scale, annual crop type maps for the irrigated areas in the Amu Darya basin. The results shed light on how institutional changes and hydroclimatic factors that affect land-use decision-making, and thus the dynamics of crop type composition, in the vast irrigated areas of Central Asia.&lt;/p&gt;