Future Precipitation Research Articles

Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

Abstract Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.

Drivers of Caribbean precipitation change due to global warming: analyses and emergent constraint of CMIP6 simulations

Using 31 CMIP6 models we have analyzed projected future Caribbean precipitation. The model mean projection results in a 0.5 mm/day (20%) drying under a SSP5-8.5 scenario for the end of this century over the Caribbean basin. The multi-model spread is large ranging from no drying to a 1 mm/day (40%) reduction in mean precipitation. Eastern and central Pacific warming, resembling an El Niño / positive phase of Pacific Decadal Oscillation (PDO), appears to be the main driver by shifting and weakening the Walker circulation and inducing subsidence over the Caribbean, especially during the wet season (May–November). This applies for the model mean as well as for the inter-model spread. During the dry season (December–April) the southward migration of the Intertropical Convergence Zone (ITCZ) and the advection of dry air from outside the Caribbean seem to be the dominant drivers of the projected drying. Another mechanism that contributes to the drying is the land-sea contrasts that induce divergence/convergence over the Caribbean. The incapability of CMIP6 models to simulate the current tropical Pacific warming and Walker circulation trends questions the reliability of precipitation projections in the Caribbean. Based on our understanding of the physical processes affecting Caribbean drying and on an emergent constraint analysis we state that the future drying in the Caribbean is likely to be weaker than the one projected by CMIP6 models.

Constraining Projected Changes in Rare Intense Precipitation Events Across Global Land Regions

AbstractRare precipitation events with return periods of multiple decades to hundreds of years are particularly damaging to natural and societal systems. Projections of such rare, damaging precipitation events in the future climate are, however, subject to large inter‐model variations. We show that a substantial portion of these differences can be ascribed to the projected warming uncertainty, and can be robustly reduced by using the warming observed during recent decades as an observational constraint, implemented either by directly constraining the projections with the observed warming or by conditioning them on constrained warming projections, as verified by extensive model‐based cross‐validation. The temperature constraint reduces >40% of the warming‐induced uncertainty in the projected intensification of future rare daily precipitation events for a climate that is 2°C warmer than preindustrial across most regions. This uncertainty reduction together with validation of the reliability of the projections should permit more confident adaptation planning at regional levels.

Global increase in future compound heat stress-heavy precipitation hazards and associated socio-ecosystem risks

Compound extremes of lethal heat stress-heavy precipitation events (CHPEs) seriously threaten social and ecological sustainability, while their evolution and effects at the global scale under climate warming remain unclear. Here we develop the global picture of projected changes in CHPEs under various scenarios and investigate their socioeconomic and ecosystem risks combining hazard, exposure, and vulnerability through the composite indicator approach. We find a high percentage of heat stress is followed by heavy precipitation, probably driven by atmospheric conditions. Global average frequency and intensity of CHPEs are projected to increase in the future under high-emission scenarios. Joint return periods of CHPEs are projected to decrease globally, predominantly driven by changes in heat stress extremes. In the long-term future, over half of the population, gross domestic product, and gross primary productivity may face high risk in most regions, with developed regions facing the highest risks under SSP5-8.5 and developing regions facing the highest risks under SSP3-7.0.

Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Abstract. Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convection-permitting climate models (capable of simulating sub-hourly precipitation adequately) and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available.

Wetter trend in source region of Yangtze River by runoff simulating based on Grid-RCCC-WBM

Exploring the future hydroclimatic conditions of source region of Yangtze River (SRYaR), an alpine affected by climate change significantly, is essential for basin water resources management and development ss global climate change intensifies and the process of climate warming and humidification in Northwest China. This study proposed a practical framework for assessing water resource response to the context of climate changes in alpine catchments from the respective of both runoff and hydroclimatic conditions. Utilizing Grid-RCCC-WBM driven by corrected climatic forcing from the global climate models, this study estimate the prospective overall warmer and wetter pattern in the source region of Yangtze River. The key results indicated that: (1) Under all future scenarios, both temperature and precipitation within the catchment exhibit a significant upward trend. Projections from multi-model ensembles (MME) suggest that during the mid-term period (2041–2060, MT), temperatures are expected to rise by [0.74 °C, 3.08 °C] compared to the baseline period (1995–2014), with precipitation changes ranging from [4.8%, 21.4%]. (2) Future runoff within the catchment exhibits a consistent increase, with a linear trend rate of 1.1 mm/decade. runoff changes in MT compared to the baseline period vary from [−5.1%, 33.7%]. Runoff decreases in the northern part of the catchment, while notable increases occur in the southeastern and western regions. (3) In the future, the ratio of catchment evaporation capacity to precipitation decreases in comparison to the baseline period with an augmentation in soil moisture, enhancing its capacity for water retention and reducing the conversion of precipitation to evaporation, resulting a wetting trend of the catchment. (4) The future snowpack in the catchment continues to decrease, with a significant reduction in both the proportion of snowfall relative to total precipitation and the proportion of snowmelt runoff relative to total runoff, the risk of water resources crisis in the watershed is escalating.

Assessing Future Precipitation Patterns, Extremes and Variability in Major Nile Basin Cities: An Ensemble Approach with CORDEX CORE Regional Climate Models

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study’s findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change.

Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021–2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995–2020. The methodology developed implements spatio-temporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995–2020 based on historical data, and risk maps for each quarter of the period 2021–2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline.

Uncertainty reduction for precipitation prediction in North America.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

Forecasting extreme precipitations by using polynomial regression

It is well known that the recent global warming intensifies the magnitude of rainfalls due to the increase in water content in the atmosphere. Therefore, the probability of exceeding the previously observed extreme precipitation values also increases with the experienced climate change, and forecasting extreme weather events is becoming more important. This paper presents a new polynomial regression approach and software (PolReg), where future extreme precipitations exceeding all previous observations are estimated for each month of year by using prediction bounds with a level of certainty at 95%. The presented method determines the degrees and coefficients of best-fitting polynomials for each precipitation station and forecasts the expected extreme value for each month of year by using the determined polynomials. The performance of the method is tested by removing and estimating a total of 792 highest observed monthly total precipitation values of 66 precipitation stations in Turkey (the highest observation for each month of year for each station). The results show that the proposed method and the provided software have a high performance and accuracy in estimating future precipitation extremes and might be applied in many disciplines dealing with forecasting probable extreme values.

Comparative Study of Observed and Projected Future Climate Evolution in Two Watersheds (Souss-Massa and Ouergha, Morocco) Using the Statistical Downscaling Model (SDSM)

The hydrological cycle and local climate of a region are directly impacted by climate change. Rapid fluctuations in climate alter weather patterns, resulting in the occurrence of extreme weather phenomena. This study examines two basins situated in distinct regions of Morocco, each influenced by specific climatic conditions. The primary aim of this study is to examine the climatic changes occurring within the two watersheds. Initially, it involves a comparative analysis of annual precipitation and temperatures from 1982 to 2022. Subsequently, the study projects precipitation and temperatures for the period spanning from 2024 to 2100. This analysis relies on data collected from 13 stations, 8 in the Souss-Massa and 5 in the Ouergha regions, using the Statistical Downscaling Model (SDSM) version 4.2.9. The investigation employs the fourth generation coupled global climate model CanESM2. Precipitation data and historical temperature models are used to forecast future temperatures and precipitation, based on Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. The projected scenarios indicate a decrease in observed precipitation for the years 2040, 2060, and 2080, while temperatures are predicted to rise in both watersheds across all future scenarios.

Unveiling climate change impacts on agricultural sustainability: insights from the united states

Climate change poses significant threats to agricultural systems, necessitating a comprehensive understanding of its impact for effective adaptation strategies. This study examines the influence of climate change on agriculture in the United States, primarily focusing on temperature shifts and precipitation variations. Utilizing historical data and climate projections, we analyse trends in mean temperature and precipitation patterns from 1950 to 2020. The results indicate a noteworthy increase in annual mean temperature over the years, attributed to climate change. This rise in temperature has multifaceted implications, including heat stress in crops and exacerbation of drought conditions. The study also explores the complex relationship between precipitation patterns and climate change, highlighting regional variations and potential shifts in future precipitation trends. By projecting data under SSP 1-1.9 emission scenario, we assess potential changes in mean temperatures, hot days, and precipitation for 2040-2059 periods. In conclusion, this study sheds light on the interplay between climate change and agricultural systems, emphasizing the need for adaptive strategies to secure food production and mitigate potential risks. The findings underscore the urgency of proactive measures to safeguard agricultural sustainability and resilience in the face of evolving climatic conditions.

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models. These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation. In terms of perspective, we plan to utilize this dataset and conduct a comprehensive comparative study to evaluate the performance of different ML models. Our objective is to demonstrate the effectiveness and potential of these algorithms in improving weather forecasting capabilities and enhancing the accuracy of rainfall estimation methods in the specific study area.

Prediction of Clausius–Clapeyron Scaling of Daily Precipitation Extremes over China

Abstract Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment.

Scenario Dependence of Future Precipitation Changes across Japan in CMIP6

Scenario Dependence of Future Precipitation Changes across Japan in CMIP6

Future projections of precipitation and temperature extremes at Sohra (Cherrapunji) using Statistical Downscaling Model

The Statistical Downscaling Model (SDSM 4.2) is used to project the future precipitation and maximum and minimum temperatures at Sohra, one of the extreme places on earth, using the predictors of the Second-Generation Canadian Earth System Model (CanESM2). The SDSM was calibrated with daily precipitation and temperature data from 1979 to 2005 and validated from 2006 to 2020. Future scenarios generated under the three Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 are divided into three future periods, Near Future (2021-2040), Mid Future (2041-2071), and Far Future (2071-2100). It is found that the precipitation and maximum/minimum temperature at Sohra are influenced mainly by the major global predictors; specific humidity at 850 hPa height (s850) and mean temperature at 2 m (temp)/near surface specific humidity (shum), respectively. The downscaled result reveals an increase in Monsoon precipitation in the range of 266-1543 mm under various RCPs compared with the base periods 1985-2005 during the Near Future and 1979-2008 during the Mid and Far Future. Also, annual maximum and minimum temperature increases in the range of 1-2.8 °C and 1.2-3.6 °C for all RCPs in the future.

Late Pleistocene to modern precipitation changes at the Paranal clay pan, central Atacama Desert

The Atacama Desert in Chile is known to be one of the driest deserts on Earth, with dominating hyperaridity at least since the Miocene. During recent times, however, especially the southern part of the Atacama repeatedly experienced exceptional precipitation events, like in 2015 and 2017. Locally, these events with high rainfall rates caused catastrophic floods with significant destruction and human fatalities. Although the meteorological drivers of these heavy rains are widely understood, only little is known about the frequency and amplitude of similar events on geological timescales. Here we present the results of a study on an endorheic clay pan at the southern edge of the hyperarid core of the Atacama, an area with a mean precipitation of approx. 5 mm per year. A modern ground-truthing approach combining sediment data, remote-sensing and meteorological data as well as climate-modelling was applied. Our observations indicate that the clay pan reacted very sensitively to local precipitation during the past 30 years, with four events >20 mm total rain causing sufficient surface run-off in the catchment to partially flood the basin. Comparative analyses of the four events illustrate that the amount of run-off is dependent on the maximum rain rate during the events rather than the total rain sum. A 1.88-m long sediment core recovered from the centre of the clay pan records the local hydrological and -environmental history since the Late Pleistocene. Sedimentological, mineralogical, geochemical, and biological core analyses imply strong variations in the amplitude of the recorded rainfall, with a clear shift from enhanced alluvial activity caused by higher-amplitude rain events during the Late Pleistocene to lower-amplitude Holocene events. The Holocene background sedimentation is superimposed by seven severe “Millennial-scale rain events”, which imply precipitation maxima on sub-orbital timescales that are potentially driven by changes in the El Niño Southern Oscillation (ENSO). The results of the study shed new light on the glacial-interglacial but also the sub-orbital precipitation variability in the Coastal Cordillera of the Atacama Desert and its potential driving mechanisms, and provide perspectives of the future precipitation development in the region under progressive global warming.

Assessing hydrological response to future climate change in the Bouregreg watershed, Morocco

Hydrological systems in watersheds encounter growing challenges attributed to climate change, marked by shifts in precipitation patterns, heightened drought severity, and altered runoff dynamics. This study explores the implications of these changes for streamflow in the vulnerable Bouregreg catchment.This study used a novel approach in the Bouregreg watershed to address this issue by applying the underutilized GR2M model, deviating from the prevalence of ArcSWAT and other models in previous studies. It incorporates bias-corrected outputs from the CNRM-CM5 climate model, sourced from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The objective was to project monthly streamflow for the 2040s (2030-2050) and compare it with the reference period (1982-2001), considering the Representative Concentration Pathways (RCPs): medium RCP4.5 and high RCP8.5. The methodology started with predicting future precipitation and temperatures using the regional climate model. Then, the future average rainfall for each sub-basin was determined using the Thiessen method, and the future potential evapotranspiration (PET) was estimated through the Thornthwaite formula, considering the model-predicted temperatures. Following this, the hydrological model was calibrated and validated, leading to streamflow projection for the 2040s period under the two emission scenarios.The choice to utilize climate models and hydrological modeling techniques was driven by the need to provide valuable insights for water resource management. By projecting future streamflow under different emission scenarios, the study aimed to inform decision-makers about the potential impacts of climate change on water resources.The study's findings revealed that significant climatic changes would occur in the Bouregreg catchment under the RCPs. Projections indicated a noticeable increase in mean temperatures, with expected rises of about 1.32°C for RCP4.5 and 1.69°C for RCP8.5. Additionally, the PET is foreseen to rise by 5.38 mm under RCP4.5 and 6.27 mm under RCP8.5, while precipitation levels are anticipated to experience significant reductions, decreasing by 33.74% for RCP4.5 and 40.20% for RCP8.5. These pronounced climate alterations are projected to lead to an annual decrease in streamflow. For RCP4.5, this reduction is estimated at 44.63%, and for RCP8.5, it is even more significant at 64.30%. The alterations projected for the 2040s signify a substantial departure from the conditions observed during the reference period of 1982-2001.The importance of these findings lies in their utility for decision-makers in developing effective strategies and facilitating the implementation of adaptation measures for managing water resources and ensuring their sustainable use amid evolving climate conditions.

IWRAM: A hybrid model for irrigation water demand forecasting to quantify the impacts of climate change

The phenomenon of climate change exerts a substantial influence on the water consumption patterns of agricultural crops, thereby affecting the overall demand for irrigation water and regional water security. The quantitative assessment of future changes in irrigation water requirements has great importance for long-term water resource planning and the water-energy-food-ecosystem nexus. This work employed a comprehensive approach (IWRAM) by integrating many models and techniques, including a global climate model (CanESM2), a statistical downscaling model (SDSM), a bias correction technique (QTM), a temperature-based crop phenology method (GDD), and deep learning technology (LSTM), in order to assess the potential impact of climate change on irrigation water demand. Therein, the QTM is utilized for the purpose of bias correction for the CanESM2-SDSMed daily precipitation data. Additionally, the LSTM is implemented to forecast ETo using the CanESM2-SDSMed daily temperature data. The findings from the case study conducted in the Jinghuiqu irrigation area (JIA) indicate that the application of bias correction techniques resulted in notable enhancements in the frequency distribution of predicted precipitation data. Consequently, this led to a more coherent relationship between historical observed data and anticipated future data, aligning them more closely with natural patterns. The deep learning technique exhibited a strong degree of concordance, suggesting its exceptional capacity for modeling daily reference evapotranspiration. By using the IWRAM model, relatively accurate predictions were made for future precipitation, temperature, crop evapotranspiration, and effective precipitation. Consequently, an estimation was made regarding the anticipated need for irrigation water in JIA. These results highlighted that JIA total irrigation water demand (or net irrigation water demand) will increase by 121 mm (net irrigation water: 189 mm) and 119 mm (net irrigation water: 187 mm) in the year 2050 under RCP 2.6 and 8.5 scenarios, respectively. Climate change could potentially affect the seasonal distribution of precipitation. Therefore, it is crucial for the authorities in JIA to not only take into account the overall irrigation water requirement but also give priority to the water requirements of each stage of crop growth.

Fluctuations of discharge and hydropower potential in the upper Amu Darya under the background of climate change

Study RegionUpper Amu Darya (UAD). Study FocusThe UAD has abundant hydropower potential (HP). This study focuses on the potential sites of small hydropower stations (sHPs) and explores the impact of future climate change on discharge and HP. New Hydrological Insights for the RegionThe conflict between water resources and hydropower in Central Asia is a key issue affecting regional development. Developing sHPs provides a new direction for alleviating energy shortages in Central Asia. Combining the hydrological model with the geographic information decision-making method, we have determined the potential sites for ten sHPs in the UAD and calculated the HP for each station. Based on future climate data from two shared socioeconomic pathways and four global climate models, we found that future precipitation and temperature are projected to increase, with precipitation increasing mainly in spring and winter. The average discharge will increase by 19.1∼36.6% in the near-term (2031–2050) and by 29.7∼106.8% in the long-term (2071–2090). In addition, climate change has significantly increased HP in winter, and the increase of HP in relatively high altitudes is higher than that in low altitudes. This implies that the HP development prospect of UAD is broad in future, but for sHPs construction schemes, it is necessary to further consider the impact on both environment and society.