Low-intensity Precipitation Research Articles

Temporal land use change and its effects on climate in Elmalı basin using GIS and remote sensing

Abstract Land use is vital in sustaining human activities, particularly in rapidly urbanizing regions like the Istanbul Metropolitan Area, where large-scale projects such as the Canal Istanbul, Third Airport, and Yavuz Sultan Selim Bridge have significantly impacted land use. These developments have driven population growth, leading to unplanned urbanization and environmental degradation, particularly in watersheds. In this study, the Elmalı Basin, a critical water source on the Anatolian side of Istanbul, was analyzed using Landsat-8 satellite imagery with a spatial resolution of 30 m for multispectral bands, covering the years 2013, 2015, 2018, and 2020. The results reveal a 36% increase in settlement areas, a decline in forest cover, and an increase in surface runoff from 78.76% in 2013 to 80.56% in 2020, mainly due to unplanned construction and climate changes. Temperature trends from 1979 to 2023 show an increase from 14.7 °C to 16.1 °C, while monthly precipitation anomalies highlight increasing variability in rainfall patterns. Moreover, the frequency distributions of monthly precipitation at the Florya and Göztepe stations show that low-intensity precipitation events are expected. In contrast, high-intensity events are rare, signaling drought and flood risks, which underscore the need for effective water management strategies. This study, which investigates the temporal changes in land use and their effects on climate, is crucial for providing preliminary insights that can inform future planning efforts.

Sub-Daily Performance of a Convection-Permitting Model in Simulating Decade-Long Precipitation over Northwestern Türkiye

One of the main differences between regional climate model and convection-permitting model simulations is not just how well topographic characteristics are represented, but also how deep convection is treated. The convection process frequently occurs within hours, thus a sub-daily scale becomes appropriate to evaluate these changes. To do this, a series of simulations has been carried out at different spatial resolutions (0.11 and 0.025) using the COSMO-CLM (CCLM) climate model forced by the ECMWF Reanalysis v5 (ERA5) between 2011 and 2020 over a domain covering northwestern Türkiye. Hourly precipitation and heavy precipitation simulated by both models were compared with the observations by Turkish State Meteorological Service (TSMS) stations and Integrated Multi-satellitE Retrievals for GPM (IMERG). Subsequently, we aimed to identify the reasons behind these differences by computing several atmospheric stability parameters and conducting event-scale analysis using atmospheric sounding data. CCLM12 displays notable discrepancies in the timing of the diurnal cycle, exhibiting a premature shift of several hours when compared to the TSMS. CCLM2.5 offers an accurate representation of the peak times, considering all hours and especially those occurring during the wet hours of the warm season. Despite this, there is a tendency for peak intensities to be overestimated. In both seasons, intensity and extreme precipitation are highly underestimated by CCLM12 compared to IMERG. In terms of statistical metrics, the CCLM2.5 model performs better than the CCLM12 model under extreme precipitation conditions. The comparison between CCLM12 and CCLM2.5 at 12:00 UTC reveals differences in atmospheric conditions, with CCLM12 being wetter and colder in the lower troposphere but warmer at higher altitudes, overestimating low-level clouds and producing lower TTI and KI values. These conditions can promote faster air saturation in CCLM12, resulting in lower LCL and CCL, which foster the development of low-level clouds and frequent low-intensity precipitation. In contrast, the simulation of higher TTI and KI values and a steeper lapse rate in CCLM2.5 enables air parcels to enhance instability, reach the LFC more rapidly, increase EL, and finally promote deeper convection, as evidenced by higher CAPE values and intense low-frequency precipitation.

Performance of GPM-IMERG satellite precipitation for rainfall-runoff modeling in Indonesia

ABSTRACT Accurate discharge predictions are essential for effective water resource management, including irrigation, drinking water supply, hydropower, reservoir management, environmental flow, and flood assessment. Satellite precipitation data, such as GPM-IMERG, offers a viable solution for estimating river discharge, especially in ungauged or sparsely gauged watersheds. This study evaluates the use of GPM-IMERG data in the HEC-HMS hydrological model to predict discharge in the Mayang Watershed, Jember Regency, Indonesia. The study employs the Arithmetic, Polygon Thiessen, and Isohyet methods to analyze rainfall distribution. Results indicate that the Polygon Thiessen method, when paired with GPM-IMERG data, provides more reliable precipitation estimates due to its consideration of rain gauge proximity. Calibration and validation confirm the superiority of GPM-IMERG data over traditional observational data, particularly under high-flow conditions. Despite some limitations in detecting low-intensity precipitation, GPM-IMERG outperforms other satellite products like TRMM in terms of accuracy. Sensitivity analysis highlights the significant influence of parameters such as curve Number and time of concentration on model outcomes. Further research should explore emerging rainfall data products to improve hydrological modeling accuracy.

Incorporation crisis lifecycle theory into full-stage flash drought spatio-temporal pattern identification and risk analysis

Incorporation crisis lifecycle theory into full-stage flash drought spatio-temporal pattern identification and risk analysis

Influence of precipitation infiltration recharge on hydrological processes of the karst aquifer system and adjacent river

Influence of precipitation infiltration recharge on hydrological processes of the karst aquifer system and adjacent river

How is climate change altering the precipitation pattern over Northwestern India?

AbstractClimate change in India is causing devastating downpour events and shifts in spatial characterization, with apparent regional differences. The Northwest India (NWI), which is in the Aravalli rain shadow zone and was formerly drier, has attracted great attention in recent years due to its changing rainfall patterns. This study throws light on the astonishing behaviour of ISMR over NWI with the advent of intensified rainfall over the region in the recent time frame. The Pettitt test of change point (CP) detection is utilized in this analysis to measure the change in rainfall patterns over a period of 70 years, from 1950 to 2019. This analysis suggests significant variation in the CP's time frame for different months of ISMR. The earliest change was noticed in July, while the latest was for September for the mean as well as for the intense precipitation. Moreover, we found a maximum change in the precipitation during the peak monsoon month (i.e., July and August). The difference in the precipitation of different percentile values before and after CP indicates a decrease (increase) in low (high) intensity precipitation for all the months and seasons as a whole with varying magnitude. The highest reduction of low‐intensity precipitation is noticed for the months of July and August, while the highest increase of high‐intensity precipitation (>95th percentile) is noticed for June and September. The heavier precipitation contributes largely to the mean increase of precipitation, expecting to receive more mean and precipitation extremes in the future. Unlike the increasing precipitation trend, potential evapotranspiration (source of local moisture) shows a declining trend, revealing the negative association among them and the possibility of enhanced advection of remote moisture responsible for enhanced precipitation over NWI. The enhanced vertically integrated moisture transport of the Arabian Sea and Bay of Bengal and its strengthening relationship with precipitation further confirm the contribution of remote moisture to intensified precipitation over NWI, though the in‐depth dynamical cause remains unclear. The increased Convective Available Potential Energy for entire monsoon seasons as a whole and individual months facilitates a favourable condition for enhanced convective activity over the region, resulting in strengthening the precipitation.

Root distributions predict shrub–steppe responses to precipitation intensity

Abstract. Precipitation events are becoming more intense around the world, changing the way water moves through soils and plants. Plant rooting strategies that sustain water uptake under these conditions are likely to become more abundant (e.g., shrub encroachment). Yet, it remains difficult to predict species responses to climate change because we typically do not know where active roots are located or how much water they absorb. Here, we applied a water tracer experiment to describe forb, grass, and shrub root distributions. These measurements were made in 8 m by 8 m field shelters with low or high precipitation intensity. We used tracer uptake data in a soil water flow model to estimate how much water respective plant root tissues absorb over time. In low-precipitation-intensity plots, deep shrub roots were estimated to absorb the most water (93 mm yr−1) and shrubs had the greatest aboveground cover (27 %). Grass root distributions were estimated to absorb an intermediate amount of water (80 mm yr−1) and grasses had intermediate aboveground cover (18 %). Forb root distributions were estimated to absorb the least water (79 mm yr−1) and had the least aboveground cover (12 %). In high-precipitation-intensity plots, shrub and forb root distributions changed in ways that increased their water uptake relative to grasses, predicting the increased aboveground growth of shrubs and forbs in these plots. In short, water uptake caused by different rooting distributions predicted plant aboveground cover. Our results suggest that detailed descriptions of active plant root distributions can predict plant growth responses to climate change in arid and semi-arid ecosystems.

Validation of precipitation obtained from GPM and H-SAF satellite data with relation to Ukrainian ground weather stations' data

The main objective of this study is to validate H-SAF and GPM satellite precipitation data for the territory of Ukraine. Validation was carried out by comparing satellite data with data from ground weather stations of Ukrainian Hydrometeorological Center and included the following stages: data collection and processing, bringing the data to the same spatial and temporal resolution, calculation of correlation coefficients and construction of confusion matrices. Correlation coefficients were calculated for each of the stations for two studied periods (the first period - April-September 2020, the second period - April-September 2021). In addition, to ensure a more detailed analysis, confusion matrices were constructed based on division of precipitation into classes of different intensity. The comparative analysis showed that a larger number of stations (117) has a correlation coefficient of 0.5 - 0.8 with relation to GPM data, and, as for HSAF precipitation data, a larger number of stations (59) has a low correlation coefficient (0 - 0.3). Confusion matrices were calculated for the following four classes of rainfalls (mm): “0-2”, “2-5”, “5-10”, “>10”. The confusion matrices coefficients showed that low-intensity precipitation or “no precipitation”, as well as high-intensity precipitation, can be determined by a satellite with high accuracy as opposed to the ground-based weather station measurements. Thus, according to the "specificity" indicator, a precipitation class >10 mm has the highest level of correspondence of satellite data (H-SAF, GPM Late, GPM Early) to ground data. The low value of the "specificity" for the range of 0-2 mm is explained by the fact that satellite methods are able to detect very low values of precipitation intensity, while the station shows their absence – 0 mm. Thus, according to the values of the confusion matrices, we see that a large part of the values of ground measurements of precipitation in the range of 0-2 mm are shown by the satellite method as “2-5 mm” class. We can also see that the “2-5 mm” class by satellite has the most confusion with the 0-2 mm class by station (for H-SAF, GPM Late, GPM Early). But based on the high values of the "recall" for the range of 0-2 mm (for H-SAF, GPM Late, GPM Early), we can conclude that when the station does not show this amount of precipitation (0-2 mm), then the satellite also shows another class of values. The values of "precision" coefficient show that the satellite measurements are actually reliable. This means that presence or absence of precipitation may be determined precisely, since the highest value of the "precision" coefficient is typical for low and high amounts of precipitation (on average 0.76 and 0.66, respectively). Detected by satellites, heavy precipitation or no precipitation are also confirmed in most cases by ground stations. Based on the results of the validation, GPM and H-SAF satellite precipitation data products can be used for rainfall monitoring and considered as an additional source of meteorological information.

Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients

Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients

Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands

Abstract Precipitation nowcasting is essential for weather-dependent decision-making, but it remains a challenging problem despite active research. The combination of radar data and deep learning methods has opened a new avenue for research. Radar data are well suited for precipitation nowcasting due to the high space–time resolution of the precipitation field. On the other hand, deep learning methods allow the exploitation of possible nonlinearities in the precipitation process. Thus far, deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. In this study, we have built a deep generative model with various extensions to improve nowcasting of heavy precipitation intensities. Specifically, we consider different loss functions and how the incorporation of temperature data as an additional feature affects the model’s performance. Using radar data from KNMI and 5–90-min lead times, we demonstrate that the deep generative model with the proposed loss function and temperature feature outperforms other state-of-the-art models and benchmarks. Our model, with both loss function and feature extensions, is skillful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time.

Soil moisture precipitation feedbacks in the Eastern European Alpine region in convection-permitting climate simulations.

A novel convection permitting modelling framework that combines a pseudo-global warming approach with continuously forced deep soil moisture from prescribed perturbation storylines is applied in the Eastern European Alpine region and parts of the Pannonian Basin to investigate soil moisture precipitation (SMP) feedbacks on summertime precipitation and the feedbacks' role under changed climate conditions. A set of 1-year convection-permitting (3 km horizontal grid spacing) soil moisture sensitivity simulations with the regional climate model of the Consortium for Small-Scale Modelling in Climate Mode are conducted. In order to account for global warming, end-of-the-century climate change effects from four global climate models, projecting the greenhouse gas concentration scenario RCP 8.5, are imprinted. The simulations reveal that (1) the locations of precipitation events are highly sensitive to soil moisture modifications while intensities and the internal structure of precipitation events are nearly unaffected and (2) high precipitation intensities are more likely in combinations with positive temporal but distinctive (either strong positive or strong negative) spatial SMP coupling. Low precipitation intensities are in favour of combinations of negative temporal and positive spatial coupling. The analyses suggest that soil moisture at a given time acts as a guiding field for the location of the next precipitation event. Interestingly, this behaviour is independent of climate change, although the coupling strength's increase is 1.5-1.7 times larger than expected from linear climate change scaling when climate becomes 50% dryer. Finally, it is found that (1) local deviations in the climate change signal of summertime precipitation in the range of up to ±40% are caused by uncertainty in deep soil moisture in the range of ±10% and (2) these local deviations in the climate change signal are dominated by soil moisture uncertainty in future climate conditions.

Climatic regulation of atmospheric mercury deposition: Evidence from mercury isotopes in an alpine peat core

Abstract Mercury is a global pollutant that can undergo long-range transport in the atmosphere. While anthropogenic activities have largely increased atmospheric Hg emission and deposition since global industrialization, mechanisms governing the atmospheric Hg cycle in preindustrial periods remain unclear. Alpine peatlands receive Hg mainly from atmospheric Hg deposition and are sensitive to climatic changes, therefore alpine peat cores can be a useful archive for understanding the relationship between atmospheric Hg deposition and climate change. Here we reconstruct a 1200-year Hg deposition record based on a core drilled from an ombrotrophic peat bog in Yunnan-Guizhou Plateau, SW China. This core shows a dramatic change of Hg deposition fluxes (30.3–515 μg/m2/yr) associated with variable Hg isotopic composition (δ202Hg: –1.02‰ to –2.01‰; Δ199Hg: –0.16‰ to –0.50‰; Δ200Hg: –0.08‰ to 0.07‰). Using a Δ200Hg-based model, deposition fluxes of atmospheric Hg(0) and Hg(II) were estimated, which vary coherently with climatic indices (e.g., temperature, precipitation, and monsoon intensity). Results of this study imply that atmospheric Hg deposition in SW China is governed by two climate scenarios. In warm-house climates, low precipitation intensity and vegetation growth rates in SW China caused low atmospheric Hg(II) and Hg(0) deposition fluxes, respectively. In cold-house climates, high precipitation intensity and vegetation growth rates caused high atmospheric Hg(II) and Hg(0) deposition fluxes. This study highlights that climate changes can have a strong control on atmospheric Hg deposition.

Flood generating mechanisms investigation and rainfall threshold identification for regional flood early warning

A cost effective and easily applied methodological approach for the identification of the main factors involved in flood generation mechanisms and the development of rainfall threshold for incorporation in flood early warning systems at regional scale is proposed. The methodology was tested at the Pinios upstream flood-prone area in Greece. High frequency monitoring rainfall and water level/discharge time-series were investigated statistically. Based on the results, the study area is impacted by “long-rain floods” triggered by several days long and low-intensity precipitation events in the mountainous areas, that saturate the catchment and cause high flow conditions. Time lag between the peaks of rainfall and water level was 17–25 h. The relationship between cumulative rainfall Rsum on the mountainous areas and maximum water level MaxWL of the river at the particular river site can be expressed as: MaxWL = 1.55ln(Rsum) − 3.70 and the rainfall threshold estimated for the mountainous stations can be expressed as: Rsum = 20.4*D0.3, where D is the duration of the event. The effect of antecedent moisture conditions prior each event was limited to the decrease of the time lag between rainfall and water level response. The limitations of the specific methodological approach are related to the uncertainties that arise due to the other variables contributing to the complex flood generating mechanisms not considered (e.g., the effect of snowmelt and air temperature, soil characteristics, the contribution of tributaries, or the inadequate maintenance of river network that may cause debris accumulation and river bank failure).

The effect of altitude on the uncertainty of radar-based precipitation estimates over Switzerland

ABSTRACT Despite high spatial coverage, radar-based Quantitative Precipitation Estimates (QPE) are affected by different sources of errors. Regardless of various techniques to merge these estimates with rain gauges, some errors still remain in the final product. This study focused on the relationship between these errors and altitude for a radar-only product (RZC) and a cross-validated-merged-with-gauge product (CPC-CV) in Switzerland using 16 years of hourly data based on two log-transformed metrics (bias and scatter). For each site, the bias is a measure of the mean error and the scatter is a robust estimate of the dispersion of the error around the mean. The results showed that the bias has a negative Pearson correlation coefficient with altitude, while the scatter has a positive correlation for both RZC and CPC-CV products during the entire period. Considering the effect of the old and the new networks, the errors are generally reduced using the new radar network. However, the relationship between altitude and the errors is still present. Regarding the seasonality effect, the results showed a significant underestimation at low altitudes and the weakest bias correlation in winter, while many stations showed overestimation in summer. For scatter, the correlation with altitude was highest in summer, while it was low in winter. Moreover, for low-intensity precipitation (below 1.1 mm . h − 1 ) there is a negative correlation for bias and a positive correlation for scatter with the gauge altitudes in both products. Interestingly, for high intensities (above 7 mm . h − 1 ), a positive correlation for bias and a negative correlation for scatter was observed . Also, we added the bias of eight glaciers based on the snow depth measurements in addition to the gauge data. In conclusion, the results showed that almost 30% of the variance of the errors in both products can be statistically explained using the gauge altitudes as a single explanatory variable.

High resolution ancient sedimentary DNA shows that alpine plant diversity is associated with human land use and climate change

The European Alps are highly rich in species, but their future may be threatened by ongoing changes in human land use and climate. Here, we reconstructed vegetation, temperature, human impact and livestock over the past ~12,000 years from Lake Sulsseewli, based on sedimentary ancient plant and mammal DNA, pollen, spores, chironomids, and microcharcoal. We assembled a highly-complete local DNA reference library (PhyloAlps, 3923 plant taxa), and used this to obtain an exceptionally rich sedaDNA record of 366 plant taxa. Vegetation mainly responded to climate during the early Holocene, while human activity had an additional influence on vegetation from 6 ka onwards. Land-use shifted from episodic grazing during the Neolithic and Bronze Age to agropastoralism in the Middle Ages. Associated human deforestation allowed the coexistence of plant species typically found at different elevational belts, leading to levels of plant richness that characterise the current high diversity of this region. Our findings indicate a positive association between low intensity agropastoral activities and precipitation with the maintenance of the unique subalpine and alpine plant diversity of the European Alps.

The impact of automated drainage water management on groundwater, soil moisture, and tile outlet discharge following storm events

Tile outlet discharge, soil moisture, and groundwater response were monitored for one year from a 10-ha catchment in north-central Ohio to understand the impact of automated drainage water management (ADWM) following 16 storm events. Under ADWM the tile outlet valve was programmed to open and release water when the water level in the control structure reached a user defined set point below the soil surface. Average groundwater level, soil moisture, and percentage of surface flooding indicated by groundwater levels were not significantly different following precipitation events during periods of ADWM compared to free drainage. However, the time to achieve maximum soil water moisture following precipitation onset was significantly longer and lateral hydraulic gradients were greater during periods of free drainage compared to periods of ADWM, which potentially alters how agricultural fields contribute water through edge-of-field buffers and waterways. In addition, maximum discharge occurred significantly later for storm events that occurred during ADWM; however, total tile outlet discharge was surprisingly not significantly different for events that happened under ADWM compared to events that occurred during free drainage. Tile outlet discharge for precipitation events occurring under ADWM was significantly correlated with the percentage of time during the event in which the tile outlet valve was opened. ADWM for this study was thus more effective at retaining water in the field and reducing tile outlet to near zero discharge during low-intensity precipitation events and when dry antecedent conditions were present. Our results highlight that fields under ADWM are not isolated from the surrounding landscape as they still allow water to move beyond the boundary of the field through combinations of (1) redistributions or reconnections of subsurface flow pathways and (2) periodic opening of outlet valves to release tile drainage water during large storm events and wet conditions. • Maximum groundwater and soil moisture after storms were not significantly different during free drainage compared to ADWM • Hydraulic gradients were significantly greater during times of free drainage compared to automated drainage water management • After storms, tile outlet discharge was not significantly different during free drainage conditions compared to ADWM • Automated drainage water management delayed the time to peak tile outlet discharge compared to free drainage

Assessment of three gridded satellite-based precipitation products and their performance variabilities during typhoons over Zhejiang, southeastern China

• IMERG-Early, GSMaP-NRT and FY4A QPE were evaluated for overall performance and five typhoons during 2018–2019. • IMERG-Early and GSMaP-NRT performed much better than FY4A QPE. • Satellite-based products tended to underestimate heavy precipitation but overestimate light precipitation. • Satellite-based products showed unstable performance and were more likely to present low estimations during typhoons. • Satellite-based products were difficult to detect heavy precipitation centers during typhoons in some parts of southwestern Zhejiang with complex topography. Effective and accurate estimation of heavy precipitation from typhoons is critical for the early warning of hydrological extremes and disaster mitigation. Satellite-based precipitation products are widely used for various purposes, including supporting climate, hydrology, and severe weather event monitoring and research activities. In this study, the performance of Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Chinese Fengyun-4A quantitative precipitation estimates (FY4A QPE) were compared to daily observations from 2018 to 2019. Further comparison of these products at an hourly scale was implemented for five typhoons that made landfall in the coastal areas of southeastern China. The results demonstrate that IMERG-Early and GSMaP-NRT performed much better than FY4A QPE, in both overall assessment and specific typhoons. All the satellite-based products showed the best correlations with the gauge observations for heavy precipitation events, and their correlations became worse with lower precipitation intensity. They tended to underestimate heavy precipitation but overestimate light precipitation. Furthermore, their random errors increased with increasing precipitation, according to an analysis of unbiased root mean squared error (ubRMSE). Based upon analyses of the probability of detection (POD) and critical success index (CSI), satellite-based products performed better during light precipitation events than moderate and heavy precipitation events. During typhoons, IMERG-Early and GSMaP-NRT had better accuracy in detecting typhoon precipitation with higher POD and CSI values. FY4A QPE showed instability in different typhoons and a low correlation with gauge observations. In addition, satellite-based products produced precipitation estimates more reliably in northern and eastern Zhejiang, but were difficult to detect heavy precipitation centers in some parts of southwestern Zhejiang with complex topography. These findings not only provide valuable references for the development of satellite-based retrieval algorithms to enhance the ability to monitor typhoon heavy precipitation but also support diverse hydrological models and applications.

Evaluation of GPM IMERG Products for Extreme Precipitation over Indonesia

Accurate information on extreme rain is essential for vulnerability analysis and early warning systems of hydrometeorological disasters. One newly launched satellite that can provide information about extreme precipitation is the Global Precipitation Measurement (GPM), which produces half-hour grid data through the Integrated Multi-Satellite Retrieval for GPM (IMERG) system. This work evaluates the performance of IMERG data to measure extreme precipitation indices over Indonesia. Three types of GPM IMERG data: IMERG-Early, IMERG-Late, and IMERG-Final, were validated by rain gauge data in Indonesia for extreme rain indices from 2016 to 2019. Overestimated values for extreme precipitation indices were dominated by low-intensity precipitation such as PRCPTOT, R1mm, and CDD indices. On the contrary, underestimated values of extreme precipitation indices dominated by high-intensity precipitation such as R90p, R95p, R50mm, and RX1day indices. Moreover, the different types of IMERG data showed a similar pattern with slightly different values for identifying extreme precipitation indices. Thus, a more comprehensive study is needed to improve GPM IMERG in observing the extreme precipitation over Indonesia.

Unpacking future climate extremes and their sectoral implications in western Nepal

Existing climate projections and impact assessments in Nepal only consider a limited number of generic climate indices such as means. Few studies have explored climate extremes and their sectoral implications. This study evaluates future scenarios of extreme climate indices from the list of the Expert Team on Sector-specific Climate Indices (ET-SCI) and their sectoral implications in the Karnali Basin in western Nepal. First, future projections of 26 climate indices relevant to six climate-sensitive sectors in Karnali are made for the near (2021–2045), mid (2046–2070), and far (2071–2095) future for low- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) using bias-corrected ensembles of 19 regional climate models from the COordinated Regional Downscaling EXperiment for South Asia (CORDEX-SA). Second, a qualitative analysis based on expert interviews and a literature review on the impact of the projected climate extremes on the climate-sensitive sectors is undertaken. Both the temperature and precipitation patterns are projected to deviate significantly from the historical reference already from the near future with increased occurrences of extreme events. Winter in the highlands is expected to become warmer and dryer. The hot and wet tropical summer in the lowlands will become hotter with longer warm spells and fewer cold days. Low-intensity precipitation events will decline, but the magnitude and frequency of extreme precipitation events will increase. The compounding effects of the increase in extreme temperature and precipitation events will have largely negative implications for the six climate-sensitive sectors considered here.

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures.

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-16066-9.