Average Annual Precipitation Research Articles

Coupling of WetSpass-M and MODFLOW models for groundwater flow assessment

Recharge is considered a key parameter in groundwater models for sustainable management of aquifers, which is influenced by factors such as land use, soil and weather. The present study was conducted to couple WetSpass-M and MODFLOW models in the Neyshabur–Rokh Basin. To this aim, the simulated recharge by the WetSpass-M model was applied as an input into MODFLOW to assess the groundwater balance. The hydrodynamic coefficients were determined by calibrating the model, and the model sensitivity to the hydraulic conductivity coefficient, specific yield and recharge was evaluated. The results indicated that the annual average of surface runoff, actual evapotranspiration, interception and recharge during 1991–2017 equalled 18, 36, 7.6 and 42.6% of the average annual precipitation in the basin, respectively. The accurate alignment of simulated and observed water levels, along with the achievement of suitable evaluation criteria values in both steady and transient states, demonstrates the WetSpass-M model's precision in estimating recharge and successfully integrating the two models. The groundwater balance assessment revealed a significant deficit in the aquifer, with the model demonstrating greater sensitivity to the hydraulic conductivity coefficient and providing valuable insights for the sustainable management of the Neyshabur aquifer. Thematic collection: This article is part of the Monitoring the aquifers collection available at: https://www.lyellcollection.org/topic/collections/monitoring-the-aquifers

Role of Soil Microbiota Enzymes in Soil Health and Activity Changes Depending on Climate Change and the Type of Soil Ecosystem.

The extracellular enzymes secreted by soil microorganisms play a pivotal role in the decomposition of organic matter and the global cycles of carbon (C), phosphorus (P), and nitrogen (N), also serving as indicators of soil health and fertility. Current research is extensively analyzing these microbial populations and enzyme activities in diverse soil ecosystems and climatic regions, such as forests, grasslands, tropics, arctic regions and deserts. Climate change, global warming, and intensive agriculture are altering soil enzyme activities. Yet, few reviews have thoroughly explored the key enzymes required for soil fertility and the effects of abiotic factors on their functionality. A comprehensive review is thus essential to better understand the role of soil microbial enzymes in C, P, and N cycles, and their response to climate changes, soil ecosystems, organic farming, and fertilization. Studies indicate that the soil temperature, moisture, water content, pH, substrate availability, and average annual temperature and precipitation significantly impact enzyme activities. Additionally, climate change has shown ambiguous effects on these activities, causing both reductions and enhancements in enzyme catalytic functions.

Catchment response to climate change under CMIP6 scenarios: a case study of the Krishna River Basin

Abstract This study assessed the impacts of climate change on the water balance of the Krishna River Basin (KRB) in India. A frequency-based metric, known as symmetric uncertainty, was used to select the top 50% of global climate models (GCMs) from a pool of 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs for hydrological modelling. The impact of climate change was projected for three future time frames, namely, near future (NF: 2026–2050), mid-future (MF: 2051–2075) and far future (FF: 2076–2100), using four scenarios from shared socio-economic pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The Soil and Water Assessment Tool model was used to simulate climate change impact during historical and future periods in the basin. The results showed a significant increase in the annual average precipitation, surface runoff, water yield and streamflow in the future under all SSP scenarios. The increase in the projected annual average precipitation ranges from 12 to 54% for four SSP scenarios compared to the historical ensemble average. Future periods showed a shift in the monthly peak flows compared to the baseline period. More availability of water in the future in the KRB can be effectively used for various water management works.

Estudo de suscetibilidade a movimentos gravitacionais de massa e inundações na região urbana de Patos de Minas, Minas Gerais

Every year Brazil registers thousands of accidents considered natural disasters, where the State of Minas Gerais contributes yearly, mainly accidents linked to the intense rains. Thus, this study aims to indicate areas that have the propensity to develop natural physical processes linked to mass movement and flooding in the urban perimeter of the city of Patos de Minas. From maps of land use and occupation, soil classes, hypsometry, average annual precipitation, slope, road mesh and NDVI, map algebra and analytical hierarchy process was applied. As the result, the susceptibility classification of the area was obtained. It was concluded that the urban perimeter has medium to high susceptibility for mass movements and flooding, preventive measures should be taken to avoid major accidents.

Drought analysis based on SPI and RDI drought indices in the Burdur Basin

Drought is the most complex of the recurrent extreme weather events and is defined as a natural disaster with severe environmental, economic, and agricultural impacts resulting from a significant decrease in the average rainfall recorded in an area and the average rainfall recorded in the same place. Droughts have become more frequent and severe in many parts of the world, including Türkiye, due to global warming and climate change (increasing temperatures and changing precipitation patterns). Water resources and the agricultural sector are most severely affected by droughts. In this study, drought analyses of the Burdur Basin, located in the Aegean region, one of Türkiye's seven geographical regions, were carried out. For drought analysis, annual average total precipitation, annual maximum temperature, annual minimum temperature, and annual average temperature data of 17238 Burdur and 17892 Tefenni meteorological observation stations were used. Both meteorological and agricultural drought analyzes are included in the analysis of droughts. Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological and agricultural drought, respectively. SPI and RDI values were obtained for 1-, 3-, 6- and 12-month time periods, and the severity, size, and distribution of dry and humid periods were determined for both stations separately. When the results of both methods were examined, severe droughts were observed in the study area in 1973, 1978, 1981, and 2017.

Exploring the Drivers of Soil Conservation Variation in the Source of Yellow River under Diverse Development Scenarios from a Geospatial Perspective

Soil conservation (SC) plays a vital role in preventing soil erosion and ensuring ecological security. While current research on SC primarily focuses on historical spatiotemporal variations, there remains a dearth of sufficient simulation research exploring future development scenarios. In this study, simulations were applied to the source of Yellow River (SYR), a representative ecologically fragile area. Satellite remote sensing and product data, including precipitation, soil, land use/cover, DEM, and SPOT/VEGETATION NDVI, were utilized. The historical and future evolutionary trends of SC in the SYR were quantitatively assessed using the Revised Universal Soil Loss Equation (RUSLE) and trend analysis method, and the geographical detector was employed to explore the forces driving spatial differentiations in SC. The results demonstrated that: (1) 2000–2020, the spatial heterogeneity of SC in the SYR was characterized by the distribution of “gradually decreasing from Southeast to Northwest”, demonstrated a trend of “increasing, decreasing, and then increasing”. (2) Under the diverse development scenarios, the trend of SC change in the SYR was predominantly rising, and the natural change scenario (NCS) > ecological conservation scenario (ECS) > economic expansion scenario (EES). (3) Slope was the most important single driver affecting the spatiotemporal differentiation of SC, and the interaction of slope with average annual precipitation, and NDVI on the spatiotemporal heterogeneity of SC had the strongest explanatory ability. The results can serve as a scientific basis for regional SC and ecological protection and construction of the SYR.

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

The impact of climate and land use changes on nitrogen and phosphorus pollution in the Luhun Lake Basin, China

Introduction: Water is an important ecological factor, and non-point source pollution (NPSP) seriously affects water security. In recent years, NPSP has affected water security and human survival in China.Methods: The Luhun Lake Basin has been selected as an example region of Yellow River Basin (YRB) in China, and the trends and impacts of nitrogen (N) and phosphorus (P) has been quantitatively evaluated by using geographic information system (GIS), remote sensing (RS) and InVEST model.Results: The results show that (1) the arable land and water bodies decreased, while forest land, grass land, construction land and unutilized land increased in the 20 years. The average annual precipitation increased from the northeast to the southwest in the 20 years; (2) the N and P exports first increased in 2000–2010 and then decreased in 2010–2020, and finally increased in 2000–2020; (3) with the increase of precipitation, the N and P exports decreased in 2000–2010, 2010–2020 and 2000–2020; (4) the amount of N and P exports on arable land and construction land were more than that on the other land use types. The N and P exports on each land use type all first increased and then decreased, and the overall change trend all increased in 2000–2020.Discussion: This study demonstrates how geographic information system (GIS), remote sensing (RS) and InVEST model can be used to assess nitrogen and phosphorus pollution in data scarce regions.

Estimation of Average Annual Precipitation Considering Spatio-Temporal Variability Through the Use of Multiple Linear Regression in the State of Paraíba

Objective: The objective of this work was to calibrate a multiple linear regression equation to relate average annual precipitations to spatial localization of 145 stations placed on three mesoregions of Paraíba State, Brazil, Borborema, Agreste, and Mata Zone. Theoretical framework: The variability of precipitations in space and time is directly related to the hydrological cycle, land use and occupation, and local economy. In this sense, regional models for rainfall may provide relevant information, supporting the decision-making process associated with water resources planning and management. Method: For the study case, stations with rainfall series with more than 10 years of daily data were considered. The posts that their few flaws in their series had the data filled in by the Inverse Distance Weighted (IDW) deterministic method, which assings weitghted weights to the sample data, Where the influence of one station over another decreases with the distance to the station to be estimated. Results and conclusion: The results were moderate, with a determination coefficient of 0.62 for the studied mesoregions. The average percentage errors of the estimate showed that most of the analyzed surface is classified with errors between 8.3% and 22,8%. Implications of the research: The multiple regression statistical tool was used for precipitation data with high variability, proving effective modeling. This type of study helps with decision-making in water resource management and climate monitoring studies. Originality/value: Using the multiple regression statistical tool, it was possible to obtain a linear equation to estimate the average annual precipitation in three mesoregions of the Paraíba State.

Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States.

Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47-65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose-response to low (<30 μg/L) concentrations of Li.

Quantitative analysis of fractional vegetation cover in southern Sichuan urban agglomeration using optimal parameter geographic detector model, China

Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It is crucial for us to discover the temporal and spatial features and potential drivers of vegetation change to promote regional ecological environment protection and management. However, it can be difficult to pinpoint the causes of vegetation change when considering both human activity and climate change. We used trend and stability method to study the temporal and spatial patterns of vegetation evolution in the South Sichuan Urban Agglomeration (SSUA) from 2001 to 2021 with the Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied to optimize the spatial scale and zoning effect of geographic data, effectively solving the problem of spatial data heterogeneity. It compensates for the inadequacies of conventional approaches that neglect the modifiable areal units problem (MAUP), and improving the science and accuracy of quantitative analysis and identification of vegetation drivers. We studied demonstrate that (1) During the last 21 years, Fractional Vegetation Cover (FVC) has generally been in good condition, with a multi-year average FVC greater than 0.4 of 71.74 %, and the vegetation is significantly characterized by a low fluctuation of 78.16 %. However, there is a significant trend of vegetation degradation, accounting for 8.89 %, mainly in the main urban areas of Neijiang and Zizhong County, Lu County of Luzhou City, Gao County of Yibin City, and other areas with rapid urbanization. In general, FVC is low in the built-up areas of towns and along transportation roads, while the mountainous and agricultural areas have a high level of vegetation cover. (2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its drivers. (3) The explanatory power of urbanization rate, land use type, slope, GDP, population density, and average annual precipitation were all above 20 % and were the main drivers of vegetation change. Moreover, any two factors interacted in a nonlinear enhancement and a bi-variable enhancement, increasing the impact on vegetation spatial variation. When the slope is 26.9°∼87.4°, the elevation is 967 m ∼ 4207 m, the average annual temperature is 0.18 °C ∼ 13.6 °C, the average annual precipitation is 328 mm ∼ 439 mm, the GDP is 4.07 ∼ 5.23 million yuan km−2, the population density is 12.7 ∼ 21.1 people/km2, the urbanization rate is 33.4 %∼37.7 %, and the land-use type is forest land, the FVC value is the highest and suitable for vegetation growth. The study showed that using the OPGD model to detect the zoning effects and spatial scale of the explanatory variables solves for the shortcomings of the previous methods for variable regional units and discrete methods, may more precisely explore the features of temporal and spatial changes in the vegetation and the driving mechanisms, offers scientific references for environmental conservation and long-term economic growth in the region.

Carbon stock in aboveground biomass and necromass in the Atlantic Forest: an analysis of data published between 2000 and 2021.

Synthesising knowledge on carbon stocks is an essential tool for understanding the potential of forests to store carbon and its drivers. However, such a synthesis needs to be constructed for the Atlantic Forest due to various methodological approaches and biogeographic heterogeneity. Thus, here we conducted a bibliographic search (2000 to 2021) on carbon stocks in the biomass and necromass of Atlantic Forest ecosystems to understand the variation in stocks and their explanatory variables. Drivers included spatial (altitude, forest size) and climatic (precipitation and temperature) variables, and successional stages. Based on the information in 46 articles, biomass exhibited the highest carbon stock (96%), in Mature Forests (MF), with an average of 125.34±40.3 MgC.ha-1, whereas Secondary Forests (SF) stored 82.7±38.2 MgC.ha-1. The carbon in the necromass varied from 1.63 to 11.47 MgC.ha-1, with SF exhibiting 3.90±2.73 MgC.ha-1 and MF 4.31±2.82 MgC.ha-1. Only average annual precipitation and successional stage positively explained the carbon in Atlantic Forest. This research clarifies the function and potential of Atlantic Forest fragments for storing carbon and reinforces need for conserving mature forest patches throughout the biome since one hectare of mature forest can store almost twice as much carbon as one hectare of secondary young patches.

Palaeoecological reconstruction of the Late Glacial and Early Holocene based on pollen data from lake Novozhilovo (Perm region, Russia)

This paper provides the first paleolimnological studies performed in the upper Kama basin (Perm region). These yielded a detailed pollen record characterizing the changes in the natural conditions of the transition from the periglacial environment of the Late Glacial period to the interglacial conditions of the Holocene between 14.15 and 10.0 cal ka BP. It was found that open birch woodland with larch existed in the Allerød period (14.15–12.7 сal ka BP) and was replaced by larch forest-steppe during the Younger Dryas (12.7–11.5 cal ka BP). The spread of spruce began in the early Holocene between 11.5 and 10.7 cal ka BP, whereas open larch and spruce woodlands became dominant between 10.7 and 10.0 cal ka BP. Quantitative palaeoclimatic reconstruction reveals that the average temperature of the warmest month (July) and the annual precipitation were considerably lower than modern values. The average July temperature increased from 10 to 16ºС between 14.15 and 12.7 cal ka BP. Then, small fluctuations within 1ºС occurred between 12.7 and 10.0 cal ka BP. The average annual precipitation was 200-300 mm below modern values between 14.15 and 10.7 cal ka BP and reached them only after 10.7 cal ka BP.

Effectiveness and resilience of BMPs to watershed climate adaptation considering the uncertainty of hydrological model and GCMs

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

Socioeconomic and environmental factors associated with dengue fever incidence in Guatemala: Rising temperatures increase dengue risk.

Dengue fever is a mosquito-borne illness that infects 390 million people annually. Dengue outbreaks in Guatemala have been occurring more often and at increased rates since the first dengue outbreak in Guatemala in the 1970s. This study will examine environmental and socioeconomic factors associated with dengue in Guatemala at the municipality (county) level. Socioeconomic factors included population density, Mayan population, economic activity, and attending school. Environmental factors included average minimum annual temperature and annual precipitation. The relationship between environmental and socioeconomic variables and dengue fever incidence was initially evaluated through univariate zero-inflated negative binomial models, and then again through three zero-inflated multivariate negative binomial regression models. For all three models, elevation was considered a predictor of zero-inflation. In the combined model, there was a positive relationship between minimum temperature, economic activity and dengue fever incidence, and a negative relationship between population density, Mayan population and dengue fever. Predicted rates of dengue fever incidence and adjusted confidence intervals were calculated after increasing minimum yearly temperature by 1°C and 2°C. The three municipalities with the highest minimum yearly temperature (El Estor, Iztapa, and Panzós) and the municipality of Guatemala, all had an increase in the magnitude of the risk of dengue fever incidence following 1°C and 2°C increase in temperature. This research suggests that these socioeconomic and environmental factors are associated with risk of dengue in Guatemala. The predicted rates of dengue fever also highlight the potential effect that climate change in the form of increasing temperature can have on dengue in Guatemala.

Assessment and efficiency of CMIP6 models in simulation and prediction of climatic parameters of precipitation and temperature in the Samalghan basin, Iran

In the present study, four global climate models MRI-ESM2-0, IPSL-CM6A-LR, CanESM5, and GFDL-ESM4 from the set of CMIP6 models are assessed to select the best model and determine the effects of climate change on temperature and precipitation parameters under three shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the base period (1988–2017) and a future period (2020–2049) in the Samalghan basin. Statistical measures such as mean absolute error, root mean square error, mean bias error are applied to test the models, and the correlation coefficient is used to compare the results of the historical period of the models with the observational data of the selected stations. Taking the obtained results into account, the global climatic model IPSL-CM6A-LR is chosen to study the trend of temperature and precipitation changes in the future period under scenarios. The results of this study indicate an increasing trend of the average annual precipitation in the desied period compared to the base period for the SSP1-2.6 and SSP2-4.5 scenarios at all stations. Also, it increases in the SSP5-8.5 scenario for all stations except Besh Ghardash, Hesegah and Darkesh stations. The predictions of temperature show an increase in the minimum and maximum temperature values under all scenarios compared to the base period.

Assessment of Climate Predictions for the Oued Laou watershed in Northern Morocco: A comparison of two predictive models

Focusing on the Oued Laou watershed, this study presents an innovative approach to simulate daily precipitation over a 35-year period in northern Morocco. We used the SARIMAX (AutoRegressive Seasonal Moving Average with Exogenous Variables) model and combined its potential with the Python programming language to achieve these Predictions. The SARIMAX model is an advanced time series analysis tool that integrates exogenous, autoregressive, seasonal and integrated variables. This model is capable of accurately predicting daily, monthly and annual precipitation averages for the study period. The climate projections obtained suggest that precipitation in the study area will decrease and it’s accompanied by increasing temperatures. This could have significant impacts on ecosystems, water resources and the agricultural sector. This study provides essential information for a better understanding of the particular climatic trends in northern Morocco. It provides a sound basis for making informed decisions on the sustainable management of natural resources. This information will be essential in anticipating and mitigating the effects of climate change, particularly with regard to precipitation and its potential impacts it could have on the environment and agriculture.

Protecting isolated reptile populations outside their main area of distribution: a predictive model of the Dice snake, Natrixtessellata, distribution in the Czech Republic.

Marginal populations of animals are highly susceptible to environmental pressures associated with climatic changes. Understanding their distribution and ecological requirements is, thus, essential for the development of efficient conservation strategies. The dice snake, Natrixtessellata, is listed as critically endangered in the Czech Republic. In certain regions (Bohemia and Silesia), its populations are located beyond the northern border of the continuous range of the species, while the south Moravian populations are connected to it. Based on the statewide database of the Czech Nature Conservation Agency, we created a predictive model and determined key factors influencing the species distribution. The most relevant factors were: watercourses and bodies, average annual temperatures, altitude, slope inclination and precipitation seasonality. The model fits the presence records well and is applicable in both theory and practice of the species conservation - for example, focusing faunistic research to certain areas, critical analysis of controversial presence reports and as an input for species management in the form of repatriation and introduction.

Assessing the Impacts of Future Climate and Land-Use Changes on Streamflow under Multiple Scenarios: A Case Study of the Upper Reaches of the Tarim River in Northwest China

Climate change and land use/cover change (LUCC) are two major factors that alter hydrological processes. The upper reaches of the Tarim River, situated in the northwest region of China, experience a dry and less rainy climate and are significantly influenced by human activities. This study comprehensively assessed the impacts of individual and combined climate changes and LUCCs on streamflow. Three general circulation models (GCMs) were utilized to predict future climate changes under three shared socioeconomic pathways (SSP119, SSP245, and SSP585). Cellular Automata–Markov (CA–Markov) was employed to predict future LUCC under three scenarios (i.e., ecological protection, historical trend, and farmland development). Streamflow for the period 2021–2050 was simulated using the calibrated MIKE SHE model with multiple scenarios. The results showed that from 2021 to 2050, increments in both average annual precipitation and average annual temperature under the three SSPs were predicted to lead to an increased streamflow. In comparison to the conditions observed in 2000, under three LUCC scenarios for 2030, the grassland area decreased by 1.04% to 1.21%, while the farmland area increased by 1.97% to 2.26%, resulting in reduced streamflow. The related changes analysis indicated that the variation in streamflow during winter is most significant, followed by spring. The study predicted that climate change would increase streamflow, while LUCC would decrease it. Due to the greater impact of LUCC, considering the combined effect of both factors, runoff would decrease. The contribution analysis indicated that climate change contributed between −7.16% and −18.66%, while LUCC contributed between 107.16% and 118.66%.

Can the Accuracy of Fine-Resolution Precipitation Products Be Assessed from the Surrounding Water Balance and Drought Chain (WBDC) in the Qinghai–Tibetan Plateau?

The Qinghai–Tibetan Plateau (QTP), which has a unique and severe environment, suffers from the absence of rainfall gauges in western arid land. Using different precipitation products in this region would easily lead to contradictory results. To evaluate nine fine-resolution precipitation products in the QTP, we propose a “down to top” methodology, based on water balance and drought chain, by forecasting two accuracy assessment indices—multi-year precipitation bias and precipitation correlation. We assessed the biases of all products in the Jinsha–Yalong, Yellow, Heihe, Yangtze, Yarlung Zangbo catchments and interior drainage areas. And we assessed gauge-based correlation of precipitation products, based on the correlations between precipitation product-based effective drought index (EDI), Soil Moisture Active Passive (SMAP)-based soil moisture anomaly, and the moderate-resolution imaging spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI) anomaly (R = 0.712, R = 0.36, and R = 0.785, respectively) for cross-sectional rainfall observations on the Tibetan Plateau in 2018. The results showed that ERA5-Land and IMERG merged precipitation dataset (EIMD) can efficiently close the water budget at the catchment scale. Moreover, the EIMD-based EDI exhibited the best performance in correlation with both the SMAP-based soil moisture anomaly and MODIS-based NDVI anomaly for the three main herbaceous species areas—Kobresia pygmaea meadow, Stipa purpurea steppe, and Carex moorcroftii steppe. Overall, we find that EIMD is the most accurate among the nine products. The annual average precipitation (2001–2018) was determined to be 568.16 mm in the QTP. Our assessment methodology has a remote sensing basis with low cost and can be used for other arid lands in the future.