Climate Input Research Articles

Kahramanmaraş İlinde Tarımsal Mekanizasyon Düzeyinin İlçelere Göre Poisson Regresyon ile Belirlenmesi

In this study, agricultural mechanization level indicators of the districts of Kahramanmaraş between the years 2012-2021 were calculated. A model has been established for this. While the dependent variable in the model is the agricultural areas planted in the districts according to the years, the independent variables are the number of tractors in the districts according to the years and other determined tools, equipment and machines. Poisson regression test was used, there was 25.022 chi-square probability relationship between the planted agricultural areas in Kahramanmaraş districts between 2012 and 2021, with the number of tractors in the districts and other determined equipment by years. At the level of agricultural mechanization, it was determined that there was a 3.32 decrease in the average number of tractors per 1000 hectares of land in Kahramanmaraş districts between 2012 and 2021, and a decrease of 12.45 in the number of specific equipment per tractor over the years. Among this decrease, it was thought that reasons such as climate change, input costs and job change were among the main reasons for leaving agriculture. In the comparison made from 2012 to 2021, the number of tractors in all districts increased by 54.18%, while the number of tractor equipment was also observed. An increase of 46.11% was observed. In addition, as a result of the trend analysis value applied, it was concluded that the average absolute percentage errors in the districts were 4.10443, the average number of tractors per 1000 hectares by years, and the number of specific equipment per tractor in the districts according to the years was 1.96718, which is a good estimation.

Hybrid ARIMA-IIS Approach for Wheat Yield Forecasting: An Integrated Approach

This study explores the application of a hybrid approach, combining the Impulse Indicator Saturation (IIS) method with an ARIMA(x) model, to forecast wheat yield. The IIS method is employed to find potential impulse responses, which are then integrated into the ARIMA(x) framework. The IIS method captures the potential joint effects of the weather, climate and other inputs on the data generating process of the wheat yield time series. The performance of the hybrid ARIMA(x) model is compared with that of the standalone ARIMA model using various error metrics, including Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Additionally, model selection criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Schwarz Bayesian Information Criterion (SBIC), and Hannan-Quinn Criterion (HQ) are used to identify the optimal model for forecasting. The training data of wheat yield from 1948-2018 was used to fit both the ARIMA and ARIMA(x) models, while the remaining observations until 2023 are used for model validation. The results of the study reveal that the hybrid ARIMA(x) model exhibits superior forecasting ability, demonstrating lower error metrics compared to the standalone ARIMA model. Notably, the ex-ante forecasts for the 2023-24 period predict a wheat production of 29.916 million tons using the ARIMA(x) model and 29.656 million tons using the ARIMA (2,1,2) model. These findings underscore the efficacy of the hybrid approach in enhancing production forecasting accuracy, thereby serving as a valuable basis for early warning systems to address potential demand and supply gaps in wheat production.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Stacking nitrogen mitigation strategies for future pasture-based dairy farms: impacts on leaching and profit

Producers in New Zealand’s pasture-based, seasonal dairy sector are striving to reduce nitrogen (N) losses to the environment whilst maintaining or increasing farm profitability. This study examined the cost-effectiveness of stacking different combinations of five N leaching mitigation strategies within the whole farm system; 1) reduced N fertilizer input, 2) off-paddock infrastructure, 3) recycling N by growing maize silage on a dedicated area on the farm using effluent as a fertilizer source followed by a catch-crop, 4) dietary salt supplementation to dilute urinary N, and 5) applying a nitrification inhibitor (NI) to slow the release of nitrate in the soil. The reference point (baseline) was a typical current dairy farm (CF) system in the Waikato region of New Zealand. We modelled four Future Farm scenarios by stacking mitigation strategies as follows: baseline plus reduced N fertilizer input, reduced stocking rate, and off-paddock infrastructure (FF); FF plus a dedicated maize block (FFP); FFP plus dietary salt (FFPS); and FFPS plus NI (FFPSNI). These systems were modelled using the Whole Farm Model coupled with the Urine Patch Framework, and APSIM models, using observed climate and economic input data over five consecutive years from 2013-2018. Relative to CF, the FF system achieved a N leaching reduction of 31% with a reduction in profit of 16%. The FFP system had a smaller N leaching reduction (22%), but the reduction in profit was smaller (11%). The fully stacked system (FFPSNI) demonstrated the largest leaching reduction of 33%, but also the largest profit reduction of 27%, compared with the CF. Stacking these five N mitigation strategies can achieve substantial N leaching reductions at the farm-scale. Including a dedicated, effluent-fertilized maize block followed by a catch-crop as part of the stack can reduce the negative impact on profitability but has a trade-off in N leaching. Farmers will have to weigh up these compromises between profit and leaching, considering risk factors not modelled here.

Testing and refinement of elemental proxies in tropical lakes from the Indian subcontinent

AbstractThe conventional approach of using modern values of lake hydrochemistry and sediment composition as key parameters for identifying proxies for environmental reconstruction is complicated by regional climate, catchment geology, lake morphometry and anthropogenic input. In contrast to modern studies from lakes in the north‐temperate regions, reliable proxy indicators for past environmental change from tropical lake systems are still lacking. In this study, we compare new surface geochemical and mineralogical data from the Manasbal Lake, Kashmir (India), with other lakes situated in climatically and geomorphologically distinct regimes with varying degrees of pollution (Tso Moriri in north‐western Himalaya and Lonar in central India). We characterise external (e.g., geology and weathering), internal (e.g., lake water mixing and evaporation) factors and human activities influencing sediment composition and spatial distribution of proxies in each of these lakes to evaluate the usefulness of some of the commonly used proxies, as well as ascertain conditions where they might be applicable. Our results show that (i) morphometric parameters (such as lake size, water depth and catchment vs. lake area) variably influence the spatial distribution of sediments and proxies on the lake bottom highlighting the limitations of using single cores for long‐term palaeoclimate reconstruction; (ii) hydrochemical proxies (such as Na+/Na++Ca2+ and Mg/Ca) can delineate endogenic (Lonar and Tso Moriri lakes) versus allogenic (Manasbal Lake) influence on modern sediment characteristics; (iii) the compilation of heavy metal data (e.g., Fe, Zn, Mn, Cr and Ni) from all three lake basins with varying degrees of pollution highlights the limitations of using them to characterise weathering conditions in these basins; and (iv) human impact must be precluded before using the most promising endogenic minerals (calcite, aragonite, gypsum and pyrite) as palaeoenvironmental/palaeoclimate indicators.

Estimating irrigation requirement and scheduling for major crops: Case study in Kampong Thom, Cambodia

The situation is the outcome of both increased water demand and climate change, the shortage of water resources is increasing in Cambodia. In this regard, efficient irrigation water management for vegetable production is a crucial need for farmers. The main crops farmed in Chroab commune, Santuk district, Kampong Thom province, Cambodia, are cabbage, potato, and tomato. This article uses the CROPWAT model to predict the water requirement. The pressure plant was used to measure the soil’s soil water capacity, and the findings ranged from 45.65 to 234.159 mm/m. The analysis showed that the overall CWR for crop water for the entire growing season for cabbage, potato, and tomato was 589.4 mm/165 days; 442.5 mm/130 days; and 585.4 mm/145 days, respectively. The findings of the study contribute to investigating the irrigation water requirements with scientific soil and climate inputs.

Simulation of Long-Term Water Erosion in Rainfed Croplands of Eastern Washington

Highlights Water erosion in the inland Pacific Northwest was modeled with WEPP for the past (1940–1982) and present (1983–2020). Water erosion decreased from past to present in the study watersheds. Major factors are increased adoption of conservation practices and decrease in large precipitation events. Abstract. Water erosion is an ongoing problem in eastern Washington due to its hilly terrain, highly erodible silt loam soils, rain on thawing soil, and the prevalence of conventional tillage. The region is characterized by a Mediterranean-type climate with warm, dry summers and cool, wet winters. Three distinct precipitation zones, with annual totals low (<380 mm), intermediate (380–460 mm), and high (>460 mm), dictate the area’s crop rotations. A unique 43-year (1940–1982) dataset of winter erosion measured on multiple agricultural fields in Whitman County, eastern Washington, by Verle Kaiser, a USDA Soil Conservation Service agronomist, showed annual erosion rates averaging 53.8 Mg ha-1, far exceeding the current Natural Resources Conservation Service tolerable limit of 11 Mg ha-1 yr–1 for the soils in the area. Kaiser’s field data allowed us to compare the historical field-measured erosion rates with those simulated by the WEPP (Water Erosion Prediction Project) model. Anthropogenic factors, such as tillage and crop rotation, change with time. Conservation tillage, including reduced- and no-till, has been increasingly adopted in eastern Washington since the mid-1980s. The specific objectives of this study were to (1) apply the WEPP model to simulate soil erosion in eastern Washington and evaluate the interactive effects of climate and management, in addition to topography and soil, on water erosion in the study area, and (2) compare the simulation results with Kaiser’s historical field dataset and elucidate the long-term soil erosion trend. The WEPPcloud interface was used to delineate a watershed within each precipitation zone of the study area. Climate inputs were divided into two periods: the past (1939–1982) and the present (1983–2020). Erosion has noticeably decreased from the past to the present, with WEPP simulated annual erosion averaging 13.5, 34.5, and 52.6 Mg ha-1 for the past, and 9.5, 14.1, and 15.5 Mg ha-1 for the present, in the selected watersheds in the low-, intermediate-, and high-precipitation zones, respectively. The decreasing trend was primarily due to the increased adoption of conservation tillage and crop rotation, as well as a decrease in the number of high-intensity precipitation events in the present climate. Keywords: Inland Pacific Northwest, Soil erosion by water, Temporal trend, WEPP.

Assessment of the Potential Impacts of Climate Change on the Hydrology and Canola Yield Using the DRAINMOD Model

HighlightsTotal precipitation and average temperature are projected to increase in the Interlake region of Manitoba.DRAINMOD model results suggest that controlled drainage (CD) would significantly decrease subsurface drainage.Due to dry stress, canola yield is projected to decrease under free drainage (FD) and controlled drainage (CD).Simulation results suggest that capturing, storing, and reusing drainage water could be an adaptive and mitigative strategy for climate change impacts.Abstract.Climate change is a major concern for agricultural production regions like the Canadian Prairies. Therefore, understanding the hydrologic and crop yield response to climate change is important to developing adaptative and mitigative strategies. Downscaled climate model projections from two GCMs for historical (1981-2010), midcentury (2041-2070), and late-century (2071–2100) periods under three representative concentration pathways (RCP2.6, RCP4.5, and RCP 8.5) were used as climate inputs to drive a calibrated and validated DRAINMOD model under two water management scenarios: free drainage (FD) and controlled drainage (CD). Field data, including water table depth, was collected for two canola growing seasons at the PESAI (Prairies East Sustainable Agriculture Initiative) research site in Arborg, Manitoba, Canada. The model was calibrated and validated using the 2019 and 2020 water table depth. The projected changes in the climatic variables showed a slight increase in the mean annual precipitation and the mean temperature across the seasons. DRAINMOD simulation results suggest that CD would significantly decrease subsurface drainage, while water loss through evapotranspiration (ET) and surface runoff are projected to increase considerably under CD and FD. Furthermore, results showed that the relative canola yield would decrease under FD and CD. Stressor analysis showed that canola yield reduction was driven by dry stress due to the projected temperature rise, which outweighs the slight increase in precipitation. Simulation results suggest that the capture, storage, and reuse of drainage water could be an adaptive and mitigative strategy to address the predicted impacts. Keywords: Canola yield, Climate change, DRAINMOD model, Subsurface drainage.

Are Plant Functional Types Fit for Purpose?

AbstractFor over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies.

Quantification of cogeneration vulnerability towards the climate change: framework development

Cogeneration is a vigorous measure to both decrease greenhouse gases emissions and improve an overall efficiency of the primary energy use. The climate change poses serious risks on operation of combined heat and power (CHP) plants due to the shifts in heating load patterns. This work investigates linking of the heating demand to the climate conditions and introduces a framework which can be used to develop an adaptation strategy for district heating systems. We demonstrate viability of the proposed approach by a case study assessing sensitivity of the CHP efficiency to the climate warming across China considering a modern combined-cycle gas-fired power plant with steam extraction. Outputs of the case study have demonstrated that CHP plants are generally more vulnerable towards the climate change in world regions with harsher climate conditions. An effective measure to increase resilience of the cogeneration sector is to account for anticipated climate change effects by applying adjustments to the reference climate inputs used when designing CHP plants.

Trend Analysis of Temperature and rainfall in Kashmir Valley

Abstract: Weather is the key source of uncertainty affecting crop yield especially in the context of climate change. Among variables relevant to weather, rainfall and temperature are two important factors which have a large effect on crop yield. Typically, temperature affects the length of the growing season and rainfall affects plant production. Rainfall and temperature are important climatic inputs for agricultural production, especially in the context of climate change. The interdependence of rainfall and temperature has a direct influence on the output of agriculture, occurrence and design of hydraulic structures. Earlier the correlation of temperature and rainfall could not be studied with precision but the developments in the meteorology science have however rendered the study possible and precise. The study of temporal and spatial variation of temperature and rainfall of six stations of Kashmir valley for the period of thirty years which has been used for determination of various rainfall-temperature graphs. The rainfall-temperature curve has been further used to determine the trend or pattern in temporal and spatial variation and to determine periods of maximum and minimum rainfall and temperature and also the stations of maximum and minimum rainfall and temperature. The study also computes correlations between temperature and rainfall in Kashmir valley for each station and for each month of the corresponding stations. Modern statistical methods were used to test thestatistical significance of our results.

Constraining the power-function complementary relationship with a steady-state Budyko equation for predicting terrestrial evapotranspiration globally

The power-function complementary relationship (CR) represents the latest advancement in definitive CR models, offering enhanced predictions of terrestrial evapotranspiration (E), all while elucidating the physical interpretations of its associated parameters. In this study, we introduced practical approaches to integrate the traditional Budyko framework into the novel CR model for global-scale E prediction. Under a steady-state assumption, the power-function CR was equated with the Turc-Mezentsev equation, thereby introducing an additional constraint for determining key parameters. We presented two illustrative cases where the aridity index and land-specific parameter constrain the exponent and the Priestley-Taylor coefficient of the CR, respectively. Compared to its calibration-free counterpart, the CR model constrained by the Budyko framework demonstrated improved performance in capturing spatial and temporal variations of E in point locations, mesoscale catchments, and large river basins. This improvement was not only from considering long-term precipitation partitioning, but also from the ability of the combined framework to rectify biases in input data. While the calibration-free methods exhibited poorer performance, they hold potential for superior results with higher-quality forcing inputs. By merging the Turc-Mezentsev equation with the power-function CR, this study provides insights into refining E predictions, accounting for climatic conditions and input data quality.

Development of climatic damage predictive tool for timber façade moisture-related damage

Development of the method for automated and adaptive assessment of mould growth in the timber frame facade is presented. A heat, air, and moisture (HAM) transport simulation using the open-source Python library HAMOPY is validated against a well-established software (WUFI Pro 1D). Climate input of the reference year in Norway, Oslo is used in validation. The material parameters of the 1D numerical model that influence mold growth conditions most are identified via a parametric study. The increased water vapor permeability and thermal conductivity of the outside envelope as well as the critical relative humidity threshold are selected as the model updating parameters to account for the risk of the thermal bridge, moisture leakage, and mold growth conditions variation in the model. The example of the automated parametric computation of the mold growth conditions in the facade is presented for a reference climate based on the developed and validated HAM model.

Agent-based sensor location strategy for smart irrigation of large crop fields

Efficient monitoring of large crop fields is important to ensure the optimal use of resources such as water in irrigation policies, but at the same time represents a challenge to determine the structure of the sensor network. A balance must be accomplished between the acquisition, operation, and maintenance costs of this sensor network with the amount of information that can be collected in real-time to support the optimal use of the resource, e.g., an optimal irrigation policy. In this study, a sensor location strategy is proposed based on an agent-based model (ABM) of the crop–soil system, a state estimation algorithm reconstructing non-measured variables, and an objective function balancing the convergence of the estimation technique and the costs of the sensor network. The ABM model describes the crop–soil dynamics and allows conveniently representing uneven landscapes where water exchanges take place between different portions of the land. Various state estimation techniques can be considered and an extended Kalman filter is implemented in the present study, whose error covariance matrix can be exploited to assess practical observability and observer convergence. Finally, an economic cost function combines the observability measure with the sensor costs in order to select an optimal or suboptimal sensor array. For validation purposes, a numerical simulation case study, corresponding to a rugged land located in Colombia, is used to test various scenarios including the variability of climatic inputs.

Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model

Sustainable management of aquifers requires long-term predictions of their aquifer-scale groundwater balance under climate change with meaningfully quantified uncertainty. In our analysis, we account for uncertainty in model parameters, measured data, model errors, stochastic model inputs (e.g., rainfall, potential evapotranspiration), and climate scenarios. To afford this analysis, we rely on an extension of lumped hydrological models that includes groundwater flow and storage, called lumped geohydrological model (LGhM). It shows good model performance and is three orders of magnitude faster than 2D or 3D numerical groundwater models. We use Markov chain Monte Carlo (MCMC) for Bayesian inference on a calibration period to quantify posterior parameter uncertainty and lumped data-and-model error. Then, we perform Monte Carlo forward runs with stochastic weather inputs under three different climate scenarios to quantify uncertainty in long-term predictions up to the year 2040. We apply our approach on a virtual reality representation of the Wairau Plain aquifer, New Zealand. We hypothesize that the variability in future climate is the most important impact factor. To test this hypothesis, we disentangle the overall uncertainty between data/model, parameters, weather, and climate inputs. We expect our approach to be highly useful for arbitrary types of catchments because the LGhM model structure can easily be adapted.

Budyko Framework Based Analysis of the Effect of Climate Change on Watershed Evaporation Efficiency and Its Impact on Discharge Over Europe

AbstractIn the context of climate change, the stakes surrounding water availability are rapidly intensifying. Decomposing and quantifying the effects of climate on discharge allows us to understand their impact on water resources better. We propose a methodology to separate the effect of change in the annual mean of climate variables from the effect of the intra‐annual distribution of precipitation. It combines the Budyko framework with land surface model (LSM) outputs. The LSM is used to reproduce the behavior of 2,134 reconstructed watersheds across Europe between 1902 and 2010, with climate inputs as the only source of change. We fit a one‐parameter approximation of the Budyko framework to the LSM outputs. It accounts for the evolution of the annual mean in precipitation (P) and potential evapotranspiration (PET). We introduce a varying parameter in the equation, representing the effect of long‐term variations in the intra‐annual distribution of P and PET. To better assess the effects of changes in annual means or intra‐annual distribution of P, we construct synthetic forcings fixing one or the other. European results show that the trends in the annual averages of P dominate the trends in discharge due to climate. The second main climate driver is PET, except over the Mediterranean area, where changes in intra‐annual variations of P have a higher impact on discharge than trends in PET. Therefore, the effects of changes in the intra‐annual distribution of climate variables are to be addressed when looking at changes in annual discharge.

A model of temporal and spatial river network evolution with climatic inputs

Predicting the temporal and spatial evolution of the river network is part of the Earth's critical zone investigations, which has become an important endeavor. However, modeling integration of the river network and critical zone over millions of years is rare. We address the problem of how to predict integrated river length development as a function of time within a framework of addressing the critical zone depth as a function of time. In case of groundwater-river interaction, we find a non-linear spatio-temporal scaling relationship between time, t, and total river length L, given by t≈Lp with power p being near 1.2. The basis of our model is the presumption that groundwater flow paths are relevant to river integration. As river integration may proceed over disconnected basins with irregular relief, the relevant optimal subsurface flow paths are proposed to be defined within a 3D network, with optimal path exponent 1.43. Because the 2D model of the river length has already been shown to relate to a power of the Euclidean distance across a drainage basin with the predicted universal optimal path exponent from percolation theory, Dopt = 1.21, the optimal groundwater paths should relate to the surface river length with an exponent equaling the ratio 1.43/1.21 = 1.18. To define a predictive relationship for the river length, we need to use specific length and time scales. We assume that the fundamental specific length scale is a characteristic particle size (which is commonly used to define the pore scale flow network), and the fundamental time scale is the ratio of the particle size to the regional groundwater flow rate. In this paper, we consider cases of predicting spatio-temporal scaling of drainage organization in the southwestern USA–the Amargosa, Mojave, Gila (and its tributaries) and the Rio Grande, and Pecos Rivers. For the Mojave and Gila Rivers, theoretical results for time scales of river integration since ca. 10 Ma are quite predictive, though the predicted time scales exceed observation for the Rio Grande and Pecos.

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.

Performance evaluation of simpler reference crop evapotranspiration estimation equations with and without local calibration

The present study was taken up to demonstrate an overall methodology for local calibration of simpler reference crop evapotranspiration (ET0) estimation equations relative to the physically-based, albeit more data-intensive Penman-Monteith (PM) method. Climate records for the historical period 2006-2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used for this purpose. The numerical coefficients in the original forms of the simpler equations namely, Blaney-Criddle (BC), Priestley-Taylor (PT), Radiation (RAD), and Turc (TC) were treated as unknown parameters and optimized for each station using PM ET0 estimates as reference. Results of local calibration indicated significant improvement in prediction accuracies and bias reduction for all the equations with the calibrated PT equation exhibiting the best performance. Spatial maps depicting the variations in the optimal values of parameters were developed to aid the accurate estimation of monthly ET0 values in the study area using only minimal climate inputs.

A study on hydrological responses of the Fuhe River Basin to combined effects of land use and climate change

Study regionThe Fuhe River Basin in Jiangxi Province, China. Study focusGlobal climate change and intensified human activities are making the hydrological processes at Fuhe River Basin experiencing dramatic changes. Although some studies have investigated their individual impacts on basin-scale water resources, their combined effects on hydrology have received little attention. In this study, future scenarios were constructed for three future periods, based on five global climate model outputs (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a dataset of future land use projections under three shared socioeconomic pathways and representative concentration pathways (SSP-RCPs). Then, the Soil and Water Assessment Tool (SWAT) model was used to assess the relative changes in water balance components and extreme flow frequency under these developed scenarios. Furthermore, the hydrological response assessment methodology was improved from the original multiscenario ensemble flow forecast (MESF) framework, which not only strengthens the connection between climate and land use input changes but also adds more assessment items. New hydrological insights for the regionThe flow at the outlet of Fuhe River Basin is expected to increase by approximately 27.1%− 30.2%, 24.7–39.0% and 35.5%− 43.5% in the 2030 s, 2060 s and 2090 s, respectively. Water availability will increase significantly in February, August and October and decrease in November and December. To the end of 21st century, surface runoff will have more than 100% increase. Future floods and droughts will be more frequent and severe under SSP5–8.5.