Elevated nutrient inputs and reduced riverine concentration variability challenge the health and functioning of aquatic ecosystems. To improve riverine water quality management, it is necessary to understand the underlying biogeochemical and physical processes and their interactions at catchment scale. We hypothesize that spatial heterogeneity of nutrient sources dominantly controls the variability of instream concentrations among different catchments. Therefore, we investigated controls of mean nitrate (NO), phosphate (PO), and total organic carbon (TOC) concentrations and concentration-discharge (C-Q) relationships from observations in 787 German catchments covering a wide range of physiographic and anthropogenic settings. Using partial least square regressions and random forests we linked water quality metrics to catchment characteristics. We found archetypal C-Q patterns with enrichment dominating NO and TOC, and dilution dominating PO export. Across the catchments, we found a positive but heteroscedastic relation between mean NO concentrations and agricultural land use. We argue that denitrification, particularly pronounced in sedimentary aquifers, buffers high inputs and causes a decline in concentration with depth resulting in chemodynamic, strongly positive C-Q patterns. Consequently, chemodynamic NO enrichment patterns could indicate effective subsurface denitrification. Mean PO concentrations were related to point sources though the low predictive power suggests effects of unaccounted processes. In contrast, diffuse inputs better explained the spatial differences in PO C-Q patterns. TOC levels were positively linked to the abundance of riparian wetlands as well as negatively to NO concentrations suggesting interacting processes. This study shows that vertical concentration heterogeneity mainly drives nutrient export dynamics, partially modified by interactions with other controls.

Climate change and variability threatens the sustainability of future food productions, especially in semi-arid regions where water resources are limited, and irrigated agriculture is widespread. Increasing temperatures will exacerbate evaporative losses and increase plant water needs. Consequently, higher irrigation intensities would be a logical measure to mitigate climate change impacts in these regions. Using an ensemble of well-parameterized crop model simulations, we show that this mitigation measure is oversimplified and that besides water resources availability, strong temperature increases play a crucial role in crop developments and resulting plant water needs. Our analysis encompasses agricultural areas of the Lower Chenab Canal System in Pakistan (15 000 km2), which is part of the Indus River irrigation system, the largest irrigation system in the world; and covers economically important crop growing areas (e.g., of cotton, rice and maize crops). Climate models project an above average increase in temperature over the study region, and the agro-hydrological and biophysical crops models respond with a strong decline of up to -24% (±12%) in future crop productions. Our modeling results further suggest that evaporative and irrigation demands do not align with increasing future temperature trends. The resulting decline in crop productions is consistent among model projections despite an intensification of irrigation measures and the positive effect of future CO2 enrichments. Overall, our study emphasizes the role of elevated temperature stress, its effects on agricultural production as well as water demand, and its implications for climate change adaption strategies to mitigate adverse impacts in an intensively irrigated region.

Defining effective measures to reduce nitrate pollution in heterogeneous mesoscale catchments remains challenging if based on concentration measurements at the outlet only. One reason is our limited understanding of the sub-catchment contributions to nitrate export and their importance at different time scales. While upstream sub-catchments often disproportionally contribute to runoff generation and in turn to nutrient export, agricultural areas, which are typically found in downstream lowlands, are known to be a major source for nitrate pollution. To disentangle the interplay of these contrasting drivers of nitrate export, we analyzed seasonal long-term trends and event dynamics of nitrate concentrations, loads and the concentration-discharge relationship in three nested catchments within the Selke catchment (456 km²), Germany. The upstream sub-catchments (40.4 % of total catchment area, 34.5 % of N input) had short transit times and dynamic concentration-discharge relationships with elevated nitrate concentrations during wet seasons and events. Consequently, the upstream sub-catchments dominated nitrate export during high flow and disproportionally contributed to overall annual nitrate loads at the outlet (64 %). The downstream sub-catchment was characterized by higher N input, longer transit times and relatively constant nitrate concentrations between seasons, dominating nitrate export during low flow periods. Neglecting the disproportional role of upstream sub-catchments for temporally elevated nitrate concentrations and net annual loads can lead to an overestimation of the role of agricultural lowlands. Nonetheless, in agricultural lowlands, constantly high concentrations from nitrate legacies pose a long-term threat to water quality. This knowledge is crucial for an effective and site-specific water quality management.

Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g. soil, groundwater). Catchment-scale N models, that are commonly used to investigate in-stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long-term fate of the N inputs and its uncertainties, using a legacy-driven N model (ELEMeNT) in Germany’s largest national river basin (Weser; 38,450 km2) over the period 1960-2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational datasets. We demonstrate that beyond in-stream N loading, soil N content and in-stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480-2210 kg ha-1) and the stream export amounts to around 18% (410-640 kg ha-1), leaving behind as much as around 230-780 kg ha-1 of N in the (soil) source zone and 10-105 kg ha-1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterising the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modelling.

Understanding onset of droughts and its potential linkage to resulting responses like severity (deficit volume) is crucial for providing timely information related to drought sectors including the cultivation planning and monitoring crop productivity. Using high-quality daily observed streamflow records from 82 medium-to-large sized catchments over (tropical) peninsular India, we show that the variability in onset timing drives the severity of hydrological droughts. The strength of onset timing-severity relationships using observed records indicate seasonality of rainfall and catchment characteristics mainly modulate hydrological drought responses in peninsular India, which is not readily apparent from land-surface model simulations. The observed trend for mean onset of drought depicts delayed occurrence for more than half of the catchments. Around one-third of the catchments shows a stronger non-linear significant dependency (>0.7) between severity and onset of drought. The findings of the study highlight the need for accounting feedback between drought onset and severity and their concurrent changes for seasonal-to-sub-seasonal predictability of droughts; and contributes to discussions on building resilience to extreme droughts in a changing climate.