Hydrology in many agricultural landscapes around the world is changing in unprecedented ways due to the development of extensive surface and subsurface drainage systems that optimize productivity. This plumbing of the landscape alters water pathways, timings, and storage, creating new regimes of hydrologic response and driving a chain of environmental changes in sediment dynamics, nutrient cycling, and river ecology. In this work, we nonparametrically quantify the nature of hydrologic change in the Minnesota River Basin, an intensively managed agricultural landscape, and study how this change might modulate ecological transitions. During the growing season when climate effects are shown to be minimal, daily streamflow hydrographs exhibit sharper rising limbs and stronger dependence on the previous-day precipitation. We also find a changed storage-discharge relationship and show that the artificial landscape connectivity has most drastically affected the rainfall-runoff relationship at intermediate quantiles. Considering the whole year, we show that the combined climate and land use change effects reduce the inherent nonlinearity in the dynamics of daily streamflow, perhaps reflecting a more linearized engineered hydrologic system. Using a simplified dynamic interaction model that couples hydrology to river ecology, we demonstrate how the observed hydrologic change and/or the discharge-driven sediment generation dynamics may have modulated a regime shift in river ecology, namely extirpation of native mussel populations. We posit that such nonparametric analyses and reduced complexity modeling can provide more insight than highly parameterized models and can guide development of vulnerability assessments and integrated watershed management frameworks.

Abstract. Complete transformations of land cover from prairie, wetlands, and hardwood forests to row crop agriculture and urban centers are thought to have caused profound changes in hydrology in the Upper Midwestern US since the 1800s. In this study, we investigate four large (23 000-69 000 km 2 ) Midwest river basins that span climate and land use gradients to understand how climate and agricultural drainage have influenced basin hydrology over the last 79 years. We use daily, monthly, and annual flow metrics to document streamflow changes and discuss those changes in the context of precipitation and land use changes. Since 1935, flow, precipitation, artificial drainage extent, and corn and soybean acreage have increased across the region. In extensively drained basins, we observe 2 to 4 fold increases in low flows and 1.5 to 3 fold increases in high and extreme flows. Using a water budget, we determined that the storage term has decreased in intensively drained and cultivated basins by 30-200 % since 1975, but increased by roughly 30 % in the less agricultural basin. Storage has generally decreased during spring and summer months and increased during fall and winter months in all watersheds. Thus, the loss of storage and enhanced hydrologic connectivity and efficiency imparted by artificial agricultural drainage appear to have amplified the streamflow response to precipitation increases in the Midwest. Future increases in precipitation are likely to further intensify drainage practices and increase streamflows. Increased streamflow has implications for flood risk, channel adjustment, and sediment and nutrient transport and presents unique challenges for agriculture and water resource management in the Midwest. Better documentation of existing and future drain tile and ditch installation is needed to further understand the role of climate versus drainage across multiple spatial and temporal scales.

This comment cautions against dismissing agricultural practices as a significant cause of hydrologic change in Midwestern agricultural landscapes.In a recent paper, Gupta et al. [2015] considered the important issue of quantifying the relative contributions of climate and land use/land cover (LULC) change on the observed hydrologic changes in Midwestern agricultural landscapes. They reached the conclusion that ''higher streamflows for most watersheds in the Upper Midwest are mainly due to increased precipitation'' (p. 5315), implying that LULC changes exerted minimal effect on the hydrologic response of agricultural landscapes.

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snowcover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10 percent. In particular, the probability of precipitation detection and its solid phase increases by 11 and 8 percent, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.